Abstract
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.
Highlights
Recent technological advances in genotyping along with the capacity to detect increasingly large numbers of single nucleotide polymorphisms (SNPs) have created great demand for developing new strategies to identify genes that underlie phenotypic variation
We have presented a bivariate test of association for quantitative traits in families, by the use of the multivariate variance-components model
The proposed method uses principal component analysis to correct for population stratification
Summary
Recent technological advances in genotyping along with the capacity to detect increasingly large numbers of single nucleotide polymorphisms (SNPs) have created great demand for developing new strategies to identify genes that underlie phenotypic variation. The availability of high-throughput SNP genotype data is prompting the development of genetic association analyses, including family based association tests (FBAT). While most of the current analyses focus only on traits individually, explicitly modeling genetic and environmental correlations among traits can theoretically extract more information and provide a greater power of test. It has been shown that joint analyses of two correlated traits may substantially improve power for localizing genes that jointly influence complex traits, and for evaluating their effects [2,3,4,5,6,7]. Therein, Lange et al [10] proposed a multivariate generalized estimating equations (GEEs) based method, termed FBAT-GEE. Lange et al [9] proposed a generalized principal component analysis (PCA), termed FBATPC, which is more powerful than FBAT-GEE
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