Fast and efficient estimation of individual ancestry coefficients.
Inference of individual ancestry coefficients, which is important for population genetic and association studies, is commonly performed using computer-intensive likelihood algorithms. With the availability of large population genomic data sets, fast versions of likelihood algorithms have attracted considerable attention. Reducing the computational burden of estimation algorithms remains, however, a major challenge. Here, we present a fast and efficient method for estimating individual ancestry coefficients based on sparse nonnegative matrix factorization algorithms. We implemented our method in the computer program sNMF and applied it to human and plant data sets. The performances of sNMF were then compared to the likelihood algorithm implemented in the computer program ADMIXTURE. Without loss of accuracy, sNMF computed estimates of ancestry coefficients with runtimes ∼10-30 times shorter than those of ADMIXTURE.
Université Joseph Fourier Grenoble 1, Centre National de la Recherche Scientifique, Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble Unité Mixte de Recherche 5525, 38042 Grenoble, France.
Eric
E
Frichot
François
F
Mathieu
Théo
T
Trouillon
Guillaume
G
Bouchard
Olivier
O
François
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D005787:Gene Frequency; D056726:Genetic Association Studies; D005828:Genetics, Population; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D010944:Plants / Q000235:genetics; D044382:Population Groups; D012984:Software; D013997:Time Factors
ancestry coefficients; inference of population structure; nonnegative matrix factorization algorithms