@article {3092965,
	title = {LINADMIX: evaluating the effect of ancient admixture events on modern populations},
	journal = {Bioinformatics},
	volume = {37},
	number = {24},
	year = {2021},
	pages = {4744-4755},
	abstract = {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.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.LINADMIX is available as a python code at https://github.com/swidler/linadmix.Supplementary data are available at Bioinformatics online.},
	isbn = {1367-4803},
	author = {Lily Agranat-Tamir and Waldman, Shamam and Rosen, Naomi and Yakir, Benjamin and Carmi, Shai and Liran Carmel}
}
