This article is part of the supplement: SNP-SIG 2012: Identification and annotation of SNPs in the context of structure, function, and disease
Pathway analysis of genome-wide data improves warfarin dose prediction
1 Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
2 Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
3 Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA
4 Wellcome Trust Sanger Institute, Hinxton, UK
5 University of Illinois at Chicago, Department of Pharmacy Practice, Chicago, IL 60612, USA
6 Clinical Research Center, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA
7 Research Group for Medical Informatics, Center for Genomic Medicine, RIKEN, Tokyo, Japan
8 Aurora St. Luke's Medical Center, Milwaukee, WI, USA
9 Department of Neurology, University of Alabama at Birmingham, AL 35294, USA
10 University of Chicago, Department of Medicine, Chicago, IL 60637, USA
11 University of Florida, Department of Pharmacotherapy and Translational Research, Gainsville, FL 32610, USA
12 Department of Bioengineering, Stanford University School of Medicine, Stanford, CA 94305, USA
BMC Genomics 2013, 14(Suppl 3):S11 doi:10.1186/1471-2164-14-S3-S11Published: 28 May 2013
Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.
Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.
Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.