US20020119451A1 - System and method for predicting chromosomal regions that control phenotypic traits - Google Patents

System and method for predicting chromosomal regions that control phenotypic traits Download PDF

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US20020119451A1
US20020119451A1 US09/737,918 US73791800A US2002119451A1 US 20020119451 A1 US20020119451 A1 US 20020119451A1 US 73791800 A US73791800 A US 73791800A US 2002119451 A1 US2002119451 A1 US 2002119451A1
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organism
data structure
genotypic
strains
correlation value
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Jonathan Usuka
Andrew Grupe
Gary Peltz
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Leland Stanford Junior University
Sandhill Bio Corp
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    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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    • 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
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Definitions

  • Experimental murine models have the following advantages for genetic analysis: inbred (homozygous) parental strains are available, controlled breeding, common environment, controlled experimental intervention, and ready access to tissue. A large number of murine models of human disease biology have been described, and many have been available for a decade or more. Despite this, relatively limited progress has been made in identifying genetic susceptibility loci for complex disease using murine models. Genetic analysis of murine models requires generation, phenotypic screening and genotyping of a large number of intercross progeny.
  • the present invention provides a system and method for associating a phenotype with one or more candidate chromosomal regions in the genome of an organism.
  • phenotypic differences between a plurality of strains of the organism are correlated with variations and/or similarities in the respective genomes of the plurality of strains of the organism.
  • the invention relies on the use of a genotypic database that includes variations and similarities of representative strains of the organism of interest.
  • Representative genotypic databases include, but are not limited to, single nucleotide polymorphism databases, microsatellite marker databases, restriction fragment length polymorphism databases, short tandem repeat databases, sequence length polymorphism databases, expression profile databases, and DNA methylation databases.
  • One embodiment of the present invention provides a method for associating a phenotype with one or more candidate chromosomal regions in a genome of an organism.
  • a phenotypic data structure that represents a difference in one or more phenotypes between different strains of the organism is derived.
  • the phenotypic data structure comprises a definition of one or more phenotypes exhibited by the organism together with a measure of each of these phenotypes.
  • a hypothetical phenotypic data structure for rabbits could include the phenotypes “tail length” and “hair color” and the respective measure for each of these phenotypes could be “7 centimeters” and “brown.”
  • a genotypic data structure is established in accordance with one embodiment of the present invention.
  • the genotypic data structure is identified by a particular locus selected from a plurality of loci present in the genome of the organism.
  • the genotypic data structure includes one or more positions within the locus. For each of these positions, the genotypic data structure provides information on the extent of a variation between different strains of the organism.
  • a hypothetical example of a genotypic data structure in accordance with the present invention is an data structure for a locus that includes genes A and B.
  • the genotypic data structure includes the positions of genes A and B within the locus as well as some measurement related to genes A and B, such as the mRNA expression level that has been measured for each of these genes.
  • the mRNA expression-level defines the extent of variation between different strains of the organism.
  • the phenotypic and genotypic data structures are then compared to form a correlation value.
  • the process continues with the establishment of another genotypic data structure that corresponds to a different loci and the concomitant comparison of this genotypic data structure to the phenotypic structure until several of the loci in the genome of the organism have been tested in this manner.
  • one or more genotypic data structures are identified that form a high correlation value relative to all other genotypic data structures that have been compared to the phenotypic data structure.
  • the loci in the genome of the organism that correspond to the highly correlated genotypic data structures represent one or more candidate chromosomal regions that may be associated with the phenotype of interest.
  • each element in a phenotypic data structure represents a variation in the phenotype between a different first and second strain of the organism of interest. Such variations may be determined by measurement of an attribute corresponding to the phenotype in the respective strains of the organism. Representative phenotypic variations include, for example, eye color, hair color, and susceptibility to a particular disease. In other embodiments, each element in a phenotypic data structure represents a variation in the phenotype between a different first and second cluster of strains of the organism of interest.
  • the genotypic data structure represents a variation of at least one component of a locus between two strains of the organism of interest.
  • each element in the genotypic data structure represents a variation of at least one component of the locus between a different first cluster of strains of the organism and a different second cluster of strains of the organism.
  • the phenotypic and genotypic data structures represent a subset of all strains of the organism of interest.
  • the present invention contemplates a considerable number of different methods for comparing the phenotypic and genotypic data structures.
