Dataset Information

Ancestry estimation and control of population stratification for sequence-based association studies.

ABSTRACT: Estimating individual ancestry is important in genetic association studies where population structure leads to false positive signals, although assigning ancestry remains challenging with targeted sequence data. We propose a new method for the accurate estimation of individual genetic ancestry, based on direct analysis of off-target sequence reads, and implement our method in the publicly available LASER software. We validate the method using simulated and empirical data and show that the method can accurately infer worldwide continental ancestry when used with sequencing data sets with whole-genome shotgun coverage as low as 0.001×. For estimates of fine-scale ancestry within Europe, the method performs well with coverage of 0.1×. On an even finer scale, the method improves discrimination between exome-sequenced study participants originating from different provinces within Finland. Finally, we show that our method can be used to improve case-control matching in genetic association studies and to reduce the risk of spurious findings due to population structure.

SUBMITTER: Wang C

PROVIDER: S-EPMC4084909 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Publications

Ancestry estimation and control of population stratification for sequence-based association studies.

Wang Chaolong C Zhan Xiaowei X Bragg-Gresham Jennifer J Kang Hyun Min HM Stambolian Dwight D Chew Emily Y EY Branham Kari E KE Heckenlively John J Fulton Robert R Wilson Richard K RK Mardis Elaine R ER Lin Xihong X Swaroop Anand A Zöllner Sebastian S Abecasis Gonçalo R GR

Nature genetics 20140316 4

Estimating individual ancestry is important in genetic association studies where population structure leads to false positive signals, although assigning ancestry remains challenging with targeted sequence data. We propose a new method for the accurate estimation of individual genetic ancestry, based on direct analysis of off-target sequence reads, and implement our method in the publicly available LASER software. We validate the method using simulated and empirical data and show that the method ...[more]

PMID: 24633160

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Project description:BACKGROUND:Association studies are useful to unravel the genetic basis of common human diseases. However, the presence of undetected population structure can lead to both false positive results and failures to detect genuine associations. Even when most of the approaches to deal with population stratification require genome-wide data, the use of a well-selected panel of ancestry informative markers (AIMs) may appropriately correct for population stratification. Few panels of AIMs have been developed for Latino populations and most contain a high number of markers (>?100 AIMs). For some association studies such as candidate gene approaches, it may be unfeasible to genotype a numerous set of markers to avoid false positive results. In such cases, methods that use fewer AIMs may be appropriate. RESULTS:We validated an accurate and cost-effective panel of AIMs, for use in population stratification correction of association studies and global ancestry estimation in Mexicans, as well as in populations having large proportions of both European and Native American ancestries. Based on genome-wide data from 1953 Mexican individuals, we performed a PCA and SNP weights were calculated to select subsets of unlinked AIMs within percentiles 0.10 and 0.90, ensuring that all chromosomes were represented. Correlations between PC1 calculated using genome-wide data versus each subset of AIMs (16, 32, 48 and 64) were r2?=?0.923, 0.959, 0.972 and 0.978, respectively. When evaluating PCs performance as population stratification adjustment covariates, no correlation was found between P values obtained from uncorrected and genome-wide corrected association analyses (r2?=?0.141), highlighting that population stratification correction is compulsory for association analyses in admixed populations. In contrast, high correlations were found when adjusting for both PC1 and PC2 for either subset of AIMs (r2?>?0.900). After multiple validations, including an independent sample, we selected a minimal panel of 32 AIMs, which are highly informative of the major ancestral components of Mexican mestizos, namely European and Native American ancestries. Finally, the correlation between the global ancestry proportions calculated using genome-wide data and our panel of 32 AIMs was r2?=?0.972. CONCLUSIONS:Our panel of 32 AIMs accurately estimated global ancestry and corrected for population stratification in association studies in Mexican individuals.

Dataset Information

Ancestry estimation and control of population stratification for sequence-based association studies.

Publications

Ancestry estimation and control of population stratification for sequence-based association studies.

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