Graduate Group in Biostatistics, Department of Statistics, University of California at Davis, Davis, CA 95616, USA.
Department of Evolution and Ecology, University of California at Davis, Davis, CA 95616, USA.
Genetics. 2023 Apr 6;223(4). doi: 10.1093/genetics/iyad011.
The identification of individuals that have a recent hybrid ancestry (between populations or species) has been a goal of naturalists for centuries. Since the 1960s, codominant genetic markers have been used with statistical and computational methods to identify F1 hybrids and backcrosses. Existing hybrid inference methods assume that alleles at different loci undergo independent assortment (are unlinked or in population linkage equilibrium). Genomic datasets include thousands of markers that are located on the same chromosome and are in population linkage disequilibrium which violate this assumption. Existing methods may therefore be viewed as composite likelihoods when applied to genomic datasets and their performance in identifying hybrid ancestry (which is a model-choice problem) is unknown. Here, we develop a new program Mongrail that implements a full-likelihood Bayesian hybrid inference method that explicitly models linkage and recombination, generating the posterior probability of different F1 or F2 hybrid, or backcross, genealogical classes. We use simulations to compare the statistical performance of Mongrail with that of an existing composite likelihood method (NewHybrids) and apply the method to analyze genome sequence data for hybridizing species of barred and spotted owls.
鉴定具有近期杂种血统（种群或物种之间）的个体是自然学家几个世纪以来的目标。自 20 世纪 60 年代以来，共显性遗传标记已与统计和计算方法一起用于鉴定 F1 杂种和回交。现有的杂种推断方法假设不同基因座上的等位基因经历独立分配（不连锁或处于种群连锁平衡）。基因组数据集包含数千个位于同一染色体上且处于种群连锁不平衡的标记，违反了这一假设。因此，当应用于基因组数据集时，现有方法可能被视为复合似然，并且它们在鉴定杂种血统（这是一个模型选择问题）方面的性能是未知的。在这里，我们开发了一个新程序 Mongrail，它实现了一种全似然贝叶斯杂种推断方法，该方法明确地对连锁和重组进行建模，生成不同 F1 或 F2 杂种或回交谱系类别的后验概率。我们使用模拟来比较 Mongrail 与现有复合似然方法（NewHybrids）的统计性能，并将该方法应用于分析杂交种的 barred 和 spotted 猫头鹰的基因组序列数据。
