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  • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
  • C12N15/09—Recombinant DNA-technology
  • C12N15/63—Introduction of foreign genetic material using vectors; Vectors; Use of hosts therefor; Regulation of expression
  • C12N15/79—Vectors or expression systems specially adapted for eukaryotic hosts
  • C12N15/82—Vectors or expression systems specially adapted for eukaryotic hosts for plant cells, e.g. plant artificial chromosomes (PACs)
  • C12N15/8241—Phenotypically and genetically modified plants via recombinant DNA technology
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  • 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
  • G16B20/10—Ploidy or copy number detection
• • A—HUMAN NECESSITIES
  • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
  • A01H—NEW PLANTS OR NON-TRANSGENIC PROCESSES FOR OBTAINING THEM; PLANT REPRODUCTION BY TISSUE CULTURE TECHNIQUES
  • A01H1/00—Processes for modifying genotypes ; Plants characterised by associated natural traits
  • A01H1/04—Processes of selection involving genotypic or phenotypic markers; Methods of using phenotypic markers for selection
• • 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
  • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
  • G16B20/40—Population genetics; Linkage disequilibrium

Definitions

• MQM MAPPING USING HAPLOTYPED PUTATIVE QTL-ALLELES A SIMPLE APPROACH FOR MAPPING QTL' S IN PLANT BREEDING POPULATIONS
• the present invention relates to the field of plant breeding.
• mapping of QTLs using statistical models to correlate haplotypes with phenotypic traits are known in the art.
• this experimental protocol involves deriving 100 to 300 segregating progeny from a single cross of two divergent inbred lines. The segregating progeny are genotyped for multiple marker loci and evaluated for one to several quantitative traits in several environments. QTLs are then identified as significant statistical associations between genotypic values and phenotypic variability among the segregating progeny.
• the strength of this experimental protocol comes from the utilization of the inbred cross because the resulting Fi parents all have the same linkage phase. Thus, after selfing of the F 1 plants, all segregating progeny (F 2 ) are informative and linkage disequilibrium is maximized.
• F 2 segregating progeny
• the present invention provides a novel and powerful method for mapping phenotypic traits in multiple related plant families. Central to this method is the clustering of the original parents into groups on the basis of their haplotype for multiple genetic markers. The effect of a putative genetic locus is then modeled per haplotype group, instead of per family of progeny, using statistical models which correlate the haplotype with numerical values assigned to the phenotypic trait.
• Embodiments of the invention provide for methods of mapping phenotypic traits to a corresponding chromosomal location.
• the invention provides for mapping phenotypic traits of agronomic importance, including such properties as yield, grain composition, and insect, disease and drought resistance.
• Progeny of multiple related crosses are assigned numerical values corresponding to a phenotypic trait, and their genotype at clusters of genetic marker loci, i.e., their haplotype, is ascertained. Using statistical methods, correspondence between the haplotype and the phenotypic value is determined. In preferred embodiments, QTL are mapped.
• the methods of the invention can be practiced in essentially any plant population, in preferred embodiments, the methods of the invention are used to map phenotypic traits in corn, soybean, sunflower, sorghum, wheat, rice and canola.
• the related parents used to generate progeny are inbred lines. In a preferred embodiment the lines are between 0 and 85% related. In one embodiment, the progeny are derived from a topcross and/or a backcross.
• the statistical method utilized accounts for identical by descent (IBD) data derived by correlating pedigrees and haplotypes for the genetic markers under evaluation.
• the model is selected from among a HAPLO-IM + model, a HAPLO-MQM model and a HAPLO-MQM" " model.
• the invention provides for the use of molecular genetic markers to define genetic haplotypes.
• markers are restriction fragment length polymorphisms (RFLP), isozyme markers, allele specific hybridization (ASH), amplified variable sequences of the plant genome, self-sustained sequence replication, simple sequence repeat (SSR), single nucleotide polymorphism (SNP), or arbitrary fragment length polymorphisms (AFLP).
• RFLP restriction fragment length polymorphisms
• ASH allele specific hybridization
• amplified variable sequences of the plant genome self-sustained sequence replication
• simple sequence repeat SSR
• SNP single nucleotide polymorphism
• AFLP arbitrary fragment length polymorphisms
• the haplotypes are determined by high throughput screening methods.
• one or more steps of the method is performed with computer assistance.
• Another aspect of the invention provides for the selection of phenotypic traits in a plant breeding population, as well as plants selected using the methods of the invention.
• the selection is performed
• the invention further provides for the cloning of a nucleic acid sequence or fragment in linkage disequilibrium with a phenotypic trait, and for transducing the cloned nucleic acid sequence or fragment into a plant.
• the nucleic acid is operably linked to a promoter in an expression cassette.
• FIG. 1 A line drawing which graphically depicts relationships between multiple related crosses used in plant breeding. D indicates donor parents; E indicates elite parents;
• P indicates progeny; a shaded box indicates a bi-parental cross made; a solid arrow indicates parent-offspring relation; a dashed arrow indicates a half-sib relation; and a dotted arrow indicates and indirect relation.
• FIG. 1 Diagram of the interval mapping (Evl) and multiple-QTL mapping (MQM) methods. 1 indicates QTL-allele substitution-effect per family; 2 indicates QTL- haplotype effects explain within-family variance; and 3 indicates QTL-haplotype effects explain within-and between-family variances.
• Figure 3 a and b A schematic depiction of haplotypes in a "window" of four markers for parents with identical and different haplotypes, respectively. Numbers indicate type of marker allele.
• Figure 4. A line graph showing a comparison of the , HAPLO-IM + , HAPLO-MQM and
• HAPLO-MQM + models for a single chromosome (simulation 2.1).
• the present invention provides a novel and powerful method for joint analysis of multiple related families.
• the key to increasing the power of the analysis as compared to the prior art, is the clustering of the original parents into groups on the basis of their haplotype.
• the effect of a QTL on the phenotype is then modeled per haplotype group instead of per family. This permits an examination of the effects of haplotype-alleles across families.
• Simulations of realistic plant breeding schemes demonstrate a significant increase in power of QTL detection compared to existing methods.
• the present invention offers new opportunities for the mapping and exploitation of QTLs in commercial breeding activities.
• phenotypic trait refers to an observable trait of an organism.
• the phenotypic trait can be observable to the naked eye, or by any other means of evaluation known in the art, e.g., microscopy, biochemical analysis, etc.
• a phenotype is directly controlled by a single gene, a "single gene trait.”
• a phenotype is the result of several "quantitative trait loci" acting together.
• Such a phenotype can generally be described in quantitative terms, e.g., height, weight, oil content, days to germination, etc, and therefore can be assigned a "phenotypic value" which corresponds to a quantitative value for the phenotypic trait.
• genotype refers to the genetic constitution, as contrasted with the observable trait, i.e., the phenotype.
• a genotype is an individual's genetic make-up for all the genes in its genome (chromosome compelement).
• haplotype is an individual's genotype at multiple, generally linked, loci.
• haplotype can be an individual's genotype for multiple loci or genetic markers on a single chromosome.
• chromosomal haplotype is, alternatively, used.
• an individual's genotype for multiple loci (or markers) within a defined region of a chromosome is, optionally, referred to as a "regional haplotype.”
• Genetic markers are loci, or DNA sequences which both vary (are polymorphic) between individual's in a population, and can be detected by one or more analytic methods, e.g., RFLP, AFLP, isozyme, SNP, SSR, and the like.
• RFLP Radioactive Polypeptide
• AFLP AFLP
• isozyme SNP
• SSR SSR
• MAS Marker Assisted Selection
• correspondence in the context of the invention refers to a genetic marker locus or loci in linkage disequilibrium with a phenotypic trait.
• a genetic marker is said to be in "linkage disequilibrium” with a gene or genes that control part or all of the variance for a trait (or locus), when the marker and the gene that controls the variance of the trait (or locus) do not segregate independently, i.e., they are inherited together more often than is expected by chance.
