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Mark E. Sorrells and Flavio Breseghello Department of Plant Breeding & Genetics Cornell University Association Mapping as a Breeding Strategy.

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Presentation on theme: "Mark E. Sorrells and Flavio Breseghello Department of Plant Breeding & Genetics Cornell University Association Mapping as a Breeding Strategy."— Presentation transcript:

1 Mark E. Sorrells and Flavio Breseghello Department of Plant Breeding & Genetics Cornell University Association Mapping as a Breeding Strategy

2 Presentation Overview A Genetic Model for Association Mapping in Plant Breeding Populations Comparison of Different Plant Breeding Materials for Association Mapping Association Mapping of Kernel Size and Milling Quality in Soft Winter Wheat Cultivars

3 A Genetic Model for AM in Plant Breeding Populations: Association as Conditional Probabilities GeneMarker Recombination (c) Breeding Pool Gene={a} Marker={m,M} New Parent (A,M) Pr(A,M)= φ Pr(a,M)= θ Pr(a,m)=1- φ - θ Pr(A,m)=0 (Hedrick 2005) Recombination (c) Selection on A or M (w) Pr(A|M,c,t, φ, θ,w) “Probability of a plant with marker allele M to have gene allele A, t generations after the introduction of A” t generations Population genetics theory

4 Recombination x initial frequency of M in the breeding pool A novel marker allele at 10 cM distance can be more predictive of the QTL allele than an allele 1 cM away if it was present in the original pop at a freq of 0.05 Freq. new parent: φ=0.05 Relative fitness: w=1 Freq. M in original population = θ Freq. Recombination c θ =0 ~8~18 θ =0 θ =0.05 θ =0.25 Pr(A|M) t Generations

5 Recombination x selection for M Freq. new parent: φ=0.05 Relative fitness: w = 4 (red), 2 (green), 1.25 (blue) Freq. M in original pop: 0 Freq. Recombination: c = 0.01, 0.05, 0.10 Pr(A|M) Pr(A) The generation at which the marker is depleted [Pr(A|M)=Pr(A)], depends on the selection intensity applied; The final frequency of A depends on selection and tightness of linkage between marker and gene. Generations

6 Summary In plant breeding populations, the locus most associated with the trait is not necessarily the closest locus; Loosely linked markers can still be useful for MAS if high intensity of selection is applied.

7 MAS for Complex Traits: Issues Accurate detection and estimation of QTL effects Pre-existing marker alleles in a breeding population can be linked to non-target QTL alleles Multiple QTL alleles can have different relative values Gene x gene and gene by environment interactions

8 Association Analysis as a Breeding Strategy Most association studies have focused on estimating linkage disequilibrium and fine mapping. Breeding programs are dynamic, complex genetic entities that require frequent evaluation of marker / phenotype relationships. Breseghello, F., and M.E. Sorrells Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172: Breseghello, F., and M.E. Sorrells Association analysis as a strategy for improvement of quantitative traits in plants. Crop Sci. In press.

9 Association Mapping versus QTL Mapping Association Mapping can be conducted directly on the breeding material, therefore: Direct inference from data analysis to breeding is possible Phenotypic variation is observed for most traits of interest Marker polymorphism is higher than in biparental populations Routine variety trial evaluations provide phenotypic data Association Mapping provides other useful information about: Organization of genetic variation in relevant breeding populations Novel alleles can be identified and their relative value can be assessed as often as necessary

10 Type I error (false positives) can be higher because of: Unaccounted population structure Simultaneous selection of combinations of alleles at different loci High sampling variance of rare alleles Type II error can be higher (low power) because of: Lower LD than in biparental mapping populations Unbalanced design due to differences in allele frequencies A larger multiple-testing problem because of lower LD Association Mapping versus QTL Mapping

11 Germplasm New Populations New Synthetics, Lines, Varieties Elite Synthetics, Lines, Varieties Hybridization Selection (Intermating) Evaluation Trials Genotypic & Phenotypic data Parental Selection Marker Assisted Selection Novel & Validated QTL/MarkerAssociations Integration of Association Analysis in a Breeding Program Elite germplasm feeds back into hybridization nursery

12 Types of Populations Germplasm Bank Collection A collection of genetic resources including landraces, exotic material and wild relatives. Synthetic Populations Outcrossing populations (either male-sterile or manually crossed) synthesized from inbred lines. May be used for recurrent selection. Elite Lines Inbred lines (and checks) manipulated with the objective of releasing new varieties in the short term.

13 Aspects of AMGermplasm bank Synthetic Populations Elite Germplasm SampleCore-collectionSegregating progenies Elite lines and checks Sample turnoverStaticEphemeralGradually substituted Source of phenotypic data ScreeningsProgeny testsYield trials Type of traitsHigh heritability traits; Domestication traits Depends on the evaluation scheme Low heritability traits: yield, resistance to abiotic stresses Characteristics Related to Association Mapping: Practical aspects

14 Characteristics Related to Association Mapping: Genetic Expectations Aspects of AMGermplasm bankSynthetic PopulationsElite Germplasm Linkage Disequilibrium LowIntermediate and fast-decaying High Population structure MediumLowHigh Allele diversity among samples HighIntermediateLow Allele diversity within samples Variable1 or 2 alleles (diploid species) 1 allele (inbred lines)