  • c(P, G L ) is the correlation value
  • p(i) is a value of the i th element of the phenotypic data structure
  • g(i) is a value of the i th element of the genotypic data structure
  • ⁇ P> is a mean value of all elements in the phenotypic data structure
  • ⁇ G L > is a mean value of all elements in the genotypic data structure
  • Other methods for forming a correlation value between the phenotypic data structure and a particular genotypic data structure include but are not limited to regression analysis, regression analysis with data transformations, a Pearson correlation, a Spearman rank correlation, a regression tree and concomitant data reduction, partial least squares, and canonical analysis.
  • a mean correlation value that represents a mean of correlation values is computed between the phenotypic data structure and a particular genotypic data structure. Further, a standard deviation of the mean correlation is computed. Genotypic data structures having a correlation value that is a number of standard deviations above the mean correlation value are considered to be the data structures that correspond to loci that are associated with the genotypic trait. The number of standard deviations that is chosen for the cutoff is dynamically chosen so that a specific percentage of the genome, such as ten percent, is identified as positive.
  • FIG. 1 illustrates a computer system for associating a phenotype with one or more candidate chromosomal regions in a genome of an organism in accordance with one embodiment of the present invention.
  • FIG. 2 illustrates the processing steps for associating a phenotype with one or more candidate chromosomal regions in a genome of an organism in accordance with one embodiment of the present invention.
  • FIG. 3 illustrates a hypothetical representation of the method for computational prediction of QTL intervals in accordance with one embodiment of the present invention.
  • FIGS. 4A - 4 D illustrate the computational prediction of chromosomal regions containing genes that determine MHC haplotype
  • FIG. 4C retinal ganglion number
  • FIG. 4D in accordance with one embodiment of the present invention.
  • FIG. 5 illustrates an analysis of the sensitivity of the computational genome scanning method for prediction using ten experimentally verified QTL intervals. A graph of the percentage of correct predictions as a function of the amount of genomic sequence (percent) contained within the predicted regions is plotted.
  • FIG. 6 illustrates the comparison of a genotypic database 52 that includes SNP data versus a genotypic database that includes microsatellite data in identifying the murine chromosomal location for the phenotypic trait of retinal ganglion cell formation, in accordance with one embodiment of the present invention.
  • a key aspect of research in genetics is associating sequence variations with heritable phenotypes.
  • the most common variations are single nucleotide polymorphisms (SNPs), which occur approximately once every 100 to 300 bases in a genome.
  • SNPs single nucleotide polymorphisms
  • the present invention contemplates the use of genotypic databases such as SNP databases in order to correlate genetic variances in an organism with one or more phenotypic variances.
  • a searchable database of mouse SNPs that contains alleles for 15 common inbred mouse strains and information for performing high throughput, inexpensive genotyping assays for each SNP was built.
  • SNP-based genotyping of pooled samples requires at least twenty-fold fewer assays than genotyping individual samples with microsatellite markers, and identified the same linkage regions.
  • genotypic databases include databases that have various types of gene expression data from platform types such as spotted microarray (microarray), high-density oligonucleotide array (HDA), hybridization filter (filter) and serial analysis of gene expression (SAGE) data.
  • platform types such as spotted microarray (microarray), high-density oligonucleotide array (HDA), hybridization filter (filter) and serial analysis of gene expression (SAGE) data.
  • MSA high-density oligonucleotide array
  • filter hybridization filter
  • SAGE serial analysis of gene expression
  • the chromosomal location is either known (physical mapping or mouse genomic sequencing) or can be estimated by syntenic mapping based upon homology with human genes.
  • Another example of a genetic database that can be used is a DNA methylation database.
  • a DNA methylation database For details on a representative DNA methylation database, see Grunau et al., “MethDB—a public database for DNA methylation data,” Nucleic Acids Research, in press; or the URL:
  • the present invention provides tools for scanning genotypic databases, such as SNP databases, to predict quantitative trait loci (QTL) after phenotypic information obtained from common strains of the organism is provided.
  • the computational QTL prediction method is capable of correctly predicting the chromosomal regions that have been previously identified by tedious and laborious analysis of experimental intercross populations for the multiple traits that are analyzed.
  • the present invention bypasses the burdensome requirement for generation and characterization of intercross progeny, enabling QTL regions to be predicted within a millisecond time frame.
  • FIG. 1 shows a system 20 for associating a phenotype with one or more candidate chromosomal regions in a genome of an organism.
