Data associated with "Metabolic reaction fluxes as amplifiers and buffers of risk alleles for coronary artery disease"

Abstract Genome-wide association studies have identified thousands of variants associated with disease risk but the mechanism by which such variants contribute to disease remains largely unknown. Indeed, a major challenge is that variants do not act in isolation but rather in the framework of highly complex biological networks, such as the human metabolic network, which can amplify or buffer the effect of specific risk alleles on disease susceptibility. Here we use genetically predicted reaction fluxes to perform a systematic search for metabolic fluxes acting as buffers or amplifiers of coronary artery disease (CAD) risk alleles. Our analysis identifies 30 risk locus - reaction flux pairs with significant interaction on CAD susceptibility involving 18 individual reaction fluxes and 8 independent risk loci. Notably, many of these reactions are linked to processes with putative roles in the disease such as the metabolism of inflammatory mediators. In summary, this work establishes proof of concept that biochemical reaction fluxes can have non-additive effects with risk alleles and provides novel insights into the interplay between metabolism and genetic variation on disease susceptibility. Description This dataset provides summary statistics for the interaction effects between risk allele dosage and reaction fluxes on CAD and myocardial infarction (MI) risk in UK Biobank participants of European genetic ancestries. We use two complementary methods to evaluate buffering/amplification effects. First, we test for a significant interaction effect size between risk allele dosage and reaction flux value using a Cox proportional-hazards model for disease risk. Second, for each pair of reaction flux values and risk alleles, we estimate the effect of reaction flux value on disease risk within each dosage of the risk allele (0, 1, or 2), and Welch's ANOVA is then used to evaluate the significance of the differences between effect sizes across risk allele dosages. We consider that there is a buffering/amplification of disease susceptibility between a variant and a reaction flux when it is statistically significant with both approaches. To facilitate the exploration of these results, we provide two interactive HTML files that allow users to visualize and query all interactions with P < 0.001 for CAD and MI risk. Additional files with reaction and variant annotation are also provided.摘要 全基因组关联研究已鉴定出数千种与疾病风险相关的遗传变异，但此类变异促成疾病的具体机制仍未被完全阐明。其中一项核心挑战在于，遗传变异并非独立发挥功能，而是处于高度复杂的生物网络框架之中——例如人类代谢网络，这类网络可放大或缓冲特定风险等位基因对疾病易感性的影响。 本研究借助遗传预测的反应通量，系统搜寻可作为冠状动脉疾病（coronary artery disease, CAD）风险等位基因缓冲因子或放大因子的代谢通量。经分析，本研究共鉴定出30个风险位点-反应通量配对，这些配对在CAD易感性上存在显著交互作用，涉及18种独立的反应通量与8个独立的风险位点。值得注意的是，其中诸多反应与已被推测参与该疾病进程的生物学过程密切相关，例如炎症介质代谢。 综上，本研究验证了生化反应通量可与风险等位基因产生非加性效应的概念，并为阐明代谢与遗传变异在疾病易感性中的相互作用提供了全新视角。 数据集说明 本数据集提供了欧洲遗传血统的英国生物银行（UK Biobank）参与者中，风险等位基因剂量与反应通量之间的交互效应对冠状动脉疾病（CAD）及心肌梗死（myocardial infarction, MI）风险的汇总统计量。 本研究采用两种互补的分析方法评估缓冲/放大效应：其一，针对疾病风险构建考克斯比例风险模型（Cox proportional-hazards model），以此检验风险等位基因剂量与反应通量值之间是否存在显著的交互效应量；其二，针对每一组反应通量值与风险等位基因配对，我们分别在风险等位基因剂量为0、1或2的亚组中，估算反应通量值对疾病风险的效应大小，随后采用韦尔奇方差分析（Welch's ANOVA）检验不同风险等位基因剂量组间的效应大小差异是否具有统计学意义。若某一变异与反应通量的交互效应在两种方法下均达到统计学显著性，则认为二者之间存在疾病易感性的缓冲或放大作用。 为便于用户探索本研究结果，我们提供了两份交互式HTML文件，支持用户可视化查询与CAD及MI风险相关且P值小于0.001的所有交互效应。 本数据集同时附带反应通量与遗传变异的注释文件。