LINADMIX: evaluating the effect of ancient admixture events on modern populations

Author:

Agranat-Tamir Lily12,Waldman Shamam3,Rosen Naomi1,Yakir Benjamin2,Carmi Shai3,Carmel Liran1ORCID

Affiliation:

1. Department of Genetics, The Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 9112102, Israel

2. Department of Statistics and Data Science, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel

3. Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem 9112102, Israel

Abstract

Abstract Motivation The rise in the number of genotyped ancient individuals provides an opportunity to estimate population admixture models for many populations. However, in models describing modern populations as mixtures of ancient ones, it is typically difficult to estimate the model mixing coefficients and to evaluate its fit to the data. Results We present LINADMIX, designed to tackle this problem by solving a constrained linear model when both the ancient and the modern genotypes are represented in a low-dimensional space. LINADMIX estimates the mixing coefficients and their standard errors, and computes a P-value for testing the model fit to the data. We quantified the performance of LINADMIX using an extensive set of simulated studies. We show that LINADMIX can accurately estimate admixture coefficients, and is robust to factors such as population size, genetic drift, proportion of missing data and various types of model misspecification. Availability and implementation LINADMIX is available as a python code at https://github.com/swidler/linadmix. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Israel Science Foundation

ISF

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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