  • System 20 preferably includes:
  • a central processing unit 22 a central processing unit 22 ;
  • a main non-volatile storage unit 34 preferably a hard disk drive, for storing software and data, the storage unit 34 controlled by disk controller 32 ;
  • system memory 38 preferably high speed random-access memory (RAM), for storing system control programs, data, and application programs, including programs and data loaded from non-volatile storage unit 34 ; system memory 38 may also include read-only memory (ROM);
  • RAM random-access memory
  • ROM read-only memory
  • a user interface 24 including one or more input devices ( 26 , 30 ) and a display 28 ;
  • a network interface card 36 for connecting to any wired or wireless communication network
  • an internal bus 33 for interconnecting the aforementioned elements of the system.
  • Operating system 40 may be stored in system memory 38 .
  • system memory 38 includes:
  • file system 42 for controlling access to the various files and data structures used by the present invention
  • phenotype/genotype processing module 44 for associating a phenotype with one or more candidate chromosomal regions in a genome of an organism
  • genotypic database 52 for storing variations in genomic sequences of a plurality of strains of an organism
  • phenotypic data 60 that includes measured differences in one or phenotypic traits associated with the organism.
  • phenotype/genotype processing module 44 includes:
  • a phenotypic data structure derivation subroutine 46 for deriving a phenotypic data structure that represents a variation in a phenotype between different strains of an organism of interest;
  • a genotypic data structure derivation subroutine 48 for establishing a genotypic data structure that corresponds to a locus in the genome of the organism of interest
  • a phenotype/genotype comparison subroutine 50 for comparing the phenotypic array to the genotypic array to form a correlation value.
  • Genotypic database 52 is any type of genetic database that tracks variations in the genome of an organism of interest. Information that is typically represented in genotypic database 52 is a collection of loci 54 within the genome of the organism of interest. For each locus 54 , strains 56 for which genetic variation information is available are represented. For each represented strain 56 , variation information 58 is provided. Variation information 58 is any type of genetic variation information. Representative genetic variation information 58 includes, but is not limited to, single nucleotide polymorphisms, restriction fragment length polymorphisms, microsatellite markers, restriction fragment length polymorphisms, and short tandem repeats.
  • genotypic databases 52 include, but are not limited to: Genetic variation type Uniform resource location SNP http://bioinfo.pal.roche.com/usuka_bioinformatics/ cgi-bin/msnp/msnp.pl SNP http://snp.cshl.org/ SNP http://www.ibc.wustl.edu/SNP/ SNP http://www-genome.wi.mit.edu/SNP/mouse/ SNP http://www.ncbi.nlm.nih.gov/SNP/ Microsatellite http://www.informatics.jax.org/searches/ markers polymorphism_form.shtml Restriction http://www.informatics.jax.org/searches/ fragment polymorphism_form.shtml length polymorphisms Short tandem http://www.cidr.jhmi.edu/mouse/mmset.html repeats Sequence http://mcbio.med
  • genotypic databases 52 within the scope of the present invention include a wide array of expression profile databases such as the one found at the URL:
  • genotypic database 52 is a variation in the expression level of a gene rather than a variation in the genome, there is no requirement that genomic database 52 be populated with elements such as locus 54 .
  • a phenotypic data structure is derived from phenotypic data 60 (FIG. 1) using phenotypic data structure derivation subroutine 46 (FIG. 1).
  • the phenotypic data structure tracks measured differences in traits between strains of an organism of interest.
  • the phenotypic data structure used is a phenotypic array.
  • the phenotypic array is formed in a stepwise fashion by subroutine 46 .
  • an N ⁇ N phenotypic distance matrix, P is established where both the ith row and the jth column are associated with a given strain for which quantitative information t i exists for a given trait.
  • This matrix is populated with the differences between strains in regard to the examined trait as follows:
  • each element in the matrix corresponds to a distance between strains using the quantitative trait as a metric for the space.
  • This matrix has the following properties:
  • the matrix is symmetric, because
  • a phenotypic distance matrix that tracks the lifespan for these five species members has the form: P A/J AKR/J C3H/HeJ C57BL/6J DBA/2J A/J 0 495 267 118 209 AKR/J 495 0 228 613 286 C3H/HeJ 267 228 0 385 58 C57BL/6J 118 613 385 0 327 DBA/2J 209 286 58 327 0
  • Each value in this illustrative phenotypic distance matrix represents the difference in life span between the designated members.
