An efficient family-based association test using multiple markers

In genetic association studies, multiple markers are usually employed to cover a genomic region of interest for localizing a trait locus. In this report, we propose a novel multi-marker family-based association test (T-LC) that linearly combines the single-marker test statistics using data-driven weights. We examine the type-I error rate in a numerical study and compare its power to identify a common trait locus using tag single nucleotide polymorphisms (SNPs) within the same haplotype block that the trait locus resides with three competing tests including a global haplotype test (T-H), a multi-marker test similar to the Hotelling-T-2 test for the population-based data (T-MM), and a single-marker test with Bonferroni's correction for multiple testing (T-B). The type-I error rate of T-LC is well maintained in our numeric study. In all the scenarios we examined, T-LC is the most powerful, followed by T-B. T-MM and T-H are the poorest. T-H and T-MM have essentially the same power when parents are available. However, when both parents are missing, T-MM is substantially more powerful than TH. We also apply this new test on a data set from a previous association study on nicotine dependence.