Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 714-722 |
| Number of pages | 9 |
| Journal | Clinical pharmacology and therapeutics |
| Volume | 110 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 2021 |
Funding
A.M. is supported by a grant from the American Heart Association (20PRE35180088) and from the Vanderbilt Medical Scientist Training Program (T32GM007347). This work was supported by the National Institutes of Health (NIH) (R01GM132204) to S.L.V.D. The ICPC research reported in this publication was supported by the National Heart, Lung, and Blood Institute U01HL105198, National Institute of General Medical Sciences R24GM61374 and NIH Genome Research Institute U24HG010615. Genome‐wide SNP genotyping was supported by the Pharmacogenomics Research Network & CGM Global Alliance. Other support provided by the Deutsche Forschungsgemeinschaft (DFG), Germany grant numbers SCHW858/1‐2, 374031971—TRR 240, Klinische Forschungsgruppe‐KFO‐274 and in part, by the EU Horizon 2020 UPGx grant number 668353, and the Robert Bosch Stiftung, Stuttgart, Germany. The ACE inhibitor data set from electronic Medical Records and Genomics (eMERGE) Phase II data was supported by U01HG04603 (Vanderbilt), 1U02HG004608‐01, 1U01HG006389 and NCATS/NIH grant UL1TR000427 (Marshfield/EIRH/Penn State), U01HG006375 and U01AG06781 (Group Health and University of Washington), U01HG04599 (Mayo Clinic), U01HG004609 (Northwestern University), U01HG006382 (Geisinger), an ARRA grant RC2GM092618, a Vanderbilt PGRN grant U19HL065962 and the Vanderbilt CTSA grant UL1TR000445 from NCATS/NIH. At Geisinger, the sample collection was supported by NIH (P30DK072488, R01DK088231, and R01DK091601), Pennsylvania Commonwealth Universal Research Enhancement Program, the Ben Franklin Technology Development Fund of PA, the Geisinger Clinical Research Fund and a Grant‐In‐Aid from the American Heart Association. This work used data sets from Vanderbilt University Medical Center’s BioVU, which is supported by institutional funding and by UL1TR000445 from NCATS. The tacrolimus data set was supported by National Institutes of Health, NIGMS, K23GM100183. The MTX clearance 9900 data set was generated at St Jude Children’s Research Hospital and by the Children’s Oncology Group, and supported by grants CA36401, GM92666, CA98453, CA98413, CA114766, and CA21765 from the National Institutes of Health, ALSAC, the Jeffrey Pride Foundation, and the National Childhood Cancer Foundation. This content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. A.M. is supported by a grant from the American Heart Association (20PRE35180088) and from the Vanderbilt Medical Scientist Training Program (T32GM007347). This work was supported by the National Institutes of Health (NIH) (R01GM132204) to S.L.V.D. The ICPC research reported in this publication was supported by the National Heart, Lung, and Blood Institute U01HL105198, National Institute of General Medical Sciences R24GM61374 and NIH Genome Research Institute U24HG010615. Genome-wide SNP genotyping was supported by the Pharmacogenomics Research Network & CGM Global Alliance. Other support provided by the Deutsche Forschungsgemeinschaft (DFG), Germany grant numbers SCHW858/1-2, 374031971?TRR 240, Klinische Forschungsgruppe-KFO-274 and in part, by the EU Horizon 2020 UPGx grant number 668353, and the Robert Bosch Stiftung, Stuttgart, Germany. The ACE inhibitor data set from electronic Medical Records and Genomics (eMERGE) Phase II data was supported by U01HG04603 (Vanderbilt), 1U02HG004608-01, 1U01HG006389 and NCATS/NIH grant UL1TR000427 (Marshfield/EIRH/Penn State), U01HG006375 and U01AG06781 (Group Health and University of Washington), U01HG04599 (Mayo Clinic), U01HG004609 (Northwestern University), U01HG006382 (Geisinger), an ARRA grant RC2GM092618, a Vanderbilt PGRN grant U19HL065962 and the Vanderbilt CTSA grant UL1TR000445 from NCATS/NIH. At Geisinger, the sample collection was supported by NIH (P30DK072488, R01DK088231, and R01DK091601), Pennsylvania Commonwealth Universal Research Enhancement Program, the Ben Franklin Technology Development Fund of PA, the Geisinger Clinical Research Fund and a Grant-In-Aid from the American Heart Association. This work used data sets from Vanderbilt University Medical Center?s BioVU, which is supported by institutional funding and by UL1TR000445 from NCATS. The tacrolimus data set was supported by National Institutes of Health, NIGMS, K23GM100183. The MTX clearance 9900 data set was generated at St Jude Children?s Research Hospital and by the Children?s Oncology Group, and supported by grants CA36401, GM92666, CA98453, CA98413, CA114766, and CA21765 from the National Institutes of Health, ALSAC, the Jeffrey Pride Foundation, and the National Childhood Cancer Foundation. This content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors would like to thank Christian M. Shaffer for help with data extraction and coding resources, and the International Clopidogrel Pharmacogenomics Consortium (ICPC) for contributing data. This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, Tennessee.
ASJC Scopus subject areas
- Pharmacology (medical)
- Pharmacology