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

Research output: Contribution to journalArticle

11 Citations (Scopus)

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
JournalUnknown Journal
Volume14 Suppl 3
Publication statusPublished - 01-01-2013
Externally publishedYes

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Warfarin
Single Nucleotide Polymorphism
Genome
Linkage Disequilibrium
Phenotype
Genes
Genome-Wide Association Study
Pharmacogenetics
Metabolic Networks and Pathways
African Americans
Research Personnel
Weights and Measures
Mutation

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics

Cite this

Daneshjou, R., Tatonetti, N. P., Karczewski, K. J., Sagreiya, H., Bourgeois, S., Drozda, K., ... Altman, R. B. (2013). Pathway analysis of genome-wide data improves warfarin dose prediction. Unknown Journal, 14 Suppl 3.
Daneshjou, Roxana ; Tatonetti, Nicholas P. ; Karczewski, Konrad J. ; Sagreiya, Hersh ; Bourgeois, Stephane ; Drozda, Katarzyna ; Burmester, James K. ; Tsunoda, Tatsuhiko ; Nakamura, Yusuke ; Kubo, Michiaki ; Tector, Matthew ; Limdi, Nita A. ; Cavallari, Larisa H. ; Perera, Minoli ; Johnson, Julie A. ; Klein, Teri E. ; Altman, Russ B. / Pathway analysis of genome-wide data improves warfarin dose prediction. In: Unknown Journal. 2013 ; Vol. 14 Suppl 3.
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Daneshjou, R, Tatonetti, NP, Karczewski, KJ, Sagreiya, H, Bourgeois, S, Drozda, K, Burmester, JK, Tsunoda, T, Nakamura, Y, Kubo, M, Tector, M, Limdi, NA, Cavallari, LH, Perera, M, Johnson, JA, Klein, TE & Altman, RB 2013, 'Pathway analysis of genome-wide data improves warfarin dose prediction.', Unknown Journal, vol. 14 Suppl 3.

Pathway analysis of genome-wide data improves warfarin dose prediction. / Daneshjou, Roxana; Tatonetti, Nicholas P.; Karczewski, Konrad J.; Sagreiya, Hersh; Bourgeois, Stephane; Drozda, Katarzyna; Burmester, James K.; Tsunoda, Tatsuhiko; Nakamura, Yusuke; Kubo, Michiaki; Tector, Matthew; Limdi, Nita A.; Cavallari, Larisa H.; Perera, Minoli; Johnson, Julie A.; Klein, Teri E.; Altman, Russ B.

In: Unknown Journal, Vol. 14 Suppl 3, 01.01.2013.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Pathway analysis of genome-wide data improves warfarin dose prediction.

AU - Daneshjou, Roxana

AU - Tatonetti, Nicholas P.

AU - Karczewski, Konrad J.

AU - Sagreiya, Hersh

AU - Bourgeois, Stephane

AU - Drozda, Katarzyna

AU - Burmester, James K.

AU - Tsunoda, Tatsuhiko

AU - Nakamura, Yusuke

AU - Kubo, Michiaki

AU - Tector, Matthew

AU - Limdi, Nita A.

AU - Cavallari, Larisa H.

AU - Perera, Minoli

AU - Johnson, Julie A.

AU - Klein, Teri E.

AU - Altman, Russ B.

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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.

AB - 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.

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Daneshjou R, Tatonetti NP, Karczewski KJ, Sagreiya H, Bourgeois S, Drozda K et al. Pathway analysis of genome-wide data improves warfarin dose prediction. Unknown Journal. 2013 Jan 1;14 Suppl 3.