Inference of individual admixture coefficients, which is important for population genetic and association studies, is commonly performed using compute-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 however remains a major challenge.
Here, we present a fast and efficient program for estimating individual admixture coefficients based on sparse non-negative matrix factorization and population genetics. We implemented our algorithm in the computer program sNMF. We already successfully applied sNMF to large human and plant genomic 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 admixture coefficients within run-times approximately 10 to 30 times faster than those of ADMIXTURE.
This work was supported by a grant (CIBLE-2010) from
la région Rhône-Alpes.