Dataset Information

Geographic population structure analysis of worldwide human populations infers their biogeographical origins.

ABSTRACT: The search for a method that utilizes biological information to predict humans' place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its accuracy with three data sets using 40,000-130,000 SNPs. GPS placed 83% of worldwide individuals in their country of origin. Applied to over 200 Sardinians villagers, GPS placed a quarter of them in their villages and most of the rest within 50 km of their villages. GPS's accuracy and power to infer the biogeography of worldwide individuals down to their country or, in some cases, village, of origin, underscores the promise of admixture-based methods for biogeography and has ramifications for genetic ancestry testing.

SUBMITTER: Elhaik E

PROVIDER: S-EPMC4007635 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Geographic population structure analysis of worldwide human populations infers their biogeographical origins.

Elhaik Eran E Tatarinova Tatiana T Chebotarev Dmitri D Piras Ignazio S IS Maria Calò Carla C De Montis Antonella A Atzori Manuela M Marini Monica M Tofanelli Sergio S Francalacci Paolo P Pagani Luca L Tyler-Smith Chris C Xue Yali Y Cucca Francesco F Schurr Theodore G TG Gaieski Jill B JB Melendez Carlalynne C Vilar Miguel G MG Owings Amanda C AC Gómez Rocío R Fujita Ricardo R Santos Fabrício R FR Comas David D Balanovsky Oleg O Balanovska Elena E Zalloua Pierre P Soodyall Himla H Pitchappan Ramasamy R Ganeshprasad Arunkumar A Hammer Michael M Matisoo-Smith Lisa L Wells R Spencer RS

Nature communications 20140429

The search for a method that utilizes biological information to predict humans' place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its ...[more]

PMID: 24781250

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Project description:Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.

Dataset Information

Geographic population structure analysis of worldwide human populations infers their biogeographical origins.

Publications

Geographic population structure analysis of worldwide human populations infers their biogeographical origins.

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