Family-based bivariate association tests for quantitative traits.
The availability of a large number of dense SNPs, high-throughput genotyping and computation methods promotes the application of family-based association tests. While most of the current family-based analyses focus only on individual traits, joint analyses of correlated traits can extract more information and potentially improve the statistical power. However, current TDT-based methods are low-powered. Here, we develop a method for tests of association for bivariate quantitative traits in families. In particular, we correct for population stratification by the use of an integration of principal component analysis and TDT. A score test statistic in the variance-components model is proposed. Extensive simulation studies indicate that the proposed method not only outperforms approaches limited to individual traits when pleiotropic effect is present, but also surpasses the power of two popular bivariate association tests termed FBAT-GEE and FBAT-PC, respectively, while correcting for population stratification. When applied to the GAW16 datasets, the proposed method successfully identifies at the genome-wide level the two SNPs that present pleiotropic effects to HDL and TG traits.
Zhang
Lei
L
Key Laboratory of Biomedical Information Engineering, Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
Bonham
Aaron J
AJ
Li
Jian
J
Pei
Yu-Fang
YF
Chen
Jie
J
Papasian
Christopher J
CJ
Deng
Hong-Wen
HW
D003198:Computer Simulation; D003226:Congresses as Topic; D030541:Databases, Genetic; D005190:Family; D020022:Genetic Predisposition to Disease; D005828:Genetics, Population; D006801:Humans; D015810:Linkage Disequilibrium / Q000235:genetics; D010290:Parents; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D040641:Quantitative Trait Loci / Q000235:genetics*; D012107:Research Design