US20050026173A1 - Genetic diagnosis using multiple sequence variant analysis combined with mass spectrometry - Google Patents

Genetic diagnosis using multiple sequence variant analysis combined with mass spectrometry Download PDF

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US20050026173A1
US20050026173A1 US10/788,043 US78804304A US2005026173A1 US 20050026173 A1 US20050026173 A1 US 20050026173A1 US 78804304 A US78804304 A US 78804304A US 2005026173 A1 US2005026173 A1 US 2005026173A1
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spc
spcs
polymorphisms
genetic
polymorphism
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Marc Zabeau
Patrick Stanssens
Yannick Gansemans
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METHEXIS NV
Methexis Genomics NV
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Assigned to METHEXIS NV reassignment METHEXIS NV ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: GANSEMANS, YANNICK, STANSSENS, PATRICK, ZABEAU, MARC
Publication of US20050026173A1 publication Critical patent/US20050026173A1/en
Priority to US11/077,564 priority Critical patent/US7593818B2/en
Priority to US11/312,088 priority patent/US20060257888A1/en
Priority to US11/908,094 priority patent/US20090104601A1/en
Priority to US12/553,445 priority patent/US7996157B2/en
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present invention is in the field of nucleic acid-based genetic analysis. More particularly, it discloses novel insights into the overall structure of genetic variation in all living species.
  • Variation in the human genome sequence is an important determinative factor in the etiology of many common medical conditions. Heterozygosity in the human population is attributable to common variants of a given genetic sequence, and those skilled in the art have sought to comprehensively identify common genetic variations and to link such variations to medical conditions (Lander, Science 274:536, 1996; Collins et al., Science 278:1580, 1997; Risch, Science 273:1516, 1996].
  • LD linkage disequilibrium
  • LD is not a simple function of distance and the patterns of genetic polymorphisms, shaped by the various genomic processes and demographic events, appear complex.
  • Gene-mapping studies critically depend on knowledge of the extent and spatial structure of LD because the number of genetic markers should be kept as small as possible so that such studies can be applied in large cohorts at an affordable cost.
  • an important analytical challenge is to identify the minimal set of SNPs with maximum total relevant information and to balance any reduction in the variation that is examined against the potential reduction in utility/efficiency of the genome-wide survey.
  • Any SNP selection algorithm that is ultimately used should also account for the cost and difficulty of designing an assay for a given SNP on a given platform—a particular SNP may be the most informative in a region but it may also be difficult to measure.
  • Multi-SNP haplotypes have been proposed as more efficient and informative genetic markers than individual SNPs [Judson et al., Pharmacogenomics 1: 15-26, 2000; Judson et al., Pharmacogenomics 3: 379-391, 2002; Stephens et al., Science 293: 489-493, 2001; Drysdale et al., Proc. Natl. Acad. Sci. USA 97: 10483-10488, 2000; Johnson et al., Nat. Genet. 29: 233-237, 2001].
  • Haplotypes capture the organization of variation in the genome and provide a record of a population's genetic history. Therefore, disequilibrium tests based on haplotypes have greater power than single markers to track an unobserved, but evolutionary linked, variable site.
  • haplotype block The major attraction of the ‘haplotype block’ model is that it may simplify the analysis of genetic variation across a genomic region—the idea is that a limited number of common haplotypes capture most of the genetic variation across sizeable regions and that these prevalent haplotypes (and the undiscovered variants contained in these haplotypes) can be diagnosed with the use of a small number of ‘haplotype tag’ SNPs (htSNPs).
  • the ‘haplotype block’ concept has fuelled the International HapMap Project [http://www.hapmap.org; Dennis C., Nature 425: 758-759 (2003)]. So far, the haplotype block structure has only been investigated in humans.
  • haplotypes from diploid unrelated individuals, heterozygous at multiple loci is difficult.
  • Conventional genotyping techniques do not permit determination of the phase of several different markers.
  • a genomic region with N bi-allelic SNPs can theoretically yield 2 N haplotypes in the case of complete equilibrium, whereas the actual number should be less than the number of SNPs in the absence of recombination events and recurrent mutations [Harding et al., Am. J. Hum. Genet. 60: 772-789, 1997; Fullerton et al., Am. J. Hum. Genet. 67: 881-900, 2000].
  • Large-scale studies [Stephens et al., Science 293: 489-493, 2001] indicate that the haplotype variation is slightly greater than the number of SNPs.
  • haplotypes may be resolved (partially) when the genotypes of first-degree relatives are available, e.g. father-mother-offspring trios [Wijsman E. M., Am. J. Hum. Genet. 41: 356-373, 1987; Daly et al., Nat. Genet. 29: 229-232, 2001].
  • the present invention discloses novel insights into the overall structure of genetic variation in all living species.
  • the present invention is directed to a method of producing an SPC map of a genomic region of interest comprising the steps of obtaining the nucleic acid of said genomic region of interest from a plurality of subjects; subjecting said nucleic acid to one or more separate base specific, sequence specific or site specific complementary cleavage reactions, wherein each cleavage reaction generates a non ordered set of fragments; analyzing the sets of non ordered fragments obtained using mass spectrometry; performing a systematic computational analysis on the mass spectra obtained to identify a plurality of spectral changes indicative of a plurality of polymorphism; and identifying one or more SPCs, wherein each SPC comprises a subset of polymorphisms from said nucleic acid sequence wherein said polymorphisms of said subset coincide with each other polymorphism of said subset.
  • the complementary cleavage reactions are selected from the group consisting of enzymatic cleavage, chemical
  • the complementary cleavage reactions are characterized by a relaxed mono nucleotide, mono nucleotide, relaxed di nucleotide, or di nucleotide specificity.
  • the one or more target nucleic acids are subjected to chemical digestion reaction consisting of treatment with alkali or with reagents used in the Maxam & Gilbert sequencing method.
  • the one or more target nucleic acids are subjected to enzymatic cleavage reaction using one or more enzymes selected from the group consisting of endonucleases and exonucleases.
  • Such endonucleases may be selected from the group consisting of restriction enzymes, RNA endonucleases, DNA endonucleases and non specific phosphodiesterases. More specifically, the one or more endonucleases are one or more selective or non selective RNA endonucleases, selected from the group consisting of the G specific T1 ribonuclease, the A specific U2 ribonuclease, the A/U specific phyM ribonuclease, the U/C specific ribonuclease A, the C specific chicken liver ribonuclease (RNaseCL3) and cusativin, non specific RNase I, and pyrimidine adenosine preferring RNases isolated from E. coli, Enterobacter sp., or Saccharomyces cerevisiae.
  • the one or more target nucleic acids being analyzed may be phosphorothioate modified single stranded DNA or RNA, and wherein the cleavage reactions are performed with the nuclease P1.
  • Alternative embodiments show that one or more target nucleic acids are mosaic RNA/DNA nucleic acids or modified mosaic RNA/DNA nucleic acids, prepared with mutant polymerases, and wherein the cleavage reagents are RNA endonucleases, DNA endonucleases or alkali.
  • the one or more target nucleic acids may be further defined as transcripts, modified transcripts, mosaic RNA/DNA transcripts or modified mosaic RNA/DNA transcripts, prepared with wild type or mutant RNA polymerases, and wherein the cleavage reagents are one or more selective or non selective RNA endonucleases or alkali.
  • the one or more target nucleic acids are mosaic RNA/DNA transcripts that incorporate either dCMP, dUMP or dTMP, prepared with mutant T7 or SP6 polymerase, and wherein the cleavage reagent is a pyrimidine specific RNase.
  • the set of non ordered fragments may additionally be purified using an ion exchange beads, or be spotted onto a solid support.
  • the solid support may be any solid support commonly used in nucleic acid, e.g., it may be a solid surfaces, plates and chips.
  • the mass spectrometric analysis of the nucleic acid fragments may be performed using a mass spectrometric method selected from the group consisting of Matrix Assisted Laser Desorption/Ionization Time of flight (MALDI TOF), Electrospray Ionization (ESI), and Fourier Transform Ion Cyclotron Resonance (FT ICR).
  • MALDI TOF Matrix Assisted Laser Desorption/Ionization Time of flight
  • ESI Electrospray Ionization
  • FT ICR Fourier Transform Ion Cyclotron Resonance
  • the identification of the one or more SPCs comprises identifying each polymorphism of said subset that coincides with each other polymorphism of said subset according to a percentage coincidence of the minor alleles of said polymorphisms of between 75% and 100%. Such identifying may comprise multiple rounds of coincidence analysis. In specific embodiments, each successive round of coincidence analysis is performed at a decreasing percentage coincidence from 100% coincidence to 75% coincidence.
  • the coincidence of each said polymorphism of said subset with each other polymorphism of said subset is calculated according to a parameter selected from the group consisting of a pairwise C value, a r2 linkage disequilibrium value, and a d linkage disequilibrium value. Preferably, the coincidence is calculated using pairwise C value of from 0.75 to 1.
  • Also contemplated herein is a method of selecting one or more polymorphisms from a genomic region of interest for use in genotyping, comprising the steps of obtaining an SPC map according to a method that employs mass spectrometry fragmentation analysis of nucleic acids; selecting at least one cluster tag polymorphism which identifies a unique SPC in said SPC map; and selecting a sufficient number of cluster tag polymorphisms for use in a genotyping study of the genomic region of interest.
  • the cluster tag polymorphism is a known SNP associated with a genetic trait.
  • Also taught herein is a method of identifying a marker for a trait or phenotype comprising: obtaining a sufficient number of cluster tag polymorphisms as described above and assessing said cluster tag polymorphisms to identify an association between a trait or phenotype and at least one cluster tag polymorphism, wherein identification of said association identifies said cluster tag polymorphism as a marker for said trait or phenotype.
  • the cluster tag polymorphism is correlated with a trait or phenotype selected from the group comprising a genetic disorder, a predisposition to a genetic disorder, susceptibility to a disease, an agronomic or livestock performance trait, a product quality trait.
  • the marker is a marker of a genetic disorder and said SPC map is prepared as described above, and the plurality of subjects each manifests the same genetic disorder.
  • Also described is a method for in vitro diagnosis of a trait or a phenotype in a subject comprising obtaining a marker for said trait or phenotype as discussed above; obtaining a target nucleic acid sample from said subject; and determining the presence of said marker for said trait or a phenotype in said target nucleic acid sample, wherein the presence of said marker in said target nucleic acid indicates that said subject has the trait or the phenotype.
  • Another aspect of the invention involves a method of determining the genetic identity of a subject comprising obtaining a reference SPC map of one or more genomic regions from a plurality of subjects according to the mass spectrometry based methods described above; selecting a sufficient number of cluster tag polymorphisms for said genomic regions; obtaining a target nucleic acid of said genomic regions from a subject to be identified; determining the genotype of said cluster tag polymorphisms of said genomic regions of said subject to be identified; and comparing said genotype of said cluster tag polymorphisms with said reference SPC map to determine the genetic identity of said subject.
  • Also described is a method of determining the SPC-haplotypes from unphased diploid genotype of a genomic region of interest of a subject comprising using the above described mass spectrometry-based determination of an SPC map; determining the SPC-haplotypes from said SPC map, wherein each SPC-haplotype comprises a subset of SPCs from a genomic region wherein said SPCs of said subset coincide; and identifying the SPC-haplotype of a test subject by comparing the SPCs of said subject with the SPC-haplotypes determined from said SPC map.
  • an article comprising a machine-accessible medium having stored thereon instructions that, when executed by a machine, cause the machine to obtain nucleic acid information of a genomic region of interest from a plurality of subjects where the nucleic acid information is obtained from one or more separate base specific, sequence specific or site specific complementary cleavage reactions of a genomic region of interest, wherein each cleavage reaction generates a non ordered set of fragments; analyze the sets of non ordered fragments by mass spectrometry; perform a systematic computational analysis on the mass spectra obtained from the sets of non ordered fragments to identify a plurality of spectral changes indicative of a plurality of polymorphism; and identify one or more SPCs, wherein each SPC comprises a subset of polymorphisms from said nucleic acid sequence wherein said polymorphisms of said subset coincide with each other polymorphism of said subset.
  • the article may further have instructions that, when executed by the machine, cause the machine to analyze the results of chemical digestion reaction of one or more target nucleic acids the reaction consisting of treatment with alkali or with reagents used in the Maxam & Gilbert sequencing method.
  • the article may further have instructions that, when executed by the machine, cause the machine to select at least one cluster tag polymorphism which identifies a unique SPC in said SPC map; and select a sufficient number of cluster tag polymorphisms for use in a genotyping study of the genomic region of interest.
  • the article may have further instructions that, when executed by the machine, cause the machine to obtain a sufficient number of cluster tag polymorphisms; and assess said cluster tag polymorphisms to identify an association between a trait or phenotype and at least one cluster tag polymorphism, wherein identification of said association identifies said cluster tag polymorphism as a marker for said trait or phenotype.
  • the article may have further instructions that, when executed by the machine, cause the machine to obtain a target nucleic acid sample from said subject; and determine the presence of said marker for said trait or a phenotype in said target nucleic acid sample, wherein the presence of said marker in said target nucleic acid indicates that said subject has the trait or the phenotype.
  • the article described herein may further have instructions that, when executed by the machine, cause the machine to: select a reference SPC map of one or more genomic regions from said plurality of subjects; select a sufficient number of cluster tag polymorphisms for said genomic regions; obtain a target nucleic acid information of said genomic regions from a subject to be identified; determine the genotype of said cluster tag polymorphisms of said genomic regions of said subject to be identified; and compare said genotype of said cluster tag polymorphisms with said reference SPC map to determine the genetic identity of said subject.
  • the article of the invention may further have instructions that, when executed by the machine, cause the machine to determine the SPC-haplotypes from said SPC map, wherein each SPC-haplotype comprises a subset of SPCs from a genomic region wherein said SPCs of said subset coincide; and identify the SPC-haplotype of a test subject by comparing the SPCs of said subject with the SPC-haplotypes determined from said map.
  • FIGS. 1 through 21 The results shown in FIGS. 1 through 21 that are part of the present invention can best be represented and viewed on color printouts.
  • the Figures are however also legible on black/white printouts where the different colors, referred to in the Figure legends, are represented/replaced by different shades of grey or by any other means of differentially representing or visualizing results. Additionally, the Figures may also incorporate alternative indications (for example a numbering of the originally coloured or shaded regions) to facilitate the readability of such black/white representations.
  • FIG. 1 illustrates an SPC structure that consists of a number of independent SPCs.
  • An idealized imaginary genetic variation data set essentially devoid of confounding data, was used.
  • the various SPCs more specifically the minor alleles of the SNPs that belong to these SPCs, are differentially highlighted. Different colors are used to indicate the various SPCs.
  • the representations in FIGS. 1A and 1B correspond to the output of the algorithm.
  • the first two rows in FIGS. 1A and 1B indicate respectively the SNPs and the SPCs to which the SNPs belong.
  • FIG. 1A shows the genetic variation table (in which each column represents a polymorphic site and each row represents a sample) onto which the SPCs are visualized.
  • FIG. 1C shows the SPC network. SPCs are numbered as in FIG. 1A ; the putative source sequence that is devoid of an SPC is referred to as SPC-0.
  • FIG. 2 illustrates an SPC structure that consists of a number of dependent SPCs.
  • the representations in FIGS. 2A and 2B correspond to the output of the algorithm. The first two rows in FIGS. 2A and 2B indicate respectively the SNPs and the SPCs to which the SNPs belong.
  • FIG. 2A shows the genetic variation table (in which each column represents a polymorphic site and each row represents a sample) onto which the SPCs are depicted. The original table is organized such that individuals that share the same SPCs are grouped. Polymorphic sites that do not cluster are marked in grey (e.g. SNPs 2, 8, 29, 34 and 38).
  • FIG. 2C shows a network representation of the SPC relationships. SPCs are numbered to reflect the hierarchy; the putative source sequence that is devoid of an SPC is referred to as SPC-0.
  • FIGS. 2D and 2E show the SPCs identified in the genetic variation table and the corresponding networks using a threshold value for C of 0.9. It should be noted that in this case there is no longer a distinction between SPC-1 and SPC-1.1 of FIG. 2A .
  • FIG. 3 illustrates a complex SPC structure with both independent and dependent relationships between a total of 12 SPCs.
  • An idealized imaginary genetic variation data set essentially devoid of confounding data, was used. Different colors are used to indicate the various SPCs.
  • FIG. 3A corresponds to the output of the algorithm and shows the genetic variation table (in which each column represents a polymorphic site and each row represents a sample) onto which the SPCs are depicted.
  • the first two rows in FIG. 3A indicate respectively the SNPs and the SPCs to which the SNPs belong.
  • the original table is sorted such that individuals that share the same SPCs are grouped. For the sake of simplicity, non-clustering polymorphisms were left out.
  • the network representation in FIG. 3B shows the hierarchical relationships between the SPCs.
  • FIG. 4 represents the SPC structure at various stringencies using a data set containing missing genotype calls.
  • the data set is the same as that used for FIG. 1 wherein 4.5% of the allele calls were replaced by “N”, symbolizing a missing data point, and 0.5% of the allele calls were replaced by the opposite allele, to mimic incorrect data.
  • Different colors are used to indicate the various SPCs.
  • the first two rows in FIGS. 4A, 4B and 4 C indicate respectively the SNPs and the SPCs to which the SNPs belong.
  • FIGS. 4G, 4H , and 4 I illustrate the selection of ctSNPs that tag the SPCs 1, 3 and 4, respectively. For each SPC, a condensed genetic variation table lists the scores observed at the polymorphic sites that belong to that cluster.
  • the accompanying matrix shows the pairwise C-values as well as a calculation of the average strength of association of each polymorphism with the other polymorphisms of the cluster. These average C-values are given along the diagonal as well as in the right margin. The most preferred ctSNP is highlighted.
  • FIG. 5 exemplifies the effect of a limited number of historical recombination events on the SPC structure.
  • An imaginary genetic variation data set was used; non-clustering polymorphisms were omitted for the sake of simplicity. Different colors are used to indicate the various SPCs. Throughout the Figure, the same numbering is used to indicate the various SPCs.
  • the first two rows in FIG. 5A indicate respectively the SNPs and the SPCs to which the SNPs belong.
  • the original table was sorted such that individuals that share the same SPC are grouped. Certain samples reveal recombination events between SPC-0 and SPC-1.
  • FIG. 5C shows an SPC map of the locus in question. While SPC-1 is interrupted on both sides, the other SPCs are continuous.
  • FIGS. 5E and 5F show the various SPCs found at a threshold level of C ⁇ 0.9 and the corresponding network.
  • FIGS. 5G and 5H show the various SPCs at threshold level C ⁇ 0.8 and the corresponding network.
  • FIG. 6 exemplifies the effect of a recombination hotspot on the SPC structure.
  • An imaginary genetic variation data set was used. Different colors are used to indicate the various SPCs.
  • the recombination hotspot demarcates two adjacent regions. A black bar indicates the junction and in the two regions the major alleles (i.e. SPC-0) are differentially highlighted.
  • FIG. 6A shows the original genetic variation table onto which the SPCs are depicted. The first two rows in FIG. 6A indicate respectively the SNPs and the SPCs to which the SNPs belong.
  • the genetic variation table is arranged such that individuals that share the same SPCs in the left region are grouped. Polymorphic sites that do not cluster are marked in grey (e.g.
