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首页> 外文期刊>Journal of evolutionary biology >A framework for power and sensitivity analyses for quantitative genetic studies of natural populations, and case studies in Soay sheep (Ovis aries)
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A framework for power and sensitivity analyses for quantitative genetic studies of natural populations, and case studies in Soay sheep (Ovis aries)

机译：进行自然种群定量遗传研究的能力和敏感性分析的框架，以及大豆（绵羊）的案例研究

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摘要

Studies of the quantitative genetics of natural populations have contributed greatly to evolutionary biology in recent years. However, while pedigree data required are often uncertain (i.e. incomplete and partly erroneous) and limited, means to evaluate the effects of such uncertainties have not been developed. We have therefore developed a general framework for power and sensitivity analyses of such studies. We propose that researchers first generate a set of pedigree data that they wish to use in a quantitative genetic study, as well as data regarding errors that occur in that pedigree. This pedigree is then permuted using the data regarding errors to generate hypothetical 'true' and 'assumed' pedigrees that differ so as to mimic pedigree errors that might occur in the study system under consideration. Phenotypic data are then simulated across the true pedigree (according to user-defined genetic and environmental covariance structures), before being analysed with standard quantitative genetic techniques in conjunction with the 'assumed' pedigree data. To illustrate this approach, we conducted power and sensitivity analyses in a well-known study of Soay sheep (Ovis aries). We found that, although the estimation of simple genetic (co)variance structures is fairly robust to pedigree errors, some potentially serious biases were detected under more complex scenarios involving maternal effects. Power analyses also showed that this study system provides high power to detect heritabilities as low as about 0.09. Given this range of results, we suggest that such power and sensitivity analyses could greatly complement empirical studies, and we provide the computer program pedantics to aid in their application.
机译：近年来，自然种群数量遗传学的研究为进化生物学做出了巨大贡献。但是，尽管所需的谱系数据通常是不确定的（即不完整且部分错误）且有限，但尚未开发出评估此类不确定性影响的方法。因此，我们为此类研究的功效和敏感性分析建立了通用框架。我们建议研究人员首先生成一套谱系数据，以供他们在定量遗传研究中使用，以及有关该谱系中发生的错误的数据。然后使用关于错误的数据对这个谱系进行置换，以生成不同的假设“真实”和“假定”谱系，以便模拟可能在研究系统中出现的谱系错误。然后在真实的家谱（根据用户定义的遗传和环境协方差结构）上模拟表型数据，然后使用标准的定量遗传技术结合“假定的”家谱数据进行分析。为了说明这种方法，我们在著名的Soay羊（Ovis aries）研究中进行了功效和敏感性分析。我们发现，尽管简单的遗传（协方差）结构的估计对于谱系错误是相当可靠的，但是在涉及母体效应的更复杂的情况下，发现了一些潜在的严重偏差。功效分析还显示，该研究系统提供了强大的功能，可检测低至约0.09的遗传力。鉴于结果范围如此，我们建议这种功效和敏感性分析可以极大地补充经验研究，并且我们提供计算机程序学来辅助其应用。

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