A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Abstract

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.

Figure 1

Simulation results. (a–d) illustrate the scenarios simulated: (a) positive pleiotropy, alt. common effect across environments; (b) positive pleiotropy, alt. common effect across environments, with size of effect differing between traits/environments; (c) effect only on one trait, alt. only in one environment; (d) negative pleiotropy, alt. opposite effect across environments. (e) shows the estimated relationship between power and false discovery rate (FDR) using six different statistical tests (see text and Online Methods) for the scenario described in (a). (f) shows the estimated relationship between power and false discovery rate (FDR) for the scenario described in (b). (g) shows the estimated relationship between power and false discovery rate (FDR) for the scenario described in (c). (h) shows the estimated relationship between power and false discovery rate (FDR) for the scenario described in (d). Dots on curves denote nominal Bonferroni-corrected 5% significance thresholds. Note that both power and FDR are calculated with respect to the single focal locus, only.

Figure 2

GWAS of LDL and TG. (a–b) Manhattan plots for the marginal, single-trait analyses of LDL(a) and TG(b), respectively. (c–e) Manhattan plots for the joint MTMM analyses: (c) full model; (d) interaction effect, and; (e) common effect. The dashed horizontal line denotes the 5%Bonferroni adjusted genome-wide significance level. (f) Closeup of the FADS1-FADS2 region on chromosome 11. The points for the single-trait analyses are shown in light (TG) and dark blue (LDL), while the point for MTMM are shown in orange (full test), light green (interaction effect), and red (common effect). The gray shading denotes the FADS1 gene region. (g) Estimated phenotypic effect of the rs174546 SNP in light (TG) and dark blue (LDL).

Figure 3

Venn diagrams summarizing the GWAS of A. thaliana flowering data. (a) Classification of the 41 significant SNPs according to the test(s) in which they were significant. (b) Classification of the 41 SNPs (black) and corresponding gene regions (red) according to whether they were found using marginal (MM) or joint (MTMM) analysis.

Figure 4

Summary of FRS6 results. (a) Closeup of a 50kb region on chromosome 1 showing significant G _ E associations. The gene FRS6 is highlighted in gray. The results for the four marginal analysis (using a single trait MM) are shown in blue, while the MTMM results are shown in orange (full test), light green (three-way interaction), green (genotype by location), dark green (genotype by season), and red (common effects).(b) Phenotypic distribution as a function of experimental condition and genotype. (c–f) Plots contrasting the allelic effect in different comparisons: (c) the effect of the season in ‘Spain’; (d) the effect of the season in ‘Sweden’; (e) the effect of the location in ‘Spring’; (f) the effect of the location in ‘Summer’. The effect depends strongly on the season within each location (c–d) and less strongly on location with season (e–f).