  • FIG. 6C shows an SPC map of the locus in question. The SPCs found in the two distinct regions are shown separately (since they can occur in various combinations).
  • FIG. 6D shows that each region is characterized by a distinct SPC network.
  • FIG. 7 illustrates the identification of SPCs that are in an independent configuration starting from diploid genotype data as well as the deconvolution of these genotype data.
  • FIG. 7A is a visual representation of the diploid genotypes, with positions homozygous for the major allele having a pale taint, the minor allele having a dark taint and the heterozygous calls (“H”) having a grey taint.
  • the genotype data were generated by random pairwise combination of the SPC-haplotypes of FIG. 7E . Haplotypes are named according to the SPCs thereby neglecting the non-clustering SNPs. The haplotype combinations are shown for each genotype on the left side. In FIGS.
  • FIGS. 7C and 7D show the metatype table, onto which the SPCs are visualized, and which for the sake of representation is shown in two halves. In essence, this table was obtained by duplicating FIG. 7A wherein the “H” positions were replaced once by the minor allele (the resulting minor metatypes are indicated by the letter “a” after the haplotype combination and are shown in FIG.
  • FIG. 7C shows the major allele (the resulting major metatype are indicated by the letter “b” after the haplotype combination and are shown in FIG. 7D ).
  • the two tables are sorted such that metatypes that share the same SPC are grouped as much as possible. Polymorphic sites that do not cluster (positions 33 and 38) are marked in grey.
  • FIG. 7F shows the SPC relationship which can be deduced from the data in FIGS. 7C and 7D . This SPC structure permits the deconvolution of the diploid genotypes into the component SPC-haplotypes shown in FIG. 7E .
  • FIG. 8 illustrates the identification of a complex SPC structure starting from diploid genotype data as well as the deconvolution of these data.
  • FIG. 8A is a visual representation of the diploid genotypes, with positions homozygous for the major allele having a pale taint, the minor allele having a dark taint and the heterozygous calls (“H”) having a grey taint.
  • the genotype data were generated by random pairwise combination of the SPC-haplotypes in FIG. 8E . In case the combined alleles were different, these were replaced by “H”.
  • the haplotype combinations are shown for each genotype on the left side.
  • FIGS. 8B to 8 F different colors are used to indicate the various SPCs.
  • FIG. 8A is a visual representation of the diploid genotypes, with positions homozygous for the major allele having a pale taint, the minor allele having a dark taint and the heterozygous calls (“H”)
  • FIGS. 8C and 7D show the metatype table, onto which the SPCs are visualized, and which for the sake of representation is shown in two halves. In essence, this table was obtained by duplicating FIG. 8A wherein the “H” positions were replaced once by the minor allele (the resulting minor metatypes are indicated by the letter “a” after the haplotype combination and are shown in FIG.
  • FIG. 8C shows the SPC relationship which can be deduced from the data in FIG. 8C .
  • This SPC structure permits the deconvolution of the diploid genotypes into the component SPC-haplotypes shown in FIG. 8E .
  • FIG. 9 shows the intraspecies SPC map of the sh2 locus of maize. Different colors are used to indicate the various SPCs.
  • FIG. 9A corresponds to the output of the algorithm and shows the genetic variation table onto which the SPCs are depicted. The maize lines for each genotype are shown in the left most column. The position of each variation on the physical map of the 7 kb sh2 locus is indicated above the columns. The polymorphic sites in the middle segment of the locus are omitted to bring down the size of the table. The table is organized such that individuals that share the same SPCs are grouped. Polymorphic sites that do not cluster are for the most part omitted—the ones that are shown are colored in grey and are located at positions 924, 936, 1834, 1907 and 1971.
  • FIG. 9B shows the SPC network of the locus. The putative source sequence that is devoid of an SPC is referred to as SPC-0.
  • FIG. 10 shows the intraspecies SPC map of the sh1 locus of maize. Different colors are used to indicate the various SPCs.
  • the upper part of the figure is a schematic representation of the physical map of the 7 kb sh1 locus, in which the differentially highlighted rectangles indicate the map positions of the polymorphic sites that are listed in the genetic variation table.
  • the middle panel corresponds to the output of the algorithm and lists the different SPCs in the locus. Each row represents the polymorphic sites that belong to a particular SPC.
  • the lower panel corresponds to the output of the algorithm and shows the genetic variation table onto which the SPCs are depicted.
  • the maize lines for each genotype are shown in the left most column. The table is organized such that individuals that share the same SPCs are grouped as much as possible. Polymorphic sites that do not cluster are not shown.
  • FIG. 11 shows the intraspecies SPC map of the Y1 locus of maize. Different colors are used to indicate the various SPCs.
  • FIG. 11A is a schematic representation of the physical map of the 6 kb Y1 locus, in which the differentially highlighted rectangles indicate the map positions of the polymorphic sites that are listed in the genetic variation table of FIG. 11B .
  • FIG. 11B corresponds to the output of the algorithm and shows the genetic variation table onto which the SPCs are depicted. The maize lines for each genotype are shown in the left most column.
  • the upper panel of FIG. 11B shows the SPCs in the white endosperm lines.
  • the lower panel of FIG. 11B shows the SPCs in the orange/yellow endosperm lines.
  • the table is organized such that individuals that share the same SPCs are grouped as much as possible.
  • the arrows indicate the positions of some putative historical recombination events. Polymorphic sites that do not cluster are not shown.
  • FIG. 12 shows the interspecies SPC map of the globulin 1 locus of maize. Different colors are used to indicate the various SPCs.
  • the representation in FIG. 12A corresponds to the output of the algorithm and shows the genetic variation table onto which the SPCs are depicted. Non-clustering polymorphisms and some SPCs that cannot be placed in the network structure were omitted. The abbreviated species and accession numbers for each genotype are shown in the second column. The table is organized such that individuals that share the same independent SPC are grouped as indicated by the differentially highlighted left most column. The arrows indicate the Zea mays accessions that share SPCs with Zea perennis .
  • FIG. 12B shows the SPC network and the Zea species. The atypical branching of SPCs 1 and 3 symbolizes that both these SPCs share one polymorphism with SPC-2. The putative source sequence that is devoid of an SPC is referred to as SPC-0.
  • FIG. 13 shows the SPC map of the FRI locus of Arabidopsis thaliana . Different colors are used to indicate the various SPCs.
  • FIG. 13A is a schematic representation of the physical map of the 450 kb FRI locus, in which the differentially highlighted rectangles symbolize the sequenced regions and also indicate the map positions of the polymorphic sites that are listed in the genetic variation table of FIG. 13B .
  • FIG. 13B corresponds to the output of the algorithm and shows the genetic variation table onto which the SPCs are depicted. The Arabidopsis lines for each genotype are shown in the left most column. The table is organized such that individuals that share the same SPCs are grouped as much as possible.
  • FIG. 14 shows the SPC maps of 31 amplicons from a 3.76 Mb segment of chromosome 1 of Arabidopsis thaliana . Different colors are used to indicate the various SPCs.
  • the figure is composed of 6 panels, numbered 1 through 6, which represent 100 polymorphic sites each. The rectangles at the top of each panel represent the amplicons from which the polymorphic sites were analyzed. The amplicons are numbered from 134 through 165, corresponding respectively to positions 16,157,725 and 19,926,385 on chromosome 1. Note that the missing amplicon 149 has no polymorphic sites.
  • the dotted lines that divide the panels mark the boundaries of the blocks of polymorphisms that belong to each amplicon.
  • Each SPC is represented on a different row and marked by a different color.
  • SPCs that span adjacent amplicons are outlined and marked by black arrows.
  • the empty blocks represent the amplicons that have no SPCs. Note that amplicons may be represented in consecutive panels, and that corresponding SPCs may be represented on different rows and marked by a different color.
  • FIG. 15 shows the SPC structure of the human CYP4A11 gene. Different colors are used to indicate the various SPCs.
  • FIG. 15A corresponds to the output of the algorithm and shows the metatype table onto which the SPCs are depicted. The sample names for each metatype are shown in the left most column, and are denoted with the extension “ ⁇ 1” for the minor metatype and the extension “ ⁇ 2” for the major metatype. The position of each polymorphic site in the sequence of the CYP4A11 gene is indicated above the columns. Polymorphic sites that do not cluster are omitted. The table is organized such that metatypes that share the same SPCs are grouped. The upper panel shows the major metatypes and the lower panel the minor metatypes.
  • FIG. 15B shows the different SPC combinations observed in the three classes of metatypes. Each rectangle of two rows shows the minor and the major metatype of a sample, the SPCs observed and the SPC combinations. The two SPC-haplotypes are obtained after deconvolution of the genotype.
  • FIG. 15C presents the hierarchical relationship between the SPCs of the CYP4A11 gene. The putative source sequence that is devoid of an SPC is referred to as SPC-0. The full and dotted lines represent respectively confirmed and putative relationships.
  • SPC-0 The putative source sequence that is devoid of an SPC
  • FIGS. 15D shows the SPC map of the CYP4A11 gene.
  • the upper panel shows the inferred SPC-haplotypes onto which the SPCs are depicted.
  • the lower panel represents the SPCs such that each SPC is represented on a different row and marked by a different color.
  • FIGS. 15E , F and G illustrate the selection of ctSNPs that tag the SPCs 1, 2 and 4, respectively.
  • a condensed metatype table lists the scores observed at the polymorphic sites that belong to that cluster.
  • the accompanying matrix shows the pairwise C-values as well as a calculation of the average strength of association of each polymorphism with the other polymorphisms of the cluster. These average C-values are given along the diagonal as well as in the right margin. The most preferred ctSNPs are highlighted.
  • FIG. 16 shows the SPC structure of a segment of the human MHC locus. Different colors are used to indicate the various SPCs.
  • FIG. 16A is a schematic representation of the physical map of the 200 kb Class II region of the MHC locus, in which the differentially highlighted rectangles symbolize the 7 domains from FIGS. 16B and C. The positions of the hotspots of recombination are indicated by the vertical arrows.
  • FIGS. 16B and C show the SPC map of the region in which each SPC is represented on a different row and marked by a different color. The differentially highlighted rectangles represent the domains inferred from the SPC maps.
  • FIG. 16B represents the SPC map of the subgroup of SNPs with high frequency minor alleles (frequency >16%) and FIG.
  • FIG. 16C represents the SPC map of the subgroup the SNPs characterized by low frequency minor alleles ( ⁇ 16%). SPCs that span different domains are outlined and marked by horizontal arrows.
  • FIG. 16D shows an SPC map of domain 4 of FIG. 16A from position 35,095 to position 89,298. In the upper row the polymorphic sites are numbered consecutively and the physical map position of each polymorphic site is indicated above the columns. Polymorphic sites that do not cluster are omitted.
  • the upper panel shows the inferred SPC-haplotypes onto which the SPCs are depicted.
  • the lower panel shows the SPCs in which each SPC is represented on a different row and marked by a different color.
  • FIG. 16E presents the hierarchical relationship between the SPCs of domain 4.
  • FIG. 17 shows the SPC map of the HapMap SNPs of human Chromosome 22.
  • FIG. 17A is a schematic representation of the physical map of a segment of 2.27 Mb of chromosome 22 in which the differentially highlighted and numbered rectangles symbolize the 11 domains of FIG. 17B . The domains are drawn to scale. The map positions represent the positions on chromosome 22.
  • FIG. 17B shows the SPC map of 700 SNPs of chromosome 22. The figure is composed of 7 panels, numbered 1 through 7, which represent 100 polymorphic sites each. The rectangles at the top of each panel represent the domains comprising 10 or more clustered SNPs.
  • FIG. 17C shows the SPC map of domain 9 of FIG. 17B from position 17,399,935 to position 17,400,240. The chromosomal map position of each SNP is indicated above the columns. The figure shows the inferred SPC-haplotypes onto which the SPCs are depicted. Polymorphic sites that do not cluster are omitted.
  • FIG. 17D presents the hierarchical relationship between the SPCs of domain 9.
  • FIG. 17E corresponds to the output of the algorithm and shows the metatypes of three trios (parents and child) onto which the SPCs are depicted, with their corresponding SPC-haplotypes.
  • the metatypes are shown in the order: parents (father and mother; marked P) and child (marked C).
  • the alleles marked by a black frame and arrows represent the genotyping errors.
  • FIG. 18 shows the SPC map of 500 kilobases on chromosome 5q31. Different colors are used to indicate the various SPCs which are represented on different rows. SNPs that do not cluster are shown on the bottom row. The SNP names are indicated above the columns. The grey rectangles, numbered 1 through 11, represent the haplotype blocks identified by Daly et al. [Daly et al., Nat. Genet. 29: 229-232, 2001]. SPCs than span different haplotype blocks are framed in their respective colors.
  • FIG. 19 shows the SPC map of single-feature polymorphisms (SFPs) in yeast. Different colors are used to indicate the various SPCs.
  • the upper panel shows the SPCs in which each SPC is represented on a different row and marked by a different color.
  • the lower panel corresponds to the output of the algorithm and shows the genetic variation table onto which the SPCs are depicted. Only those SFPs that belong to SPCs having 4 or more SFPs are shown.
  • the yeast strains for each genotype are shown in the left most column. The position of each variation on the physical map of chromosome 1 is indicated above the columns.
  • FIG. 20 shows the SPC map of the gInA locus of Campylobacter jejuni . Different colors are used to indicate the various SPCs.
  • the upper panel shows the SPCs in which each SPC is represented on a different row and marked by a different color.
  • the lower panel corresponds to the output of the algorithm and shows the genetic variation table onto which the SPCs are depicted. Only those polymorphisms that belong to SPCs having 3 or more polymorphisms are shown.
  • the Campylobacter jejuni strains for each genotype are shown in the left most column. The position of each variation is indicated above the columns.
  • FIG. 21 shows the SPC map of the asg62 locus of maize. Different colors are used to indicate the various SPCs.
  • the two panels correspond to the output of the algorithm.
  • the upper panel shows the spectral variation table onto which the SPCs are depicted.
  • the numbers above the columns refer to the position on the nucleotide sequence of the 3′-terminal nucleotide of cleavage fragments that are present in some samples (indicated by “A”) while absent in other maize lines (indicated by “C”).
  • the lower panel shows the genetic variation table onto which the SPCs are depicted.
  • the position on the nucleotide sequence of each variation is indicated above the columns.
  • the maize lines for each genotype or DNA mass profiling are shown in the left most column of each panel.
  • FIG. 22 is a schematic diagram of some of the components of a computer.
  • FIG. 23 is an exemplary flowchart showing some of the steps used to facilitate the production of an SPC map of a genomic region of interest.
  • FIG. 24 is an exemplary flowchart showing some of the steps used in an alternative embodiment to the embodiment shown in FIG. 23 .
  • FIG. 25 is an exemplary flowchart showing some of the steps used in a method of selecting one or more polymorphisms from a genomic region of interest for use in genotyping.
  • FIG. 26 is an exemplary flow chart describing some of the steps used to facilitate the identification of a marker trait or phenotype.
  • FIG. 27 is an exemplary flow chart describing some of the steps used to facilitate the identification of a location of a gene associated with a trait or phenotype.
  • FIG. 28 is an exemplary flow chart describing some of the steps used in a method for in vitro diagnosis of a trait or phenotype.
  • FIG. 29 is an exemplary flow chart describing some of the steps used in a method of determining the genetic identity of a subject.
  • FIG. 30 is an exemplary flow chart describing some of the steps used in a method of determining the SPC-haplotypes from unphased diploid genotype of a genomic region of interest.
  • FIG. 31 is an exemplary flow chart 230 describing some of the steps used in a method of producing an SPC map of a genomic region of interest.
  • the flowchart 230 begins with the step of obtaining a nucleic acid of a genomic region of interest from a plurality of subjects (block 232 ). After obtaining the nucleic acid, the flow proceeds to subjecting the nucleic acid to one or more separate sequence specific cleavage reactions, wherein each cleavage reaction generates a set of fragments (block 234 ) and analyzing the sets of fragments by mass spectrometry (block 236 ).
  • the flowchart 230 may then continue by performing an analysis of the mass spectra obtained from the sets of fragments to identify a plurality of spectral changes indicative of a plurality of polymorphism (block 238 ) and proceed with identifying one or more SPCs, wherein each SPC includes a subset of polymorphisms from the nucleic acid sequence wherein the polymorphisms of the subset coincide with each other polymorphism of the subset (block 240 ).
  • FIG. 32 is an exemplary flow chart 250 describing some of the steps used in a method of selecting one or more polymorphisms from a genomic region of interest for use in genotyping.
  • the flowchart 250 begins with the step of obtaining a nucleic acid of a genomic region of interest from a plurality of subjects (block 252 ). After obtaining the nucleic acid, the flow proceeds to subjecting the nucleic acid to one or more separate sequence specific cleavage reactions, wherein each cleavage reaction generates a set of fragments (block 254 ) and analyzing the sets of fragments by mass spectrometry (block 256 ).
  • the flowchart 250 may then continue by performing an analysis of the mass spectra obtained from the sets of fragments to identify a plurality of spectral changes indicative of a plurality of polymorphism (block 258 ) and proceed with identifying one or more SPCs, wherein each SPC includes a subset of polymorphisms from the nucleic acid sequence wherein the polymorphisms of the subset coincide with each other polymorphism of the subset (block 260 ).
  • the flow continues with selecting at least one cluster tag polymorphism which identifies a unique SPC in the SPC map (block 262 ) and selecting a sufficient number of cluster tag polymorphisms for use in a genotyping study of the genomic region of interest (block 264 ).
  • FIG. 33 is an exemplary flow chart 270 describing some of the steps used in a method of determining the SPC-haplotypes from unphased diploid genotype of a genomic region of interest of a subject.
  • the flowchart 270 begins with the step of obtaining a nucleic acid of a genomic region of interest from a plurality of subjects (block 272 ). After obtaining the nucleic acid, the flow proceeds to subjecting the nucleic acid to one or more separate sequence specific cleavage reactions, wherein each cleavage reaction generates a set of fragments (block 274 ) and analyzing the sets of fragments by mass spectrometry (block 276 ).
  • the flowchart 270 may then continue by performing an analysis of the mass spectra obtained from the sets of fragments to identify a plurality of spectral changes indicative of a plurality of polymorphism (block 278 ) and proceed with identifying one or more SPCs, wherein each SPC includes a subset of polymorphisms from the nucleic acid sequence wherein the polymorphisms of the subset coincide with each other polymorphism of the subset (block 280 ).
  • each SPC-haplotype includes a subset of SPCs from a genomic region wherein the SPCs of the subset coincide (block 282 ) and identifying the SPC-haplotype of a test subject by comparing the SPCs of the subject with the SPC-haplotypes determined from the map (block 284 ).
  • the present invention is directed to methods, algorithms and computer programs for revealing the structure of genetic variation and to the selection of the most informative markers on the basis of the underlying structure.
  • the methods can be applied on any data set of genetic variation from a particular locus.
  • the analysis of the genetic variation is based on haplotype data.
  • the structure is uncovered using diploid genotype data, thereby avoiding the need to either experimentally or computationally infer the component haplotypes.
  • the present method can be applied onto uncharacterized allelic variation that results from the interrogation of a target nucleic acid with an experimental procedure that provides a record of the sequence variation present but does not actually provide the entire sequence or, in particular, the sequence at the variable positions.