• the Fi is fully informative, linkage disequilibrium is maximized, the linkage phase is known, there are only two QTL alleles, and, except for backcross progeny, the frequency of each QTL allele is 0.5.
• the selection of lines for breeding is based- on maximizing genetic variability of traits useful for agronomic performance.
• the crosses are not necessarily informative at all marker loci and QTLs, linkage disequilibrium exists among the (F 2 ) progeny within families, but not necessarily across the breeding population.
• the linkage phase is not consistent across the breeding population, multiple QTL alleles can exist and the frequency of each will vary between 0 and 1.
• a plant breeding population usually consists of multiple related instead of unrelated families.
• higher mapping resolution and power can be obtained by methods properly including these relationships into the model.
• Current technologies are making such detailed information about relationships readily available. It is not unrealistic to assume that plant-breeding populations will be fingerprinted on a regular basis at 200-500 marker loci, and with chip technology soon at 1000 or more loci. Marker information can monitor identity-by-descent (IBD) from parent to offspring throughout the populations.
• Figure 1 shows a typical design of multiple related crosses. There are clear and direct half- sib relationships between families if the same line is used as parent in two or more different crosses.
• IBD identical-by-descent
• the present invention exploits this kind of US and IBD information.
• We previously developed the MQM approach for analyzing experimental populations Jansen (1996) supra; and Beavis, PCT Application WO 99/32661, published January 7, 1999, QTL MAPPING IN PLANT BREEDING POPULATIONS, herein incorporated in their entirety).
• the present invention for mapping phenotypic traits in plant breeding populations significantly extends the previous methodologies and is described in detail herein.
• One important feature of the methods of the present invention is the clustering of the original parents into groups on the basis of their haplotype for multiple genetic markers.
• the effect of a putative genetic locus is then modeled per parental haplotype group, instead of per family of progeny as previously performed.
• other existing QTL analysis methods e.g., frequentist or Bayesian analysis procedures, with fixed, random, or mixed QTL effects, etc., can also be utilized.
• Figure 1 illustrates a typical design of multiple related crosses.
• Figure 2 provides an overview of the methods of analysis.
• a single F? ⁇ test-cross population An F 2:3 population is, in short, a standard F 2 population in which the phenotypic scores of F individuals are obtained not by evaluation of the F plants themselves but rather by evaluation of F 3 offspring of the F 2 plants. Each F 2 individual is crossed with the same homozygous tester to generate a number of F offspring per F 2 plant and those F 3 plants are evaluated for traits of interest. Like a standard F 2 , each F 2 plant has genotype and phenotype scores. But in an F :3 the trait value of an F plant is computed as the average trait value of its F 3 testcross offspring.
• the F 2 population is a mixture of the three QTL genotypes: QiQi, Q 2 Q 2 and Q ⁇ Q 2 .
• Each F 2 plant is crossed to a tester QQ to generate a F 2:3 topcross.
• Table 1 shows two characteristics important for QTL modeling in this testcross design, namely (a) only heterozygous F 2 plants generate segregating F 3 offspring and (b) the expected trait value of a heterozygous F 2 plant is halfway between the expected trait values of the homozygous F 2 plants.
• the latter implies that QTL models will not contain parameters for dominance.
• the former implies that we can expect heterogeneous residual variance.
• IM was developed for analyzing a single family obtained from one bi-parental cross (Lander and Botstein (1989) Genetics 121:185). In the case of multiple families, one can analyze each family separately by IM and sum up the QTL likelihood over the families. This straightforward approach does not model QTL activity across families (in statistical terms, QTLs are nested within families).
• the allele effects (a x and a 2 in Table 1) need to be indexed by family number: a ⁇ f and a 2f .
• This F 2 plant can obtain 0, 1 or 2 copies of the QTL allele of the first parent, Q 1# In the first case it has received two copies of allele a 2f from the second parent and the model for the model reads
• N fam be the number of families. QTLs are nested within families and in total there are 2Nf am regression parameters and N fam residual variance parameters.
• N QTL be the number of QTLs.
• a ⁇ and a 2f to denote the allele effects of a single QTL in family f.
• the QTL model for a given F2 plant in family f reads
• haplotypes are now numbered from 1 to N HaP i o (i)- Let h(lf) and h(2f) indicate the haplotype of the allele from the first and second parent, respectively. Then, a h (i f ) and a h ( 2f ) indicate the allele type after clustering into haplotype alleles. This will depend on the size of the window with larger windows having more different haplotypes.
• the models now include a parameter for any between-family differences not yet accounted for
• NQ ⁇ _N fam effects a lf (i) and a 2f (i), but in HAPLO-IM + and HAPLO-MQM + they are 'mapped' to the smaller set of N Hap i o (i) parameters for the effects of the haplotype clusters (Fig 2).
• the model includes NH ap ⁇ 0 (i)-l free parameters for the effect of the i-th QTL and this number can be significantly lower than the N fam parameters in the "full parameterization" with QTLs nested within families as in TM and MQM. The number of QTL parameters can be reduced without loss of information.
• the '+' in the name HAPLO-MQM 1" indicates that parameters for family effects have been included. In the following, models without parameters for family effects are considered, and the method is indicated without '+' by 'just' HAPLO-MQM. Analyzing multiple related F? ⁇ families via HAPLO-MQM:
• the IBD concept is based on between-family genotypic information.
• the next step is to additionally explore the potential of between-family phenotypic information.
• the Fi of one family can be homozygous Q]Q X for a given QTL, others can be homozygous Q 2 Q 2 .
• Clearly non-segregation of (major) genes will show up only as different mean trait values of the families. In some cases there can be no other sources generating additional between- family variation.
• the effects of QTL alleles can then be estimated from populations in which they are segregating, and twice their effect contributes to the mean of any family in which they are not segregating.
• the multiple-QTL model for the phenotype y of a given F 2 plant in family f is equivalent to the MQM model:
• HAPLO-MQM exploits the within- families and between-families phenotypic information. HAPLO-MQM exploits the full power of QTL detection by combining the sources of information. Gain of power will be highest if between-family differences originate mainly from multiple additively acting and "detectable" QTLs only (see discussion).
• the models include a parameter for the mean trait value of a population and a parameter for the effect of substitution of one allele by another allele within the population genetic background.
• the BVI analysis requires 60 parameters for the family means plus 60 parameters for QTL-allele substitutions, i.e., 120 parameters all together.
• the HAPLO- MQM method of the present invention is based on a different paradigm. The effects of haplotyped QTL-alleles across families, and not the effects of allele substitution within families, are evaluated across families.
• the present invention provides models which can cope with QTLs segregating in only a subset of the families and which exploit within-family variation, but in addition also consider between-family variation.
• the number of parents is 120, or less if some parents are used more than once.
• EVI and HAPLO-MQM require the same maximum number of parameters.
• the allele effects of segregating and non-segregating QTLs contribute to the differences between families, but there can also be other genetic and non-genetic sources of variation (e.g., epistatic interactions).
• the HAPLO-MQM* model includes parameters to account and test for these differences. Note that these additional parameters do not play the role of "mean value of families" as in IM. In fact, they quantify the deviance between the observed trait values and the predicted trait values under the HAPLO-MQM model. This deviance is a measure for how well the sum of estimated allele effects can explain the between-family differences.
• TM and HAPLO-MQM in no-OTL regions In the present IBD approach, the parents are clustered into less than 120 groups on the basis of their haplotype in a window of, e.g., four, markers around the QTL position under study. The same approach is carried out for each marker cofactor (each time leading to different groupings). The number of parameters per QTL (or marker cofactor) is equal to the number of different haplotypes.
• the models of HAPLO-MQM required -15 parameters in the 1 st set of simulations, -15 in the 2 nd set and -37 in the 3 rd set. In each set of simulations IM takes 60 allele-substitution parameters per QTL.