15 Characteristics Related to Association Mapping: Potential Applications AspectsGermplasm bankSynthetic PopulationsElite Germplasm PowerLowIntermediate and decreasing High; could allow genome scan ResolutionHigh; could allow fine mapping Intermediate and increasing Low Use of significant markers Transfer of new alleles by marker-assisted backcross Incorporation in selection index Forward Breeding - MAS in progenies (requires validation)

16 Previous QTL information Doubled-Haploid Population AC Reed x Grandin QTL for kernel size (width) near Xwmc18-2D Recombinant Inbred Population Synthetic W7984 x Opata (ITMI population) QTL for kernel size (length) on 5A and 5B Length 5B Width 2D

17 Association Analysis Materials 95/149 soft winter wheat cultivars from the Northeastern US: Mostly recent releases, representing 35 seed companies / institutions 93 SSR loci: 33 on 2D, 20 on 5A, 9 on 5B, 31 on 16 other chromosomes Rare alleles (freq<5%):considered as missing for LD and population structure analysis; considered as allele for AM analysis Methods Population Structure: 36 “unlinked” SSR markers- Structure without admixture, SPAGeDi (Hardy & Vekemans) program for Kinship ; Visualization: Factorial (Multiple) Correspondence Analysis (Benzecri, 1973 L' Analyse des correspondances. Dunod) Linkage Disequilibrium: Tassel (maizegenetics.net) used to compute r 2, with p-values from 1000 permutations Association Analysis: R stats package lme used to analyze Linear mixed-effects model with marker as fixed effects (selected from previously identified QTL regions) and subpopulations or Kinship as random effects (no obvious differentiating characteristics); Two-marker models: tested by likelihood ratio test Jianming Yu, Gael Pressoir, et al. (2006) A Unified Mixed-Model Method for Association Mapping Accounting for Multiple Levels of Relatedness Nature Genetics 38:

18 Estimating Relatedness The K Matrix Relatedness (K) i j Θ ij ≅ F ij F 11 F nn F nj …… ………… In cattle studies the analogous matrix is estimated from pedigrees, and it controls for the polygene effect Ji anming Yu, Gael Pressoir, et al. (2006) A Unified Mixed-Model Method for Association Mapping Accounting for Multiple Levels of Relatedness Nature Genetics 38: Fij = (Qij-Qm)/(1-Qm) (Ritland, Loiselle) If Fij is negative, then it is set to zero.

19 Subpopulation No. of Varieties Fst Total Population Structure: Sample Subdivisions Moderate Population Subdivision

20 Population Structure: Factorial Correspondence Analysis S2 S4 S1 S3 Orthogonal views of 4 soft winter wheat subpopulations

21 Linkage Disequilibrium: Germplasm Sample Selection 149 lines genotyped with 18 unlinked SSR markers Most similar lines were excluded "Normalizing" the sample drastically reduced LD among unlinked markers p<.0001 p<.001 p< lines 95 lines R 2 probability for unlinked SSR markers

22 Definition of a baseline-LD specific for our sample Defined as the 95 th percentile of the distribution of r 2 among unlinked loci r 2 estimates above this value are probably due to genetic linkage Baseline LD for this sample: r 2 = Normal curve Normal Distr. 95 th percentile LD baseline

23 Linkage Disequilibrium: Chromosome 2D Consistent LD was below 1 cM, localized LD 1-5 cM

24 Linkage Disequilibrium: Chromosome 5A Significant LD extended for 5 cM in pericentromeric region ~5 cM

25 LocusWeightAreaLengthWidth cMNameNYOHNYOHNYOHNYOH 7Xcfd * Xwmc ’ *0.000** 23Xgwm * 28Xwmc * Xgwm ** Xgwm * Loci Associated with Kernel Size (p-values) Chromosome 2D Kernel Size Milling Quality None of the loci on 2D were significant after multiple testing correction ** Likelihood Ratio Test Agreed with QTL in Reed x Grandin

26 LocusWeightAreaLengthWidth cMNameNYOHNYOHNYOHNYOH 55Xcfa * Xwmc150b0.002* * Xbarc * Xbarc * Loci Associated with Kernel Size (p-values) Chromosome 5A cMLocus Milling Score Flour YieldESIFriability Break-Flour Yield 55Xcfa *0.081 Kernel Size Milling Quality n.s. ** Likelihood Ratio Test Agreed with QTL in M6 x Opata

27 B.L.U.E. of allele effects Kernel Length N. of Cultivars:

28 B.L.U.E. of allele effects Kernel Width N. of Cultivars:

29 B.L.U.E of allele effects Kernel Weight N. of Cultivars:

30 Conclusions Linkage Disequilibrium Variation in LD across the genome can be characterized in relevant germplasm Markers closely linked to QTL of interest can be identified and allelic effects quantified Association Mapping as a Breeding Strategy For recurrent selection, markers could be used to carry information from a “good year” to a “bad year” In pedigree breeding, markers could carry information about traits of interest from replicated field trials to single row or single plant selection Allelic values of previously identified alleles can be updated annually based on advanced trial data combined with genotypic data New alleles can be identified and characterized to determine their relative value A selection index can be used to incorporate both phenotypic and molecular data

31 Acknowledgements USDA Soft Wheat Quality Lab, Wooster, OH Embrapa Technical Support: David Benscher James Tanaka Gretchen Salm

32 Kangaroo Island Wayne Powell


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