从单个基因组标记数据样本估计当前大种群的有效规模:模拟估算器的比较

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Jinliang Wang
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引用次数: 0

摘要

全基因组单核苷酸多态性(snp)数据越来越多地用于估计当前有效种群大小(N e),为濒危物种保护和指导开发物种管理提供信息。以往的兄弟姐妹频率(SF)和连锁不平衡(LD)估计集中在遗传漂变强的小群体,因此N e很容易估计。基因组单核苷酸多态性(SNP)数据提供了充足的信息,并具有将这些估计器应用于遗传漂变相当弱的大群体的潜力,因此N e难以估计。在这项研究中,我模拟了非常大的种群,并采样了大量可变的个体(基因分型为10,000个snp),以便通过SF和LD方法估计N - e。我还考虑了更现实的情况,即群体经历瓶颈,标记数据遭受基因分型错误。模拟结果表明,当样本数量足够多时,SF和LD方法都能对非常大的种群产生准确的N - e估计。然而,当n远远小于nne时,nne的估计值呈双峰分布,其中相当大比例的估计值为无限大。对于有瓶颈的种群,LD估计器分别高估和低估了在瓶颈处和瓶颈后采样的亲本种群的N e。LD估计器在应用于存在等位基因缺失和假等位基因的数据时,也会大大高估N e。相比之下,SF估计是无偏和准确的,当群体的大小变化或标记遭受基因分型错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating current effective sizes of large populations from a single sample of genomic marker data: A comparison of estimators by simulations
Abstract Genome‐wide single nucleotide polymorphisms (SNPs) data are increasingly used in estimating the current effective population sizes ( N e ) for informing the conservation of endangered species and guiding the management of exploited species. Previous assessments of sibship frequency (SF) and linkage disequilibrium (LD) estimators of N e focused on small populations where genetic drift is strong and thus N e is easy to estimate. Genomic single nucleotide polymorphism (SNP) data provide ample information and hold the potential for application of these estimators to large populations where genetic drift is rather weak and thus N e is difficult to estimate. In this study, I simulated very large populations and sampled a widely variable number of individuals (genotyped at 10,000 SNPs) for estimating N e by both SF and LD methods. I also considered the more realistic situation where a population experiences a bottleneck, and where marker data suffer from genotyping errors. The simulations show that both SF and LD methods can yield accurate N e estimates of very large populations when sampled individuals are sufficiently numerous. When n is much smaller than N e , however, N e estimates are in a bimodal distribution with a substantial proportion of the estimates being infinitely large. For a population with a bottleneck, LD estimator overestimates and underestimates the N e of the parental population from samples taken at and after the bottleneck, respectively. LD estimator also overestimates N e substantially when applied to data suffering from allelic dropouts and false alleles. In contrast, SF estimator is unbiased and accurate when populations are changing in size or markers suffer from genotyping errors.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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