  • the underlying structure of genetic variation is also useful for the deduction of the constituent haplotypes from diploid genotype data.
  • polymorphism refers to a condition in which two or more different nucleotide sequences can exist at a particular locus in DNA. Polymorphisms can serve as genetic markers. Polymorphisms include “single nucleotide polymorphism” (SNP) and indels. Such polymorphisms also are known as restriction fragment length polymorphisms (RFLP).
  • SNP single nucleotide polymorphism
  • RFLP restriction fragment length polymorphisms
  • a RFLP is a variation in DNA sequence that alters the length of a restriction fragment, as described in Botstein et al., Am. J. Hum. Genet. 32:314-331 (1980).
  • the restriction fragment length polymorphism may create or delete a restriction site, thus changing the length of the restriction fragment.
  • RFLPs have been widely used in human and animal genetic analyses (see WO 90/13668; W090/11369; Donis-Keller, Cell 51:319-337 (1987); Lander et al., Genetics 121:85-99 (1989)).
  • a heritable trait can be linked to a particular RFLP, the presence of the RFLP in an individual can be used to predict the likelihood that the animal will also exhibit the trait.
  • VNTR variable number tandem repeat
  • allele(s) indicate mutually exclusive forms (sequences) of a single polymorphic site or of a combination of polymorphic sites.
  • single nucleotide polymorphism is used to indicate a polymorphism or genetic marker that involves a single nucleotide.
  • SNPs are bi-allelic polymorphisms/markers.
  • the term “indel”, as used herein, indicates an insertion/deletion polymorphism that involves two or more nucleotides.
  • major allele refers to the most frequent of two or more alleles at a polymorphic locus.
  • minor allele(s) refers to the less frequent allele(s) found at a polymorphic locus.
  • diploid refers to the state of having each chromosome in two copies per nucleus or cell.
  • haplotype denotes the combination of alleles found at multiple contiguous polymorphic loci (e.g. SNPs) on the same copy of a chromosome or haploid DNA molecule.
  • genotype indicates the allele or pair of alleles present at one or more polymorphic loci.
  • haplotype indicates the allele or pair of alleles present at one or more polymorphic loci.
  • two haplotypes make up a genotype.
  • genotype corresponds to the haplotype.
  • Metatype refers to an artificial haplotype. Metatypes originate from the replacement of the heterozygous calls in a genotype by either the minor or the major allele observed at the applicable positions.
  • sequence polymorphism cluster refers to a set of tightly linked (coinciding, co-occurring; co-segregating) sequence polymorphisms. More specifically, the term SPC indicates the set of coinciding minor alleles.
  • cluster tag SNP(s) refers to one or more SNPs that best represent the sequence polymorphism cluster to which the SNP(s) belong and that are preferred as markers for the detection of that sequence polymorphism cluster.
  • cluster tag polymorphism(s), refers to one or more polymorphisms that best represent the sequence polymorphism cluster to which the polymorphisms belong and that can serve as markers for the detection of that sequence polymorphism cluster.
  • Cluster tag SNP(s) are preferred cluster tag polymorphisms.
  • SPC-haplotype refers to the haplotype formed by those polymorphisms that belong to one or more SPCs.
  • leton means an instance of a category that has only one element or occurs only once; the context makes clear what is meant. A singleton SNP or SPC occurs only once in the sample under investigation.
  • clade denotes a group of sequences or haplotypes that are related in that these haplotypes have one or more SPCs in common while also differing from one another in at least one SPC.
  • sequence polymorphism clusters SPCs
  • SPCs sequence polymorphism clusters
  • SPCs are identified by first quantifying the percentage coincidence between pairs of (bi-allelic) sites followed by the stepwise assembly of marker alleles that exhibit coincidence above a gradually less stringent threshold.
  • Coincident marker alleles can be identified with the use of certain measures for assessing the strength of LD.
  • Many different LD statistics have been proposed [Lewontin R. C., Genetics 140: 377-388, 1995; Devlin & Risch, Genomics 29: 311-322, 1995].
  • One frequently used LD measure that is suitable with the present invention is r 2 (sometimes denoted ⁇ 2 ). r 2 ranges from zero to one and represents the statistical correlation between two sites; it takes the value of 1 if only two out of the four possible two-site haplotypes are observed in the sample.
  • statistic and similar measures [e.g. Q; see Devlin & Risch, Genomics 29: 311-322, 1995] are not appropriate for the present algorithm as these measures return the maximum value irrespective of whether there are two or three haplotypes formed by the pair of markers.
  • the denominator which serves to standardize D is however such that, in contrast to the more commonly used
  • measure, C* 1, if, and only if, two out of the four possible two-locus haplotypes are observed in the sample.
  • C* can be positive (coupling) or negative (repulsion) and that in this case absolute values are taken into consideration.
  • the formula consistently used herein simply measures the proportion (%) of the haplotype consisting of the minor alleles a and b (P ab ), relative to the frequency of the most common minor allele (i.e.
  • the missing data points were treated in a statistical way and were taken as both the minor and major allele in proportion to the observed allele ratio at that polymorphic position.
  • the two-site haplotypes may also occur as fractions.
  • the number of alleles or haplotypes was divided by the total number of samples.
  • only those samples that have an allele call at both polymorphic positions are considered to calculate the haplotype as well as the allele frequency.
  • the allele frequencies at one particular polymorphic site are not fixed but depend on the site with which association is being calculated. The latter approach tends to overestimate the strength of association and may be utilized for the detection of SPCs in data sets with numerous missing allele calls. It will be understood that the different approaches are identical when the sample genotypes are devoid of missing data.
  • the input consists of a genetic variation table containing the alleles present at a given number of polymorphic sites (columns) for a plurality of subjects (rows), i.e. basically a set of haplotypes (although it is shown herein that diploid genotype data may also be processed).
  • the program can derive this table from a ‘multiple sequence alignment file’.
  • the first step in the algorithm consists of the generation of a matrix with all pairwise calculations of the strength of coincidence (e.g. values of C as defined above). Subsequently, a clustering operation is performed whereby one or more sequence polymorphism clusters (SPC) are formed and an SPC map is assembled.
  • SPC sequence polymorphism clusters
  • An SPC assembles sequence polymorphisms that coincide with each other to an extent that exceeds an empirically defined threshold level.
  • the minimum number of polymorphisms that an SPC has to incorporate as well as its occurrence frequency in the sample in order for that SPC to be statistically meaningful varies from one data set to the other.
  • the clustering operation is an iterative process.
  • the clusters that are formed are allowed to expand and new clusters are to emerge by gradually decreasing (e.g. using steps of 0.1, 0.05 or 0.025) the threshold value down to a bottom value.
  • SPCs can be defined at any threshold value, including 1, ⁇ 0.95, ⁇ 0.90, ⁇ 0.85, ⁇ 0.80, ⁇ 0.75, ⁇ 0.70, ⁇ 0.65, ⁇ 0.60, ⁇ 0.55, and ⁇ 0.50.
  • the adequacy of the threshold settings depends, among other things, on the measure that is used to calculate the strength of association of the marker alleles.
  • the clustering operation may be performed according to several different criteria. In one approach, all pairwise coincidence values of the cluster polymorphisms must exceed the chosen threshold level. Alternatively, individual polymorphisms or entire clusters are merged when the average association value exceeds a certain practical threshold level. Yet another option requires that at least one polymorphism is in association with all other polymorphisms of the cluster above the threshold value.
  • a cluster may assemble not only the group of primary polymorphisms whose pairwise association surpasses the threshold but also secondary polymorphisms that are in association above the threshold with one of the primary polymorphisms.
  • the SPCs that the program has identified can be visualized in a number of different ways including a color-coded version of the above-mentioned matrix with coincidence values (C-values) and a color-coded version of the original input genetic variation table (sorted such that the individuals that share the same SPCs are grouped).
  • C-values coincidence values
  • a color-coded version of the original input genetic variation table sorted such that the individuals that share the same SPCs are grouped.
  • the SPC-program incorporates a module for the selection of cluster tag polymorphisms. This selection is based on the identification of the one or more polymorphisms that best represent the SPC they belong to. Typically, SNPs are chosen as cluster tag polymorphisms; cluster tag SNPs are herein also named ctSNPs. According to a preferred method, the average strength of association (herein also referred to as Average Linkage Value or ALV) of each polymorphism with all other polymorphisms of the cluster is calculated and used as the decisive criterion: the one or more polymorphisms/SNPs that exhibit the highest ALV are retained as markers for subsequent genotyping experiments.
  • ALV Average Linkage Value
  • indels were identified by two dots at, respectively, the start and the end position of the deletion. In between these dots blank spaces may be present whenever polymorphic sites occur at intervening positions in the other samples. Blank spaces in the genetic variation table are ignored and frequencies are calculated by simply dividing the observed number of a particular allele or two-site haplotype by the total number of samples.
  • the algorithm can not only be applied to a data set of genetic variants from a particular locus but also, in a generic sense, to experimental data that capture all or part of that genetic variation.
  • the genetic variation table can also consist of diploid genotype data.
  • the input table is adapted to contain each individual twice; all heterozygous scores are then replaced by the minor allele in one entry and by the major allele in the second entry.
  • the resultant artificial haplotypes are herein named metatypes and the adapted genetic variation table is called a metatype table.
  • the present clustering method may presumably also be performed with the use of other measures for the strength of association between marker alleles than those mentioned herein. These measures can either be known or newly conceived. For instance, a statistic that measures the strength of association between multi-allelic rather than bi-allelic loci could be utilized [e.g. refer to Hedrick P. W., Genetics 117: 331-341, 1987 for a multi-allelic version of D′]. In general, the use of alternative measures in combination with appropriate threshold levels will expose a set of SPCs. This, and other variations in the algorithm may be readily adapted by those skilled in the art. These variations may to a certain extent affect the output of the program (as is often the case with iterative clustering procedures) but are equally useful in exposing the fundamental SPC structure of genetic variation data—these variations are therefore also within the scope of the present invention.
  • FIG. 22 is a schematic diagram of one possible embodiment of a computer (i.e., machine) 30 .
  • the computer 30 may be used to accumulate, analyze, and download data relating to defining the subset of variations that are most suited as genetic markers to search for correlations with certain phenotypic traits.
  • the computer 30 may have a controller 100 that is operatively connected to a database 102 via a link 106 . It should be noted that, while not shown, additional databases may be linked to the controller 100 in a known manner.
  • the controller 100 may include a program memory 120 , a microcontroller or a microprocessor (MP) 122 , a random-access memory (RAM) 124 , and an input/output (I/O) circuit 126 , all of which may be interconnected via an address/data bus 130 . It should be appreciated that although only one microprocessor 122 is shown, the controller 100 may include multiple microprocessors 122 . Similarly, the memory of the controller 100 may include multiple RAMs 124 and multiple program memories 120 . Although the I/O circuit 126 is shown as a single block, it should be appreciated that the I/O circuit 126 may include a number of different types of I/O circuits.
  • the RAM(s) 124 and programs memories 120 may be implemented as semiconductor memories, magnetically readable memories, and/or optically readable memories, for example. All of these memories or data repositories may be referred to as machine-accessible mediums.
  • the controller 100 may also be operatively connected to a network 32 via a link 132 .
  • a machine-accessible medium includes any mechanism that provides (i.e., stores and/or transmits) information in a form accessible by a machine (e.g., a computer, network device, personal digital assistant, manufacturing tool, any device with a set of one or more processors).
  • a machine-accessible medium includes recordable/non-recordable media (e.g., read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices), as well as electrical, optical, acoustical or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals); etc.
  • FIG. 23 is a flow chart 150 describing some of the steps used to facilitate the production of a sequence polymorphism cluster (SPC) map of a genomic region of interest.
  • the flowchart 150 begins with the step of obtaining the nucleic acid sequence of a genomic region of interest from a plurality of subjects (block 152 ). After obtaining the nucleic acid sequence, the flow chart 150 proceeds to identifying a plurality of polymorphisms in the nucleic acid sequences (block 154 ) and then to identifying one or more SPCS, wherein each SPC comprises a subset of polymorphisms from the nucleic acid sequence wherein the polymorphisms of the subset coincide with each other polymorphism of the subset (block 156 ).
  • SPC sequence polymorphism cluster
  • the identification of the one or more SPCs may include identifying each polymorphism of the subset that coincides with each other polymorphism of the subset according to a percentage coincidence of the minor alleles of the polymorphisms of between 75% and 100%.
  • the identification of the one or more SPCs also may include multiple rounds of coincidence analysis, wherein each successive round of coincidence analysis is performed at a decreasing percentage coincidence from 100% coincidence to 75% coincidence.
  • the coincidence of each of the polymorphism of the subset with each other polymorphism of the subset may be calculated according to a parameter, such as, for example, a pairwise C value, a r2 linkage disequilibrium value, and a d linkage disequilibrium value, wherein the pairwise C value ranges from 0.75 to 1.
  • a parameter such as, for example, a pairwise C value, a r2 linkage disequilibrium value, and a d linkage disequilibrium value, wherein the pairwise C value ranges from 0.75 to 1.
  • the identification of a plurality of polymorphisms in the target nucleic acid sequences may be determined by an assay, such as, for example, direct sequence analysis, differential nucleic acid analysis, sequence based genotyping DNA chip analysis, and PCR analysis.
  • FIG. 24 is a flow chart 160 describing some of the steps used to facilitate the production of an SPC map of a genomic region of interest from unphased diploid genotypes.
  • the flowchart 160 may begin with the step of obtaining the unphased diploid genotypes of a genomic region of interest from a plurality of subjects (block 162 ). After obtaining the unphased diploid genotypes, the flow proceeds to determining the major and minor metatypes found in the unphased diploid genotypes (block 164 ) and then to identifying one or more SPCs, wherein each SPC comprises a subset of polymorphisms from the metatypes wherein the polymorphisms of the subset coincide with each other polymorphism of the subset (block 166 ).
  • the step of identifying the one or more SPCs may include identifying each polymorphism of the subset that coincides with each other polymorphism of the subset according to a percentage coincidence of the minor alleles of the polymorphisms of between 85% and 100%.
  • the exemplary method disclosed in FIG. 24 may include multiple rounds of coincidence analysis, wherein each successive round of coincidence analysis is performed at a decreasing percentage coincidence from 100% coincidence to 75% coincidence.
  • the coincidence of each of the polymorphism of the subset with each other polymorphism of the subset may be calculated according to a parameter, such as, for example, a pairwise C value, a r2 linkage disequilibrium value, and a d linkage disequilibrium value, wherein the pairwise C value ranges from 0.75 to 1.
  • the identification of a plurality of polymorphisms in the target nucleic acid sequences may be determined by an assay, such as, for example, direct sequence analysis, differential nucleic acid analysis, sequence based genotyping DNA chip analysis, and PCR analysis.
  • FIG. 25 is an exemplary flow chart 170 describing some of the steps used in a method of selecting one or more polymorphisms from a genomic region of interest for use in genotyping.
  • the flowchart 170 may begin with the step of obtaining an SPC map of a genomic region of interest (block 172 ). After obtaining the SPC map, the flow chart 170 may proceed to selecting at least one cluster tag polymorphism which identifies a unique SPC in the SPC map (block 174 ) and then to selecting a sufficient number of cluster tag polymorphisms for use in a genotyping study of the genomic region of interest (block 176 ).
  • the cluster tag polymorphism may be, for example, a single nucleotide polymorphism (SNP), a deletion polymorphism, an insertion polymorphism; or a short tandem repeat polymorphism (STR). Also, the cluster tag polymorphism may be a known SNP associated with a genetic trait.
  • SNP single nucleotide polymorphism
  • STR short tandem repeat polymorphism
  • FIG. 26 is a flow chart 180 describing some of the steps used to facilitate the identification of a marker trait or phenotype.
  • the flowchart 180 may begin with the step of obtaining a sufficient number of cluster tag polymorphisms from a genomic region of interest (block 182 ). After obtaining the sufficient number of cluster tag polymorphisms, the flow proceeds to assessing the cluster tag polymorphisms to identify an association between a trait or phenotype and at least one cluster tag polymorphism, wherein identification of the association identifies the cluster tag polymorphism as a marker for the trait or phenotype (block 184 ).
  • the cluster tag polymorphism may be correlated with a variety of traits or phenotypes, such as, for example, a genetic disorder, a predisposition to a genetic disorder, susceptibility to a disease, an agronomic or livestock performance trait, a product quality trait.
  • the marker may be a marker of a genetic disorder and the SPC map may be prepared according to the method described in FIG. 23 , and the plurality of subjects each manifests the same genetic disorder.
  • the identification of the plurality of polymorphisms in the target nucleic acid sequences may be determined by a number of assays, including, for example, direct sequence analysis, differential nucleic acid analysis, sequence based genotyping, DNA chip analysis and polymerase chain reaction analysis.
  • FIG. 27 is an exemplary flow chart 190 describing some of the steps used to facilitate the identification of a location of a gene associated with a trait or phenotype.
  • the flowchart 190 may begin with the step of identifying a plurality of SPCs identified in a given genomic region associated with the trait or phenotype, wherein each SPC comprises a subset of polymorphisms from the genomic region wherein the polymorphisms of the subset are associated with each other polymorphism of the subset (block 192 ).
  • the flow proceeds to identifying a set of cluster tag polymorphisms wherein each member of the set of cluster tag polymorphisms identifies a unique SPC in the plurality of SPCs (block 194 ).
  • the flow may then continue with assessing the set of cluster tag polymorphisms to identify an association between a trait or phenotype and at least one cluster tag polymorphism, wherein identification of the association between the cluster tag polymorphism and the trait or phenotype is indicative of the location of the gene (block 196 ).
  • the phenotype may be, for example, a genetic disorder, a predisposition to a genetic disorder, susceptibility to a disease, an agronomic or livestock performance trait, or a product quality trait.
  • FIG. 28 is an exemplary flow chart 200 describing some of the steps used in a method for in vitro diagnosis of a trait or phenotype.
  • the flowchart 200 may begin with the step of obtaining a marker for a trait or phenotype in a subject (block 202 ). After obtaining the marker, the flow proceeds to obtaining a target nucleic acid sample from the subject (block 204 ) and determining the presence of the marker for the trait or a phenotype in the target nucleic acid sample, wherein the presence of the marker in the target nucleic acid indicates that the subject has the trait or the phenotype (block 206 ).
  • the trait or phenotype may be, for example, a genetic disorder, a predisposition to a genetic disorder, susceptibility to a disease, an agronomic or livestock performance trait, or a product quality trait.
  • FIG. 29 is an exemplary flow chart 210 describing some of the steps used in a method of determining the genetic identity of a subject.
  • the flowchart 210 may begin with the step of obtaining a reference SPC map of one or more genomic regions from a plurality of subjects (block 212 ). After obtaining the reference SPC map, the flow proceeds to selecting a sufficient number of cluster tag polymorphisms for the genomic regions (block 214 ) and obtaining a target nucleic acid of the genomic regions from a subject to be identified (block 216 ).
  • the flow may continue with determining the genotype of the cluster tag polymorphisms of the genomic regions of the subject to be identified (block 218 ) and comparing the genotype of the cluster tag polymorphisms with the reference SPC map to determine the genetic identity of the subject of interest (block 219 ).
  • the reference SPC map may be prepared according to the methods described in connection with FIGS. 22 or 23 .
