Format

Send to

Choose Destination
Nat Genet. 2014 Apr;46(4):409-15. doi: 10.1038/ng.2924. Epub 2014 Mar 16.

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

Author information

1
1] Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. [2] Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA. [3].
2
1] Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA. [2].
3
Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.
4
Department of Ophthalmology, University of Pennsylvania Medical School, Philadelphia, Pennsylvania, USA.
5
Division of Epidemiology and Clinical Research, National Eye Institute, Bethesda, Maryland, USA.
6
Department of Ophthalmology, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan, USA.
7
Genome Institute, Washington University School of Medicine, St. Louis, Missouri, USA.
8
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.
9
Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, Bethesda, Maryland, USA.
10
1] Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA. [2] Department of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan, USA.

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.

PMID:
24633160
PMCID:
PMC4084909
DOI:
10.1038/ng.2924
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center