Genome-wide Association Studies A population-based survey to identify non-random associations between phenotypes and genetic markers across the genome Does not rely on linkage analysis or trace the inheritance of traits and markers from a cross Relies on historic linkage disequilibrium between genetic markers and QTL Also called Association mapping Linkage disequilibrium mapping

Advantages of GWAS approach More opportunity for recombination than in a biparental mapping population Fine mapping of QTL Validate candidate genes Determine which polymorphisms within a gene determine different phenotypes Surveys a broader gene pool More than two individuals are represented Identify multiple alleles for QTL Evaluate effects of QTL in diverse genetic backgrounds No need to create mapping populations for linkage analysis New possibilities for QTL analysis in species with a long generation time where controlled crossing is difficult

Higher resolution maps with GWAS Source: Conifer Genomics Learning Modules (Modified from Rafalski (2002), COPB 5: 94-100)

Disadvantages of GWAS approach Not a controlled experiment! Risk of false positives due to population structure Results will be confounded by any background LD in the population that is not due to close linkage It is critical to either confirm that the background LD is negligible or use statistical approaches to adjust for it Need to know extent and structure of LD in order to identify best association mapping strategy Power to detect QTL is unpredictable Ideally… LD has decayed to a large extent in the population as a whole and over fairly small map distances Adequate LD still exists between marker loci and closely linked QTL

Steps in association mapping Select an association panel Measure phenotypes Genotype the panel Quantify extent of linkage disequilibrium Assess population structure Estimate kinship Apply appropriate statistical model to detect associations between markers and traits

LD decay Extent of LD “decays” as the distance between markers increases Can also think of “decay” as distance along a chromosome D is the covariance between alleles at different loci Can consider r2 to be the square of the correlation coefficient r2 r2 0.2 10 kb 100 kb distance between markers

Low LD requires high marker density High LD Low density Low LD Low density Low LD High density High power to detect QTL High resolution of QTL

Extent of LD in barley Wild barley: LD decays within a gene Landraces: ~ 90 kb European germplasm - significant LD: mean 3.9 cM, median 1.16 cM, maximum >60 cM Modern European barley Landraces (ICARDA) Wild barley

Population Structure Population structure may arise from various causes geographic isolation selection breeding history Population structure may cause false positive associations between genotypes and phenotypes Methods to account for populations structure Genomic control (GC) Structured Association (SA) Software: Structure 2.3.4 Principle Component Analysis (PCA)

Population Structure Many individuals will not belong uniquely to one subpopulation, but will be the descendents of crosses between two or more ancestral populations Estimates the proportion of ancestry attributable to each population for each individual

Slide courtesy of Alfonso Cuesta-Marcos Marker Distance Line 1 Line 2 Line 3 Line 4 Line 5 Line 6 Line 7 Line 8 Line 9 Line 10 Line 11 Line 12 Line 13 Line 14 Line 15 Line 16 _3_0363_ A B _1_1061_ 0.8 _3_0703_ 1.5 _1_1505_ _1_0498_ _2_1005_ 3.8 _1_1054_ _2_0674_ 6 _1_0297_ 8.8 _1_0638_ 10.7 _1_1302_ 11.4 _1_0422_ _2_0929_ 15.3 _3_1474_ 15.4 _1_1522_ 17.3 _2_1388_ _3_0259_ 18.1 _1_0325_ _2_0602_ 20.8 _1_0733_ 23.9 _2_0729 _1_1272_ _2_0891_ 26.1 _2_0748_ 26.6 _3_0251_ 27.4 _1_0997_ 35.5 _1_1133_ 41.8 _2_0500_ 42.5 _3_0634_ 43.3 10 Desease severity 5 Slide courtesy of Alfonso Cuesta-Marcos

Q + K model Y = Xß + S + Qv + Zu + e random effects Mixed Model – includes fixed and random effects random effects Y = Xß + S + Qv + Zu + e Y is the individual observations of the phenotype Xß includes fixed effects: population means, environments S includes marker allele effects (fixed) Q is a subpopulation incidence matrix (adjusts for structure) v is a matrix of estimates of subpopulation mean effects (fixed) Zu represents degree of relatedness not captured by population structure (adjusts for kinship) u is the polygenic effect generated by other loci that are unlinked to the one being tested Yu et al. (2006) Nature Genetics 38: 203-208

Linkage analysis + association mapping Can we combine the benefits of both approaches? Nested Association Mapping (NAM) Method Crossed 25 diverse inbreds to a common inbred B73 Derived recombinant inbred lines from each cross Pros and Cons Diverse and representative High power to detect QTL High resolution of QTL A lot of work!!! Yu, et al. (2008) Genetics McMullen, et al. (2009) Science

Linkage analysis + association mapping Multi-parent Advanced Generation Intercrosses (MAGIC) (A) Select founders (C) Intercross individuals across funnels (B) Make defined crosses (funnels) (D) Self or create double haploids Huang et al. (2015) Theor Appl Genet 128:999-1017