Inferring the ancestry of parents and grandparents from genetic data

• Published in: PLoS computational biology 2020-08, Vol.16 (8), p.e1008065-e1008065
• Main Authors: Pei, Jingwen, Zhang, Yiming, Nielsen, Rasmus, Wu, Yufeng
• Format: Article
• Language: English
• Subjects: Algorithms; Biology and Life Sciences; Computer and Information Sciences; Computer science; Computer simulation; Computers; Engineering and Technology; Families & family life; Family relations; Genealogy; Genetic aspects; Genetics; Genomes; Genotype & phenotype; Grandparents; Haplotypes; Markov analysis; Markov chains; Markov processes; Methods; Parents & parenting; Physical sciences; Population; Population genetics; Probabilistic inference; Research and Analysis Methods; Software; Statistical analysis; Statistical inference
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008065

Inference of admixture proportions is a classical statistical problem in population genetics. Standard methods implicitly assume that both parents of an individual have the same admixture fraction. However, this is rarely the case in real data. In this paper we show that the distribution of admixture tract lengths in a genome contains information about the admixture proportions of the ancestors of an individual. We develop a Hidden Markov Model (HMM) framework for estimating the admixture proportions of the immediate ancestors of an individual, i.e. a type of decomposition of an individual's admixture proportions into further subsets of ancestral proportions in the ancestors. Based on a genealogical model for admixture tracts, we develop an efficient algorithm for computing the sampling probability of the genome from a single individual, as a function of the admixture proportions of the ancestors of this individual. This allows us to perform probabilistic inference of admixture proportions of ancestors only using the genome of an extant individual. We perform extensive simulations to quantify the error in the estimation of ancestral admixture proportions under various conditions. To illustrate the utility of the method, we apply it to real genetic data.