Abstract
BackgroundA key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects. Pharmacogenomics GWAS attempt to elucidate genotypes predictive of drug response. However, the size of these studies has severely limited their power and potential application. We propose a novel knowledge integration and SNP aggregation approach for identifying genes impacting drug response. Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with drug response. We first use pre-existing knowledge sources to rank pharmacogenes by their likelihood to affect drug response. We then define a summary score for each gene based on allele frequencies and train linear and logistic regression classifiers to predict drug response phenotypes.ResultsWe applied our method to a published warfarin GWAS data set comprising 181 individuals. We find that our method can increase the power of the GWAS to identify both VKORC1 and CYP2C9 as warfarin pharmacogenes, where the original analysis had only identified VKORC1. Additionally, we find that our method can be used to discriminate between low-dose (AUROC=0.886) and high-dose (AUROC=0.764) responders.ConclusionsOur method offers a new route for candidate pharmacogene discovery from pharmacogenomics GWAS, and serves as a foundation for future work in methods for predictive pharmacogenomics.
Highlights
A key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects
Using knowledge to limit the hypothesis testing space We identified 228 genes which were likely to be pharmacogenes for warfarin and which contained SNPs measured in the Cooper warfarin response Genome-wide association studies (GWAS) data set (See Methods)
Derivation and evaluation of a candidate gene-score based on allele frequencies W assigned each SNP to a gene if the SNP was within 5 kbp of the boundary of the gene
Summary
A key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects. Given the its broad use, narrow therapeutic range, and severity of side effects, a comprehensive pharmacogenomic characterization of warfarin dose-response offers the potential for substantial clinical impact [8,9]. A method to estimate stable warfarin dose was developed by integrating information on patient VKORC1 and CYP2C9 genotypes with clinical factors [16]. This method incorporated genotype information for only two warfarin pharmacogenes, the genotype-based method was able to explain ~49% of the variance in stable dose, substantially outperforming the pure-clinical and fixed-dose approaches. The elucidation of additional large-effect pharmacogenes for warfarin and other genomic features might serve to dramatically increase the ability to predict warfarin dose response from genotype
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