Estimating individual admixture from finite reference databases.
Year: 2021
Type: preprint
Abstract: The concept of individual admixture (IA) assumes that the genome of individuals is composed of alleles inherited from $K$ ancestral populations. Each copy of each allele has the same chance $q_k$ to originate from population $k$, and together with the allele frequencies in all populations $p$ comprises the admixture model, which is the basis for software like {\sc STRUCTURE} and {\sc ADMIXTURE}. Here, we assume that $p$ is given through a finite reference database, and $q$ is estimated via maximum likelihood. Above all, we are interested in efficient estimation of $q$, and the variance of the estimator which originates from finiteness of the reference database, i.e. a variance in $p$. We provide a central limit theorem for the maximum-likelihood estimator, give simulation results, and discuss applications in forensic genetics.
Source: arXiv (Cornell University)
Authors: Peter Pfaffelhuber, Angelika Rohde
Institution: University of Freiburg
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3205617427