• the computed QTL likelihood is expected to be on average -15 and -37 under HAPLO-MQM, and -60 under IM.
• Table 3 and Figure 4 show that this "background" likelihood often takes values in the predicted ranges.
• An important consequence is that the threshold for genome-wide significance in HAPLO-MQM is much lower than that in multi-family IM. This increases the power of QTL detection using the HAPLO-MQM methods of the invention.
• HAPLO-MQM approach requires -15 parameters per QTL in the 1 st and 2 nd set, and -37 in the 3 rd set, but the IM approach requires 60 parameters. Therefore TM and MQM are still highly over-parameterized. On the other hand, the additional parameters do not really cause problems: the model has the flexibility to fit the data well, the JJVI can be more over-fitted.
• Figure 4 allows us to compare the single-QTL approach of IM to that of HAPLO-JJVT 1" , which is a single-QTL HAPLO-MQM* " model with family effects included to eliminate effects of the other QTLs.
• the QTL-likelihood peaks for EVI and HAPLO-Uvf " are approximately of the same height. However, the "background" likelihood is lower for the latter, which indicates that HAPLO-fM "1" is the more powerful approach.
• the power of QTL detection is determined by the ratio between the variance induced by the given QTL and the unexplained residual variance (Jansen 1994, 1996, both supra). In simulations provided for illustration: f ⁇ 2 QTL / ( f ⁇ 2 QT + f ⁇ a + f ⁇ 2 e ). With JM, the genetic background QTL-effects are part of the unexplained variance.
• the HAPLO-MQM 1" model included 10-30 marker cofactors simultaneously.
• h 2 QTL ⁇ 2 QTL / (O" 2 Q TL + f ⁇ 2 a + f ⁇ 2 e ).
• ⁇ 2 QT L / ( ⁇ 2 QTL + f ⁇ 2 a + f ⁇ 2 e ) 0.05.
• HAPLO-MQM 4" successfully removed the 70% ⁇ QTL / ( ⁇ QTL + ⁇ e ) ⁇ 0.16.
• simulation 2.3 with smaller proportion of QTL-induced variation f ⁇ 2 QTL / (O "2 QTL + ⁇ 2 e ) - 0.09.
• the HAPLO-MQM " models use all together -200-600 parameters for QTLs, leaving still -2400-2800 degrees of freedom for estimating residual error variance.
• QTL- likelihood peaks are clearly higher for HAPLO-MQM 4" than for IM in simulations 2.1 and 2.2 (Table 3b and Figure 4). Modeling of multiple QTLs can be a second step to increase the power of QTL detection.
• HAPLO-MOM + versus HAPLO-MQM
• the present invention provides a third possibility for further increasing the
• QTL power the use of between-family information in addition to within-family information. Families are usually not segregating for all QTLs involved. But that does not imply that the effect of those non-segregating QTLs cannot be detected: they generate differences between the mean values of the families. Therefore, the QTL-allele effects in our models should not only explain the within-family variation but they should also capture the between-family variation. This new multiple-QTL model can meet these additional constraints. Of course, this approach works efficiently when most of the (unexplained) differences between families are indeed induced by additively acting QTLs. This can be tested by comparing the HAPLO- MQM and HAPLO-MQM 4" models.
• Markers in a window around the QTL position under study are used to group the parents into haplotype categories.
• a relatively large window of four markers (Figure 3) was used. To some extent this was an arbitrary choice. With more markers, the windows would have become very large, given the sparse marker map of the simulations (200 markers at 2000 cM).
• all markers are used simultaneously for haplotyping, and a one to one relation between haplotype and parent is established.
• the HAPLO-MQM model then includes as many haplotype effects as there are different parents and correctly models all half-sib relationships.
• there are good reasons for using few(er) markers in haplotyping Haplotyping on the basis of fewer markers tends to result in less haplotype classes, so that fewer QTL parameters are required. This increases the power of QTL detection and allows us to fit more complex models, e.g., with interactions.
• a larger number of markers can be used in haplotyping, in particular if the marker map is dense. In this case it is assumed that two parents with identical haplotype in the window under study have the same QTL allele within this region. The probability that this is indeed true increases when the haplotyping is based on more markers, because more markers decreases the chance of erroneous grouping. It will be appreciated that there is an optimal balance, and it is likely that the optimum can change, e.g., when different marker densities are used, or when different types of marker are used. In breeding populations, bi-allelic markers (e.g., AFLPs) are expected to be less informative in fingerprinting than multi-allelic markers (e.g., microsatellites).
• lower informative marker types are available at a higher map density to achieve indirectly a high multilocus information content.
• IBD identity-by-descent
• US identical-in-state
• the methods of the present invention focus on QTLs with fixed effects across populations. In reality, the effects of QTL alleles are modified by genetic background. With the current HAPLO-MQM models, the "average" allele effect across the population is estimated. In order to keep the number of parameters within reasonable bounds, one can extend the use of the HAPLO-MQM model as follows. Use a priori criteria, such as genetic distance, to classify families into sub-populations and include QTL x sub-population instead of QTL x family interactions as fixed or random effects in the models.
• Genotype x environment interaction is a very important issue in breeding. It will be appreciated that any methodological concept developed for QTL x E interaction in IM or MQM models can also be applied to HAPLO-MQM. Furthermore, the likelihood of the HAPLO-MQM and that of the HAPLO-MQM 4" can be compared to assess the amount of such interaction. Computation:
• the HAPLO-MQM models contain many parameters. Up to 30 QTLs with
• the "QTL-allele breeding values" (Best Linear Unbiased Prediction) can then be predicted.
• marker-assisted selection BLUPs are calculated for the breeding values of individuals. With HAPLO-MQM, selection occurs at the level of the QTL-allele predictions rather than at the level of the individual ' s predictions.
• HAPLO-MQM can, in most cases, detect the QTLs and even dissect the linked ones. Higher genetic complexity (smaller heritability, more QTLs, tightly linked QTLs) is compensated by increasing the number of families and/or the progeny sizes per family. In any circumstance, the application of more powerful analysis tools, such as HAPLO-MQM, will improve the chance of successful dissection of the effects of linked and unlinked QTLs.
• the genome for the simulations consisted of 10 linkage groups of 200 centiMorgan (cM) each, e.g., as in maize.
• the genotype and phenotype data were generated in a number of steps. First, a base population of more or less related inbred lines was simulated, all lines belonging to one and the same heterotic group. Then, pairs of parents for multiple crosses were selected from the base population. Next, F 2 offspring of the multiple crosses were generated and each F 2 plant was testcrossed to a tester from another heterotic group to generate F 2:3 offspring. Finally, phenotypic values were assigned to the segregating F 2 ; 3 and F progeny. Certain aspects of the simulation steps are described below in more detail.
• the following (ad-hoc) protocol was used for generating a base population of inbred lines with different (re)combinations of "ancestral" linkage blocks.
• the genome consisted of 10 linkage groups, each containing 101 bi-allelic marker loci with 2 cM of recombination between adjacent pairs per riieiosis.
• the ad-hoc procedure for generating linkage blocks is as follows: a set of 400 homozygous recombinant lines from the cross between hypothetical parents with genotypes 1111 (and so on) and 2222 (and so on) was simulated.
• the genotype of a homozygous line consisted of linkage blocks of l's and of 2's; the genotype will be an expression like 111211222 (and so on).
• 400 doubled haploid lines were simulated using a recombination frequency of 0.02 and 0.2 between adjacent markers, respectively. Linkage disequilibrium between adjacent loci is 0.20 and 0, respectively.
• the next step of the procedure was to move from linkage blocks to biallelic marker genotypes (1 st set of simulations) or multi-allelic marker genotypes (five alleles per marker in the 2 nd and 3 rd set of simulations). This was accomplished by assigning types of marker allele to the linkage blocks.
