Pathway analysis of genome-wide data improves warfarin dose prediction View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-05-28

AUTHORS

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

ABSTRACT

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. More... »

PAGES

s11-s11

References to SciGraph publications

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  • Journal

    TITLE

    BMC Genomics

    ISSUE

    Suppl 3

    VOLUME

    14

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2164-14-s3-s11

    DOI

    http://dx.doi.org/10.1186/1471-2164-14-s3-s11

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1016081843

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/23819817


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