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首页> 外文期刊>BMC Genomics >Pathway analysis of genome-wide data improves warfarin dose prediction
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Pathway analysis of genome-wide data improves warfarin dose prediction

机译：全基因组数据的通路分析可改善华法林剂量预测

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BackgroundMany 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.ResultsHere, 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.ConclusionsOur 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.
机译：背景许多全基因组关联研究致力于将单个基因座与目标表型相关联。但是，在稀有变异的情况下，很难收集足够的样本来评估这些关联。此外，一个通路中一个基因或一组基因中的多个变异都可能导致该表型，这表明在该基因或通路上发现的变异的聚集可能有助于提高检测关联的能力。沿生物学相关途径聚集单核苷酸多态性（SNP）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，并没有消除连锁不平衡（LD）中的变异。相反，它使用新颖的SNP加权方案来降低相关SNP的贡献的权重。我们将我们的方法应用于三个服用华法林的患者队列：两个欧洲血统队列和一个非裔美国人队列。尽管已经确定了华法令的临床协变量和关键的药物遗传学位点，但我们的关联度指标确定了与分布在整个华法令代谢途径中的突变的显着关联。在VKORC1和CYP2C9中使用所有已知的临床协变量和药物遗传学变异体后，我们改善了剂量预测。特别是，我们发现华法令剂量中至少有1％的遗传力缺失可能是由于华法令代谢途径变化的综合效应所致，即使SNP并未单独显示出显着的相关性。结论我们的方法允许研究人员进行研究通过不预先选择SNP，以无偏见的方式聚集SNP。它通过权衡LD结构来保留所有可用信息，从而消除了LD修剪的需要。

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