• a (new) type of marker allele using preset marker allele frequencies was independently sampled, linkage block by linkage block, from a multinomial distribution.
• the original genotype 111221 contains three linkage blocks, 111, 22 and 1. Each linkage block gets randomly assigned one of the types of a marker allele. Thus with 5 marker alleles, the l's in the first linkage block can be replaced by 5's.
• After sampling types of marker allele for the other blocks the original genotype 111221 can be converted into the new configuration 555133.
• the 400 lines in the base population can be crossed amongst each other in various combinations.
• pairs of parents which approximately showed a preset level of relatedness, say -45% are selected.
• only pairs of parents having a preset level of relatedness were used for crossings and all the other possible pairs with other levels of relatedness were ignored.
• different levels of relatedness within pairs of parents were used (10%, 40% and 45%).
• the heterozygous F t offspring of the crosses were selfed in order to generate segregating F 2 families.
• Each F 2 individual was crossed with one and the same homozygous tester to generate a number of F 3 offspring per F 2 plant and those F 3 plants were "evaluated" for traits of interest.
• QTLs were placed at marker positions, which made it easy to derive genotypic values.
• the trait value was calculated as the sum of genotype and random Gaussian noise.
• h 2 QT L ⁇ 2 QT L / ( ⁇ 2 QTL + ⁇ 2 a + ⁇ 2 e ) was used.
• the set of 1010 markers was reduced: in each of the three simulations two hundred loci were randomly sampled from the genome and only these marker data were available for analysis.
• the average recombination frequency between adjacent markers is 0.1 and 0.5, and the average linkage disequilibrium is approximately 0.20 and zero, respectively.
• the two parents of each 60 crosses were about 45% related, i.e., it is expected that 55% of the loci are polymorphic in the progeny.
• Linkage disequilibrium between adjacent pairs of markers in the breeding population was investigated at values of approximately zero (i.e., loci are statistically independent) and 0.20.
• Family sizes of either 10 or 50 F 2 progeny were investigated in a topcross combination with a single unrelated tester. For any given simulation all families were of equal size, and populations of 600 or 3000 progeny were evaluated. All F 2 progeny were genotyped and all F 2:3 progeny were evaluated for a quantitatively expressed trait.
• this first set of simulations has many similarities with a population derived from a single cross. The distinctions are that the number of sampled progeny is larger, different sets of QTLs can be segregating in each F 2:3 family, and linkage phase between QTLs and marker loci are not consistent across the population. Under these conditions, the impact of population size, number of segregating QTLs, and linkage disequilibrium among breeding lines upon analysis methods was investigated. 2 nd set of simulations: For the inbred parents in the base populations one of five alleles can occur at each locus (markers and QTLs). The allelic genotypes indicate ancestral alleles that are identical by descent (IBD) among the breeding lines.
• IBD identical by descent
• Such information is obtained by genotyping all the important lines involved in the pedigrees of the breeding populations.
• the frequencies of each allele in the population were 0.55, 0.24, 0.12, 0.06, and 0.03 respectively. Pairs of parents were selected in such a way that the two parents of a cross were about 10% related, i.e., approximately 90% of the loci are polymorphic between any pair of parents. Linkage disequilibrium between adjacent pairs of markers was approximately 0.20.
• Family sizes consisted of 50 F 2:3 progeny that were top-crossed with a single unrelated tester. Each progeny was genotyped and evaluated for a quantitatively expressed trait. Expression of quantitative traits was due to five or ten QTLs.
• the QTLs segregated independently and they were located in the middle of the linkage groups.
• pairs of QTLs were located 50 centiMorgan (cM) from each other on the same chromosome.
• the QTLs were functionally bi-allelic, i.e., one of the five alleles that could occur at a QTL was chosen to have a positive (+) effect, while all remaining alleles were simulated to have an equal negative effect when combined with the tester allele.
• marker loci are multi-allelic and the allelic state of the marker loci is independent of the functional state of the QTLs.
• the multi-allelic state of marker loci is similar to the polymorphism index that has been observed in simple sequence repeat markers in maize (Senior et al. 1996).
• the family size remained at 50 progeny.
• different sets of QTLs are segregating in each family, and linkage phases between QTLs and marker loci are not consistent across the population.
• the third set of simulations was very much like the second set except that the parents of crosses were about 40% related and that linkage disequilibrium between adjacent pairs of markers was zero (worst case scenario). Thus, for the third set of simulations the primary changes in available information were due to changes in population structure. RESULTS OF THE SIMULATION STUDY
• HAPLO-MQM results are compared to those of HAPLO-MQM 4" , to see what the effect is of exclusion versus inclusion of parameters for family effects.
• the QTL likelihood peak is higher for HAPLO-MQM than for HAPLO-MQM + . This is caused by the fact that HAPLO- MQM exploits the between-family information in the trait means of the families.
• the figures of the "likelihood elsewhere" seem to be upwards biased under HAPLO-MQM in simulation 1.2. Under the null-hypothesis of "no QTL segregating," the expected QTL likelihood in no-QTL regions should be equal to the number of QTL parameters (-15).
• Results of simulation 1.2 support the initial expectation that bi-allelic and widely spread markers are not very suitable for an analysis of the HAPLO-MQM type: the families are clustered into -15 haplotype groups and a number of families is probably wrongly classified. With HAPLO-MQM, QTLs and fitted cofactors were found near the simulated positions.
• the "effective" population size for a QTL which is here defined as the expected number of families segregating for the QTL times the progeny size, is now approximated.
• One of the five QTL alleles was assigned a positive (+) effect, the other QTL alleles each had an equal negative effect.
• the + allele had a frequency of 0.55 or 0.12.
• any given QTL was segregating for the + allele in only a subset of the entire population and the "effective" population sizes were relatively high, namely -1500 (30 families) and -300 (6 families) for the + allele frequencies of 0.55 and 0.12, respectively.
• 3 rd set of simulations The third set of simulations differs in two aspects from the 2 nd set.
• the breeding population consisted of fully inbred lines that were about 45% instead of 10% related.
• linkage disequilibrium between adjacent pairs of markers in the breeding population was zero instead of 0.20. Therefore the "effective" population size is now much smaller, namely -600 (12 families) and -120 (2 families) for the two cases with positive QTL-allele frequencies of 0.55 and 0.12, respectively.
• the QTL likelihood peaks in the 3 rd set are much lower than in the 2 nd set (Tables 3b and 3c).
• the parents of the families are clustered on the basis of their haplotype in a window of 4 markers around the putative QTL.
• the same approach was used for cofactor markers.
• the 120 parents were clustered into -15 groups.
• the 3 rd set the number of clusters increased to -37, partly because several parents were used more than once. This number of parameters per QTL or marker cofactor is still much lower than the 60 parameters per QTL in the IM approach.
• phenotypic traits relies on the ability to detect genetic differences between individuals. These genetic differences, or "genetic markers" are then correlated with phenotypic variations using the statistical methods of the present invention.
• a single gene encoding a protein responsible for a phenotypic trait is detectable directly by a mutation which results in the variation in phenotype. More frequently, it is the case that multiple genetic loci each contribute to the observed phenotype.
• a quantifiable phenotype e.g., height, weight, grain yield, oil content, etc.
• the genes underlying a phenotype are designated quantitative trait loci, or QTL.
• Detection and mapping of QTL typically utilizes the detection and correlation of genetic markers with the phenotypic trait under investigation.
• regions of DNA which are non-coding, or which encode proteins or portions of proteins which lack critical function tend to accumulate mutations, and therefore, are variable between members of the same species. Such regions provide the basis for numerous molecular genetic markers. Markers identify alterations in the genome which can be insertions, deletions, point mutations, recombination events, or the presence and sequence of transposable elements.
• nucleic acid refers to single- stranded or double-stranded deoxyribonucleotides or ribonucleotides and polymers thereof.
