Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies.
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DOI：
10.1371/journal.pone.0015006
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发表时间：
2010-11-16
期刊：
影响因子：
3.7
通讯作者：
Da Y
中科院分区：
文献类型：
--
作者：
Ma L;Han S;Yang J;Da Y
复杂的疾病或表型可能涉及多种遗传变异以及遗传、环境和其他因素之间的相互作用。目前的全基因组关联研究（GWAS）大多使用单位点分析，并通过多重确认鉴定了遗传效应。这种已证实的单核苷酸多态性（SNP）效应很可能是真正的遗传效应，并且在测试相同表型的新效应时忽略此信息会导致统计功效下降，因为残余方差增加，而残余方差是被遗漏效应的一部分。在这项研究中，提出了一种多位点关联检验（MLT），用于 GWAS 分析，条件是 SNP 已证实具有提高统计能力的效果。推导了统计功效的分析公式，并通过模拟对已确认的 SNP 进行 MLT 计算和不考虑已确认的 SNP 的单基因座测试 (SLT) 进行验证。通过案例研究与模拟数据和弗雷明汉心脏研究 (FHS) GWAS 数据比较了两种方法的统计功效。结果表明，MLT 方法比 SLT 方法具有更高的统计功效。在针对四种胆固醇表型和血清代谢物的 GWAS 案例研究中，MLT 方法将统计功效提高了 5% 至 38%，具体取决于条件 SNP 的数量和效应大小。对于 FHS 数据的 HDL 胆固醇 (HDL-C) 和总胆固醇 (TC) 分析，以 GWAS 目录和 NCBI 中确认的 SNP 为条件的 MLT 方法比 SLT 具有更显着的结果。
Complex diseases or phenotypes may involve multiple genetic variants and interactions between genetic, environmental and other factors. Current genome-wide association studies (GWAS) mostly used single-locus analysis and had identified genetic effects with multiple confirmations. Such confirmed single-nucleotide polymorphism (SNP) effects were likely to be true genetic effects and ignoring this information in testing new effects of the same phenotype results in decreased statistical power due to increased residual variance that has a component of the omitted effects. In this study, a multi-locus association test (MLT) was proposed for GWAS analysis conditional on SNPs with confirmed effects to improve statistical power. Analytical formulae for statistical power were derived and were verified by simulation for MLT accounting for confirmed SNPs and for single-locus test (SLT) without accounting for confirmed SNPs. Statistical power of the two methods was compared by case studies with simulated and the Framingham Heart Study (FHS) GWAS data. Results showed that the MLT method had increased statistical power over SLT. In the GWAS case study on four cholesterol phenotypes and serum metabolites, the MLT method improved statistical power by 5% to 38% depending on the number and effect sizes of the conditional SNPs. For the analysis of HDL cholesterol (HDL-C) and total cholesterol (TC) of the FHS data, the MLT method conditional on confirmed SNPs from GWAS catalog and NCBI had considerably more significant results than SLT.
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DOI：
10.1073/pnas.0903103106
发表时间：
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