Empirical likelihood-based robust tests for genetic association analysis with quantitative traits

Genome-wide association studies (GWAS) are effective in investigating the loci related with complex diseases. For most of these studies, the genetic inheritance model is not known in advance and therefore robust tests are preferred. Empirical likelihood (EL) method is well known for its flexibility and nonparametric properties, but is rarely investigated in GWAS. In this study, we develop EL-based test statistics to detect the association of a disease and genetic loci while the genetic model is unknown. The performance of proposed tests is evaluated by simulations and compared with several existing methods. For illustration, we apply these tests to identify the single nucleotide polymorphisms associated with alkaline phosphatase level on mouse chromosome 6.