Pathway analysis of genome-wide data improves warfarin dose prediction.

Roxana Daneshjou*, Nicholas P. Tatonetti, Konrad J. Karczewski, Hersh Sagreiya, Stephane Bourgeois, Katarzyna Drozda, James K. Burmester, Tatsuhiko Tsunoda, Yusuke Nakamura, Michiaki Kubo, Matthew Tector, Nita A. Limdi, Larisa H. Cavallari, Minoli Perera, Julie A. Johnson, Teri E. Klein, Russ B. Altman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish (US)
JournalUnknown Journal
Volume14 Suppl 3
DOIs
StatePublished - 2013

Funding

Geoffery M. Cooper, for allowing us to use the Cooper. et. al. warfarin data. The International Warfarin Pharmacogenetic Consortium. RD was funded by the Howard Hughes Medical Institute Medical Fellows, Stanford School of Medicine Medical Scholars Grant, and the Stanford School of Medicine Medical Scientist Training Program, NPT was funded by DOESCGF. JKB is a member of the Wisconsin Network for Health Research and funded by grant UL155025011. LHC is funded by American Heart Association Midwest Affiliate Grant-In-Aid (10GRNT3750024) and the University of Illinois Hans Vahlteich Pharmacy Endowment Award. NAL was funded by the National Heart Lung and Blood Institute (RO1HL092173) and the National Institute of Neurological Disorders and Stroke (K23NS45598). MP is funded by K23 HL089808-01A2. MT is funded by Wisconsin Network for Health Research. RBA is funded by. NIH/NIGMS R24 GM61374, BioX2 NSF Grant (CNS-0619926). Declarations The publication costs for this article were funded by the above grants.

ASJC Scopus subject areas

  • Genetics
  • Biotechnology

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