  • the phenotypic data structure derivation subroutine 46 converts the phenotypic matrix to the phenotypic array by taking the non-redundant, non-diagonal elements of the matrix and arranging them into a vector P:
  • the linear format of P facilitates the ordered comparison of the phenotype and genotype of respective strains of an organism of interest in subsequent computational steps.
  • the phenotypic data used by phenotypic data structure derivation subroutine 46 (FIG. 1) in processing step 202 (FIG. 2) is entered by hand into system 20 by a computer operator.
  • the phenotypic data is read from a source such as phenotypic data file 60 (FIG. 1). It will be appreciated that there are no limitations on the format of the phenotypic data.
  • the phenotypic data can, for example, represent a series of measurements for a quantifiable phenotypic trait in a collection of strains of a species.
  • Such quantifiable phenotypic traits may include, for example, murine tail length, lifespan, eye color, size and weight.
  • the phenotypic data can be in binary form that tracks the absence or presence of some phenotypic trait.
  • a “1” may indicate that a particular species of the organism of interest possesses a given phenotypic trait and a “0” may indicate that a particular species of the organism of interest lacks the phenotypic trait.
  • the phenotypic data structure can be populated with any form of biological data that is representative of the phenotype of the organism of interest.
  • the phenotypic data can be expression data such as mRNA expression data or protein expression level data.
  • each element in the phenotypic data structure is populated with differences in mRNA or protein expression levels between strains of the organism of interest or of cells cultured from the organism of interest.
  • processing step 204 a particular locus, or position, is selected within the genome of the organism of interest.
  • Processing step 204 is the first step of a repetitive loop formed by processing steps 204 through 212 that is repeated for several different loci, or positions, within the genome of the organism of interest.
  • processing step 206 a genotypic data structure is established for the selected locus.
  • processing step 206 is performed by genotypic data structure derivation subroutine 48 (FIG. 1).
  • the genotypic data structure is typically formed in a method similar to the construction of the phenotypic data structure. While the phenotypic data structure's values are typically the differences in quantitative traits exhibited by several strains of an organism of interest, the values in the genotypic data structure correspond to counts of the polymorphic differences between strains for a given locus L that contains M genetic variations, such as SNPs. That is, a given locus L may have several independent genetic variations M, and the goal of the genotypic array that corresponds to this locus is to quantify the number of these independent genetic variations.
  • an individual variation matrix S x is established for each variation in every position x within locus L.
  • S x , the i th row and the j th column are associated with the allele value 1 x (i) for strain i at locus position x.
  • the elements of these S matrices are populated according to the following rule:
  • locus position l x may contain information on the allele for strain i, but not for strain j. In this situation, the assumption is made that strain j has equal probability of containing either allele, and the corresponding entry is set equal to one half.
  • each individual variation matrix S contains elements that take on one of three values: 0, 1 ⁇ 2, or 1. It will be appreciated that many other types of schemes may be used when allelic information is not presently known and use of the value “1 ⁇ 2” in such instances merely illustrates one example of a scheme that may be used in such instances. Similarly, any number of weighting schemes can be used rather than a “0” or “1” and all such weighting schemes are within the scope of the present invention.
  • a variation matrix S that tracks an individual locus position l x for five members (M1 through M5) of a species may have the form: S M1 M2 M3 M4 M5 M1 0 0.5 0.5 1 0 M2 0.5 0 0.5 0 1 M3 0.5 0.5 0 1 1 M4 1 0 1 0 0.5 M5 0 1 1 0.5 0
  • an illustrative genotypic matrix G that represents a specific locus in five species members (M1 through M5) has the form: G M1 M2 M3 M4 M5 M1 0 3.5 2 4 3 M2 3.5 0 3 2.5 1 M3 2 3 0 1 1 M4 4 2.5 1 0 0.5 M5 3 1 1 0.5 0
  • genotypic data structure derivation subroutine 48 converts the genotypic matrix to a genotypic array by taking the non-redundant, non-diagonal elements of the matrix and arranging them into the vector G:
  • ⁇ A> ⁇ i a(i)/I, 1 ⁇ i ⁇ I; (The mean of the scalars comprising vector A);
  • c(P, G L ) is the correlation value between the phenotypic array and the genotypic array that corresponds to locus L;
  • p(i) is a value of the i th element of the phenotypic array
  • g(i) is a value of the i th element of the genotypic array
  • ⁇ P> is a mean value of all elements in the phenotypic array
  • phenotypic and genotypic arrays can be compared in processing step 208 using any number of algorithms other than linear regression.
