Pathway analysis of genome-wide data improves warfarin dose prediction
Pathway analysis of genome-wide data improves warfarin dose prediction
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DOI：
10.1186/1471-2164-14-s3-s11
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发表时间：
2013-05-28
期刊：
影响因子：
4.4
通讯作者：
Altman, Russ B.
中科院分区：
文献类型：
--
作者：
Daneshjou, Roxana;Tatonetti, Nicholas P.;Altman, Russ B.
背景：许多全基因组关联研究集中于单个基因座与目标表型的关联。然而，在罕见变异的情况下，积累足够的样本来评估这些关联可能是困难的。此外，一个基因或一个途径内的一组基因的多个变异都可能导致表型，这表明在该基因或途径上发现的变异的聚集可能有助于提高检测关联的能力。结果：我们提出了一种沿着生物相关途径聚集单核苷酸多态(SNPs)的方法，以寻求与表型的遗传关联。我们的方法使用了所有可用的遗传变异，并且不会删除那些处于连锁不平衡(LD)的遗传变异。相反，它使用了一种新的SNP加权方案来降低相关SNP的贡献。我们将我们的方法应用于三组服用华法林的患者：两名欧洲血统患者和一名非裔美国人。尽管华法林的临床协变量和关键的药物遗传位点已经被表征，但我们的关联度量确定了华法林代谢途径中分布的突变与显著的关联。在使用了所有已知的临床协变量和VKORC1和CYP2C9中的药物遗传变异后，我们改进了剂量预测。特别是，我们发现华法林剂量中至少1%的缺失遗传率可能是由于华法林代谢途径的变异的聚集效应，即使SNP没有单独显示出显著的相关性。结论：我们的方法允许研究人员通过不预先选择SNP来以公正的方式研究聚集的SNP效应。它通过加权来考虑LD-结构，从而保留了所有可用的信息，从而消除了对LD剪枝的需要。
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.