Ryman Nils, Palm Stefan, André Carl, Carvalho Gary R, Dahlgren Thomas G, Jorde Per Erik, Laikre Linda, Larsson Lena C, Palmé Anna, Ruzzante Daniel E
Division of Population Genetics, Department of Zoology, Stockholm University, S-10691 Stockholm, Sweden.
Mol Ecol. 2006 Jul;15(8):2031-45. doi: 10.1111/j.1365-294X.2006.02839.x.
Information on statistical power is critical when planning investigations and evaluating empirical data, but actual power estimates are rarely presented in population genetic studies. We used computer simulations to assess and evaluate power when testing for genetic differentiation at multiple loci through combining test statistics or P values obtained by four different statistical approaches, viz. Pearson's chi-square, the log-likelihood ratio G-test, Fisher's exact test, and an F(ST)-based permutation test. Factors considered in the comparisons include the number of samples, their size, and the number and type of genetic marker loci. It is shown that power for detecting divergence may be substantial for frequently used sample sizes and sets of markers, also at quite low levels of differentiation. The choice of statistical method may be critical, though. For multi-allelic loci such as microsatellites, combining exact P values using Fisher's method is robust and generally provides a high resolving power. In contrast, for few-allele loci (e.g. allozymes and single nucleotide polymorphisms) and when making pairwise sample comparisons, this approach may yield a remarkably low power. In such situations chi-square typically represents a better alternative. The G-test without Williams's correction frequently tends to provide an unduly high proportion of false significances, and results from this test should be interpreted with great care. Our results are not confined to population genetic analyses but applicable to contingency testing in general.
在规划调查和评估实证数据时，关于统计功效的信息至关重要，但在群体遗传学研究中，实际的功效估计却很少呈现。我们通过计算机模拟，结合四种不同统计方法（即皮尔逊卡方检验、对数似然比G检验、费舍尔精确检验以及基于F(ST)的置换检验）所获得的检验统计量或P值，来评估在多个位点检测遗传分化时的功效。比较中考虑的因素包括样本数量、样本大小以及遗传标记位点的数量和类型。结果表明，对于常用的样本量和标记集，即使在分化水平相当低的情况下，检测分化的功效也可能相当可观。不过，统计方法的选择可能至关重要。对于多等位基因位点，如微卫星，使用费舍尔方法合并精确P值是稳健的，通常具有较高的分辨能力。相比之下，对于少等位基因位点（如等位酶和单核苷酸多态性）以及进行成对样本比较时，这种方法可能会产生非常低的功效。在这种情况下，卡方检验通常是更好的选择。未经威廉姆斯校正的G检验往往会产生过高比例的假显著性，对该检验的结果应谨慎解释。我们的结果不仅限于群体遗传学分析，而是普遍适用于列联检验。
