Genome-wide approach to measure variant-based heritability of drug outcome phenotypes.

Muhammad, Ayesha; Aka, Ida T; Birdwell, Kelly A; Gordon, Adam S; Roden, Dan M; Wei, Wei-Qi; Mosley, Jonathan D; Van Driest, Sara L

Pharmacogenomic studies have successfully identified variants - typically with large effect sizes in drug target and metabolism enzymes - that predict drug outcome phenotypes. However, these variants may account for a limited proportion of phenotype variability attributable to the genome. Using genome-wide common variation, we measured the narrow-sense heritability [ h SNP 2 ] of 7 pharmacodynamic and 5 pharmacokinetic phenotypes across 3 cardiovascular drugs, 2 antibiotics, and 3 immunosuppressants. We used a Bayesian Hierarchical Mixed Model, BayesR, to model the distribution of genome-wide variant effect sizes for each drug phenotype as a mixture of 4 normal distributions of fixed variance (0, 0.01%, 0.1% and 1% of the total additive genetic variance). This model allowed us to parse h SNP 2 into bins representing contributions of no-, small-, moderate- and large-effect size variants respectively. For the 12 phenotypes, a median of 969 (range 235-6,304) unique individuals of European ancestry and a median of 1,201,626 (range 777,427-1,514,275) variants were included in our analyses. The number of variants contributing to h SNP 2 ranged from 2,791 to 5,356 (median 3,347). Estimates for h SNP 2 ranged from 0.05 (ACE-inhibitor induced cough) to 0.59 (gentamicin concentration). Small- and moderate-effect variants contributed a majority to h SNP 2 for every phenotype (range 61-95%). We conclude that drug outcome phenotypes are highly polygenic. Thus, larger genome-wide association studies of drug phenotypes are needed both to discover novel variants and to determine how genome-wide approaches may improve clinical prediction of drug outcomes.