Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
Am J Hum Genet. 2022 Aug 4;109(8):1405-1420. doi: 10.1016/j.ajhg.2022.06.016. Epub 2022 Jul 30.
Population genetic analyses of local ancestry tracts routinely assume that the ancestral admixture process is identical for both parents of an individual, an assumption that may be invalid when considering recent admixture. Here, we present Parental Admixture Proportion Inference (PAPI), a Bayesian tool for inferring the admixture proportions and admixture times for each parent of a single admixed individual. PAPI analyzes unphased local ancestry tracts and has two components: a binomial model that leverages genome-wide ancestry fractions to infer parental admixture proportions and a hidden Markov model (HMM) that infers admixture times from tract lengths. Crucially, the HMM accounts for unobserved within-ancestry recombination by approximating the pedigree crossover dynamics, enabling inference of parental admixture times. In simulations, we find that PAPI's admixture proportion estimates deviate from the truth by 0.047 on average, outperforming ANCESTOR and PedMix by 46.0% and 57.6%, respectively. Moreover, PAPI's admixture time estimates were strongly correlated with the truth (R=0.76) but have an average downward bias of 1.01 generations that is partly attributable to inaccuracies in local ancestry inference. As an illustration of its utility, we ran PAPI on African American genotypes from the PAGE study (N = 5,786) and found strong evidence of assortative mating by ancestry proportion: couples' ancestry proportions are highly correlated (R = 0.87) and are closer to each other than expected under random mating (p < 10). We anticipate that PAPI will be useful in studying the population dynamics of admixture and will also be of interest to individuals seeking to learn about their personal genealogies.
人群遗传分析中的局部祖源分析通常假设个体的双亲具有相同的祖先混合过程，当考虑最近的混合时，这种假设可能是无效的。在这里，我们提出了 Parental Admixture Proportion Inference（PAPI），这是一种用于推断单个混合个体的每个父母的混合比例和混合时间的贝叶斯工具。PAPI 分析未相位的局部祖源分析，有两个组成部分：一个二项式模型，利用全基因组祖先分数推断父母的混合比例，以及一个隐马尔可夫模型（HMM），该模型从轨迹长度推断混合时间。至关重要的是，HMM 通过近似系谱交叉动态来处理未观察到的内部重组，从而能够推断父母的混合时间。在模拟中，我们发现 PAPI 的混合比例估计值平均偏离真实值 0.047，分别比 ANCESTOR 和 PedMix 高出 46.0%和 57.6%。此外，PAPI 的混合时间估计值与真实值高度相关（R=0.76），但平均存在 1.01 代的向下偏差，部分原因是局部祖源推断的不准确。作为其效用的说明，我们在 PAGE 研究（N=5786）的非裔美国人基因型上运行了 PAPI，并发现了按祖源比例进行的 assortative mating 的有力证据：夫妇的祖源比例高度相关（R=0.87），彼此之间的距离比随机交配下预期的更近（p<10）。我们预计 PAPI 将有助于研究混合的人口动态，并且也将引起寻求了解其个人族谱的个体的兴趣。