  • alternative methods for forming a correlation value in processing step 208 include, but are not limited to, regression analysis, regression analysis with data transformations, Pearson correlations, Spearman rank correlation, a regression tree and concomitant data reduction, partial least squares, and canonical analysis. (See e.g. Lui, “Statistical Genomics,” CRC Press LLC, New York, 1998; Stuart & Ord, “Kendall's Advanced Theory of Statistics,” Arnold, London, England, 1994).
  • processing steps 202 through 206 have been described with reference to linear phenotypic and genotypic arrays, it will be appreciated that the methods of the present invention are not limited to the comparison of such arrays. Indeed, any form of data structure having elements that preserve the information in the above described matrices and arrays may be used. For example, rather than using the genotypic array described above, the individual variation matrices can be used. Further, rather than using the phenotypic array, a phenotypic distance matrix can be used.
  • the correlation value is stored in processing step 210 so that it can be subsequently ranked with the correlation value of each of the other loci that are analyzed.
  • Processing step 212 is provided so that the procedure can be repeated in an iterative fashion for all suitable loci vectors L in genotypic database 52 .
  • a decision is made whether to test an additional locus by asking whether all of the loci present in genotypic database 52 (FIG. 1) have been tested.
  • processing step 212 returns a “yes” and the process continues by looping back to processing step 204 where an additional, untested locus is selected from genotypic database 52 .
  • processing step 214 comprises the arrangement of the tested loci in a vector K according their correlation scores:
  • K ( L t , L u , L v , . . . )
  • processing step 214 includes the computation of (i) a mean correlation value that represents a mean of each correlation value formed during instances of processing step 208 ; and (ii) a standard deviation of the mean correlation value based on each of the correlation values formed during instances of processing step 208 .
  • processing step 216 the genotypic data structures that achieve the highest correlation values are selected. Since each genotypic data structure corresponds to a particular locus in the genome, the selection process in processing step 216 results in the association of the phenotype with particular loci in the organism of interest. In one embodiment, the selection process in processing step 216 is performed by selecting genotypic data structures that form a correlation value that is a predetermined number of standard deviations above the mean correlation value. Typically, the predetermined number is chosen so that a small percentage of the genome of the organism, such as five percent, will be selected during processing step 216 .
  • the methods of the present invention are particularly useful in embodiments that make use of genetic information from inbred strains of an organism of interest.
  • a genotypic database 52 was developed that contains allele information across 15 inbred strains.
  • 293 SNPs at defined locations were identified in the mouse genome.
  • the SNPs were identified by direct sequencing of PCR amplification products from defined chromosomal locations.
  • This database also incorporates published allele information for 2848 SNPs, 45% of which are characterized in a subset of M. Musculus strains, and 55% of the SNPs are polymorphic between M. castaneus and one or more M.
  • musculus subspecies (Lindblad-Toh, et al., Nature Genetics Apr;24, 381-386, 2000).
  • User queries regarding SNPs found within a specified chromosomal region or between selected inbred strains are executed in real time and provided via a user interface 24 .
  • FIG. 3 shows hypothetical comparisons, in accordance with the methods of the present invention, between three mouse strains (A, B, C) using SNP information found in the murine SNP database.
  • Each of the two chromosomes sets for a given mouse strain is represented by a horizontal box along the horizontal axis of FIG. 3.
  • Each chromosome set is characterized by the hatching type (horizontal, diagonal, and vertical). Chromosomes with the same hatching style in each of the mouse strains are identical.
  • Cross hatched or diagonally hatched ovals respectively represent alleles at specific chromosomal positions.
  • a dashed horizontal line is used to differentiate each of the mouse strains and the accompanying chart at the bottom of FIG. 3.
  • strains A and B exhibit a similar phenotype. That is, strains A and B exhibit a similar phenotype (full size tail), while strain C has a different phenotype (short tail). SNP alleles at particular chromosomal regions are represented as cross hatched or diagonally hatched ovals.
  • a series of pairwise comparisons are made to establish the correlation value between the phenotype and genotype for each locus. In each of these series of pairwise comparisons, allelic differences in a respective segment of the chromosome of each of the mouse strains is correlated with the phenotypic difference between each mouse strain.
