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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. The Goddess Sekhmet Robert Masters Pdf Merge. 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. Population stratification has long been recognized as a confounding factor in genetic association studies (;; ). To correct for the effects of population stratification, association studies may take account of individuals' ancestries in their analyses, an approach known as “structured association” ().

One simple technique is to incorporate ancestry as an additional covariate in an appropriate regression model (). Self-reported ancestries can be used for this purpose, but these are often vague or inaccurate. An alternative is to estimate ancestries from the genotypes actually collected in a study. We offer the following taxonomy of ancestry estimation tools. At the highest level, we make a distinction between estimating “global ancestry” and “local ancestry.” In the local ancestry paradigm (;;;,), we imagine that each person's genome is divided into chromosome segments of definite ancestral origin. The goal then is to find the segment boundaries and assign each segment's origin.

In the global ancestry paradigm (; ), we are concerned only with estimating the proportion of ancestry from each contributing population, considered as an average over the individual's entire genome. Here we tackle estimation of global ancestry. Under the broad heading of “global ancestry estimation,” there are two approaches: “model-based ancestry estimation” and “algorithmic ancestry estimation.” Model-based approaches, exemplified by structure (), FRAPPE (), and our program ADMIXTURE, estimate ancestry coefficients as the parameters of a statistical model. Algorithmic approaches use techniques from multivariate analysis, chiefly cluster analysis and principal component analysis (PCA), to discover structure within the data in a less parametric way. Cluster analysis directly seeks the ancestral clusters in the data, while principal component analysis constructs low-dimensional projections of the data that explain the gross variation in marker genotypes, which, in practice, is the variation between populations.