Inferring Geographic Coordinates of Origin for Europeans Using Small Panels of Ancestry Informative Markers

Abstract

Recent large-scale studies of European populations have demonstrated the existence of population genetic structure within Europe and the potential to accurately infer individual ancestry when information from hundreds of thousands of genetic markers is used. In fact, when genomewide genetic variation of European populations is projected down to a two-dimensional Principal Components Analysis plot, a surprising correlation with actual geographic coordinates of self-reported ancestry has been reported. This substructure can hamper the search of susceptibility genes for common complex disorders leading to spurious correlations. The identification of genetic markers that can correct for population stratification becomes therefore of paramount importance. Analyzing 1,200 individuals from 11 populations genotyped for more than 500,000 SNPs (Population Reference Sample), we present a systematic exploration of the extent to which geographic coordinates of origin within Europe can be predicted, with small panels of SNPs. Markers are selected to correlate with the top principal components of the dataset, as we have previously demonstrated. Performing thorough cross-validation experiments we show that it is indeed possible to predict individual ancestry within Europe down to a few hundred kilometers from actual individual origin, using information from carefully selected panels of 500 or 1,000 SNPs. Furthermore, we show that these panels can be used to correctly assign the HapMap Phase 3 European populations to their geographic origin. The SNPs that we propose can prove extremely useful in a variety of different settings, such as stratification correction or genetic ancestry testing, and the study of the history of European populations.

Randomized Algorithms for Matrices and Data

“…Moreover, this algorithm and obvious heuristics motivated by it have already been applied successfully to problems of interest to geneticists such as genotype reconstruction in unassayed populations, identifying substructure in heterogeneous populations, and inference of individual ancestry [21,25,10,26,27,28,29].…”

“…Moreover, in [133] it is also proven that this algorithm runs in o(mn 2 ) time-due to the structured random projection in the first step, the running time is basically the same time as that of the fast random projection algorithm described in the previous subsection. 27 In addition, given an arbitrary rank parameter k and an arbitrary-sized m×n matrix A, this algorithm can be extended to approximate the leverage scores relative to the best rank-k approximation to A in roughly O(mnk) time. See [133] for a discussion of the technical issues associated with this.…”

“…This algorithm may be viewed as post-processing the relative-error random sampling algorithm of the previous subsection to remove redundant columns; and it has been applied successfully to a range of data analysis problems. See, e.g., [26,27,28] and [155,156,157], as well as [158] for a discussion of numerical issues associated with this algorithm.…”

Cytokine Gene Polymorphisms and Alzheimer's Disease in Brazil

“…For traits that bear evidence of differential risk among populations with distinct continental backgrounds (e.g. Europeans and Africans), estimating ancestry proportions is a strategy that can help obtain knowledge about the structure of complex traits [14,15,16,17]. The Brazilian contingent is highly admixed, with roughly 60-75% of its ancestry derived from Iberian whites, 10-30% from West Africans, and 5-20% from Native Americans [18,19].…”

Genetic ancestry inference using support vector machines, and the active emergence of a unique American population

“…For example, a European American is more likely to have interest in whether or not he/she is a Scandinavian than if he/she belongs to the third objective cluster from the right. The k-nearest neighbor (k-NN) algorithm 37 identifies the k nearest neighbors of an individual (on the PC map) and infers his/her ancestry as the average geographic sampling location of the k neighbors. Attractively, k-NN also does not require a priori class definitions.…”