  • FIG. 3 Graphic analysis of the correlation data between the respective strains is shown at the bottom of FIG. 3. The analysis indicates that while most sites exhibit a negative correlation with respect to murine tail length, two chromosomal regions ( 302 ) and ( 304 ) have a strong positive correlation. In fact, 302 and 304 are the chromosomal regions predicted to have genes regulating tail length.
  • FIG. 4 illustrates the correlation between the genotype and phenotype distributions for all 19 mouse autosomal chromosomes for a given trait. Loci are arranged proximal to distal for each chromosome. Each bar represents a 30 cM interval of the respective chromosome and neighboring bars are offset by 10 cM. Dotted line 402 represents a useful cutoff for analyzing the data, with the highest correlated ten percent of the genome being above this line.
  • the methods of the present invention were used to predict the chromosomal location of the MHC complex, which has been mapped to murine chromosome 17, using the H2 haplotypes for the MHC K locus for 10 inbred strains (Anonymous, JAX Notes 475, 1998). Phenotypic distances for strains that shared a haplotype were set to zero, and a distance of one was used for strains of different haplotypes. The SNPs within and near the MHC region had a genotypic distribution which were highly correlated with the phenotypic distances; the correlation value for interval 440 (FIG. 4A) was 5.35 standard deviations above the average for all loci analyzed. There were no other peaks throughout the mouse genome that exhibited a comparable correlation with the phenotype. The computational analysis, executed in accordance with the methods of the present invention, excluded 96% of the mouse genome from consideration without missing the genomic region known to contain the MHC.
  • chromosomal positions that regulate susceptibility to experimental allergic asthma have been investigated using prior art techniques. For example, published analyses of intercross progeny between susceptible (A/J) and resistant (C3H/HeJ) mouse strains identified QTL intervals on chromosomes 2 and 7 (Ewart, et al., Am J Respir Cell Mol Biol 23, 537-545, 2000; Karp, et al., Nature Immunology 1, 221-226, 2000). The ability of the methods of the present invention to identify these chromosomal regions was investigated.
  • the phenotypic distance used to populate the phenotypic matrix was the absolute difference between the measured airway response after allergen-challenge for each strain pair.
  • the experimentally identified QTL intervals on chromosomes 2 and 7 were among the strongest peaks identified by the methods of the present invention (FIG. 4B).
  • the computational method excluded 80% of the mouse genome from consideration without missing the experimentally mapped QTL regions using airway responsiveness data from only 5 inbred mouse strains.
  • the measured density of retinal ganglion cells was used as a phenotype.
  • the QTLs associated with this phenotype have been localized to chromosome 11 in the mouse genome (Williams et al., Journal of Neuroscience 18, 138-146, 1998).
  • the experimentally verified QTL interval on chromosome 11 was contained in the chromosomal regions predicted by the methods of the present invention, while 96% of the mouse genome was excluded (FIG. 4D).
  • chromosomal positions for these six additional quantitative traits are derived from published studies that provided mapped QTL intervals and phenotypic data across multiple inbred strains for each trait (Table 1). As shown in Table 1, a total of 10 QTLs from 6 published phenotypic studies are identified from the literature. Each QTL resides on a different chromosome. Centimorgan positions were interpreted from published marker locations on physical maps.
  • Phenotype Chromosome (cM) Notes AHR 2 (23.5), 7 (1) Allergen induced airway response (APTI) Eye weight 5 (0-10) Mouse eye weight (grams), day 75 Retinal anglion 11 (57.5) Retinal ganglion cell # Lymphoma 1 (62-73), 6 (30), Tumor incidence, lifespan 16 (50) MHC 17 (10) H2 K serotyping PKC 11 (66), 3 (16.4, 45) PKC- ⁇ protein amount, activity
  • the methods of the present invention identified all ten experimentally characterized QTL intervals. In addition, seventeen other chromosomal regions were predicted by this computational method. Whether these predicted regions affect phenotypic traits has not yet been experimentally verified. The threshold required for correct identification of a QTL varied from two percent to nineteen percent of the complete mouse genome.
  • genomic region to search for candidate genes is computationally reduced by an order of magnitude. Since the average size of a predicted genomic region was 38 cM, the 1500 cM mouse genome could be subdivided into approximately forty regions. The computational method was used for seven different phenotypes, so approximately 280 genomic intervals (38-cM in size) were examined. This method correctly identified seven of ten experimentally validated QTL intervals, while missing three, at the ten percent genome-wide threshold. The algorithm further predicted 23 genomic intervals were involved in a phenotypic trait where no QTL had been experimentally characterized.
