Multilocus approaches for the measurement of selection on correlated genetic loci
Multilocus approaches for the measurement of selection on correlated genetic loci
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DOI：
10.1111/mec.13867
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发表时间：
2017-01-01
影响因子：
4.9
通讯作者：
Nosil, Patrik
中科院分区：
文献类型：
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作者：
Gompert, Zachariah;Egan, Scott P.;Nosil, Patrik
生态物种形成的研究与选择的研究有着内在的联系。建立了基于特征值和个体的适应性(例如，存活率)之间的关联来估计世代内的表型选择的方法。这些方法试图通过多元统计方法(即对选择梯度的推断)，将直接作用于某一性状的选择与因与其他性状相关而引起的间接选择区分开来。分离遗传座位上的直接选择和间接选择也有利于对遗传类型或基因组变异的选择进行估计。然而，利用基因组数据实现这一目标是困难的，因为潜在相关遗传基因座的数量(P)相对于采样的个体数量(N)非常大。换句话说，模型参数的数量超过了观测值的数量(p>；>；n)。我们对全基因组回归方法(即贝叶斯稀疏线性混合模型)在p>；>；n情况下量化直接选择的实用性进行了模拟检验。这种模型已被用于全基因组关联图谱，并在人工育种中很常见。我们的结果表明，它们有望用于研究野生自然选择，从而研究生态物种形成。但我们也展示了该方法的重要局限性，并讨论了更可靠的推断所需的研究设计。
The study of ecological speciation is inherently linked to the study of selection. Methods for estimating phenotypic selection within a generation based on associations between trait values and fitness (e. g. survival) of individuals are established. These methods attempt to disentangle selection acting directly on a trait from indirect selection caused by correlations with other traits via multivariate statistical approaches (i. e. inference of selection gradients). The estimation of selection on genotypic or genomic variation could also benefit from disentangling direct and indirect selection on genetic loci. However, achieving this goal is difficult with genomic data because the number of potentially correlated genetic loci (p) is very large relative to the number of individuals sampled (n). In other words, the number of model parameters exceeds the number of observations (p >> n). We present simulations examining the utility of whole-genome regression approaches (i. e. Bayesian sparse linear mixed models) for quantifying direct selection in cases where p >> n. Such models have been used for genome-wide association mapping and are common in artificial breeding. Our results show they hold promise for studies of natural selection in the wild and thus of ecological speciation. But we also demonstrate important limitations to the approach and discuss study designs required for more robust inferences.