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EFFICIENT TESTS OF ASSOCIATION FOR QUANTITATIVE TRAITS AND AFFECTED-UNAFFECTED STUDIES USING POOLED DNA

机译:使用POOLED DNA进行定量性状的有效联结测试和未受影响的相关研究

摘要

Risk assessment and diagnosis of a complex disorder often requires measuring an underlying quantitative phenotype. Association studies in unrelated populations can implicate genetic factors contributing to disease risk, and experiments using pooled DNA provide a less costly but necessarily less powerful alternative to methods based on individual genotyping. Although the sample sites required for pooling and individual genotyping studies have been compared in certain instances, general results have not been reported in the context of association studies, nor have there been clear comparisons of pooling based on quantitative and qualitative (affected/unaffected) phenotypes. Here we use exact numerical calculations and analytical approximations to examine the sample size requirements of association tests for quantitative traits and affected-unaffected studies using pooled DNA. We show, in analogy with selection experiments, that the optimal design for virtually any quantitative phenotype is to pool the top and bottom 27 % of individuals, regardless of marker frequency or inheritance mode; this design requires a population only 24 % larger than that required for individual genotyping. Furthermore, this design is approximately four times more efficient than typical affected-unaffected studies of DNA pooled from individuals classified as affected or unaffected.
机译:复杂疾病的风险评估和诊断通常需要测量基本的定量表型。在不相关人群中进行的关联研究可能暗示遗传因素会导致疾病风险,并且使用合并的DNA进行的实验提供了基于个体基因分型方法的成本较低但必定功能较弱的替代方法。尽管在某些情况下已经比较了合并和个体基因分型研究所需的样本位点,但尚未在关联研究的背景下报告总体结果,也没有基于定量和定性(受影响/不受影响)表型的合并的明确比较。在这里,我们使用精确的数值计算和分析近似来检查关联测试的样本量要求,以进行定量特征和使用合并的DNA的未受影响研究。与选择实验类似,我们表明,几乎所有定量表型的最佳设计是合并顶部和底部27%的个体,而不论标记频率或遗传模式如何。这种设计所需要的种群仅比个体基因分型所需要的种群大24%。此外,该设计的效率比从分类为受影响或未受影响的个体收集的DNA的典型未受影响研究的效率高四倍。

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