  • the methods of the present invention were able to identify ten QTLs for seven phenotypic traits that had been previously identified by prior art techniques.
  • Each of the experimentally verified QTL intervals was identified by the methods of the present invention.
  • the genotypic array used to identify these chromosomal regions was derived from a murine SNP genotypic database.
  • the conventionally identified QTL interval exhibited a computational SNP distribution that was highly correlated with the tested phenotype. The correlation was well above the mean value for the entire genome, and nine of ten were greater than a full standard deviation above the mean.
  • genotypic databases include various databases that have various types of gene expression data from platform types such as spotted microarray (microarray), high-density oligonucleotide array (HDA), hybridization filter (filter) and serial analysis of gene expression (SAGE) data.
  • platform types such as spotted microarray (microarray), high-density oligonucleotide array (HDA), hybridization filter (filter) and serial analysis of gene expression (SAGE) data.
  • Genotypic database 52 was populated in manner analogous to the case when SNP data was used to populate database 52 : if the polymorphisms matched between two mouse strains, a “0” was entered, if they differed, a “1” was entered. In this way, the number of differences between mouse strains was counted for a given locus. The remainder of the analysis was performed in accordance with the methods of the present invention. For this trial, the MHC locus was identified on chromosome 17. Although the QTL for the MHC region was not as clearly distinguished when using microsatellite information as it was for SNP data, it should be noted that the microsatellite data used for the trial was sparser than the information currently available in the mouse SNP database.
  • the genotypic database 52 populated with microsatellite data as described in Example 7 was compared to the previously described genotypic database 52 that contains allele information across 15 inbred strains for 287 SNPs at defined locations in the mouse genome.
  • the phenotype is the formation of retinal ganglion cells in infant mice.
  • the experimentally verified QTL that correlates with this phenotype is on chromosome 11.
  • the genotypic database 52 populated with the microsatellite information more strongly identifies the correct QTL peak than the genotypic database 52 populated with SNP data (4.2 standard deviations with microsatellites versus 2.3 standard deviations with SNPs).
  • the results using the microsatellite data are less noisy than the results using the SNP data. See, for example, the reduced positive peak on chromosome 9 using the microsatellite data (702 versus 704).
  • inbred mouse strains limits variability due to environment, and timed experimental intervention and sampling limits error in phenotypic assessment.
  • the inbred strains are homozygous at all loci, which eliminates confounding effects due to heterozygosity found in human populations.
  • inbred strains be used to populate genotypic database 52 .
  • the methods of the present invention will greatly accelerate analysis of complex traits and mammalian disease biology. Recently, there has been increased emphasis on using chemical mutagenesis in the mouse as a method for studying complex biology. This has occurred as a result of the difficulties noted by investigators searching for complex trait loci using standard methods for QTL analysis. For a review, see Nadeau and Frankel, Nature Genetics Aug;25, 381-384, 2000. However, analysis of genetic variation among existing inbred mouse strains can be markedly accelerated by application of the methods of the present invention. Of course, understanding the genetic basis of complex disease requires additional steps beyond computational prediction of genomic intervals. Specific gene candidates must be identified and evaluated before the underlying mutations can be identified and effective treatment strategies can be designed, tested in animal models, and developed for use with humans.
  • the techniques of the invention may be applied using pooled or clustered genetic variation information as a source for the genotypic data structure or genetic variation information from individual samples.
  • the phenotypic information provided from sources such as phenotypic data file 60
  • genotypic database 52 may represent inbred species of the organism of interest or randomized species of the organism of interest that have not been inbred. Because of the overwhelming homology between murine and human genomes, the examples provided herein clearly demonstrate that the methods of the present invention provide an invaluable tool for correlating human phenotypic traits with specific loci in the human genome.
  • genotypic to phenotypic data structure comparison As a two-dimensional comparison. Higher dimensional comparisons than the two-dimensional comparison are possible.
  • one embodiment of the present invention provides for a three dimensional comparison of the class: “genotypic data structure” versus “phenotypic data structure one” versus “phenotypic data structure two.”
  • Another example of a type of comparison within the scope of the present invention includes a comparison of “SNP genotypic data” to “disease phenotypic data” to “microarray data.”

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