• nucleic acid sequence refers to single- stranded or double-stranded deoxyribonucleotides or ribonucleotides and polymers thereof.
• the term optionally includes known analogs of naturally occuring nucleotides.
• a particular nucleic acid sequence of this invention encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions) and complementary sequences, in addition to the sequence explicitly indicated.
• the term "gene” is used interchangebly for a specific genomic sequence, a cDNA and a mRNA encoded by the genomic sequence.
• Two single-stranded nucleic acids "hybridize” when they form a double- stranded duplex.
• the region of double-strandedness can include the full-length of one or both of the single-stranded nucleic acids, or all of one single stranded nucleic acid and a subsequence of the other single-stranded nucleic acid, or the region of double-strandedness can include a subsequence of each nucleic acid.
• some techniques for detecting genetic markers utilize hybridization of a probe nucleic acid to nucleic acids corresponding to the genetic marker. Markers which are restriction fragment length polymorphisms (RFLP), are detected by hybridizing a probe which is typically a sub-fragment (or a synthetic oligonucleotide corresponding to a sub-fragment) of the nucleic acid to be detected to restriction digested genomic DNA.
• RFLP restriction fragment length polymorphisms
• the restriction enzyme is selected to provide restriction fragments of at least two alternative (or polymorphic) lengths in different individuals.
• an appropriate matrix e.g., agarose
• a membrane e.g., nitrocellulose, nylon
• the labeled probe is hybridized under conditions which result in equilibrium binding of the probe to the target followed by removal of excess probe by washing.
• the hybridized probe is then detected using, most typically by autoradiography or other similar detection technique (e.g., fluorography, liquid scintillation counter, etc.). Examples of specific hybridization protocols are widely available in the art, see, e.g., Berger, Sambrook, Ausubel, all supra.
• Amplified variable sequences refer to amplified sequenes of the plant genome which exhibit high nucleic acid residue variability between members of the same species. All organisms have variable genomic seuqences and each organism (with the exception of a clone) has a different set of variable sequences. Once identified, the presence of specific variable sequence can be used to predict phenotypic traits.
• DNA from the plant serves as a template for amplification with primers that flank a variable sequence of DNA. The variable sequence is amplified and then sequenced.
• RNA polymerase mediated techniques e.g., NASBA
• PCR polymerase chain reaction
• LCR ligase chain reaction
• NASBA RNA polymerase mediated techniques
• Oligonucleotides for use as primers, e.g., in amplification reactions and for use as nucleic acid sequence probes are typically synthesized chemically according to the solid phase phosphoramidite triester method described by Beaucage and Caruthers (1981) Tetrahedron Lett. 22: 1859, or can simply be ordered commercially.
• self-sustained sequence replication can be used to identify genetic markers.
• Self-sustained sequence replication refers to a method of nucleic acid amplification using target nucleic acid sequences which are replicated exponentially in vitro under substantially isothermal conditions by using three enzymatic activities involved in retroviral replication: (1) reverse transcriptase, (2) Rnase H, and (3) a DNA-dependent RNA polymerase (Guatelli et al. (1990) Proc Natl Acad Sci USA 87:1874).
• this reaction accumulates cDNA and RNA copies of the original target.
• Arbitrary fragment length polymophisms can also be used as genetic markers (Vos et al. (1995) Nucl Acids Res 23:4407.
• the phrase "arbitrary fragment length polymorphism” refers to selected restriction fragments which are amplified before or after cleavage by a restriction endonuclease. The amplification step allows easier detection of specific restriction fragments.
• AFLP allows the detection large numbers of polymorphic markers and has been used form genetic mapping of plants (Becker et al. (1995) Mol Gen Genet 249:65; and Meksem et al. (1995) Mol Gen Genet 249:74.
• Allele-specific hybridization can be used to identify the genetic markers of the invention.
• ASH technology is based on the stable annealing of a short, single- stranded, oligonucleotide probe to a completely complementary single-strand target nucleic acid. Detection is via an isotopic or non-isotopic label attached to the probe.
• two or more different ASH probes are designed to have identical DNA sequences except at the polymorphic nucleotides. Each probe will have exact homology with one allele sequence so that the range of probes can distinguish all the known alternative allele sequences. Each probe is hybridized to the target DNA. With appropriate probe design and hybridization conditions, a single-base mismatch between the probe and target DNA will prevent hybridization. In this manner, only one of the alternative probes will hybridize to a target sample that is homozygous or homogenous for an allele. Samples that are heterozygous or heterogeneous for two alleles will hybridize to both of two alternative probes.
• ASH markers are used as dominant markers where the presence or absence of only one allele is determined from hybridization or lack of hybridization by only one probe. The alternative allele can be inferred from the lack of hybridizaiton.
• ASH probe and target molecules are optionally RNA or DNA; the target molecules are any length of nucleotides behond the sequence that is complementary to the probe; the probe is designed to hybridize with either strand of a DNA target; the probe ranges in size to conform to variously stringent hybridization conditions, etc.
• PCR allows the target sequence for ASH to be amplified from low concentrations of nucleic acid in relatively small volumes. Otherwise, the target sequence from genomic DNA is digested with a restriction endonuclease and size separated by gel electrophoresis. Hybridizations typically occur with the target sequence bound to the surface of a membrane or, as described in U.S. Patent 5,468,613, the ASH probe sequence can be bound to a membrane.
• ASH data are obtained by amplifying nucleic acid fragments (amplicons) from genomic DNA using PCR, transferring the amplicon target DNA to a membrane in a dot-blot format, hybridizing a labeled oligonucleotide probe to the amplicon target, and observing the hybridization dots by autoradiography.
• Single nucleotide polymorphisms SNP are markers that consist of a shared sequence differentiated on the basis of a single nucleotide. Typically, this distinction is detected by differential migration patterns of an amplicon comprising the SNP on e.g., an acrylamide gel.
• alternative modes of detection such as hybridization, e.g., ASH, or RFLP analysis are not excluded.
• Simple sequence repeats take advantage of high levels of di-, tri-, or tetra-nucleotide tandem repeats within a genome. Dinucleotide repeats have been reported to occur in the human genome as amny as 50,000 times with n varying from 10 to 60 or more (Jacob et al. (1991) Cell 67:213. Dinucleotide repeats have also been found in higher plants (Condit and Hubbell (1991) Genome 34:66).
• SSR data is generated by hybridizing primers to conserved regions of the plant genome which flank the SSR sequence. PCR is then used to amplify the dinucleotide repeats between the primers. The amplified sequences are then electorphoresed to determine the size and therefore the number of di-, tri-, and tetra-nucleotide repeats.
• isozyme markers are employed as genetic markers. Isozymes are multiple forms of enzymes which differ from one another in their amino acid, and therefore their nucleic acid sequences. Some isozymes are multimeric enzymes contianing slightly different subunits.
• isozymes are either multimeric or monomeric but have been cleaved from the proenzyme at different sites in the amino acid seuqence. Isozymes can be characterized and analysed at the protein level, or alternatively, isozymes which differ at the nucleic acid level can be determined. In such cases any of the nucleic acid based methods described herein can be used to analyze isozyme markers.
• a primary motivation for development of molecular markers in crop species is the potential for increased efficiency in plant breeding through marker assisted selection
• MAS Genetic marker alleles are used to identify plants that contain a desired genotype at multiple loci, and that are expected to transfer the desired genotype, along with a desired phenotype to their progeny. Genetic marker alleles can be used to identify plants that contain a desired genotype at one marker locus, several loci, or a haplotype, and that would be expected to transfer the desired genotype, along with a desired phenotype to their progeny.
• the presence and/or absence of a particular genetic marker allele in the genome of a plant exhibiting a preferred phenotypic trait is made by any method listed above, e.g., RFLP, AFLP, SSR, etc. If the nucleic acids from the plant are positive for a desired genetic marker, the plant can be selfed to create a true breeding line with the same genotype, or it can be crossed with a plant with the same marker or with other desired characteristics to create a sexually crossed hybrid generation.