  • FIG. 30 is an exemplary flow chart 220 describing some of the steps used in a method of determining the SPC-haplotypes from unphased diploid genotype of a genomic region of interest.
  • the flowchart 220 begins with the step of obtaining an SPC map of a genomic region of interest (block 222 ).
  • each SPC-haplotype includes a subset of SPCs from a genomic region wherein the SPCs of the subset coincide (block 224 ) and identifying the SPC-haplotype of a test subject by comparing the SPCs of the subject with the SPC-haplotypes determined from the SPC map (block 226 ).
  • sequence polymorphism clusters i.e. sets of polymorphisms that are essentially in absolute linkage (i.e. pairwise C-value is 1 or close to 1).
  • the inventors have found this to be true in all species for which sufficient data on genetic variation are available, including human, maize, Arabidopsis, Drosophila , and yeast.
  • the polymorphisms in an SPC are non-contiguous and the polymorphisms that belong to different SPCs are intermingled.
  • the present finding is different from the haplotype block concept in which areas of contiguous polymorphisms are identified that are essentially devoid of recombination (i.e. high values of Lewontin's D′ measure) and/or that display limited haplotype diversity [refer to Wall & Pritchard, Nature Rev. Genet. 4: 587-597, 2003 for various definitions of haplotype blocks].
  • SPCs sequence polymorphism clusters
  • FIGS. 1 to 3 The structures revealed by the method of the present invention are referred to as sequence polymorphism clusters (SPCs).
  • SPCs sequence polymorphism clusters
  • FIGS. 1 to 3 These Figures are based on idealized imaginary genetic variation data sets (containing the allele calls at all the polymorphic sites for a plurality of test subjects), which are devoid of confounding data.
  • the SPC structures observed in publicly available authentic data sets, derived from various species, are discussed in the Examples provided below.
  • FIGS. 1A and 2A typify frequently observed patterns of SPCs; in practice, mostly combinations of these two patterns are found ( FIG. 3A ).
  • Groups of interspersed polymorphisms exhibit strong linkage, e.g. the alleles at the polymorphic sites are essentially found in only two combinations. Matrices with all pairwise C-values are shown in FIGS. 1B and 2B .
  • the SPCs display one of two different relationships. Some SPCs are unrelated/independent, i.e. the minor alleles occur on distinct haplotypes ( FIG. 1A ). Other SPCs are dependent and can be ranked according to their level of inclusiveness; the minor allele of a dependent SPC occurs on a subset of the haplotypes on which the minor alleles of one or more higher-level SPCs are found ( FIG. 2A ). As a rule, an SPC is not found both in conjunction with (dependent relationship), as well as separate from another SPC (independent configuration). In other words, the minor alleles of two SPCs are not both found on distinctive haplotypes as well as jointly on a third haplotype.
  • the orderly SPC structure can be represented by means of a simple network wherein each branch corresponds to the appearance/disappearance of one particular SPC (see FIGS. 1C, 2C and 3 B).
  • each branch corresponds to the appearance/disappearance of one particular SPC (see FIGS. 1C, 2C and 3 B).
  • the nodes of the network correspond to the various sequences/haplotypes, which may or may not be observed in the plurality of samples under study (see for example FIG. 3B ).
  • Haplotypes and their closest relatives that differ only by the presence of non-clustering polymorphisms are herein named after the SPCs they contain (see FIGS. 1A and 2A ), and are herein referred to as SPC-haplotypes.
  • the network clarifies the relationship between SPCs on the one hand and haplotypes on the other hand: the SPCs can be viewed as the elements with which the various haplotypes are built. Certain SPCs are specific to one haplotype while others are common to several haplotypes, thus defining a clade of related haplotypes.
  • the SPC organization translates into one of two different hierarchical network structures. Unrelated SPCs branch off from a single central point ( FIG. 1C ); i.e.
  • the SPC network establishes an apparent genealogical relationship between the main sequences, i.e. the sequences devoid of the non-clustering polymorphisms. It should be realized that the network is unrooted (due to the lack of an “outspecies” or sequence from an accepted common ancestor) and, consequently, that evolutionary relationships deduced from the network are ambiguous.
  • the branches do not reflect evolutionary distance or extent of sequence divergence while the size of the nodes does not relate to the occurrence frequency of the various sequences.
  • Various alternative representations, that include a variable amount of evolutionary information, are known in the art, such as a dendrogram and a cladogram. Skilled persons will also recognize that the network structure depends on the (depth of) sampling as well as the population under study.
  • the method of the present invention is thus capable of revealing intrinsic structures of DNA sequence variation in any species.
  • This structure stands out against and can explain the often complex patterns of LD between adjacent markers and the overall lack of correlation between the level of LD and physical distance.
  • sequence variations in for example maize, that previously had been described as displaying very little LD [Tenaillon et al., Proc. Natl. Acad. Sci. USA 98: 9161-9166, 2001; Remington et al., Proc. Natl. Acad. Sci. USA 98: 11479-11484, 2001; Gaut & Long, The Plant Cell 15: 1502-1505, 2003], are highly structured and that SPCs extend over greater distances.
  • haplotype notion and the more recently developed haplotype block concept represent practical approaches to capture most of the common genetic variation with a small number of SNPs.
  • haplotypes and the genealogical record it provides has not been recognized.
  • the knowledge of the underlying SPC organization in a genomic region allows for the logical and most powerful design and interpretation of genetic analyses.
  • the method of the present invention is directed to an SPC map of a genomic region of interest or an entire genome and to methods of constructing such an SPC map.
  • An SPC map can be used to select an optimal set of markers, all or part of which can be assayed in subsequent genotyping studies, i.e. to establish an association between a genotype and a phenotype/trait or for in vitro diagnostic purposes.
  • the SPC map can also reveal the full breadth of genetic diversity in a species as well as its close relatives, such as certain economically important crops and livestock, and thereby provide opportunities for marker-assisted (inter)breeding.
  • the SPC map can be constructed with genetic variation data derived from any population sample.
  • the SPC map depends to some extent on the population under study as well as the depth of investigation (i.e. the size of the sample) and that the map should be used accordingly.
  • the value of certain assays is directly correlated with the validity and comprehensiveness of the SPC map on which the assays are based and that, therefore, the map has to be built starting from a representative and sufficiently large sample of the population.
  • the construction of an SPC map comprises determining the pattern of SPCs across the genomic region of interest, their relationship as well as their boundaries.
  • the pattern of SPCs is preferably analyzed at a variety of threshold levels rather than one single predetermined stringency.
  • SPCs can be defined at any threshold value, including 1, ⁇ 0.95, ⁇ 0.90, ⁇ 0.85, ⁇ 0.80, ⁇ 0.75, ⁇ 0.70, ⁇ 0.65, ⁇ 0.60, ⁇ 0.55, and ⁇ 0.50.
  • Those of ordinary skill in the art will recognize that the adequacy of the threshold settings depends, among other things, on the measure that is used to calculate the strength of association of the marker alleles.
  • This is illustrated in FIG. 4 .
  • the genetic variation data set used for this figure is the same as that for FIG. 1 except that 5% of the allele calls, chosen at random, were replaced by missing data (4.5%; symbolized by “N”) or an incorrect result (0.5%; the accurate allele was substituted for the opposite allele observed at that position).
  • SPCs are primarily ended by recombination events. This is illustrated in FIG. 5 and FIG. 6 .
  • FIG. 5A /B exemplifies the effect of a few historical recombination events on the SPC structure.
  • one particular SPC namely SPC-1
  • the recombination events are recognized by the simple fact that the SNPs of the new SPCs (e.g. SPC-1x and SPC-1y) do not intermingle with those of SPC1, as is typically be the case for SPCs in non-recombinant regions, and instead produce adjacent SPCs.
  • a recombination event results in a violation of the prevailing principle in an SPC structure, namely that an SPC pair is not found both in an independency as well as a dependency configuration.
  • the relationship between the two new SPCs and SPC-1 is one of apparent dependency (this is because SPC-1 recombined with SPC-0 which is devoid of SPCs) and an irregularity is only observed when considering the relation between SPC-1x and SPC-1y. This conflict in the relationship is indicated by the dashed lines in the network structure of FIG. 5D .
  • FIG. 5E /F and FIG. 5G /H show the identified SPCs and corresponding network at C ⁇ 0.9 and C ⁇ 0.8, respectively. It can be seen that SPC-1x and SPC-1y unite one at the time with SPC-1 at stepwise decreased stringencies.
  • FIG. 6A /B shows that the association is low for all polymorphic site pairs that are spanning a hotspot of recombination. It can be seen in the matrix of FIG. 6B that these pairwise C-values are all ⁇ 0.5 indicating that there is no clustering between the SNPs on both sides of the recombination hotspot. Recurrent recombination clearly demarcates the end of an LD-region.
  • FIG. 6C shows an SPC map of the locus of interest. The SPCs found in the two distinct regions are shown separately to reflect the fact that they can occur in various combinations.
  • SPCs that belong to neighboring regions do not obey the hierarchical principle that is observed within non-recombinant regions, namely that the minor alleles of two SPCs cannot both be found on separate and the same haplotypes.
  • the SPC relationship can only be shown for each region separately ( FIG. 6D ).
  • the haplotype map represents a ‘block-like’ partitioning of the human genome.
  • the discrete haplotype blocks are segments of various sizes over which limited recombination is observed and which are bounded by sites of recombination. There is evidence to suggest that within each such haplotype block the genetic diversity is extremely limited, with an average of three to six common haplotypes that together comprise, on average, 90% of all chromosomes in the population sample.
  • the map elements or SPCs in a region do not necessarily have the same boundaries. In many instances, one or more SPCs extend across the endpoints of other SPCs (even so when that endpoint is observed at a high frequency in the population) or encompass multiple other SPCs.
  • the map elements are also defined differently: whereas haplotype blocks essentially correspond to non-recombinant regions, SPCs require the more strict condition of co-occurrence of the marker alleles (absolute LD). Additionally, non-clustering polymorphic sites are regarded as poor markers in the SPC concept whereas, in the haplotype block model, they may be considered for inclusion in the panel of tag SNPs since they do contribute to haplotype diversity.
  • the SPC map provides a rational and superior basis for the selection of informative SNPs that are of value in the discovery of associations with certain phenotypes.
  • it represents a coherent method to reduce the number of variants that need to be assayed without the loss of information.
  • a single representative SNP referred to as a ctSNP
  • ctSNP single representative SNP
  • all other polymorphisms of the SPC can be considered redundant.
  • the inventors identified cases where SPCs are shared between related species and, therefore, predate the speciation event (refer to Example 4).
  • SPCs are ‘very old’ and indicates that these structures represent ancestral groupings of variations that have been subjected to extensive natural selection and have been retained throughout history because they effect or are linked to a particular phenotype.
  • SPCs may be viewed as most significant to test as units for association to phenotype.
  • the polymorphisms that fail to cluster are in all likelihood more recent mutations, in case they are found in conjunction with only one SPC, and may represent recurrent mutations in case the polymorphisms are in partial association with more than one SPC.
  • the present clustering approach represents a novel diagnostic method for the genetic diagnosis of biologically (medically or agriculturally) relevant genetic variation. More specifically, it is projected that the method of the present invention will be very useful for selecting DNA markers that have superior diagnostic value.
  • an SPC may contain polymorphisms other than SNPs (see Example 1)
  • the polymorphism that is specified as a tag for the cluster will preferably be an SNP.
  • This type of marker is readily assayed using one of several available procedures [Kwok P. Y., Annu. Rev. Genomics Hum. Genet. 2: 235-258, 2001; see also hereinafter].
  • the SNPs that belong to a particular SPC are not (all) equally useful as tag for that SPC.
  • the possible concept that any one SNP that is in association with all other polymorphic sites of the SPC above a chosen threshold level qualifies as ctSNP is to a large extent arbitrary.
  • an objective ranking is proposed that reflects how well the various SNPs represent the SPC they belong to. This can be achieved using one of several possible criteria—according to a preferred method the average strength of association of each SNP with all other polymorphisms of the cluster is used as the decisive criterion.
  • the selection of ctSNPs according to this measure is illustrated for three different SPCs in FIG. 4G /H/I.
  • FIGS. 4G, 4H , and 4 I show two tables for SPC-1, SPC-2 and SPC-4, respectively.
  • the first summary table lists the allele calls at each polymorphic site categorized in the respective SPCs.
  • the second table shows the matrix of pairwise C-values within each cluster. As indicated above, these values were calculated differently as compared to those shown in FIG. 4D .
  • the average C-value for each polymorphism is shown along the diagonal SNP as well as in the right margin.
  • ctSNP SNP with the highest average strength of association with the other polymorphisms of the cluster.
  • SNPs with only marginal differences in the average strength of association with the other SPC polymorphisms may be used interchangeably as ctSNP. This offers the opportunity to select an SNP that is readily assayed on the platform of choice.
  • Persons of ordinary skill in the art will appreciate that alternative ways can be conceived to rank SNPs and to select tag SNPs that best represent a cluster. It will also be understood that the validity of the choice of ctSNPs depends on the quality of the data.
  • SNPs are justifiably rejected as ctSNP when the relative weak association with the other polymorphisms is genuine, i.e. is attributable to biological phenomena such as recurrent mutation or gene conversion.
  • SNPs may also be declined inappropriately on the basis of poor assay results; it is obvious that the latter SNPs are in reality good candidate tag SNPs which may be selected by using superior data obtained, for instance, by means of an alternative assay protocol/platform.
  • the SPC structure of a locus provides a logical framework that is of use in the design of experiments to genetically characterize that locus as well as to rationalize the experimental results.
  • Association between an SPC (or the ctSNP that represents the SPC) and a particular phenotype reveals itself by an increase in the frequency of the rare allele in a population that is characterized by the phenotype as compared to a control population.
  • the relationships between SPCs also imply a certain correlation in the allele frequencies measured for the various SPCs. For instance, in the case of independent SPCs ( FIG. 1A ), an association of the phenotype with one specific SPC will be accompanied by a decrease in the rare allele frequencies of (all) other SPCs.
  • associations with SPCs in a dependency relationship do coincide: a causal relation with one particular SPC necessarily implies linkage with the lower-level dependent SPCs as well as linkage (albeit less pronounced) with the SPCs that are higher up in the hierarchical tree.
  • a clade-specific SPC that is high up in hierarchy is shared by a number of different haplotypes and can, in principle, be used to reveal an association with any of these different haplotypes.
  • SNPs can be chosen that correspond to the primary level of divergence, e.g. SNPs that tag the SPCs labeled 1, 2, and 3 in FIG. 3B .
  • SNPs can be chosen that correspond to the primary level of divergence, e.g. SNPs that tag the SPCs labeled 1, 2, and 3 in FIG. 3B .
  • a more thorough study would involve the use of a larger number of SNPs, for example those that tag the subsequent layer of dependent SPCs (e.g. SPCs 1.1, 1.2, 2.1, 2.2, 3.1 and 3.2 in FIG. 3B ).
  • the clade-specific SPC corresponds to a node in the SPC network that does not match with an actual sequences/haplotype in the sample under study. This is illustrated in FIG. 3B where the SPC-1 does not require tagging since it always coincides with either dependent SPC-1.1 or SPC-1.2 while, similarly, the detection of SPC-3.2.1 and SPC-3.2.2 render the identification of SPC-3.2 excessive.
  • a systematic genetic characterization is particularly useful for loci with a complex SPC map.
  • Analyses according to the methods of the present invention have revealed that certain loci are characterized by a highly branched SPC structure with many levels of dependency (refer to FIGS. 3A and 3B ). This has, for example, been observed in the ‘SeattleSNPs’ genetic variation data [UW-FHCRC Variation Discovery Resource; http://pga.gs.washington.edu/; see also Example 7]. It is to be anticipated that, in general, the recognition of such a highly divergent structure will require a fairly exhaustive search for the genetic variation by sequence determination of sizeable regions on a sufficient number of individuals, i.e.
  • the variation data must be sufficiently dense and contain common as well as rare polymorphisms. Rare SPCs will only progressively emerge as the population is being examined to a greater depth. For instance, while the data of the International HapMap Project, at the current level of SNP density [e.g. ⁇ 274,500 SNPs as of Jan. 7, 2003; http://www.hapmap.org; Dennis C., Nature 425: 758-759 (2003)], exhibit already some SPC structure, at least in the most SNP dense parts (refer to Example 9), it should not be expected to reveal this structure to its full depth.
  • the SPC structure and its translation into a methodical genetic characterization can be applied to genome wide scans and in addition, it also is applicable to other studies, such as in vitro diagnosis.
  • the diagnostically important human MHC locus constitutes but one possible example. Indeed, the following Examples show an investigation of the MHC genotype data generated by Jeffreys and coworkers [Jeffreys et al., Nature Genet. 29: 217-222 (2001)] and show that at least certain regions are characterized by a highly branched SPC network (refer to Example 8).
  • the method of the present invention is directed to the identification of SPCs and ctSNPs using diploid genotype data.
  • Sequence polymorphism clusters may indeed be detected by applying the present algorithm directly to diploid genotypes in place of a haplotype data set. This is less important for most economically important plant and animal species where essentially homozygous inbred lines are readily available.
  • the ability to use genotype rather than haplotype data for the detection of SPCs represents an important advantage in the case of humans. It avoids the need to determine the haplotypes, which is hard to accomplish experimentally and error prone when based on computational approaches alone.
  • the identification of SPCs on the basis of diploid genotype data is illustrated in FIGS. 7 and 8 .
  • the first example is based on essentially the same data set used in FIG. 1 , i.e. a simple case of a number of independent SPCs.
  • the second example relates to genotype data exhibiting a more complex SPC structure.
  • the input genetic variation table FIGS. 7A and 8A
  • This duplicate table is further modified in that all heterozygous scores are replaced by the minor allele in one copy and by the major allele in the second copy.
  • the resultant artificial haplotypes are herein named minor metatypes, in case the heterozygous calls are replaced by the minor allele, and major metatypes when the heterozygous calls in the diploid genotypes were substituted for the major allele.
  • the duplicated and reformatted genetic variation table is referred to as the metatype table. It is noted that two essential features are perfectly retained in the metatype format, namely the frequencies of the alleles and their co-occurrence or linkage. Indeed, the ratios of the heterozygous and homozygous alleles (i.e. 0.5:1) are correctly maintained by separating diploid genotypes in two metatypes. The linkages between the co-occurring sites are retained by the simultaneous replacement of all heterozygous genotypes on a single diploid genotype by either the minor alleles or the major alleles in respectively the minor and major metatypes.
  • FIGS. 7 B/C/D and 8 B/C/D show the SPCs revealed by the analysis of the diploid genotypes.
  • the diploid genotypes were generated by the random association of haplotypes with a known SPC structure ( FIGS. 7E and 8 E).
  • a comparison indicates that the SPCs identified on the basis of diploid genotypes are identical to those found on the starting haplotypes.
  • the analysis of the diploid genotype data would ultimately lead to the selection of the same set of ctSNPs as an analysis of the elementary haplotypes.
  • the illustrations of FIGS. 7 C/D and 8 C/D however demonstrate one notable difference with bona fide haploid genotypes, namely that independent SPCs can coincide on certain metatypes (compare FIG.
  • the methods of the present invention differ in several aspects from the method developed by Carlson and coworkers to identify maximally informative tag SNPs [Carlson et al., Am. J. Hum. Genet. 74: 106-120, 2004].
