College of Forestry, Wildlife, and Environment, Auburn University, Auburn, Alabama, United States of America.
Walker College of Business, Appalachian State University, Boone, North Carolina, United States of America.
PLoS Comput Biol. 2024 Oct 31;20(10):e1012566. doi: 10.1371/journal.pcbi.1012566. eCollection 2024 Oct.
Wild populations are increasingly threatened by human-mediated climate change and land use changes. As populations decline, the probability of inbreeding increases, along with the potential for negative effects on individual fitness. Detecting and characterizing runs of homozygosity (ROHs) is a popular strategy for assessing the extent of individual inbreeding present in a population and can also shed light on the genetic mechanisms contributing to inbreeding depression. Here, we analyze simulated and empirical datasets to demonstrate the downstream effects of program selection and long-term demographic history on ROH inference, leading to context-dependent biases in the results. Through a sensitivity analysis we evaluate how various parameter values impact ROH-calling results, highlighting its utility as a tool for parameter exploration. Our results indicate that ROH inferences are sensitive to factors such as sequencing depth and ROH length distribution, with bias direction and magnitude varying with demographic history and the programs used. Estimation biases are particularly pronounced at lower sequencing depths, potentially leading to either underestimation or overestimation of inbreeding. These results are particularly important for the management of endangered species, as underestimating inbreeding signals in the genome can substantially undermine conservation initiatives. We also found that small true ROHs can be incorrectly lumped together and called as longer ROHs, leading to erroneous inference of recent inbreeding. To address these challenges, we suggest using a combination of ROH detection tools and ROH length-specific inferences, along with sensitivity analysis, to generate robust and context-appropriate population inferences regarding inbreeding history. We outline these recommendations for ROH estimation at multiple levels of sequencing effort, which are typical of conservation genomics studies.
野生种群越来越受到人类介导的气候变化和土地利用变化的威胁。随着种群数量的减少，近亲繁殖的可能性增加，个体适应能力下降的潜在风险也随之增加。检测和描述纯合子区域（ROH）是评估群体中个体近交程度的常用策略，也可以揭示导致近交衰退的遗传机制。在这里，我们分析了模拟和实际数据集，以展示程序选择和长期人口历史对 ROH 推断的下游影响，从而导致结果存在与上下文相关的偏差。通过敏感性分析，我们评估了各种参数值如何影响 ROH 调用结果，突出了其作为参数探索工具的实用性。我们的研究结果表明，ROH 推断对测序深度和 ROH 长度分布等因素敏感，偏差的方向和程度随人口历史和使用的程序而变化。在较低的测序深度下，估计偏差尤为明显，可能导致近交程度的低估或高估。这些结果对于濒危物种的管理尤为重要，因为低估基因组中的近交信号可能会严重破坏保护计划。我们还发现，真正的小 ROH 可能会被错误地合并并称为较长的 ROH，从而导致对近期近交的错误推断。为了解决这些挑战，我们建议使用 ROH 检测工具的组合和针对 ROH 长度的特定推断，以及敏感性分析，以生成关于近交历史的稳健且适用于上下文的群体推断。我们针对多种测序水平的 ROH 估计提出了这些建议，这些建议适用于保护基因组学研究。
