A population genetic signal of polygenic adaptation.
A population genetic signal of polygenic adaptation.
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DOI：
10.1371/journal.pgen.1004412
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发表时间：
2014-08
期刊：
影响因子：
4.5
通讯作者：
Coop G
中科院分区：
文献类型：
--
作者：
Berg JJ;Coop G
多基因表型选择的适应可能通过许多基因座上微妙的等位基因频率变化而发生。目前的群体基因组技术不能很好地识别这些信号。在过去的十年中，详细的知识，具体的基因座的多基因性状已开始出现从全基因组关联研究（GWAS）。在这里，我们结合联合收割机的知识，从GWAS与强大的人口遗传建模，以确定可能已受到当地适应的性状。我们利用GWAS提供了许多位点的加性效应大小的估计，以估计许多群体中给定表型的平均加性遗传值，作为等位基因频率的简单加权和。我们使用一个一般模型的中性遗传值漂移的任意数量的人口与任意的相关性结构。基于这个模型，我们开发的方法来检测异常强的相关性之间的遗传值和特定的环境变量，以及一个概括的比较，以测试种群之间的遗传值的过度分散。最后，我们制定了一个框架，以确定个人的人口或群体的人口，有助于过度分散的信号。这些测试具有比它们的单基因座等效物大得多的功效，因为它们寻找相似效应等位基因之间的正协方差，并且也显著优于不考虑群体结构的方法。我们将我们的测试应用于人类基因组多样性小组（HGDP）数据集，使用GWAS数据进行身高，皮肤色素沉着，2型糖尿病，体重指数和两种炎症性肠病数据集。这项分析揭示了许多假定的局部适应信号，我们讨论了这些结果的生物学解释和注意事项。适应过程在进化生物学中具有根本的重要性。在过去的几十年里，基因分型技术和新的统计方法使进化生物学家能够识别基因组中可能在这一过程中起重要作用的各个区域。然而，当适应发生在由许多基因担保的性状中时，留下的遗传信号更加分散，基因组的任何单个区域都不可能显示出强烈的选择特征。因此，识别这种特征需要对与特定表型相关的位点进行详细注释。在这里，我们开发并实施了一套统计方法，将这种来自全基因组关联研究的注释与来自许多群体的等位基因频率数据相结合，为识别多基因性状中的适应信号提供了一种强有力的方法。我们应用我们的方法来测试选择对人类身高，皮肤色素沉着，体重指数，2型糖尿病风险和炎症性肠病风险的影响。我们发现身高和皮肤色素沉着的信号相对较强，炎症性肠道疾病的信号中等，而体重指数和2型糖尿病风险的证据相对较少。
Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results. The process of adaptation is of fundamental importance in evolutionary biology. Within the last few decades, genotyping technologies and new statistical methods have given evolutionary biologists the ability to identify individual regions of the genome that are likely to have been important in this process. When adaptation occurs in traits that are underwritten by many genes, however, the genetic signals left behind are more diffuse, and no individual region of the genome is likely to show strong signatures of selection. Identifying this signature therefore requires a detailed annotation of sites associated with a particular phenotype. Here we develop and implement a suite of statistical methods to integrate this sort of annotation from genome wide association studies with allele frequency data from many populations, providing a powerful way to identify the signal of adaptation in polygenic traits. We apply our methods to test for the impact of selection on human height, skin pigmentation, body mass index, type 2 diabetes risk, and inflammatory bowel disease risk. We find relatively strong signals for height and skin pigmentation, moderate signals for inflammatory bowel disease, and comparatively little evidence for body mass index and type 2 diabetes risk.
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Seielstad, Mark
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