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首页> 外文期刊>Annals of Human Genetics >Efficient Association Study Design Via Power-Optimized Tag SNP Selection
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Efficient Association Study Design Via Power-Optimized Tag SNP Selection

机译：通过功耗优化的标签SNP选择进行有效的关联研究设计

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Discovering statistical correlation between causal genetic variation and clinical traits through association studies is an important method for identifying the genetic basis of human diseases. Since fully resequencing a cohort is prohibitively costly, genetic association studies take advantage of local correlation structure (or linkage disequilibrium) between single nucleotide polymorphisms (SNPs) by selecting a subset of SNPs to be genotyped (tag SNPs). While many current association studies are performed using commercially available high-throughput genotyping products that define a set of tag SNPs, choosing tag SNPs remains an important problem for both custom follow-up studies as well as designing the high-throughput genotyping products themselves. The most widely used tag SNP selection method optimizes the correlation between SNPs (r~2). However, tag SNPs chosen based on an r~2 criterion do not necessarily maximize the statistical power of an association study. We propose a study design framework that chooses SNPs to maximize power and efficiently measures the power through empirical simulation. Empirical results based on the HapMap data show that our method gains considerable power over a widely used r~2-based method, or equivalently reduces the number of tag SNPs required to attain the desired power of a study. Our power-optimized 100k whole genome tag set provides equivalent power to the Affymetrix 500k chip for the CEU population. For the design of custom follow-up studies, our method provides up to twice the power increase using the same number of tag SNPs as r~2-based methods. Our method is publicly available via web server at http://design.cs.ucla.edu.
机译：通过关联研究发现因果遗传变异与临床特征之间的统计相关性是鉴定人类疾病遗传基础的重要方法。由于对一个队列进行完全重测序的费用高得惊人，因此遗传关联研究通过选择一个单核苷酸多态性（SNP）子集进行基因分型（标签SNP），从而利用了单核苷酸多态性（SNP）之间的局部相关结构（或连锁不平衡）。尽管目前使用定义一组标签SNP的高通量基因分型产品进行了许多关联研究，但选择标签SNP仍然是自定义随访研究以及设计高通量基因分型产品本身的重要问题。使用最广泛的标签SNP选择方法优化了SNP之间的相关性（r〜2）。但是，基于r〜2准则选择的标签SNP不一定会使关联研究的统计能力最大化。我们提出了一个研究设计框架，该框架选择SNP以最大化功率并通过经验模拟有效地测量功率。基于HapMap数据的经验结果表明，与广泛使用的基于r〜2的方法相比，我们的方法获得了可观的功效，或者等效地减少了获得研究所需功效所需的标签SNP数量。我们针对功耗进行优化的100k全基因组标签集为CEU群体提供了与Affymetrix 500k芯片同等的功能。对于自定义随访研究的设计，使用与基于r〜2的方法相同数量的标签SNP，我们的方法可提供高达两倍的功率增加。我们的方法可通过位于http://design.cs.ucla.edu的Web服务器公开获得。

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