Title: A gene-based sequential burden association test

Abstract: Detecting the association between a set of variants and a phenotype of interest is the first and important step in genetic and genomic studies. Although it attracted a large amount of attention in the scientific community and several related statistical approaches have been proposed in the literature, powerful and robust statistical tests are still highly desired and yet to be developed in this area. In this study, we propose a powerful and robust association test, which combines information from each individual single nucleotide polymorphisms (SNPs) based on a set of independent burden tests. We compare the proposed approach with some popular ones through a comprehensive simulation study and real data application. Our results show that in general the new test is more powerful; the gain in detecting power can be substantial in many situations, compared to other methods.

Bio: Professor Zhongxue Chen received his Ph.D. degree from Southern Methodist University. Currrently he is an associate professor in School of Public Health, Indiana University Bloomington. His research interests are Bioinformatics, Statistical Genetics, Biostatistical Methodology, Machine Learning, and Collaborative Research.