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

Chaolong Wang, Xiaowei Zhan, Jennifer Bragg-Gresham, Hyun Min Kang, Dwight Stambolian, Emily Y. Chew, Kari E. Branham, John Heckenlively, Robert Fulton, Richard K. Wilson, Elaine R. Mardis, Xihong Lin, Anand Swaroop, Sebastian Zöllner, Gonçalo R. Abecasis

Research output: Contribution to journalArticlepeer-review

91 Scopus citations


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.

Original languageEnglish
Pages (from-to)409-415
Number of pages7
JournalNature Genetics
Issue number4
StatePublished - Apr 2014


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