Genome-Wide Approach to Measure Variant-Based Heritability of Drug Outcome Phenotypes

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 ((Formula presented.)) of seven pharmacodynamic and five pharmacokinetic phenotypes across three cardiovascular drugs, two antibiotics, and three 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 four normal distributions of fixed variance (0, 0.01%, 0.1%, and 1% of the total additive genetic variance). This model allowed us to parse (Formula presented.) into bins representing contributions of no-effect, small-effect, moderate-effect, and large-effect 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 (Formula presented.) ranged from 2,791 to 5,356 (median 3,347). Estimates for (Formula presented.) ranged from 0.05 (angiotensin-converting enzyme inhibitor–induced cough) to 0.59 (gentamicin concentration). Small-effect and moderate-effect variants contributed a majority to (Formula presented.) 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.