A robust association test leveraging unknown genetic interactions: Application to cystic fibrosis lung disease

Abstract

AbstractFor complex traits such as lung disease in Cystic Fibrosis (CF), Gene x Gene or Gene x Environment interactions can impact disease severity but these remain largely unknown. Unaccounted-for genetic interactions introduce a distributional shift in the quantitative trait across the genotypic groups. Joint location and scale tests, or full distributional differences across genotype groups can account for unknown genetic interactions and increase power for gene identification compared with the conventional association test. Here we propose a new joint location and scale test (JLS), a quantile regression-basd JLS (qJLS), that addresses previous limitations. Specifically, qJLS is free of distributional assumptions, thus applies to non-Gaussian traits; is as powerful as the existing JLS tests under Gaussian traits; and is computationally efficient for genome-wide association studies (GWAS). Our simulation studies, which model unknown genetic interactions, demonstrate that qJLS is robust to skewed and heavy-tailed error distributions and is as powerful as other JLS tests in the literature under normality. Without any unknown genetic interaction, qJLS shows a large increase in power with non-Gaussian traits over conventional association tests and is slightly less powerful under normality. We apply the qJLS method to the Canadian CF Gene Modifier Study (n=1,997) and identified a genome-wide significant variant, rs9513900 on chromosome 13, that had not previously been reported to contribute to CF lung disease. qJLS provides a powerful alternative to conventional genetic association tests, where interactions my contribute to a quantitative trait.Author summaryCystic fibrosis (CF) is a genetic disorder caused by loss-of-function variants in CF transmembrane conductance regulator (CFTR) gene, leading to disease in several organs and notably the lungs. Even among those who share identical CF causing variants, their lung disease severity is variable, which is presumed to be caused in part by other genes besidesCFTRreferred to as modifier genes. Several genome-wide association studies of CF lung disease have identified associated loci but these account for only a small fraction of the total CF lung disease heritability. This may be due to other environmental factors such as infections, smoke exposure, socioeconomic status, treatment of lung diseases or a numerous other unknown or unmeasured factors that may interact with modifier genes. A class of new statistical methods can leverage these unknown interactions to better detect putative genetic loci. We provide a comprehensive simulation study that incorporates unknown interactions and we show that these statistical methods perform better than conventional approaches at identifying contributing genetic loci when the assumptions for these approaches are met. We then develop an approach that is robust to the typical normal assumptions, provide software for implementation and we apply it to the Canadian CF Gene Modifier Study to identify novel variants contributing to CF lung disease.