  • the present invention teaches a method to recognize sets of clustered polymorphisms in diploid genotype data.
  • the selection of ctSNPs can be performed without the prior need to infer haplotypes from these diploid genotype data (see Example 7).
  • Carlson and coworkers base their calculation of the LD-measure r 2 on inferred haplotype frequencies.
  • the experimental determination of haplotypes from unrelated diploid (human) individuals is very demanding while the computational probabilistic approaches have limitations in accuracy.
  • the present method avoids the possible errors in the computationally deduced haplotypes.
  • the structure of genetic variation is, in the present invention, fully exposed on the basis of an examination of the association of marker alleles at different stringencies.
  • Carlson and coworkers consider bins of associated markers on the basis of a fixed statistic. It is amply demonstrated herein that any given threshold is data set dependent, and that association of markers at such a threshold provide an incomplete and unrefined picture of the genetic variation. This has practical consequences concerning the number, the comprehensiveness, and the information content of the selected tag SNPs. For example, certain SNPs that do not exceed the chosen threshold of association with any other SNP may unjustly be placed in singleton bins, which ultimately increase the number of tag SNPs that are required to probe the genetic variation in a region.
  • Carlson and coworkers designate SNPs that are above the threshold of association with all other SNPs of the bin as tag SNPs for that bin; the tag SNPs are considered equivalent and anyone SNP can be selected for assay.
  • a preferred method of the present invention entails the ranking of SNPs according to their suitability as tag SNPs (ctSNP) for the SPC.
  • non-clustering polymorphisms are not considered for assaying.
  • Also encompassed by the present invention is a method to unambiguously establish the phase of the mutations starting from diploid genotype data without the need for supplementary experimental haplotype resolution.
  • the in silico inference of haplotypes from diploid genotype data is illustrated by means of the aforementioned FIGS. 7 and 8 .
  • the exemplary genotype data, assembled from known haplotypes, serve the purpose of teaching the rationale used in the deconvolution of the genotypes.
  • the SPCs were already established directly from the genotype data (see FIGS. 7 C/D and 8 C/D).
  • FIG. 7 comprises a total of 8 haplotypes ( FIG. 7E ), 5 of which correspond to independent SPCs 1 to 5, a sixth haplotype that contains no SPC (SPC-0 in FIG. 7E /F), and two additional ones, related to SPC-4 and SPC-0, that result from the presence of non-clustering SNPs.
  • SPC-0 in FIG. 7E /F
  • SPC-4 two additional ones, related to SPC-4 and SPC-0, that result from the presence of non-clustering SNPs.
  • FIG. 7C /D This can be clearly seen in FIG. 7C /D.
  • the major metatypes contain the SPCs 1, 2, 4 and 5, and the minor metatypes exhibit various combinations of the different SPCs ( FIG. 7C /D).
  • SPC-3 can only be inferred from the minor metatypes. From these Figures it would—in the absence of knowledge about the underlying haplotypes—be straightforward to ascertain the independence of the SPCs and to deduce the SPC network shown in FIG. 7F . That being established, the rules for the deconvolution of the underlying haplotypes are simple.
  • SPC-0 If the minor metatypes contain only one SPC, then this genotype is deconvoluted into one haplotype containing the SPC and one haplotype that contain no SPC (SPC-0). (2) If the minor metatypes contain two SPCs, then this genotype is deconvoluted into one haplotype containing the first and a second haplotype containing the second SPC. SNPs that are not part of an SPC may be phased as well. In the present example, this is the case for both SNP-33 and SNP-38. The simplest interpretation, which can explain all genotypes with the fewest haplotypes, is that SNP-33 is in partial association with SPC-4 only.
  • SNP-38 is associated with SPC-0 since it found in minor metatypes containing either only SPC-0 or one single SPC.
  • FIG. 8 aims to describe the deconvolution of more complex SPC structures, which are more likely to be encountered in practical reality.
  • the example comprises a total of 7 SPCs, of which 3 are unrelated/independent and 4 are dependent on them. These 7 SPCs occur on 5 different haplotypes; an additional sixth haplotype contains no SPCs ( FIG. 8E /F).
  • the resultant minor metatypes may comprise more than two SPCs, thus requiring the prior establishment of the hierarchical relationships between the SPCs before the simple rules outlined above can be applied.
  • an SPC is dependent on another SPC if the SPC is always co-occurring with that other SPC.
  • Such co-occurrences can be deduced from inspection of both the major metatypes and the minor metatypes. While a co-occurrence in the major metatypes unambiguously establishes that the SPCs are dependent, the dependency of an SPC may not be unequivocally ascertained on the basis of the minor metatypes because of co-occurrence with multiple SPCs that are in an independent relation to one another. The likelihood to unambiguously determine the hierarchy increases with the number of observations. For this reason, the SPC structure is analyzed separately, first in the major and then in the minor metatypes.
  • the novel clustering approach of the present invention can be applied to any type of sequence or genetic variation data. In cases as documented here, it can be applied to sequence variations identified in DNA sequences of a specific locus derived from different individuals of either the same species or even different (related) species. Alternatively, the method can be applied to a set of closely linked SNPs scored in a number of individuals using state of the art genotyping methods. In a generic sense the method can be used on any data set of genetic variants from a particular locus, like for instance on experimentally observed variations that reflect but do not allow definition of the genetic differences in an interrogated target nucleic acid.
  • VDAs Variation Detection Arrays
  • Array hybridization is both a polymorphism discovery tool as well as a method for the routine genotyping. There is no need to fully characterize the SFPs and to convert them to dedicated assays using different array designs on the same platform or using entirely different genotyping methodologies.
  • the preferred embodiment of DNA hybridization thus constitutes a novel method for genetic analysis in which the majority of the polymorphisms in a given DNA segment are recorded in a single assay, and are subsequently analyzed using the present novel clustering approach so as to genetically diagnose the individual using the pattern of clustered hybridization differences (refer to Example 11).
  • the DNA hybridization technology constitutes a genetic marker technology highly suited for determining the genetic state of a locus.
  • the method does not require the systematic discovery of the genetic variation that is present in a locus by full sequence determination using either conventional Sanger based methods or the above-mentioned VDAs (‘sequence-by-hybridization).
  • the hybridization patterns provide a sufficiently detailed record of the sequence variation present and application of the present novel clustering approach will reveal a clustering in the hybridization signals similar to that observed when analyzing the sequence variations directly.
  • the skilled person will understand that the successful translation of the hybridization results to an SPC map requires that a sufficiently large number of features be used per locus.
  • the hybridization reaction itself can be used for the routine determination of the allelic state at various polymorphism clusters in a single assay, where the conventional approach would require the design and validation of separate assays for several ctSNPs per locus.
  • the fact of being able to record the greater part of sequence variations present offers a unique approach for genotyping, which will in certain applications be of the uttermost importance.
  • the novel clustering approach has been used for the analysis of DNA sequence profiles generated by mass spectrometric analysis of oligonucleotide fragments obtained by fragmentation of the target nucleic acid, an approach that the present invention refers to as DNA mass profiling.
  • sequence variations either result in changes in the cleavage pattern or in shifts in the mass of particular cleavage products.
  • DNA mass profiling is that it not only allows the identification of informative markers using the present clustering approach but also the routine scoring of the allelic state of the target region—in other words, the individual markers need not be converted to dedicated assays.
  • a target nucleic acid is fragmented in a sequence specific way, either chemically or enzymatically, such that the size of the (majority of the) resultant fragments is suited for mass spectrometrical analysis.
  • a variety of cleavage assays have been devised, these include but are not limited to: (i) UDG-cleavage of DNA target nucleic acid that incorporates dU-residues [Elso et al., Genome Res. 12: 1428-1433, 2002; von Wintzingerode et al., Proc. Natl. Acad. Sci.
  • One preferred fragmentation scheme consists of the RNase-A mediated cleavage of transcripts wherein the canonical ribonucleotide CMP is replaced by a dC-residue or wherein UMP is replaced by either a dT- or a dU-residue.
  • Transcripts that incorporate dC/dT/dU-residues can be generated with good efficiency with the use of mutant phage RNA polymerases that have essentially lost the ability to discriminate between rNTP and dNTP substrates [e.g.
  • Mass-spectrometric methods useful in the practice of DNA mass profiling include ionization techniques such as matrix assisted laser desorption ionization (MALDI) and electrospray (ESI). These ion sources can be matched with various separation/detection formats such as time-of-flight (TOF; using linear or reflectron configurations), single or multiple quadrupole, Fourier transform ion cyclotron resonance (FT-ICR), ion trap, or combinations of these as is known in the art of mass spectrometry [Limbach P., Mass Spectrom. Rev. 15: 297-336, 1996; Murray K., J. Mass Spectrom. 31: 1203-1215, 1996].
  • TOF time-of-flight
  • FT-ICR Fourier transform ion cyclotron resonance
  • sequence variations will result in differences in the number and the masses of the fragmentation products and consequently result in the detection of novel oligonucleotide masses, and the concomitant disappearance of other spectral signals.
  • Applying the present novel clustering approach to the spectral differences reveals a clustering in the spectral signals similar to that observed when analyzing the sequence variations directly.
  • no prior knowledge of the exact sequence differences present is required, such that the spectral data themselves can be used for the clustering analysis.
  • the present method allows discrimination between spectral differences that are relevant—i.e. the clustered spectral differences—and those that are spurious—i.e.
  • DNA mass profiling is exemplified in FIG. 21 .
  • the illustration is the result of a simulation experiment where the SPC structure obtained by the analysis of a set of polymorphic sequences is compared with that found by DNA mass profiling.
  • the skilled person will understand that not all sequence differences can be observed in a single DNA mass profiling assay while certain other polymorphisms may be associated with more than one variable mass signal. Nonetheless, as supported by Example 13, the analysis of the variable mass signals can yield essentially the same overall view on the structure of the genetic variation in a locus.
  • the preferred embodiment of DNA mass profiling thus constitutes a novel method for genetic analysis in which the majority of the polymorphisms in a given DNA segment are recorded in a single assay, and are subsequently analyzed using the present novel clustering approach so as to genetically diagnose the individual using the pattern of clustered spectral differences.
  • the DNA mass profiling technology constitutes a genetic marker technology highly suited for determining the genetic state of a locus.
  • the advantages of the DNA mass profiling technology will be obvious to any person skilled in the art. First it obviates the need to systematically discover all the genetic variants at a locus by extensive DNA sequencing. The mass profile provides a detailed reflection of the sequence variation present.
  • the mass profiling reaction itself can be used for the routine determination of the allelic state at various polymorphism clusters in a single assay, where the conventional approach would require the design and validation of separate assays for several tag polymorphisms/SNPs per locus.
  • the methods of the present invention are particularly well suited for the genetic analysis in species that exhibit high levels of sequence variation.
  • Species like maize typically exhibit a level of sequence variation in the order of one polymorphism per 30 or 50 bases.
  • the current mass profiling technologies capable of analyzing DNA in the ranges of 500 to 1500 bases, will allow to detect some 10 to 50 polymorphisms per assay, a number that is well suited for the clustering approach. It can be anticipated however that future mass profiling technologies will be able to interrogate longer DNA segments, and will then become applicable to species exhibiting lower levels of sequence polymorphism.
  • nucleotide diversity at specific loci might be several times higher than elsewhere in a genome.
  • an unusually high polymorphism was found in the human major histocompatibility complex [e.g. >10% versus 0.08%-0.2% on average; Gaudieri et al., Genome Res. 10: 1579-1586, 2000; The MHC sequencing consortium, Nature 401: 921-923, 1999].
  • the genetic characterization of certain sites within the major histocompatibility complex may thus be accomplished by means of the DNA mass profiling technology.
  • the methods of the present invention are particularly useful in two distinct fields of application, namely for genetic analysis and diagnosis in a wide range of areas from human genetics to marker assisted breeding in agriculture and livestock and for the genetic identity determination of almost any type of organism.
  • ctSNPs superior genetic markers
  • One important field of application of ctSNPs will be genome wide association studies in a variety of organisms. In human for instance, the use of ctSNPs will be to identify genetic components responsible for predispositions, health risk factors or drug response traits. In crop and live stock improvement the use of ctSNPs will be to identify genetic factors involved in quantitative traits that determine agricultural performance such as yield and quality.
  • ctSNPs may either lead to the identification of such genetic factors either indirectly through their linkage to the causative mutations in a nearby gene or directly through their association with causative mutations that belong the same SPC.
  • SPC modules that in certain cases comprise a large number of different mutations.
  • the mere existence of such SPC modules suggests that these have not arisen by chance alone, but rather represent clusters of mutations that have been selected in the course of evolution and hence represent allelic variants of genes that confer(ed) some kind of selective advantage to the species.
  • SPCs are likely modules of genetic variation associated with traits, and complex traits in particular, and this for the simple reason that these are determined not by single mutations but rather by clusters of mutations. This is apparently the case in one of the first quantitative traits recently characterized, the so called heterochronic mutations, namely mutations that affect the timing of gene expression [Cong et al., Proc. Natl. Acad. Sci. USA 99: 13606-13611, 2002].
  • the method of the present invention whereby the SPC structure of genomic regions is examined provides a logical framework for genetic identity determination.
  • the SPC map of an individual will represent the ultimate description of the genetic identity of that individual, and this for any organism, from bacteria to humans. Consequently once the SPC map has been determined for an organism, this logical framework allows the design of an exhaustive panel of ctSNPs that can be used to determine or diagnose the genetic identity of individuals. While the utility of this application in human in vitro diagnostics is particularly contemplated, numerous other applications of this technology also are envisioned. For instance, in the in vitro diagnosis of “identity preserved foods”, through the identification of the genetic material used in the production. Another application involves the identification of bacterial strains, in particular pathogenic strains.
  • phenotypic traits which can be indicative of a particular SPC include symptoms of, or susceptibility to, diseases of which one or more components is or may be genetic, such as autoimmune diseases, inflammation, cancer, diseases of the nervous system, and infection by pathogenic microorganisms.
  • diseases of which one or more components is or may be genetic such as autoimmune diseases, inflammation, cancer, diseases of the nervous system, and infection by pathogenic microorganisms.
  • autoimmune diseases include rheumatoid arthritis, multiple sclerosis, diabetes—(insulin-dependent and non-dependent), systemic lupus erythematosus and Graves disease.
  • cancers include cancers of the bladder, brain, breast, colon, esophagus, kidney, leukemia, liver, lung, oral cavity, ovary, pancreas, prostate, skin, stomach and uterus.
  • Phenotypic traits also include characteristics such as longevity, appearance (e.g., baldness, color, obesity), strength, speed, endurance, fertility, and susceptibility or receptivity to particular drugs or therapeutic treatments.
  • Many human disease phenotypes can be simulated in animal models.
  • Examples of such models include inflammation (see e.g., Ma, Circulation 88:649-658 (1993)); multiple sclerosis (Yednock et al., Nature 356:63-66 (1992)); Alzheimer's disease (Games, Nature 373:523 (1995); Hsiao et al., Science 250:1587-1590 (1990)); cancer (see Donehower, Nature 356:215 (1992); Clark, Nature 359:328 (1992); Jacks, Nature 359:295 (1992); and Lee, Nature 359:288 (1992)); cystic fibrosis (Snouwaert, Science 257:1083 (1992)); Gaucher's Disease (Tybulewicz, Nature 357:407 (1992)); hypercholesterolemia (Piedrahita, PNAS 89:4471 (1992)); neurofibromatosis (Brannan, Genes & Dev.
  • inflammation see e.g., Ma, Circulation
  • Phenotypes and traits which can be indicative of a particular SPC also include agricultural and livestock performance traits, such as, among others, yield, product (e.g meat) quality, and stress tolerance.
  • the present invention therefore defines a powerful framework for genetic studies. Traditionally, association studies between a phenotype and a gene have involved testing individual SNPs in and around one or more candidate genes of interest. This approach is unsystematic and has no clear endpoint. More recently, a more comprehensive approach has been pioneered which is based on the selection of a sufficiently dense subset of SNPs that define the common allelic variation in so-called haplotype blocks. The present invention reveals the more basic and fundamental structure in genetic variation.
  • the SPC maps described herein can explain the general observation that LD is extremely variable within and among loci and populations and provide the basis for the most rational and systematic genetic analysis of an entire genome, a sub-genomic locus or a gene.
  • a subset of SNPs sufficient to uniquely distinguish each SPC can then be selected and associations with each SPC can be definitively determined by determining the presence of such a ctSNP.
  • a ctSNP as described herein above
  • associations with each SPC can be definitively determined by determining the presence of such a ctSNP.
  • the skilled artisan could perform an exhaustive test of whether certain population variation in a gene is associated with a particular trait, e.g., disease state.
  • the approach provides a precise framework for creating a comprehensive SPC map of any genome for any given population, human, animal or plant. By testing a sufficiently large collection of SNPs, it should be possibly to define all of the underlying SPCs. Once these SPCs are identified, one or more unique SNPs associated with each SPC can be selected to provide an optimal reference set of SNPs for examination in any subsequent genotyping study. SPCs are therefore particularly valuable because they provide a simple method for selecting a subset of SNPs capturing the full information required for population association to find phenotype/trait-associated alleles, e.g., common disease-susceptibility associated alleles.
  • SPC structure Once the SPC structure is defined, it is sufficient to genotype a single ctSNP unique for a given SPC to describe the entire SPC. Thus, SPCs across an entire genome or sub-genomic region can be exhaustively tested with a particular set of ctSNPs.
  • This SPC map contains sets of co-occurring alleles, e.g., cosegregating polymorphisms.
  • SPC map there may be one or more SPCs and each SPC may be further identified by a polymorphism that is characteristic of that particular SPC.
  • sequence variation can be captured by a relatively small number of SNPs.
  • a comprehensive description of the SPC map in a human, animal or plant population can require a high density of polymorphic markers.
  • Polymorphism information can be obtained from any sample population to produce a map of the invention.
  • “Information” as used herein in reference to sample populations is intended to encompass data regarding frequency and location of polymorphisms and other data such as background and phenotypic (e.g. health) information useful in genotype studies and the methods and maps of the invention described herein.
  • a diverse (multiethnic) population sample can include a total random sample in which no data regarding (ethnic) origin is known.
  • such a sample can include samples from two or more groups with differing (ethnic) origins.
  • Such diverse (multiethnic) samples can also include samples from three, four, five, six or more groups.
  • Ethnicity refers to the human case and can be, for example, European, Asian, African or any other ethnic classification or any subset or combination thereof.
  • the populations can consist of breeding germplasm, specific races, varieties, lines, accessions, landraces, introgression lines, wild species or any subset or combination thereof.
  • the population samples can be of any size including 5, 10, 15, 20, 25, 30, 35, 40, 50, 75, 100, 125, 150 or more individuals.
  • Information for producing a map of the invention can also be obtained from multiple sample populations. Such information can be used concurrently or sequentially. For example, studies can be performed using homogeneous (monoethnic) population samples. The results of these studies can then be utilized with the results of a study on a diverse (multiethnic) sample. Alternatively, the results from the homogeneous (monoethnic) sample can be combined to form a diverse (multiethnic) study.
  • Polymorphisms can be detected from a target nucleic acid from an individual being analyzed.
  • a target nucleic acid from an individual being analyzed.
  • tissue samples include whole blood, semen, saliva, tears, urine, fecal material, sweat, buccal, skin and hair have readily been used to assay for genomic DNA.
  • any part e.g. leaves, roots, seedlings
  • the sample must be obtained from an organ or tissue in which the target nucleic acid is expressed.
  • PCR is a generally preferred method for amplifying a target nucleic acid
  • Suitable amplification methods include the ligase chain reaction (LCR) (see Wu and Wallace, Genomics 4:560 (1989); Landegren et al., Science 241:1077 (1988)), transcription amplification (Kwoh et al., Proc. Natl. Acad. Sci. USA 86:1173 (1989)), and self-sustained sequence replication (Guatelli et al., Proc. Nat. Acad. Sci. USA 87:1874 (1990)) and nucleic acid based sequence amplification (NASBA).
  • LCR ligase chain reaction
  • ssRNA single stranded RNA
  • dsDNA double stranded DNA
  • the first type of analysis is sometimes referred to as de novo characterization and makes use of a differential nucleic acid analysis. This analysis compares target sequences in different individuals to identify points of variation, i.e., polymorphic sites. By analyzing a group of individuals representing the greatest variety characteristic patterns of alleles can be identified, and the frequencies of such alleles in the population determined. Additional allelic frequencies can be determined for subpopulations characterized by criteria such as geography, race, or gender.
  • the second type of analysis is determining which form(s) of a characterized polymorphism are present in individuals under test. There are a variety of suitable procedures for sequence-based genotyping, which are discussed in turn.
  • Allele-Specific Probes and Primers The design and use of allele-specific probes for analyzing SNPs is described by e.g., Saiki et al., Nature 324:163-166 (1986); Dattagupta, EP 235,726, Saiki, WO 89/11548. Allele-specific probes can be designed that hybridize to a segment of target DNA from one individual but do not hybridize to the corresponding segment from another individual due to the presence of different polymorphic forms in the respective segments from the two individuals. Hybridization conditions should be sufficiently stringent that there is a significant difference in hybridization intensity between alleles, and preferably be selected such that a hybridizing probe hybridizes to only one of the alleles.
  • Some probes are designed to hybridize to a segment of target DNA such that the polymorphic site aligns with a central position (e.g., in a 15 mer at the 7 position; in a 16 mer, at either the 8 or 9 position) of the probe. This design of probe achieves good discrimination in hybridization between different allelic forms.
  • Allele-specific probes are often used in pairs, one member of a pair showing a perfect match to a reference form of a target sequence and the other member showing a perfect match to a variant form. Several pairs of probes can then be immobilized on the same support for simultaneous analysis of multiple polymorphisms within the same target sequence.
  • the allele-specific primer hybridizes to a site on target DNA overlapping a SNP and only primes amplification of an allelic form to which the primer exhibits perfect complementarity. See Gibbs, Nucleic Acids Res. 17: 2427-2448 (1989). This primer is used in conjunction with a second primer which hybridizes at a distal site. Amplification proceeds from the two primers leading to a detectable product signifying the particular allelic form is present. A control is usually performed with a second pair of primers, one of which shows a single base mismatch at the polymorphic site and the other of which exhibits perfect complementarily to a distal site.
  • PCR polymerase chain reaction
  • the single-base mismatch prevents amplification and no detectable product is formed.
  • the method works best when the mismatch is included in the 3′-most position of the oligonucleotide aligned with the polymorphism because this position is most destabilizing to elongation from the primer.
  • the SNPs can also be identified by hybridization to nucleic acid arrays (DNA chip analysis).
  • Subarrays that are optimized for detection of variant forms of precharacterized polymorphisms can also be utilized.
  • Such a subarray contains probes designed to be complementary to a second reference sequence, which is an allelic variant of the first reference sequence.
  • the inclusion of a second group (or further groups) can be particular useful for analyzing short subsequences of the primary reference sequence in which multiple mutations are expected to occur within a short distance commensurate with the length of the probes (i.e., two or more mutations within 9 to 21 bases).
  • Direct Sequencing The direct analysis of a sequence of any samples for use with the present invention can be accomplished using either the dideoxy-chain termination method or the Maxam-Gilbert method (see Sambrook et al., Molecular Cloning, A Laboratory Manual (2nd Ed., CSHP, New York 1989); Zyskind et al., Recombinant DNA Laboratory Manual, (Acad. Press, 1988)).
  • SBH sequencing by hybridization
  • Methods and compositions for sequencing by hybridization are described, e.g., in U.S. Pat. No. 6,689,563; U.S. Pat. No. 6,670,133; U.S. Pat. No. 6,451,996; U.S. Pat. No. 6,399,364; U.S. Pat. No. 6,284,460, U.S. Pat. No. 6,007,987; U.S. Pat. No. 5,552,270.
  • SBH sequencing by hybridization
  • Denaturing Gradient Gel Electrophoresis Amplification products generated using the polymerase chain reaction can be analyzed by the use of denaturing gradient gel electrophoresis. Different alleles can be identified based on the different sequence-dependent melting properties and electrophoretic migration. Erlich, ed., PCR Technology, Principles and Applications for DNA Amplification. (W. H. Freeman and Co, New York, 1992), Chapter 7.
  • Alleles of target sequences can be differentiated using single-strand conformation polymorphism analysis, which identifies base differences by alteration in electrophoretic migration of single stranded PCR products, as described in Orita et al., Proc. Natl. Acad. Sci. USA 86, 2766-2770 (1989).
  • Amplified PCR products can be generated as described above, and heated or otherwise denatured, to form single stranded amplification products.
  • Single-stranded nucleic acids may refold or form secondary structures which are partially dependent on the base sequence.
  • the different electrophoretic mobilities of single-stranded amplification products can be related to base-sequence difference between alleles of target sequences.
  • Allele-specific Primer Extension Minisequencing.
  • a primer is specifically annealed upstream of the SNP site of interest, which may then be extended by the addition of an appropriate nucleotide triphosphate mixture, before detection of the allele-specific extension products on a suitable detection system. If dideoxynucleotide triphosphates labelled with different dyes are used, single base extension (SBE) products can be analyze by electrophoresis using a fluorescent sequencer, either gel or capillary based. Conventional detection methods, such as an immunochemical assay, can also be used to detect the SBE products.
  • Matrix-assisted laser desorption ionisation time-of-flight mass spectrometry can be used to separate the extension products as well as the primer to a high degree of precision by their respective molecular masses without the need for any labelled tags [Storm et al., Methods Mol. Biol. 212: 241-262, 2003].
  • MALDI-TOF-MS Matrix-assisted laser desorption ionisation time-of-flight mass spectrometry
  • Each dNTP substrate is added individually and incorporation is monitored by the release of pyrophosphate which is converted to ATP fuelling a luciferase reaction. If the dNTP is not incorporated, it is degraded with no light emission. The sequence of events is followed and is specific to the sequence of the variant.
  • OLA oligonucleotide ligation assay
  • two primers are designed that are directly next to each other when hybridized to the complementary target DNA sequence in question.
  • the two adjacent primers must be directly next to each other with no interval, or mismatch, for them to be covalently joined by ligation. This discriminates whether there is an SNP present.
  • labelling and detection methods including ELISA [Nickerson et al., Proc. Natl. Acad. Sci USA 87: 8923-8927, 1990], or electrophoresis and detection on a fluorescence sequencer.
  • This assay uses a structure-specific 5′ nuclease (or flap endonuclease) to cleave sequence-specific structures in each of two cascading reactions.
  • the cleavage structure forms when two synthetic oligonucleotide probes hybridise to the target.
  • the cleaved probes then participate in a second generic Invader reaction involving a dye-labelled fluorescence resonance energy transfer (FRET) probe. Cleavage of this FRET probe generates a signal, which can be readily analysed by fluorescence microtitre plate readers.
  • the two cascading reactions amplify the signal significantly and permit identification of single base changes directly from genomic DNA without prior target amplification [Fors et al. Pharmacogenomics 1: 219-229, 2000].
  • the genomic maps and the methods of the invention can be readily used in several ways.
  • the mapping of discrete regions which contain sequence polymorphisms permits, for example, the identification of phenotypes associated with particular SPCs, the localization of the position of a locus associated with a particular phenotype (e.g. a disease) as well as the development of in vitro diagnostic assays for (disease) phenotypes.
  • linkage studies can be performed for particular SPCs because such SPCs contain particular linked combinations of alleles at particular marker sites.
  • a marker can be, for example, a RFLP, an STR, a VNTR or a single nucleotide as in the case of SNPs. The detection of a particular marker will be indicative of a particular SPC. If, through linkage analysis, it is determined that a particular ctSNP is associated with, for example, a particular disease phenotype, then the detection of the ctSNP in a sample derived from a patient will be indicative of an increased risk for the particular disease phenotype.
  • the locus can be sequenced and scanned for coding regions that code for products that potentially lead to the disease phenotype. In this manner, the position of a disease-susceptibility locus of a disease can be located.
  • Linkage analysis can be accomplished, for example, by taking samples from individuals from a particular population and determining which allelic variants the individuals have at the marker sites that tag discrete SPCs. Using algorithms known in the art, the occurrence of a particular allele can be compared to, for example, a particular phenotype in the population. If, for example, it is found that a high proportion of the population that has a particular disease phenotype also carries a particular allele at a particular polymorphic site—then one can conclude that the particular allele is linked to the particular phenotype in that population. Linkage analyses and algorithms for such analyses are well known to those of skill in the art and exemplary methods are described in greater detail in e.g., U.S. Pat. No.
  • the marker alleles embody discrete SPCs
  • the phenotype is also determined to be linked to a discrete SPC.
  • linkage analysis can be performed that allows for the conclusion that a particular phenotype is linked to a particular SPC.
  • the present example provides proof of concept that the methods of the present invention can be used to generate an SPC map of a complete gene locus that has been sequenced in a number of individuals of a particular species.
  • GenBank http://www.ncbi.nim.nih.gov.
  • sh2 shrunken2 locus from maize was chosen to exemplify the different aspects of the invention.
  • the published shrunken2 locus sequences from 32 maize cultivars comprise a region of 7050 bp containing the promoter and the coding region of the sh2 gene [Whitt et al., Proc. Natl. Acad. Sci. USA 99: 12959-12962, 2002].
  • sequences for this analysis were retrieved from GenBank (http://www.ncbi.nlm.nih.gov) accession numbers AF544132-AF544163.
  • GenBank http://www.ncbi.nlm.nih.gov
  • accession numbers AF544132-AF544163 accession numbers AF544132-AF544163.
  • the sequences were aligned using ClustalW [Thompson et al., Nucleic Acids Res. 22: 4673-4680, 1994] and the alignments around the indels were manually optimized. Using a perl script all the polymorphic sites in the aligned sequences were scored to generate a genetic variation table in which each column represents a polymorphic site and each row represents a sample. In the columns the corresponding alleles (bases) in each sample are represented, except for indels that are represented by two dots at respectively the start and the end position of the deletion.
  • the genetic variation table of the sh2 gene comprises 212 polymorphic sites.
  • the singletons i.e. the polymorphic sites at which the minor allele occurs only once, three recombinant genotypes and the duplicate indel sites were excluded from the analysis. This reduced the number of polymorphic sites in the genetic variation table to 141.
  • the algorithm clustered a total of 124 polymorphic sites (88%) of the sh2 locus into 9 different SPCs, most of which extended throughout the entire locus.
  • the five largest SPCs comprise between 10 and 39 polymorphisms (note that not all polymorphisms are displayed in FIG. 9A ).
  • the sh2 locus thus yields a continuous SPC map, as is shown in FIG. 9A .
  • the figure shows the SPCs in 29 of the 32 non-recombinant individuals.
  • the uninterrupted SPC map of the 7 kb sh2 locus indicates that the locus has experienced few historical recombination events. This is further supported by the observation that only 3 of the 32 samples sequenced appear recombinant.
  • the present example serves to illustrate a number of specific aspects of the present invention.
  • the sh2 locus comprises 5 primary independent SPCs, each comprising a large number of different polymorphisms (SPCs 1, 2, 3, 4, and 9).
  • SPCs 1, 2, 3, 4, and 9 polymorphisms
  • several layers of dependency can be observed involving SPCs 9, 5, 8, 6, and 7.
  • SPCs comprising two polymorphisms and the SPCs comprising the singletons into account
  • additional dependent SPCs are found (not shown). Consequently, the SPC-network of FIG.
  • 9B is a simplified representation of the SPC structure of the sh2 locus. Furthermore, it can be anticipated that the actual SPC structure of the sh2 locus of maize may be even more complex, because the number of individuals that has been sequenced is relatively small, and hence may represent only a fraction of the full genetic diversity of the maize ( Zea mays subsp. mays ) germplasm.
  • a second important aspect concerns the mutations that do not cluster: only 17 of the 141 polymorphic sites could not be clustered at the threshold of C ⁇ 0.80.
  • a sample of non-clustering polymorphic sites is shown in the left part of FIG. 9A . Analysis of these polymorphic sites revealed that these comprise three types. First, some polymorphic sites are associated with only one SPC but do not occur in all samples, and thus presumably represent more recent mutations. The second type comprises polymorphic sites that are found associated with more than one SPC. For some of these it seems clear that they represent recurrent mutations. Examples of this type are the single or multiple base deletions in homopolymer tracts, which are known to be highly mutable.
  • the third type comprises polymorphic sites that are associated with two or three different SPCs. Some of these may represent ancestral mutations that are common to these SPCs. However, irrespective of the explanation for the lack of clustering, the non-clustering polymorphisms represent a subset of the polymorphic sites with an erratic association and are, consequently of poor diagnostic value. Consequently, this analysis demonstrates that the methods of the present invention provide a selection of polymorphic sites exhibiting superior diagnostic value, thus providing proof of concept for one of the principal utilities of the method of the invention, namely the selection of genetic markers for analyzing genetic traits.
  • a third aspect of the present example concerns the thresholds for calculating the SPCs.
  • the SPC analysis was performed on a subset of samples comprising the 29 non-recombinant samples.
  • Lowering the threshold to C ⁇ 0.80 added 3 additional polymorphic sites to the SPCs. These were three SNP that had one aberrant data point. In this case the use of lower thresholds had marginal effects. The reasons for this are several. For one, the sequences were obviously of high quality, and the frequency of erroneous allele calls was low. Second, by excluding the recombinants prior to clustering, the analysis was biased.
  • a fourth aspect emerging from our analysis is that the SPCs of the sh2 locus comprise both indels and SNPs, supporting that the method of clustering captures all mutational events.
  • analysis of multi-allelic polymorphic sites shows that some of these represent independent mutations of the same position that are linked to different SPCs. The latter is illustrated by the polymorphism at position 5154 in FIG. 9A .
  • a fifth aspect concerns the design of cluster tag SNPs. Since most SPCs are defined by large numbers of markers that are in absolute linkage, the choice of tag SNPs in this case is straightforward. The only remark is that one should avoid using any of the 3 markers that are not in perfect linkage.
  • the SPC network shown in FIG. 9B has considerable practical utility for the selection of genetic markers for genetic analysis of the sh2 locus. While there is a total of 9 SPCs, it is clear that a genotyping study can, depending on the desired level of resolution, address a subset of these SPCs. For instance, a genotyping could be limited to the ctSNPs that tag the 5 primary independent SPCs (i.e. SPCs 1, 2, 3, 4, and 9). Even for an exhaustive analysis of the locus only a subset of the SPCs would have to be addressed, more specifically SPCs 1, 2, 3, 4, 5, 6, and 7 because the clade-specific SPCs 8 and 9 are redundant over the dependent SPCs.
  • the present example provides proof of concept that the methods of the present invention can be used to generate an SPC map of a complete gene in which extensive recombination has occurred.
  • This example presents an analysis of the polymorphic sites in the shrunken1 (sh1) locus from maize to exemplify further aspects of the invention.
  • the published shrunken1 locus sequences from 32 maize cultivars ( Zea mays subsp. mays ) comprise a region of 6590 bp containing the promoter and the coding region of the sh2 gene [Whitt et al., Proc. Natl. Acad. Sci. USA 99: 12959-12962, 2002].
  • the sequences for this analysis were retrieved from GenBank (http://www.ncbi.nlm.nih.gov) accession numbers AF544100-AF544131.
  • GenBank http://www.ncbi.nlm.nih.gov
  • accession numbers AF544100-AF544131 accession numbers AF544100-AF544131.
  • the sequences were aligned to generate a genetic variation table as described in detail in Example 1.
  • the genetic variation table of the sh1 gene comprises 418 polymorphic sites. Because of this very large number of polymorphic sites, the singletons were excluded from the analysis. This reduced the number of polymorphic sites to 282.
  • sh2 locus from Example 1 In contrast to the sh2 locus from Example 1, in which ⁇ 90% of the polymorphic sites were clustered, only ⁇ 50% of the sh1 polymorphic sites could be clustered. While the sh2 locus yielded a relatively small number of SPCs comprising many polymorphic sites, the sh1 locus yielded a much larger number of SPCs containing on average fewer polymorphic sites. Furthermore, as can be seen from FIG. 10 , most of the SPCs identified were located in two segments (positions 1186 to 3283 and 3559 to 5243) comprising about half of the locus, and a third very short (120 bp) highly polymorphic segment (positions 6315 to 6436; not shown).
  • the sh1 locus thus yields a discontinuous SPC structure, which is represented in FIG. 10 .
  • the observed SPC structure must be the result of recurrent recombination (or recombination hotspots), in the regions between the segments exhibiting a clear SPC structure.
  • These recombination events not only generated the two distinct segments but also scrambled the polymorphic sites within the intervening regions such that none of these polymorphisms cluster, and this even at thresholds of C ⁇ 0.60.
  • FIG. 10 shows that recombination has occurred within the two segments exhibiting a clear SPC structure. This is particularly evident in the right segment where most SPCs are short.
  • the two contrasting Examples 1 and 2 illustrate that the methods of the present invention can be used to generate informative SPC maps of gene loci, irrespective of the recombination history of the locus.
  • the structure of the resulting SPC maps is determined primarily by the recombination frequency in the region of interest. Extensive recombination within a locus will result in a fragmented SPC structure with short range SPCs containing fewer polymorphic sites, while in the absence historical recombination, the locus will yield a highly continuous SCP map with SPCs comprising large numbers of polymorphic sites and extending over longer distances. Irrespective of the SPC structure of the locus, the methods of the present invention have clear practical utility.
  • the methods of the present invention provide a selection of polymorphic sites exhibiting superior diagnostic value, thus providing proof of concept for one of the principal utilities of the method of the invention, namely the selection of genetic markers for analyzing genetic traits. While in the sh2 case a mere 7 ctSNPs will suffice to capture the majority of the genetic variation within the locus without loss of information, the ctSNPs selected for genotyping the sh1 locus will cover only a fraction of the genetic variation within the locus. Persons skilled in the art will understand that this is an intrinsic limitation and not one related to the method of the present invention.
  • the present example provides proof of concept that the method of the present invention can be used to generate an SPC map of a locus in which several historical recombination events have occurred.
  • This example presents an analysis of the polymorphisms in the Y1 phytoene synthase locus of maize to exemplify further aspects of the invention.
  • the Y1 phytoene synthase gene which is involved in endosperm color, was sequenced in 75 maize inbred lines [Palaisa et al., The Plant cell 15: 1795-1806, 2003], comprising 41 orange/yellow endosperm lines and 32 white endosperm lines.