• Clones of nucleic acids linked to QTL have a variety of uses, including as genetic markers for identificaiton of additional QTLs in subsequent applications of marker assited selection (MAS). Markers which are adjacent to an open reading frame (ORF) associated with a phenotypic trait can hybridize to a DNA clone, thereby identifying a clone on which an ORF is located.
• ORF open reading frame
• a fragment containing the open reading frame is identified by successive rounds of screening and isolation of clones which together comprise a contiguous sequence of DNA, a "contig.” Protocols sufficient to guide one of skill through the isolation of clones associated with linked markers are found in, e.g., Berger, Sambrook and Ausubel, all supra.
• vectors are available in the art for the isolation and replication of the nucleic acids of the invention.
• plasmids, cosmids and phage vectors are well known in the art, and are sufficient for many applications.
• a number of vectors capable of accomodating large nucleic acids are available in the art, these include, yeast artificial chromosomes (YACs), bacterial artificial chromosomes (BACs), plant artificial chromosomes (PLACs) and the like.
• the present invention also relates to host cells and organisms which are transformed with nucleic acids corresponding to QTL and other genes identified according to the invention. Additionally, the invention provides for the production of polypeptides corresponding to QTL by recombinant techniques.
• Host cells are genetically engineered (i.e., transduced, transfected or transformed) with the vectors of this invention (i.e., vectors which comprise QTLs or other nucleic acides identified according ot the methods of the invention and as described above) which are, for example, a cloning vector or an expression vector.
• Such vectors are, for example, in the form of a plasmid, an agrobacterium, a virus, a naked polynucleotide, or a conjugated polynucleotide.
• the vectors are introduced into plant tissues, cultured plant cells or plant protoplasts by standard methods including electroporation (From et al. (1985) Proc. Natl. Acad. Sci. USA 82;5824), infection by viral vectors such as cauliflower mosaic virus (CaMV) (Hohn et al. (1982) Molecular Biology of Plant Tumors (Academic Press, New York, pp. 549-560; Howell U.S. Patent No.
• the engineered host cells can be cultured in conventional nutrient media modified as appropriate for such activities as, for example, activating promoters or selecting transformants. These cells can optionally be cultured into transgenic plants. Plant regeneration from cultured protoplasts is described in Evans et al. (1983) "Protoplast Isolation and Culture," Handbook of Plant Cell Cultures 1, 124-176 (MacMillan Publishing Co., New York,; Davey (1983) "Recent Developments in the Culture and Regeneration of Plant Protoplasts," Protoplasts, pp. 12-29, (Birkhauser, Basel); Dale (1983) "Protoplast
• the present invention also relates to the production of transgenic organisms, which can be bacteria, yeast, fungi, or plants, transduced with the nucleic acids, e.g., cloned QTL of the invention.
• transgenic organisms which can be bacteria, yeast, fungi, or plants
• nucleic acids e.g., cloned QTL of the invention.
• a thorough discussion of techniques relevant to bacteria, unicellular eukaryotes and cell culture can be found in references enumerated above and are briefly outlined as follows.
• Several well-known methods of introducing target nucleic acids into bacterial cells are available, any of which can be used in the present invention. These include: fusion of the recipient cells with bacterial protoplasts containing the DNA, electroporation, projectile bombardment, and infection with viral vectors (discussed further, below), etc.
• Bacterial cells can be used to amplify the number of plasmids containing DNA constructs of this invention.
• the bacteria are grown to log phase and the plasmids within the bacteria can be isolated by a variety of methods known in the art (see, for instance, Sambrook).
• kits are commercially available for the purification of plasmids from bacteria. For their proper use, follow the manufacturer's instructions (see, for example, EasyPrepTM, FlexiPrepTM, both from Pharmacia Biotech; StrataCleanTM, from Stratagene; and, QIAprepTM from Qiagen).
• the isolated and purified plasmids are then further manipulated to produce other plasmids, used to transfect plant cells or incorporated into Agrobacterium tumefaciens related vectors to infect plants.
• Typical vectors contain transcription and translation terminators, transcription and translation initiation sequences, and promoters useful for regulation of the expression of the particular target nucleic acid.
• the vectors optionally comprise generic expression cassettes containing at least one independent terminator sequence, sequences permitting replication of the cassette in eukaryotes, or prokaryotes, or both, (e.g., shuttle vectors) and selection markers for both prokaryotic and eukaryotic systems.
• Vectors are suitable for replication and integration in prokaryotes, eukaryotes, or preferably both.
• Embodiments of the present invention pertain to the production of transgenic plants comprising the cloned nucleic acids of the invention.
• Techniques for transforming plant cells with nucleic acids are generally available and can be adapted to the invention by the use of nucleic acids encoding QTL or other genes encoding phenotypic traits of the invention.
• useful general references for plant cell cloning, culture and regeneration include Jones (ed) (1995) Plant Gene Transfer and
• nucleic acid constructs of the invention e.g., plasmids, cosmids, artificial chromosomes, DNA and RNA polynucleotides, are introduced into plant cells, either in culture or in the organs of a plant by a variety of conventional techniques.
• sequence is expressed, the sequence is optionally combined with transcriptional and translational initiation regulatory sequences which direct the transcription or translation of the sequence from the exogenous DNA in the intended tissues of the transformed plant.
• the DNA constructs of the invention for example plasmids, cosmids, phage, naked or variously conjugated-DNA polynucleotides, (e.g., polylysine-conjugated DNA, pep tide-conjugated DNA, liposome-conjugated DNA, etc.), or artificial chromosomes, can be introduced directly into the genomic DNA of the plant cell using techniques such as electroporation and microinjection of plant cell protoplasts, or the DNA constructs can be introduced directly to plant cells using ballistic methods, such as DNA particle bombardment.
• variously conjugated-DNA polynucleotides e.g., polylysine-conjugated DNA, pep tide-conjugated DNA, liposome-conjugated DNA, etc.
• artificial chromosomes can be introduced directly into the genomic DNA of the plant cell using techniques such as electroporation and microinjection of plant cell protoplasts, or the DNA constructs can be introduced directly to plant cells using ballistic methods, such
• Microinjection techniques for injecting e.g., cells, embryos, and protoplasts are known in the art and well described in the scientific and patent literature. For example, a number of methods are described in Jones (ed) (1995) Plant Gene Transfer and Expression Protocols- Methods in Molecular Biology, Volume 49 Humana Press Towata NJ, as well as in the other references noted herein and available in the literature.
• Agrobacterium mediated transformation is employed to generate transgenic plants.
• Agrobacterium-mediated transformation techniques including disarming and use of binary vectors, are also well described in the scientific literature. See, for example Horsch, et al. (1984) Science 233:496; and Fraley et al. (1984) Proc. Nat'l. Acad. Sci. USA 80:4803 and recently reviewed in Hansen and Chilton (1998) Current Topics in Microbiology 240:22 and Das (1998) Subcellular Biochemistry 29: Plant Microbe Interactions pp343-363.
• Transformed plant cells which are derived by any of the above transformation techniques can be cultured to regenerate a whole plant which possesses the transformed genotype and thus the desired phenotype.
• Such regeneration techniques rely on manipulation of certain phytohormones in a tissue culture growth medium, typically relying on a biocide and/or herbicide marker which has been introduced together with the desired nucleotide sequences.
• Plant regeneration from cultured protoplasts is described in Evans et al. (1983) Protoplasts Isolation and Culture, Handbook of Plant Cell Culture pp. 124-176, Macmillian Publishing Company, New York; and Binding (1985) Regeneration of Plants, Plant Protoplasts pp. 21-73, CRC Press, Boca Raton. Regeneration can also be obtained from plant callus, explants, somatic embryos (Dandekar et al. (1989) J. Tissue Cult. Meth. 12:145;
• Preferred plants for the transformation and expression of QTL and other nucleic acids identified and cloned according to the present invention include agronomically and horticulturally important species.
