The impact of a fine-scale population stratification on rare variant association test results

被引:29
作者
Persyn, Elodie [1 ,2 ]
Redon, Richard [1 ,3 ]
Bellanger, Lise [4 ]
Dina, Christian [1 ]
机构
[1] UNIV Nantes, Inst Thorax, CNRS, INSERM, Nantes, France
[2] Kings Coll London, Dept Med & Mol Genet, London 2, England
[3] CHU Nantes, Inst Thorax, Nantes, France
[4] Lab Math Jean Leray, Nantes, France
关键词
PRINCIPAL-COMPONENTS; COMMON DISEASES; SEQUENCING DATA; ADJUSTMENT; RISK;
D O I
10.1371/journal.pone.0207677
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Population stratification is a well-known confounding factor in both common and rare variant association analyses. Rare variants tend to be more geographically clustered than common variants, because of their more recent origin. However, it is not yet clear if population stratification at a very fine scale (neighboring administrative regions within a country) would lead to statistical bias in rare variant analyses. As the inclusion of convenience controls from external studies is indeed a common procedure, in order to increase the power to detect genetic associations, this problem is important. We studied through simulation the impact of a fine scale population structure on different rare variant association strategies, assessing type I error and power. We showed that principal component analysis (PCA) based methods of adjustment for population stratification adequately corrected type I error inflation at the largest geographical scales, but not at finest scales. We also showed in our simulations that adding controls obviously increased power, but at a considerably lower level when controls were drawn from another population.
引用
收藏
页数:17
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