Abstract：
vonHoldt et al. ((2024), Molecular Ecology, 33, e17231) (vH24) used low-coverage (average ~ 7X read depth) restriction site-associated DNA sequence data to estimate individual inbreeding and heterozygosity, and recent effective population size (Ne), in Great Lakes (GL) and Northern Rocky Mountain (RM) wolves. They concluded that RM heterozygosity rapidly declined between 1991 and 2020, and that Ne declined substantially in GL and RM over the last 50 generations. Here, we evaluate the effects of low sequence coverage and sampling strategy on vH24\'s findings and provide general recommendations for using sequence data to evaluate inbreeding, heterozygosity and Ne. Low-coverage sequencing resulted in downwardly biased estimates of individual inbreeding and heterozygosity, and an erroneous large temporal decline in RM heterozygosity due to declining read depth through time. Additionally, vH24\'s sampling strategy-which combined individuals from several genetically differentiated populations and across at least eight wolf generations-is expected to have resulted in severe downward bias in estimates of recent Ne for RM. We recommend using high-coverage sequence data ( ≥ $$ \\ge $$ 15-20X) to estimate inbreeding and heterozygosity. Carefully filtering individuals, loci and genotypes, and using genotype imputation or likelihoods can help to minimise bias when low-coverage sequence data must be used. For estimation of contemporary Ne, the marginal benefits of using more than 103-104 loci are small, so aggressive filtering of loci with low average read depth potentially can retain most individuals without sacrificing much precision. Individuals are relatively more valuable than loci because analyses of contemporary Ne should focus on roughly single-generation samples from local breeding populations.
摘要：
vonHoldt等人。((2024),分子生态学,33，e17231）（vH24）使用低覆盖率（平均〜7X读取深度）的限制性位点相关DNA序列数据来估计个体近交和杂合性，和最近有效人口规模(Ne)，大湖（GL）和北落基山（RM）狼。他们得出结论，在1991年至2020年之间，RM杂合度迅速下降，在过去的50代中，GL和RM的Ne大幅下降。这里,我们评估了低序列覆盖率和采样策略对vH24结果的影响，并提供了使用序列数据评估近交的一般建议，杂合性和Ne。低覆盖率测序导致对个体近亲繁殖和杂合性的向下偏倚估计，以及由于随时间的读取深度下降，RM杂合性出现了错误的短暂下降。此外,vH24的采样策略-将来自多个遗传分化种群和至少八代狼的个体组合在一起-预计将导致最近对RM的Ne的估计严重下降。我们建议使用高覆盖率序列数据（≥$$\\ge$15-20X）来估计近交和杂合度。仔细过滤个人，基因座和基因型，当必须使用低覆盖率序列数据时，使用基因型估算或可能性可以帮助最小化偏差。对于当代Ne的估计，使用超过103-104个基因座的边际收益很小，因此，对具有低平均读取深度的基因座进行积极过滤可能会保留大多数个体，而不会牺牲很多精度。个人相对比基因座更有价值，因为对当代Ne的分析应集中在来自当地育种种群的大致单代样本上。
