Region-based association analysis of human quantitative traits in related individuals

Abstract

Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.

Figure 1. Type I errors for three trait transformations of three GAW17 phenotypes.

Different modes of weight function are marked as w1, w2 and w3 corresponding to the parameters of beta function equal to (0.5, 0.5), (1, 1) and (1, 25). Error bars indicate the standard errors.

Figure 2. Power for three trait transformations of two GAW17 phenotypes.

See legend in Fig. 1 for coding of weight function modes. Error bars indicate the standard errors.

Figure 3. The nominal power plotted against the empirical power for three trait transformations.

Each set of six points of the same colour represents the power values for two GAW17 phenotypes (Q1 and Q2) under three different weight function modes. The diagonal line indicates one-to-one correspondence.

Figure 4. Type I errors for four trait transformations of six human phenotypes.

BMI: body mass index; HDL, LDL: high- and low-density lipoprotein cholesterol serum levels; TC: total cholesterol; TG: triglycerides. Different modes of weight function are marked as w1, w2 and w3 corresponding to the parameters of beta function equal to (0.5, 0.5), (1, 1) and (1, 25). Error bars indicate the standard errors.