Abstract
Family-based designs have been shown to be powerful in detecting the significant rare variants associated with human diseases. However, very few significant results have been found owing to relatively small sample sizes and the fact that statistical analyses often suffer from high false-negative error rates. These limitations can be avoided by combining results from multiple studies via meta-analysis. However, statistical methods for meta-analysis with rare variants are limited for family-based samples. In this report, we propose a tool for the meta-analysis of family-based rare variant associations, metaFARVAT. metaFARVAT is based on a quasi-likelihood score for each variant. These scores are combined to generate burden test, variable-threshold test, sequence kernel association test (SKAT), and optimal SKAT statistics. The proposed method tests homogeneous and heterogeneous effects of variants among different studies and can be applied to both quantitative and dichotomous phenotypes. Simulation results demonstrated the robustness and efficiency of the proposed method in different scenarios. By applying metaFARVAT to data from a family-based study and a case-control study, we identified a few promising candidate genes, including DLEC1, which is associated with chronic obstructive pulmonary disease.
Highlights
In recent decades, genome-wide association studies (GWAS) have identified tens of thousands of common variants associated with various complex diseases
This includes burden, sequence kernel association test (SKAT), and SKAT-O statistics for both dichotomous and quantitative phenotypes, and we have shown that they are robust against population substructure and outperform other existing rare variant association tests for family samples (Wang et al, 2016)
Type-1 error estimates for both dichotomous and quantitative phenotypes were calculated at various significance levels using 20,000 replicates of 200 unbalanced families
Summary
Genome-wide association studies (GWAS) have identified tens of thousands of common variants associated with various complex diseases. Mendelian transmission results in family members sharing the same alleles, and affected relatives have a greater chance of being affected by the same disease-causing single-nucleotide polymorphisms (SNPs) than unrelated subjects. Family-based analyses have been generally recognized as an important strategy for rare variant association studies. We proposed FARVAT (Choi et al, 2014) statistics based on quasi-likelihood. This includes burden, SKAT, and SKAT-O statistics for both dichotomous and quantitative phenotypes, and we have shown that they are robust against population substructure and outperform other existing rare variant association tests for family samples (Wang et al, 2016)
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