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. 2013;14 Suppl 3(Suppl 3):S11.
doi: 10.1186/1471-2164-14-S3-S11. Epub 2013 May 28.

Pathway Analysis of Genome-Wide Data Improves Warfarin Dose Prediction

Free PMC article

Pathway Analysis of Genome-Wide Data Improves Warfarin Dose Prediction

Roxana Daneshjou et al. BMC Genomics. .
Free PMC article


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.


Figure 1
Figure 1
Aggregate number of minor alleles (A#m) scoring gives each gene 1 point for every minor allele, and then allows us to compare the phenotype to the scores across the warfarin metabolic pathway.
Figure 2
Figure 2
LD was calculated between SNPs within 1000 kb in the pathway. SNPs were clustered based on the LD r2 values. We calculated the weights of each leaf node (SNP) using the Gerstein-Sonnhammer-Chothia algorithm.
Figure 3
Figure 3
LD-weighted A#m scores are calculated by taking the sum of all minor alleles multiplied by their respective weights. In this case, the SNPs in the black box are in tight LD, while the SNPs in the green box are in moderate LD. With SNP weighting, rather than pruning the SNPs in LD, SNPs are weighted based on the contribution of independent information to the A#m score.
Figure 4
Figure 4
Warfarin pharmacokinetic pathway.

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