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 non-negative 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 run-times approximately 10 to 30 times shorter than those of ADMIXTURE.
- Received December 11, 2013.
- Accepted January 23, 2014.
- Copyright © 2014, The Genetics Society of America