• Such species include, but are not restricted to members of the families: Graminae (including corn, rye, triticale, barley, millet, rice, wheat, oats, etc.); Leguminosae (including pea, beans, lentil, peanut, yam bean, cowpeas, velvet beans, soybean, clover, alfalfa, lupine, vetch, lotus, sweet clover, wisteria, and sweetpea); Compositae (the largest family of vascular plants, including at least 1,000 genera, including important commercial crops such as sunflower) and Rosaciae (including raspberry, apricot, almond, peach, rose, etc.), as well as nut plants (including, walnut, pecan, hazelnut, etc.), and forest trees (including Pinus, Quercus, Pseutotsuga
• plants from the genera Agrostis, Allium, Antirrhinum, Apium, Arachis, Asparagus, Atropa, Avena (e.g., oats), Bambusa, Brassica, Bromus, Browaalia, Camellia, Cannabis, Capsicum, Cicer, Chenopodium, Chichorium, Citrus, Coffea, Coix, Cucumis, Curcubita, Cynodon, Dactylis, Datura, Daucus, Digitalis, Dioscorea, Elaeis, Eleusine, Festuca, Fragaria, Geranium, Glycine, Helianthus, Heterocallis, Hevea, Hordeum (e.g., barley), Hyoscyamus, Ipomoea, Lactuca, Lens, Lilium, Linum, Lolium, Lotus, Lycopersicon, Majorana, Malus, Mangif
• plants in the family Graminae are a particularly preferred target plants for transformation with cloned sequences corresponding to QTL or other nucleic acids by the methods of the invention.
• Common crop plants which are targets of the present invention include corn, rice, triticale, rye, cotton, soybean, sorghum, wheat, oats, barley, millet, sunflower, canola, peas, beans, lentils, peanuts, yam beans, cowpeas, velvet beans, clover, alfalfa, lupine, vetch, lotus, sweet clover, wisteria, sweetpea and nut plants (e.g., walnut, pecan, etc).
• corn, rice, triticale, rye, cotton, soybean, sorghum, wheat, oats, barley, millet, sunflower, canola, peas, beans, lentils, peanuts, yam beans, cowpeas, velvet beans, clover, alfalfa, lupine, vetch, lotus, sweet clover, wisteria, sweetpea and nut plants e.g., walnut, pecan, etc.
• a plant promoter fragment is optionally employed which directs expression of a nucleic acid in any or all tissues of a regenerated plant.
• constitutive promoters include the cauliflower mosaic virus (CaMV) 35S transcription initiation region, the 1'- or 2'- promoter derived from T-DNA of Agrobacterium tumefaciens, and other transcription initiation regions from various plant genes known to those of skill.
• the plant promoter can direct expression of the polynucleotide of the invention in a specific tissue (tissue-specific promoters) or can be otherwise under more precise environmental control (inducible promoters).
• tissue-specific promoters under developmental control include promoters that initiate transcription only in certain tissues, such as fruit, seeds, or flowers.
• promoters which direct transcription in plant cells can be suitable.
• the promoter can be either constitutive or inducible.
• promoters of bacterial origin which operate in plants include the octopine synthase promoter, the nopaline synthase promoter and other promoters derived from native Ti plasmids. See, Herrara-Estrella et al. (1983), Nature, 303:209.
• Viral promoters include the 35S and 19S RNA promoters of cauliflower mosaic virus. See, Odell et al. (1985) Nature, 313:810.
• Other plant promoters include the ribulose-l,3-bisphosphate carboxylase small subunit promoter and the phaseolin promoter.
• the promoter sequence from the E8 gene and other genes can also be used.
• the isolation and sequence of the E8 promoter is described in detail in Deikman and Fischer (1988) EMBO J. 7:3315.
• Many other promoters are in current use and can be coupled to an exogenous DNA sequence to direct expression of the nucleic acid.
• polyadenylation region at the 3 '-end of the coding region is typically included.
• the polyadenylation region can be derived from the natural gene, from a variety of other plant genes, or from, e.g., T-DNA.
• the vector comprising the sequences (e.g., promoters or coding regions) from genes encoding expression products and transgenes of the invention will typically include a nucleic acid subsequence, a marker gene which confers a selectable, or alternatively, a screenable, phenotype on plant cells.
• the marker can encode biocide tolerance, particularly antibiotic tolerance, such as tolerance to kanamycin, G418, bleomycin, hygromycin, or herbicide tolerance, such as tolerance to chlorosluforon, or phosphinothricin (the active ingredient in the herbicides bialaphos or Basta). See, e.g., Padgette et al.
• exogenous DNA sequence is stably incorporated in transgenic plants and confirmed to be operable, it can be introduced into other plants by sexual crossing. Any of a number of standard breeding techniques can be used, depending upon the species to be crossed.
• the determination of genetic marker alleles is performed by high throughput screening.
• High throughput screening involves providing a library of genetic markers, e.g., RFLPs, AFLPs, isozymes, specific alleles and variable seuqences, including SSR. Such libraries are then screened against plant genomes to generate a "fingerprint" for each plant under consideration. In some cases a partial fingerprint comprising a sub-portion of the markers is generated in an area of interest.
• High throughput screening can be performed in many different formats.
• Hybridization can take place in a 96-, 324-, or a 1524-well format or in a matrix on a silicon chip or other format.
• a dot blot apparatus is used to deposit samples of fragmented and denatured genomic or amplified DNA on a nylon or nitrocellulose membrane. After cross-linking the nucleic acid to the membrane, either through exposure to ultra-violet light or by heat, the membrane is incubated with a labeled hybridization probe.
• the labels are incorporated into the nucleic acid probes by any of a number of means well- known in the art.
• the membranes are washed to remove non-hybridized probes and the association of the label with the target nucleic acid sequence is determined.
• high throughput screening systems themselves are commercially available (see, e.g., Zymark Corp., Hopkinton, MA; Air Technical Industries, Mentor, OH; Beckman Instruments, Inc. FuUerton, CA; Precision Systems, Inc., Natick, MA, etc.). These systems typically automate entire procedures including all sample and reagent pipetting, liquid dispensing, timed incubations, and final readings of the microplate or membrane in detector(s) appropriate for the assay.
• These configurable systems provide high throughput and rapid start up as well as a high degree of flexibility and customization. The manufacturers of such systems provide detailed protocols for the use of their products in high throughput applications.
• solid phase arrays are adapted for the rapid and spcific detection of multiple polymorphic nucleotides.
• a nucleic acid probe is linked to a solid support and a target nucleic acid is hybridized to the probe. Either the probe, or the target, or both, can be labeled, typically with a fluoropore. If the target is labeled, hybridization is evaluated by detecting bound fluorescence. If the probe is labeled, hybridization is typically detected by quenching of the label by the bound nucleic acid. If both the probe and the target are labeled, detection of hybridizaiton is typically performed by monitoring a color shift resulting from proximity of the two bound labels.
• an array of probes are synthesized on a solid support.
• arrays which are known, e.g., as "DNA chips” or as very large scale immobilized polymer arrays (VLSTPSTM arrays) can include millions of defined probe regions on a substrate having an area of about 1 cm 2 to several cm 2 .
• capillary electorphoresis is used to analyze polymorphism. This technique works best when the polymophism is based on size, for example, AFLP and SSR. This technique is described in detail in U.S.Patent Nos. 5,534,123 and 5,728,282. Briefly, capillary electrophoresis tubes are filled with the separation matrix.
• the separation matrix contains hydroxyethyl cellulose, urea and optionally formamide.
