Haasl Ryan J, Payseur Bret A
Department of Biology, University of Wisconsin-Platteville, Platteville, Wisconsin, United States of America.
Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
PLoS Genet. 2024 Dec 30;20(12):e1011524. doi: 10.1371/journal.pgen.1011524. eCollection 2024 Dec.
Advances in DNA sequencing technology and computation now enable genome-wide scans for natural selection to be conducted on unprecedented scales. By examining patterns of sequence variation among individuals, biologists are identifying genes and variants that affect fitness. Despite this progress, most population genetic methods for characterizing selection assume that variants mutate in a simple manner and at a low rate. Because these assumptions are violated by repetitive sequences, selection remains uncharacterized for an appreciable percentage of the genome. To meet this challenge, we focus on microsatellites, repetitive variants that mutate orders of magnitude faster than single nucleotide variants, can harbor substantial variation, and are known to influence biological function in some cases. We introduce four general models of natural selection that are each characterized by just two parameters, are easily simulated, and are specifically designed for microsatellites. Using a random forests approach to approximate Bayesian computation, we fit these models to carefully chosen microsatellites genotyped in 200 humans from a diverse collection of eight populations. Altogether, we reconstruct detailed fitness landscapes for 43 microsatellites we classify as targets of selection. Microsatellite fitness surfaces are diverse, including a range of selection strengths, contributions from dominance, and variation in the number and size of optimal alleles. Microsatellites that are subject to selection include loci known to cause trinucleotide expansion disorders and modulate gene expression, as well as intergenic loci with no obvious function. The heterogeneity in fitness landscapes we report suggests that genome-scale analyses like those used to assess selection targeting single nucleotide variants run the risk of oversimplifying the evolutionary dynamics of microsatellites. Moreover, our fitness landscapes provide a valuable visualization of the selective dynamics navigated by microsatellites.
DNA测序技术和计算能力的进步，使得现在能够以前所未有的规模进行全基因组范围的自然选择扫描。通过检查个体间的序列变异模式，生物学家正在识别影响适应性的基因和变异。尽管取得了这一进展，但大多数用于表征选择的群体遗传学方法都假定变异以简单的方式且以低速率发生突变。由于这些假设被重复序列所违背，因此基因组中相当大比例的区域的选择情况仍未得到表征。为应对这一挑战，我们聚焦于微卫星，这是一种重复变异，其突变速度比单核苷酸变异快几个数量级，可携带大量变异，并且在某些情况下已知会影响生物学功能。我们引入了四种自然选择的通用模型，每个模型仅由两个参数表征，易于模拟，并且是专门为微卫星设计的。使用随机森林方法来近似贝叶斯计算，我们将这些模型应用于从八个不同群体的200个人中精心挑选的微卫星基因型。总共，我们为43个我们归类为选择目标的微卫星重建了详细的适应性景观。微卫星适应性表面多种多样，包括一系列选择强度、显性作用的贡献以及最佳等位基因数量和大小的变化。受到选择的微卫星包括已知会导致三核苷酸扩增疾病和调节基因表达的位点，以及没有明显功能的基因间位点。我们报告的适应性景观的异质性表明，像用于评估针对单核苷酸变异的选择的那些全基因组规模分析，存在过度简化微卫星进化动态的风险。此外，我们的适应性景观为微卫星所经历的选择动态提供了有价值的可视化。
