Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

Assaf Gottlieb,Stephane Bourgeois,Mia Wadelius,Roxana Daneshjou,Marianne Degorter,Stephen B Montgomery,Russ B Altman,Peter J Svensson,Panos Deloukas

doi:10.1186/s13073-017-0495-0

Abstract

BackgroundGenome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects.MethodsPrevious work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions.Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.ResultsWe build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.ConclusionsOur results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions.MATLAB code is available at https://github.com/assafgo/warfarin-cohort

Highlights

Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific
We demonstrate the utility of the signatures by predicting warfarin dose in individuals of African American (AA) and European (EUR) descent
Datasets Gene-tissue expression data Gene expression and Expression quantitative loci (eQTL) associated with 42 tissues were extracted from GenotypeTissue Expression (GTEx) consortium version 6 [15]

Summary

Introduction

Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. A crucial component to implementing precision medicine is elucidating how genetic variation affects drug response. These gene–drug associations can be used for tailored drug selection and drug dosing [1, 2]. Genome-wide association studies (GWAS) allow the association of genetic variants like single nucleotide polymorphisms (SNPs) with a drug phenotype. While GWAS have successfully identified thousands of genotype–phenotype associations, they suffer from three limitations [3]: testing a Approaches that aggregate SNPs into genes or pathways have been developed to circumvent some of these drawbacks [7, 8]. Beyond direct measurement of genetic variation, approaches for using measured or imputed gene expression can potentially provide insight into biological

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Conclusion