Waples Robin S, Do Chi
NOAA Fisheries, Northwest Fisheries Science Center Seattle, WA, USA.
Conservation Biology Division, Northwest Fisheries Science Center Seattle, WA, USA.
Evol Appl. 2010 May;3(3):244-62. doi: 10.1111/j.1752-4571.2009.00104.x. Epub 2009 Nov 24.
Genetic methods are routinely used to estimate contemporary effective population size (N e) in natural populations, but the vast majority of applications have used only the temporal (two-sample) method. We use simulated data to evaluate how highly polymorphic molecular markers affect precision and bias in the single-sample method based on linkage disequilibrium (LD). Results of this study are as follows: (1) Low-frequency alleles upwardly bias [Formula: see text], but a simple rule can reduce bias to <about 10% without sacrificing much precision. (2) With datasets routinely available today (10-20 loci with 10 alleles; 50 individuals), precise estimates can be obtained for relatively small populations (N e < 200), and small populations are not likely to be mistaken for large ones. However, it is very difficult to obtain reliable estimates for large populations. (3) With 'microsatellite' data, the LD method has greater precision than the temporal method, unless the latter is based on samples taken many generations apart. Our results indicate the LD method has widespread applicability to conservation (which typically focuses on small populations) and the study of evolutionary processes in local populations. Considerable opportunity exists to extract more information about N e in nature by wider use of single-sample estimators and by combining estimates from different methods.
遗传方法通常用于估计自然种群中当代的有效种群大小(Ne),但绝大多数应用仅使用了时间(双样本)方法。我们使用模拟数据来评估高度多态的分子标记如何影响基于连锁不平衡(LD)的单样本方法中的精度和偏差。本研究结果如下:(1)低频等位基因会使[公式:见正文]向上偏差,但一个简单规则可将偏差降低至约10%以下,且不会牺牲太多精度。(2)对于如今常规可得的数据集(10 - 20个具有10个等位基因的位点;50个个体),对于相对较小的种群(Ne < 200)能够获得精确估计,并且小种群不太可能被误判为大种群。然而,对于大种群很难获得可靠估计。(3)对于“微卫星”数据,LD方法比时间方法具有更高的精度,除非时间方法基于相隔许多代采集的样本。我们的结果表明,LD方法在保护(通常关注小种群)和当地种群进化过程研究中具有广泛的适用性。通过更广泛地使用单样本估计器以及结合不同方法的估计,存在大量机会来提取关于自然界中Ne的更多信息。