• the AFLP or SSR samples are loaded onto the capillary tube and electorphoresed. Because of the small amount of sample and separation matrix required by capillary electrophoresis, the run times are very short. The molecular sizes and therefore, the number of nucleotides present in the nucleic acid sample is determined by techniques described herein.In a high throughput format, many capillary tubes are placed in a capillary electrophoresis apparatus. The samples are loaded onto the tubes and electrophoresis of the samples is run simultaneously. See, Mathies and Huang, (1992) Nature 359:167.
• an integrated system such as a computer, software corresponding to the statistical models of the invention, and data sets corresponding to genetic markers and phenotypic values, facilitates mapping of phenotypic traits, including QTLs.
• integrated system in the context of this invention refers to a system in which data entering a computer corresponds to physical objects or processes external to the computer, e.g., nucleic acid sequence hybridization, and a process that, within a computer, causes a physical transformation of the input signals to different output signals.
• the input data e.g., hybridization on a specific region of an array is transformed to output data, e.g., the identification of the sequence hybridized.
• the process within the computer is a set of instructions, or "program,” by which positive hybridization signals are recognized by the integrated system and attributed to individual samples as a genotype.
• Additional programs correlate the genotype, and more particularly in the methods of the invention, the haplotype, of individual samples with phenotypic values, e.g., using the HAPLO-IM 4" , HAPLO-MQM, and/or HAPLO-MQM 4" models of the invention.
• the programs JoinMap® and MapQTL® are particularly suited to this type of analysis and can be extended to include the
• HAPLO-DVr HAPLO-MQM
• HAPLO-MQM HAPLO-MQM 4
• HAPLO-MQM HAPLO-MQM 4
• GUI interfaces GUI interfaces
• Active X applications e.g., Olectra Chart and True WevChart
• Other useful software tools in the context of the integrated systems of the invention include statistical packages such as SAS, Genstat, and S-Plus.
• Additional programming languages such as Fortran and the like are also suitably employed in the integrated systems of the invention.
• phenotypic values assigned to a population of progeny descending from releated or unrelated crosses are recorded in a computer readable medium, thereby establishing a database corresponding phenotypic values with unique identifiers for each member of the population of progeny.
• Data regarding gentoype for one or more haplotypes corresponding to a plurarlity of genetic markers, e.g., RFLP, AFLP, ASH, isozyme markers, SSR, SNP or other markers as described herein, are similarly recorded in a
• marker data is obtained using an integrated system that automates one or more aspects of the assay (or assays) used to determine the haplotype.
• input data corresponding to genotypes for independent genetic markers or for haplotypes are relayed from a device, e.g., an array, a scanner, a CCD, or other detection device directly to files in a computer readable medium accessible to the central processing unit.
• a set of instructions (embodied in one or more programs) encoding the statistical models of the invention is then executed by the computational device to identify correlations between phenotypic values and haplotypes.
• the integrated system also includes a user input device, such as a keyboard, a mouse, a touchscreen, or the like, for, e.g., selecting files, retrieving data, etc., and an output device (e.g., a monitor, a printer, etc.) for viewing or recovering the product of the statistical analysis.
• a user input device such as a keyboard, a mouse, a touchscreen, or the like
• an output device e.g., a monitor, a printer, etc.
• the invention provides an integrated system comprising a computer or computer readable medium comprising a database with at least one data set that corresponds to genotypes for genetic markers.
• the system also includes a user interface allowing a user to selectively view one or more databases.
• standard text manipulation software such as word processing software (e.g., Microsoft WordTM or Corel
• WordperfectTM and database or spreadsheet software (e.g., spreadsheet software such as
• ParadoxTM can be used in conjunction with a user interface (e.g., a GUI in a standard operating system such as a Windows, Macintosh or Linux system) to manipulate strings of characters.
• a user interface e.g., a GUI in a standard operating system such as a Windows, Macintosh or Linux system
• the invention also provides integrated systems for sample manipulation incorporating robotic devices as previously described.
• An input device for entering data to the digital computer to control high throughput liquid transfer by the robotic liquid control armature and, optionally, to control transfer by the armature to the solid support is commonly a feature of the integrated system.
• Integrated systems for genetic marker analysis of the present invention typically include a digital computer with one or more of high-throughput liquid control software, image analysis software, data interpretation software, a robotic liquid control armature for transferring solutions from a source to a destination operably linked to the digital computer, an input device (e.g., a computer keyboard) for entering data to the digital computer to control high throughput liquid transfer by the robotic liquid control armature and, optionally, an image scanner for digitizing label signals from labeled probes hybridized, e.g., to expression products on a solid support operably linked to the digital computer.
• an input device e.g., a computer keyboard
• an image scanner for digitizing label signals from labeled probes hybridized, e.g., to expression products on a solid support operably linked to the digital computer.
• the image scanner interfaces with the image analysis software to provide a measurement of, e.g., differentiating nucleic acid probe label intensity upon hybridization to an arrayed sample nucleic acid population, where the probe label intensity measurement is interpreted by the data interpretation software to show whether, and to what degree, the labeled probe hybridizes to a label.
• the data so derived is then correlated with phenotypic values using the statistical models of the present invention, to determine the correspondence between phenotype and genotype(s) for genetic markers, thereby, assigning chromosomal locations.
• Optical images e.g., hybridization patterns viewed (and, optionally, recorded) by a camera or other recording device (e.g., a photodiode and data storage device) are optionally further processed in any of the embodiments herein, e.g., by digitizing the image and/or storing and analyzing the image on a computer.
• a camera or other recording device e.g., a photodiode and data storage device
• a variety of commercially available peripheral equipment and software is available for digitizing, storing and analyzing a digitized video or digitized optical image, e.g., using PC (Intel x86 or pentium chip- compatible DOSTM, OS2TM WINDOWSTM, WINDOWS NTTM or WINDOWS95TM based machines), MACINTOSHTM, LINUX, or UNIX based (e.g., SUNTM work station) computers.
• PC Intel x86 or pentium chip- compatible DOSTM, OS2TM WINDOWSTM, WINDOWS NTTM or WINDOWS95TM based machines
• MACINTOSHTM e.g., LINUX
• LINUX UNIX based (e.g., SUNTM work station) computers.
• TM Family y a lf x 1 + a 2f x 2 + e '
• MQM Family y ⁇ i ⁇ a ⁇ f(i) . ⁇ l(i)+a2f(i) . ⁇ a(i) ⁇ + e
• HAPLO-MQM Haplotype y ⁇ i ⁇ a h(lf)( i). ⁇ l( i ) +a h(2f)( i). ⁇ 2 ( i) ⁇ + e HAPLO-MQM + Haplotype ⁇ ⁇ ( ⁇ QH ⁇ C ⁇ Ci) ⁇ ⁇ u f + e y trait value of a given F2 plant in family f "
• a LD linkage disequilibrium
• f(+) frequency of active QTL allele
• h 2 QTL TO" 2 QT L / ( ⁇ 2 Q T L + ⁇ 2 a + ⁇ 2 e ), where Q TL is the variation induced by the QTL if it segregates in a single F2:3 population, ⁇ 2 a refers to genetic variation when also all other QTLs segregate and ⁇ 2 e is the residual variation;
• b We used (approximate) thresholds for QTL detection; For TM ⁇ (60;0.001) ⁇ 100.
• HAPLO-MQM and HAPLO-MQM 4" ⁇ 2 (15;0.001) ⁇ 38 in the 1 st and 2 nd set and ⁇ 2 (37;0.001) ⁇ 69 in the 3 rd set; c Lowest and highest QTL peak in the regions of the simulated QTLs.
• d IM average QTL likelihood on chromosomes where no QTLs were simulated; No results (-) when each chromosome contains a QTL.
• e HAPLO-MQM and HAPLO-MQM 4" average QTL likelihood on chromosomes 1-10, regions of 30 cM on each side of simulated QTLs excluded;
• progeny e.g. 300, 3000

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