  • the sequences for this analysis were retrieved from GenBank (http://www.ncbi.nlm.nih.gov) accession numbers AY296260-AY296483 and AY300233-AY300529.
  • the sequences comprise 7 different segments from a region of 6000 bp containing the promoter and the coding region of the Y1 phytoene synthase gene.
  • the individual sequences were aligned to generate 7 genetic variation tables as described in detail in Example 1, which were subsequently combined into a single genetic variation table.
  • the combined genetic variation table of the Y1 phytoene synthase gene comprises 191 polymorphic sites.
  • the SPCs that comprise 3 or more polymorphic sites were computed with the SPC algorithm using various thresholds.
  • the algorithm clustered 85, 95 and 113 polymorphisms at a threshold value of C 1, C ⁇ 0.95 and C ⁇ 0.80, respectively.
  • the Y1 SPC map presented in FIG. 11B shows the SPCs obtained at the threshold value of C ⁇ 0.95, with in the upper half of the panel the white endosperm lines and in the lower half of the panel the orange/yellow endosperm lines. While the orange/yellow lines all share the same continuous SPC (SPC-1), the white lines exhibit a number of different SPCs, exhibiting a discontinuous pattern of SPCs. This pattern is consistent with a relatively small number of recombination events that occurred at the positions between the different SPCs, indicated by the arrows in FIG. 11B .
  • the present example also illustrates one important aspect of the present invention, namely that SPCs may be highly correlated with phenotypes. Indeed the finding that all orange/yellow endosperm lines share the same SPC indicates that the polymorphisms that make up that SPC are either tightly linked to or are responsible for the orange/yellow phenotype.
  • the present example also illustrates another important aspect of the present invention, namely the importance of using different thresholds to identify SPCs.
  • the SPCs include only those polymorphisms that are present in non-recombinant individuals, since the polymorphisms that are affected by (rare) recombination events will not exhibit complete linkage.
  • the only mutations within the single SPC present in the orange/yellow lines that are perfectly correlated with the phenotype are the polymorphisms at positions 3-701 and 3-755, which are the only ones present in InbredLo32 (see FIG. 11B ), which moreover is a complex recombinant. This illustrates that while SPCs may be well correlated with phenotypes, not all polymorphisms in the SPC have necessarily the same diagnostic value.
  • the present example provides proof of concept that the methods of the present invention can be used to generate an interspecies SPC map of a gene locus that has been sequenced in individuals from different closely related species.
  • This example presents an analysis of the polymorphic sites in the globulin 1 (glb1) locus of maize to exemplify further aspects of the invention.
  • Evidence is presented that the SPCs detected by the method of the present invention may have arisen before the split of the related species and can therefore be considered ancient.
  • the globulin 1 gene sequences analyzed in the present example have been generated in phylogenetic studies on the origins of domesticated maize [Hilton and Gaut, Genetics 150: 863-872,1998; Tenaillon et al., Proc. Natl. Acad. Sci. USA 98: 9161-9166, 2001; Tiffin and Gaut, Genetics 158: 401-412, 2001] and comprise a region of 1200 bp containing part of the coding region of the glb1 gene from 70 different accessions of maize inbred lines and landraces ( Zea mays subsp. mays ), the progenitor of cultivated maize (teosinte or Zea mays sp. parviglumis ), and the closely related species Zea perennis, Zea diploperennis and Zea luxurians.
  • the sequences for this analysis were retrieved from GenBank (http://www.ncbi.nlm.nih.gov) accession numbers AF064212-AF064235, AF377671-AF377694 and AF329790-AF329813.
  • GenBank http://www.ncbi.nlm.nih.gov
  • the sequences were aligned to generate a genetic variation table as described in detail in Example 1.
  • the genetic variation table of the glb1 gene comprises 317 polymorphic sites of which 66 were singletons. Because the primary interest of this analysis was to examine the polymorphic sites that were shared between the samples, the singletons were excluded from the analysis.
  • the SPC map of the globulin 1 gene shows that 5 primary SPCs can group all 39 sequences: SPC-1 and SPC-5 comprise different Zea mays accessions, SPC-2 comprises both Zea mays and Zea diploperennis accessions, SPC-3 comprises the Zea luxurians accessions and SPC-4 comprises the Zea perennis accessions, and can be further subdivided through the various dependent SPCs. Close inspection of FIGS. 12A and 12B shows that the SPCs are in general, but not always, specific for the different Zea species.
  • the method of the present invention provides a superior method of monitoring genetic diversity in wild accessions of the species and related species.
  • this example shows that the interspecific SPC maps of a locus can provide insights into the complex phylogenetic origins of genetic variation.
  • the present example provides proof of concept that that the methods of the present invention can be used to construct SPC maps of entire genomic segments, covering large numbers of genes.
  • Examples 1 through 3 illustrated that the analysis of gene loci with the methods of the present invention may yield different types of SPC maps depending upon the recombination history of the locus.
  • This example presents an analysis of the polymorphic sites in the genomic region surrounding the FRI locus of Arabidopsis thaliana to provide proof of concept that SPC maps can also generated for genomic regions comprising many genes using polymorphism data sampled throughout a genomic region.
  • One approach for assessing allelic diversity in genomic regions that is becoming widely used involves the sequencing of short segments (500 to 1000 bp, the length of a typical sequence run) from different places throughout the genomic region of interest. Several studies of this type have been published recently, and one of these was chosen in the present example.
  • genomic sequences analyzed in the present example were generated in the study of a 450-kb genomic region surrounding the flowering time locus FRI [Hagenblad and Nordborg, Genetics. 161: 289-298, 2002] and comprises a set of 14 amplicons sequenced from 20 accessions of Arabidopsis thaliana.
  • the sequences for this analysis were retrieved from GenBank (http://www.ncbi.nlm.nih.gov) accession numbers AY092417-AY092756.
  • GenBank http://www.ncbi.nlm.nih.gov
  • accession numbers AY092417-AY092756 accession numbers AY092417-AY092756.
  • the individual sequences were aligned to generate 14 genetic variation tables as described in detail in Example 1, which were subsequently combined into a single continuous genetic variation table.
  • the genetic variation table of the FRI locus comprises 191 polymorphic sites.
  • the algorithm clustered respectively 85 and 94 polymorphisms at clustering thresholds of C 1 and C ⁇ 0.75.
  • FIG. 13A shows a physical map of the 450-kb region surrounding the flowering time locus FRI
  • FIG. 13B shows the SPC map of the region obtained using the C ⁇ 0.75 threshold.
  • SPCs of singletons 40 out of 94 clustered polymorphisms
  • the haplotype block method will divide genomic regions into blocks according to observed recombination events, using a certain threshold.
  • the method of the present invention will detect recombination events in the SPCs that are affected, but these will not affect the other SPCs.
  • the results presented in the present example demonstrate that the SPC method is superior in capturing the structure in the genetic variation.
  • the present example provides proof of concept that that the methods of the present invention can be used to construct SPC maps of entire genomes from genome-wide genetic diversity data, and that from the SPC map ctSNP markers can be derived for genome-wide association studies.
  • SPC map ctSNP markers can be derived for genome-wide association studies.
  • Several approaches for surveying genetic diversity on a genome-wide scale are currently being pioneered, involving sequencing short fragments of 500 to 1000 bp amplified from genomic DNA from a collection of individuals representative for the species.
  • the amplicons are chosen at regular intervals (20 or 50 kb) along the genome, while other approaches rely on the systematic sequencing of regions of known genes.
  • This example presents an analysis of the polymorphic sites identified in a set of amplified fragments from chromosome 1 of Arabidopsis thaliana.
  • genomic sequences analyzed in the present example were generated in the NSF 2010 Project “A genomic survey of polymorphism and linkage disequilibrium in Arabidopsis thaliana ” [Bergelson J., Kreitman M., and Nordborg M., http://walnut.usc.edu/2010/2010.html] and comprises 255 amplicons from chromosome 1 sequenced from 98 accessions of Arabidopsis thaliana.
  • the sequences for this analysis were downloaded from the website http://walnut.usc.edu/2010/2010.html.
  • the individual sequences were aligned to generate one genetic variation table per amplicon as described in detail in Example 1. Singletons and polymorphic sites with more than 33% missing data were excluded from the analysis.
  • the individual tables were concatenated into a single genetic variation table in the same order in which the amplicons occur on the chromosome.
  • the resulting genetic variation table of chromosome 1 contains 3378 polymorphic sites.
  • the genetic variation table was analyzed with the SPC algorithm using a sliding window of 120 polymorphic sites and an overlap of 20 SNPs between each consecutive block. The following parameter settings were used in this analysis.
  • FIG. 14 shows the SPCs identified in 31 amplicons (from amplicon #134 to amplicon #165) from a 3.76 Mb segment of chromosome 1 (from position 16,157,725 to position 19,926,877). It can be seen that the amplicons that do not yield SPCs (10 of the amplicons of FIG. 14 ) generally have relatively few polymorphic sites, although occasionally amplicons are observed that have numerous polymorphisms that fail to cluster (e.g. amplicons 144 and 147).
  • the amplicons yielding SPCs were broadly classified into 2 classes, each occurring with similar frequency.
  • the class I amplicons reveal only one SPC (e.g. amplicons 142, 150, 152, 153, 154, 155 and 158).
  • the class II amplicons reveal two or more overlapping SPCs (e.g. amplicons 136, 137, 139, 143, 145, 146, 148 and 163).
  • the class I amplicons correspond to dimorphic loci, i.e. loci that have only two haplotypes (SPC-n and SPC-0), while the class II amplicons correspond to polymorphic loci, i.e. loci that have three or more haplotypes.
  • this example demonstrates that the SPC method is well suited to assess the genetic diversity at both the level of an entire genome. Moreover, the discovered SPC structures provide a logical framework for the development of useful sets of DNA markers for genetic analysis of a species. For each SPC only one representative ctSNP is chosen. This marker set will be universally applicable in the species.
  • This present method of analyzing genetic diversity has useful applications in plant and animal breeding, in that it provides both a means to develop useful genetic markers, as well as allowing breeders to select appropriate lines for introducing new genetic diversity in breeding programmes.
  • SPCs Based on the SPCs found, one can develop SPC tags which can be used for both identifying genes involved in agronomical traits and for marker assisted breeding.
  • the SPC maps are useful for identifying lines that carry novel SPCs that are not present in the breeding germplasm and that can provide novel genetic diversity.
  • the present example provides proof of concept that the methods of the present invention can be used on unphased diploid genotype data both to construct an SPC map of a gene and to select tag SNPs for genetic analysis.
  • the present example will also provide proof of concept that the methods of the present invention can be used to infer haplotypes from the unphased diploid genotypes.
  • This example presents an analysis of the polymorphic sites in the human CYP4A11 (cytochrome P450, family 4, subfamily A, polypeptide 11) gene to exemplify the different aspects of the invention.
  • the genetic variation data analyzed in the present example was generated by the UW-FHCRC Variation Discovery Resource [SeattleSNPs; http://pga.gs.washington.edu/].
  • the UW-FHCRC Variation Discovery Resource (SeattleSNPs) is a collaboration between the University of Washington and the Fred Hutchinson Cancer Research Center and is one of the Programs for Genomic Applications (PGAs) funded by the National Heart, Lung, and Blood Institute (NHLBI).
  • PGAs Programs for Genomic Applications
  • HNLBI National Heart, Lung, and Blood Institute
  • SeattleSNPs The goal of SeattleSNPs is to discover and model the associations between single nucleotide sequence differences in the genes and pathways that underlie inflammatory responses in humans.
  • the unphased diploid genotypes and the SNP allele data tables for this analysis were downloaded from the SeattleSNPs website (http://pga.gs.washington.edu/).
  • the genetic variation data for the CYP4A11 gene comprise 103 polymorphic sites (SNPs and indels) that were identified by resequencing a segment of 13 kb in 24 African American and 23 European individuals.
  • the diploid genotype data table lists the allele scores of the 103 polymorphic sites of the CYP4A11 gene in the 47 samples.
  • the diploid genotype data table was first reformatted to the standard format for genetic variation tables as described in Example 1 using the following procedure.
  • Homozygous diploid SNP genotypes were denoted by the symbols “A”, “C”, “G” or “T”, while homozygous indel genotypes were denoted by a dot for the deletion allele or, alternatively, the first base of the insertion.
  • the heterozygous diploid genotypes (polymorphic sites at which both alleles were scored) were denoted by the symbol “H”.
  • metatypes was derived from the genetic variation table using the following procedure. The table was first duplicated by adding a second copy of the sample rows. Thereafter the symbols “H” were replaced in each of the two copies respectively by the minor allele in the first copy and by the major allele in the second copy.
  • the duplicated and reformatted genetic variation table is referred to as the metatype table.
  • the diploid genotypes in which the symbols “H” were replaced by the minor allele are referred to as minor metatypes and the diploid genotypes in which the symbols “H” were replaced by the major allele are referred to as major metatypes.
  • the sample names in the metatype table are denoted with the extension “ ⁇ 1” for the minor metatypes, and with the extension “ ⁇ 2” for the major metatypes. It is noted that two essential features of the polymorphic sites are perfectly retained in the metatype format, namely the frequencies of the alleles and their co-occurrence or linkage.
  • each diploid genotype is disassembled in two metatypes, and each heterozygous genotype is correctly split into one minor and one major allele in the two metatypes.
  • the linkages between the co-occurring polymorphic sites are retained by the simultaneous replacement of all heterozygous genotypes on a single diploid genotype by either the minor or the major alleles in respectively the minor and major metatypes.
  • the polymorphisms were for most part clustered in similar SPCs at the different thresholds, with two exceptions.
  • SPC-14 was found only at the threshold of C ⁇ 0.80.
  • the SPC map of the 81 polymorphic sites clustered at the threshold of C ⁇ 0.90 is analyzed in detail, thus excluding SPC-14.
  • FIG. 15A the 13 different SPCs clustered at the threshold of C ⁇ 0.90, comprising 81 polymorphisms, are visualized onto the metatypes.
  • the SPCs found in the major metatypes (sample name followed by “ ⁇ 2”) are shown, while the lower half of FIG. 15A shows the SPCs observed in the minor metatypes (sample name followed by “ ⁇ 1”).
  • SPC-0 The metatypes that are devoid of an SPC (SPC-0) are omitted, except for one representative in each table half.
  • the minor and major metatypes were sorted according to the SPCs present.
  • a striking feature of FIG. 15A is that SPC-2 is present in all metatypes that are not SPC-0, either alone or in combination with other SPCs. This observation suggests that many (if not all) SPCs are dependent on SPC-2.
  • the relationships between the SPCs were inferred in a two step process: first, the SPC combinations observed in the major metatypes were examined; second, the SPCs observed in the minor metatypes were systematically compared to the SPCs observed in the corresponding major metatypes. This comparison between the major and minor metatypes is illustrated in FIG. 15B .
  • Examination of the SPCs found in the major metatypes (upper panel of FIG. 15A ) reveals that (1) SPC-13 is invariably found in combination with SPC-2, but not vice versa, while (2) SPC-1 and SPC-4 each appear on a fraction of the metatypes that contain both SPC-2 and SPC-13. It follows from these observations that SPC-1 and SPC-4 depend on SPC-13, which in turn depends on SPC-2.
  • Class I shown in the upper panel of FIG. 15B , represents those metatypes that exhibit identical SPCs in both the minor and the major metatype.
  • Class II shown in the middle panel of FIG. 15B , represents those metatypes that exhibit different SPCs in the minor and the major metatype.
  • Class III shown in the lower panel of FIG. 15B , represents those minor metatypes for which the major metatype exhibits SPC-0.
  • the class I metatypes reveal two SPC combinations: 1-2-13 and 2-4-13, consistent with the dependency of SPC-1, SPC-4 and SPC-13 on SPC-2.
  • the dependencies of SPCs 6, 8 and 10, which are observed in one sample only cannot be established.
  • the minor metatype D036-1 has the SPCs 2, 3, 10 and 13 and its major metatype has SPC-0.
  • SPC-10 could be dependent on SPC-3 but could also be dependent on SPC-0.
  • FIG. 15C shows a visual representation of the network of hierarchical relationships established between the 9 SPCs in the CYP4A11 gene.
  • SPC-6 (occurs twice and consists of 6 SNPs), SPC-8 (singleton, 4 SNPs), SPC-10 (singleton, two polymorphisms), SPC-11 (singleton, 2 SNPs), and SPC-9 (singleton, 3 SNPs). It is anticipated that the analysis of additional samples would enable the establishment of the relationships of these SPCs. Indeed, the skilled person will realize that the outcome of the above analysis is determined primarily by the number of informative observations, and that the remaining ambiguity is not related to inherent limitation of the method.
  • the SPC map presented in FIG. 15D shows in the upper panel the inferred haplotypes onto which the different SPC combinations observed in the metatypes are visualized, and the lower panel shows the 67 polymorphic sites that are clustered in each of the 9 SPCs.
  • the 9 SPCs are organized in a total of only 10 inferred haplotypes designated by the SPC combinations present: 2-13, 2-1-13; 2-3-13; 2-4; 2-4-13; 2-5-13; 2-7; 2-9-13; 2-12-13 and 0 (the haplotype that has no SPC).
  • the inferred haplotypes can now be used to deconvolute the diploid genotypes, as shown in the last two columns of FIG. 15B .
  • the rationale for the deconvolution is that the minor metatypes represent combinations of two of the inferred haplotypes, and that the major metatypes represent those SPCs that are common between the two inferred haplotypes.
  • the grouping of the metatypes into three classes is also useful for the deconvolution.
  • the class I metatypes have identical SPC combinations in both minor and major metatype, and these SPC combinations are also found among the inferred haplotypes. Consequently the class I metatypes are simply deconvoluted into two identical haplotypes.
  • sample E012 which has the SPC combination 1-2-13 is deconvoluted into two 1-2-13 haplotypes.
  • the class II metatypes display different SPC combinations in the minor and major metatypes. Each minor metatype must represent a combination of two inferred haplotypes other than “0”, and which share the SPCs represented in the major metatype.
  • sample D009 which has in the minor metatype the SPC combination 1-2-3-13 and 2-13 in the major metatype is deconvoluted into the two haplotypes 1-2-13 and 2-3-13.
  • the class III metatypes display SPC combinations in the minor metatypes and no SPCs in the major metatypes. Each minor metatype must thus represent a combination of two inferred haplotypes which share no SPCs. Since all the SPCs are dependent on SPC-2, one of the haplotypes must be “0”. For example, sample E019 which has in the minor metatype the SPC combination 1-2-13 is deconvoluted into the two haplotypes 1-2-13 and 0.
  • the present invention provides a means to select those polymorphic sites that most closely match the SPC and are thus most suited to serve as ctSNPs.
  • the method is based on a calculation of the average linkage value (AVL) of each polymorphism with all other polymorphisms of the SPC. As explained herein above, this calculation not only considers aberrant data (i.e. the minor alleles are not present in all samples carrying the SPC or are found in other samples) but also take missing genotypes into account to evaluate the suitability of SNPs.
  • ADL average linkage value
  • FIGS. 15E , F and G show the selection of ctSNPs for three SPCs, respectively SPC-1, SPC-2 and SPC-4.
  • SPC-1 the selection of ctSNPs for SPC-1.
