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GWAS to Sequencing: Divergence in Study Design and Analysis

机译:GWAS测序:研究设计和分析的差异

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摘要

The success of genome-wide association studies (GWAS) in uncovering genetic risk factors for complex traits has generated great promise for the complete data generated by sequencing. The bumpy transition from GWAS to whole-exome or whole-genome association studies (WGAS) based on sequencing investigations has highlighted important differences in analysis and interpretation. We show how the loss in power due to the allele frequency spectrum targeted by sequencing is difficult to compensate for with realistic effect sizes and point to study designs that may help. We discuss several issues in interpreting the results, including a special case of the winner's curse. Extrapolation and prediction using rare SNPs is complex, because of the selective ascertainment of SNPs in case-control studies and the low amount of information at each SNP, and naive procedures are biased under the alternative. We also discuss the challenges in tuning gene-based tests and accounting for multiple testing when genes have very different sets of SNPs. The examples we emphasize in this paper highlight the difficult road we must travel for a two-letter switch.
机译:全基因组关联研究(GWAS)在揭示复杂性状的遗传风险因素方面的成功,为测序产生的完整数据带来了广阔前景。从GWAS到基于测序研究的全外显子或全基因组关联研究(WGAS)的艰难过渡凸显了分析和解释方面的重要差异。我们展示了如何通过序列靶向的等位基因频谱而导致的功率损耗难以用现实的效应大小来补偿,并指出可能有帮助的研究设计。我们在解释结果时讨论了几个问题,包括获胜者诅咒的特殊情况。由于在病例对照研究中对SNP的选择性确定以及每个SNP的信息量少,因此使用稀有SNP进行外推和预测是复杂的,并且在其他方​​法下偏于朴素的程序。我们还讨论了当基因具有非常不同的SNP集时,在调整基于基因的测试和进行多重测试时所面临的挑战。我们在本文中强调的示例强调了两个字母转换必须走的艰难道路。

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