Open Access
The impact of a fine-scale population stratification on rare variant association test results
Author(s) -
Elodie Persyn,
Richard Redon,
Lise Bellanger,
Christian Dina
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0207677
Subject(s) - population stratification , confounding , population , stratification (seeds) , type i and type ii errors , principal component analysis , statistics , genetic association , econometrics , statistical power , biology , demography , evolutionary biology , genetics , mathematics , single nucleotide polymorphism , genotype , seed dormancy , botany , germination , dormancy , sociology , gene
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.