August 05, 2009

ADMIXTURE, a new program for model-based estimation of ancestry in unrelated individuals (Alexander et al. 2009)

The improvement in speed is very important, as STRUCTURE runs very slowly for large number of markers/individuals. The authors write:
We are thus hesitant to make a definitive statement regarding the speed of admixture versus structure. Let us simply state that in the experiments we have run on a 2.8GHz Intel Xeon computer on datasets with around 1,000 individuals and 10,000 markers, we have found that point estimation with admixture typically took on the order of minutes, while point estimation with structure took on the order of hours.
The paper includes free supplementary material (pdf).

UPDATE (Aug 9): Link to ADMIXTURE software.

Genome Research doi:10.1101/gr.094052.109

Fast model-based estimation of ancestry in unrelated individuals

David H. Alexander et al.


Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.


No comments: