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Tests of Association for Quantitative Traits in Nuclear Families Using Principal Components to Correct for Population Stratification
Author(s) -
Zhang Lei,
Li Jian,
Pei YuFang,
Liu Yongjun,
Deng HongWen
Publication year - 2009
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2009.00539.x
Subject(s) - population stratification , principal component analysis , transmission disequilibrium test , population , nuclear family , genetic association , statistics , disequilibrium , type i and type ii errors , association (psychology) , linkage disequilibrium , association test , biology , econometrics , mathematics , genetics , demography , psychology , medicine , allele , haplotype , single nucleotide polymorphism , genotype , sociology , gene , anthropology , ophthalmology , psychotherapist
SUMMARY Traditional transmission disequilibrium test (TDT) based methods for genetic association analyses are robust to population stratification at the cost of a substantial loss of power. We here describe a novel method for family‐based association studies that corrects for population stratification with the use of an extension of principal component analysis (PCA). Specifically, we adopt PCA on unrelated parents in each family. We then infer principal components for children from those for their parents through a TDT‐like strategy. Two test statistics within the variance‐components model are proposed for association tests. Simulation results show that the proposed tests have correct type I error rates regardless of population stratification, and have greatly improved power over two popular TDT‐based methods: QTDT and FBAT. The application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility of the proposed method.