  • SPC-2 the selection of ctSNPs for SPC-1.
  • SPC-4 the selection of ctSNPs for SPC-1.
  • the two equivalent ctSNPs of choice characterized by the largest ALV values, are SNP-33 and SNP-45. Both SNPs best represent the SPC because the minor alleles are found in all samples carrying the SPC and do not occur in other samples while, additionally, there are no missing data points.
  • FIG. 15F shows the selection of ctSNPs for SPC-2.
  • the two SNPs that have the largest ALV values, SNP-31 and SNP-40 both perfectly match with the SPC without missing data points. All other SNPs have either missing data points or exhibit aberrant scores.
  • FIG. 15G shows the selection of tag SNPs for SPC 4.
  • the present example provides further proof of concept that the methods of the present invention can be used on unphased diploid genotype data to construct SPC maps of complex genomic loci and to select ctSNPs for developing diagnostic markers for genetic analysis.
  • the present example also provides proof of concept that the methods of the present invention can be used to analyze loci in the human genome exhibiting complex patterns of recombination.
  • This example presents an analysis of polymorphic sites in the human major histocompatibility complex (MHC) locus.
  • MHC major histocompatibility complex locus.
  • the MHC locus is known to exhibit complex patterns of genetic variation and is currently the focus of intensive genetic research because of its importance in many human diseases.
  • the MHC locus is also one of the few loci in the human genome in which the existence of recombinational hotspots is well documented, and the present example comprises a 216-kb segment of the class II region of the MHC in which different recombinational hotspots have been mapped with great precision [Jeffreys et al., Nat. Genet. 29: 217-222, 2001].
  • the diploid genotypes and the SNP allele data for the “SNP genotypes from upstream of the HLA-DNA gene to the TAP2 gene in the Class II region of the MHC” [Jeffreys et al., Nat. Genet. 29: 217-222, 2001] were copied from the website http://www.le.ac.uk/genetics/ajj/HLA/Genotype.html.
  • the data comprise 296 SNPs typed in a panel of 50 unrelated UK Caucasian semen donors using allele-specific oligonucleotide hybridisation of genomic PCR products.
  • the diploid genotype table lists the allele scores of the 296 polymorphic sites of the class II region of the MHC in the 50 samples.
  • C 1, C ⁇ 0.95, C ⁇ 0.90, C ⁇ 0.85 and C ⁇ 0.80.
  • the SPC algorithm clustered 198 of the 296 polymorphisms into 40 different SPCs.
  • the pattern of SPCs is shown in FIGS. 16B and 16C . Note that, in order to reduce the size of the Figure, the analysis was performed on two separate sets of SNPs, more specifically the subgroup of SNPs with high frequency minor alleles (observed more than 8 times or >16%; FIG. 16B ) and the SNPs characterized by low frequency minor alleles ( ⁇ 16%; FIG. 16C ).
  • each subgroup cluster into 20 SPCs.
  • FIG. 16B /C clearly shows that nearly all of the SPCs are confined to 7 different domains within the 216-kb segment; these domains are represented by the differently highlighted rectangles that refer to the physical map shown in FIG. 16A .
  • each domain comprises a different set of SPCs and there are (almost) no SPCs that extend into adjacent domains. This is consistent with the presence of recombination hotspots between the domains that have disrupted the SPCs. Indeed, the domain boundaries predicted by the SPC map correspond very well with the positions of the recombination hotspots which were identified by Jeffreys and co-workers, and which are indicated by the red arrows in FIG. 16A .
  • FIG. 16B /C shows that there are a few exceptional SPCs that are spanning multiple domains, most notably SPC-2 and SPC-7 that are indicated by heavy arrows in FIG. 16C .
  • SPC-2 is found in domains 1, 3 and 6 and comprises singleton SNPs observed in one sample.
  • SPC-7 occurs in domains 4 and 7 and is observed in eight individuals.
  • the relationships between the SPCs are shown in the network structure of FIG. 16E ; they were inferred by comparing the SPCs found in the minor metatypes and their corresponding major metatypes as outlined in detail in Example 7.
  • all of the metatypes were consistent with the deduced SPC-haplotypes or occasional recombinants between these.
  • Tag SNPs (ctSNPs) that best represent the various clusters can obviously be selected in the absence of an SPC map and accompanying network structure.
  • the SPC map of the MHC locus is much more complex. This is consistent with the much higher genetic variability of the MHC locus. It can be anticipated that the SPC-haplotypes described in the present example represent only a fraction of those that may be uncovered in the human population. Indeed the data analyzed here were from a limited population sample of North Europeans. Hence the SPC mapping strategy provides a useful method to analyze the organizational patterns of SNPs and to design reliable tag SNPs for genetic resting.
  • the present example provides further proof of concept that the methods of the present invention can be used on unphased diploid genotype data to construct SPC maps of the human genome and that the SPC maps are particularly useful for selecting ctSNPs as diagnostic markers for genome-wide genetic association studies.
  • This example presents an analysis of the genetic variation data recently generated in the International human HapMap project (The International HapMap Consortium, Nature 426: 789-796, 2003) to exemplify the different aspects of the invention.
  • the aim of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome, by characterizing sequence variants, their frequencies, and correlations between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe.
  • the project will provide tools that will allow the indirect association approach to be applied readily to any functional candidate gene in the genome, to any region suggested by family-based linkage analysis, or ultimately to the whole genome for scans for disease risk factors.
  • the unphased diploid genotypes and the SNP allele data of public data release #3 for chromosome 22 was downloaded from the HapMap website http://www.hapmap.org/ (The International HapMap Consortium, Nature 426: 789-796, 2003). Chromosome 22 was chosen for this analysis because of the relatively high density of SNPs genotyped on this chromosome, averaging 1 SNP per ⁇ 5 kb.
  • the unphased diploid genotypes list the SNP allele scores of the 5865 polymorphic sites of chromosome 22, genotyped in 30 father-mother-child CEPH trios and 5 duplicate samples (95 individuals in total). The chromosomal positions of each SNP are given in basepairs on reference sequence “ncbi_b34”.
  • a genetic variation table was derived from the unphased diploid genotypes by converting the homozygous genotypes denoted by two identical symbols (e.g. “AA”) into single letter symbols (e.g. “A”) and the heterozygous genotypes denoted by two different symbols (e.g. “AG”) into the symbol “H”. Missing genotypes are represented by the symbol “N”.
  • the genetic variation table of chromosome 22 was divided into consecutive blocks of 120 SNPs with an overlap of 20 SNPs between each consecutive block. Finally, a reformatting into consecutive tables of metatypes was performed as described in Example 7.
  • the metatype table was analyzed with the SPC algorithm with the same parameter settings as in Example 7.
  • the present Example is directed at the analysis of a segment of 2.27 Mb comprising 700 SNPs, corresponding to an average of 1 SNP per 3.24 kb.
  • the SPC map obtained at a clustering threshold of C ⁇ 0.90 shown in FIG. 17B roughly half of the SNPs were clustered in domains exhibiting extensive and interspersed SPC patterns, while the other half of the SNPs yielded mostly short isolated SPCs comprising a few SNPs.
  • the methods of the present invention may furthermore be used for the selection of tag SNPs (ctSNPs).
  • ctSNPs tag SNPs
  • Such ctSNPs can be selected both in the less structured regions and in the domains of extensive SPC structure.
  • genotypes for additional SNPs become available in the future, this list can simply be updated by adding tag SNPs for the novel SPCs that will be uncovered. It should be stressed that the tag SNPs that are identified on the basis of the current analysis will, in general, remain valid in the future.
  • Domain 9 of FIG. 17B was analyzed in detail to exemplify one of the aspects of the present invention, more specifically the ability to identify potentially erroneous genotype data that one may want to verify experimentally.
  • Domain 9 comprises 59 SNPs of which 58 are clustered in 6 SPCs at a threshold of C ⁇ 0.90.
  • the relationships between 5 of the 6 SPCs, shown in the network structure of FIG. 17D were inferred by comparing the SPCs found in the minor metatypes and their corresponding major metatypes as outlined in detail in Example 7.
  • the sixth SPC comprises 3 singleton SNPs observed in one sample that was excluded from the analysis.
  • the deconvolution analysis revealed that the SPCs are organized in 6 SPC-haplotypes (including the haplotype that is devoid of SPCs) as shown in the SPC map in FIG. 17C .
  • 6 SPC-haplotypes including the haplotype that is devoid of SPCs
  • all 89 metatypes were consistent with the 6 SPC-haplotypes or occasional recombinants between these.
  • the SNP genotypes that were inconsistent with the SPC map were examined in detail.
  • An inconsistency consists of either the absence of a SNP minor allele in metatypes that contain the SPC to which the SNP belongs, or, alternatively, the presence of a minor allele in a metatype that does not carry the SPC.
  • FIG. 17E represents the metatypes of 3 trios (parents and child) with their corresponding SPC-haplotypes.
  • the minor allele of SNP-24 (belonging to SPC-1) is genotyped in one of the parents, but not in the child.
  • the second trio is genotyped in one of the parents, but not in the child.
  • the present example provides an illustration of the differences between the SPC maps constructed with the methods of the present invention and the haplotype blocks obtained with the approach proposed by Daly et al. [Daly et al., Nat. Genet. 29: 229-232, 2001; Daly et al., patent application US 2003/0170665 A1].
  • the present example also provides an illustration of the differences between the tag SNPs (ctSNPs) selected with the methods of the present invention and the haplotype tag SNPs (htSNPs) selected with the haplotype block method.
  • This example presents a reanalysis of the polymorphic sites in a 500 kb segment on chromosome 5q31, which had been used to establish the presence of haplotype blocks in the human genome [Daly et al., Nat. Genet. 29: 229-232, 2001].
  • the results of the analysis presented provides evidence that the ctSNPs selected with the methods of the present invention are superior diagnostic markers for genome wide genetic association studies, and genetic analysis in general.
  • the unphased diploid genotypes and the SNP allele data for the “High-resolution haplotype structure in the human genome” [Daly et al., Nat. Genet. 29: 229-232, 2001] were downloaded as “Download raw-data page” from the website http://www.broad.mit.edu/humgen/IBD5/haplodata.html.
  • the data of the 500 kb segment on chromosome 5q31 comprise 103 SNPs typed in a panel of 129 trios, amounting to 387 individuals.
  • the raw-data page lists numerical symbols representing the alleles of the 103 polymorphic sites genotyped in the 387 samples.
  • the numerical symbols were replaced by the symbols “A”, “C”, “G” and “T” for the homozygous genotypes and by the symbol “H” and “N” for respectively the heterozygous genotypes and the missing genotypes.
  • the genetic variation table was reformatted into a metatype table as described in Example 7.
  • the analysis of the present data set was encumbered by the large number of missing data points (i.e. 10.4%) combined with the relatively high incidence of recombination.
  • the SPC pattern that was ultimately assembled gathers information about the clustering at different stringencies. Basically, the 15 SPCs that were identified at the C ⁇ 0.875 threshold were retained and SNPs that clustered at the lower thresholds were added (without allowing the SPCs themselves to coalesce). In total 87 of the 103 SNPs were clustered.
  • FIG. 18 shows that the SPC pattern of the 103 SNPs is discontinuous at both ends of the map (short alternating SPCs), while the central part comprises long overlapping SPCs.
  • the haplotype block structure [Daly et al., Nat. Genet. 29: 229-232, 2001] is represented by the numbered grey rectangles in FIG. 18 .
  • Comparison of the SPC pattern with the 11 haplotype blocks shows that several SPCs are running across two or more blocks, illustrating that the SPC structure provides a more concise representation of the organization in the genetic variation. The principal difference between the two methods lays in the selection of tag SNP markers for genotyping.
  • tag SNPs are derived from the haplotypes identified within the blocks as SNPs that are diagnostic for each haplotype, while the methods of the present invention define (at the most) one tag SNP for each SPC. Consequently, the SPCs that are spanning multiple adjacent blocks will be tagged more than once, actually as many times as the number of blocks the SPC is encompassing. In contrast to the SPC concept, the consideration of independent blocks, leads a redundancy in the selection of markers.
  • SPC structure may be derived directly from unphased diploid genotype data whereas the inference of haplotypes is a prerequisite for the haplotype block method.
  • the present example provides proof of concept that the methods of the present invention can be used on genetic variation data other than defined sequence differences, and that the SPC maps thus obtained are particularly useful for examining genome-wide patterns of genetic variation.
  • the present example provides this proof of concept for single-feature polymorphisms (SFPs) obtained using high-density oligonucleotide arrays and demonstrates that the methods of the present invention can be used to design diagnostic microarrays that address selected tag SFPs derived from the SPC maps.
  • SFPs single-feature polymorphisms
  • This example presents an analysis of the polymorphic sites in chromosome 1 of common laboratory strains of yeast identified using high-density oligonucleotide arrays [Winzeler et. al., Genetics. 163: 79-89, 2003].
  • the Affymetrix S98 oligonucleotide array (Affymetrix Inc, Santa Clara, Calif.) containing 285,156 different 25-mers from the yeast genomic sequence was used to discover 11,115 single-feature polymorphisms (SFPs) in 14 different yeast strains and to assess the genome-wide distribution of genetic variation in this yeast population.
  • High-density oligonucleotide arrays using short 25-mer oligonucleotides are particularly useful for discovering polymorphisms because the strength of the hybridisation signal can be used to detect nucleotide changes.
  • SFPs Single-Feature Polymorphisms
  • allelic variation data of intraspecies polymorphisms between laboratory strains of yeast used in the present analysis were downloaded from the website http://www.scripps.edu/cb/winzeler/genetics_supplement/supplement.htm.
  • the allelic variation data table comprises the presence/absence scores (1/0) of 11,115 SFPs in 14 different yeast strains, together with their position on each of the 16 yeast chromosomes.
  • the allelic variation data table was converted into the standard format of the genetic variation table by substituting the numerical symbols 0 and 1 by the symbols “C” and “A” respectively.
  • the SFPs were sorted by chromosome and the genetic variation table was partitioned into 16 tables comprising the SFPs of individual chromosomes.
  • the genetic variation table of chromosome 1 analyzed in the present example, comprises 406 SFPs, of which 174 were singletons. To simplify the analysis and the representation of the results, the singletons were excluded from the analysis.
  • the SPC map of chromosome I can be used to select informative tag SFPs that are diagnostic for each SPC identified and which can be used for genotyping yeast strains.
  • a subset of 12 or 19 tag SFPs can be identified (depending on the minimum number of SFPs per cluster), representing a more than 20-fold reduction of the 406 initially observed SFPs. While the exact fold of reduction will depend on the extent of linkage of SFPs, the example demonstrates that the methods of the present invention provide a straightforward approach for selecting a subset of SFPs that have the highest diagnostic value.
  • Dedicated arrays, comprising only those oligonucleotides that interrogate the tag SFPs can then be designed.
  • the present example illustrates that the methods of the present invention provide a rational framework for analyzing complex patterns of genetic variation generated on a genome-wide scale, obtained by microarray analysis.
  • the example also demonstrates that the methods of the present invention permit the selection of tag SFPs that may be assembled on purposely designed microarrays that are useful for in vitro diagnostic tests or genetic analysis in general.
  • the present example provides proof of concept that the methods of the present invention can be used on genetic variation data obtained with multilocus sequence typing (MLST) of bacteria, and that the SPC maps thus obtained are particularly useful for determining the genetic identity of bacteria.
  • Multilocus sequence typing (MLST) is rapidly becoming one of the standard techniques for the characterization of bacteria. In this technique neutral genetic variation from multiple genomic locations is indexed by analyzing stretches of nucleotide sequence of 500 bp from loci coding for house keeping genes. Sequence data are readily compared among laboratories and lend themselves to electronic storage and distribution. A World Wide Web site for the storage and exchange of data and protocols for MLST has been established (http://mlst.zoo.ox.ac.uk). This example presents an analysis of some of the MLST data from a study of the gram-negative bacterium Campylobacter jejuni [Dingle et al., J. Clin. Microbiol. 39:14-23, 2001].
  • the aligned nucleotide sequences of the glutamine synthetase (glnA) gene from 108 C. jejuni strains used in the present analysis were downloaded from the website http://mlst.zoo.ox.ac.uk.
  • SPC 20 shows the SPC map obtained at a threshold of C ⁇ 0.90 in which the polymorphic sites are clustered into 4 SPCs. It can be seen that the majority of polymorphic sites exhibit a simple SPC structure in that they fall into three SPCs, two of which (SPC-2 and SPC-3) are dependent on SPC-1. The fourth SPC (SPC-4) contains sites at which a third allele occurs in one sample only.
  • the simple SPC pattern demonstrates that a very large number (over one hundred) of polymorphisms can be reduced to a mere three cluster tag polymorphism to type the 108 strains at this locus. Moreover, the straightforward dependency relationships observed provide a clear genealogical picture of the evolution of the glnA locus.
  • the present example illustrates that the methods of the present invention provide a rational framework for analyzing complex patterns of genetic variation generated by multilocus sequence typing (MLST) of bacteria.
  • the example also demonstrates that the methods of the present invention permit the selection of cluster tag SNPs that may be assembled on the basis of the observed SPCs at different loci, and which are useful for precise in vitro diagnostic of particular groups of bacteria in general.
  • the present example provides proof of concept that the methods of the present invention can be used on genetic variation data obtained by analyzing nucleic acid sequences with mass spectrometry.
  • This example presents an in silico comparison of the analysis of genetic variation identified in the nucleic acid sequences of a locus with spectral variation that would be obtained if the same sequences were analyzed by mass spectrometry.
  • the rationale of the present example was that when nucleic acids are subjected to base-specific cleavage prior to mass spectrometry, many of the sequence polymorphisms will give rise to predictable alterations in the spectra which are indicative of the nature and the position of the underlying sequence variation. These predicted spectral changes can readily be computed using state of the art computer programs.
  • the maize gene sequences analyzed in the present example have been generated in phylogenetic studies on the origins of domesticated maize [Tenaillon et al., Proc. Natl. Acad. Sci. USA 98: 9161-9166, 2001] and comprise a region of 420 bp from an anonymous RFLP locus (asg62) located on chromosome 1 sequenced in 25 different accessions of maize inbred lines and landraces ( Zea mays subsp. mays ).
  • GenBank http://www.ncbi.nlm.nih.gov
  • accession numbers AF377395-AF377419 accession numbers AF377395-AF377419.
  • the sequences were aligned to generate a genetic variation table as described in detail in Example 1.
  • the genetic variation table comprises 47 polymorphic sites observed in the 25 sample sequences.
  • the same sequences were used to compute the spectral differences that would be observed in the case of a mass spectrometrical analysis.
  • the fragmentation schemes used for this case in point consists of the RNase-A mediated U- and C-specific cleavage of transcripts that incorporate dC- and dT-residues, respectively.
  • Transcripts that incorporate dC/dT-residues can be generated with good efficiency with the use of mutant phage RNA polymerases that have essentially lost the ability to discriminate between rNTP and dNTP substrates [e.g.
  • FIG. 21 shows a comparison of the SPCs detected in the spectral variation table from the U-specific cleavage reaction and in the genetic variation table. Because of the great genetic diversity observed at this locus in the maize lines, 37 of the 49 sequence polymorphisms were found to cluster in a total of 9 SPCs, of which three have 5 or more clustered polymorphisms.

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