A Powerful Variant-set Association Test Based on Chi-square Distribution
Dr. Zhongxue Chen
Associate Professor, Indian university Bloomington
About the Speaker
Dr. Chen received his PhD in Statistics from Southern Methodist University in 2007. He is currently a tenured associate professor of biostatistics at Indian university Bloomington. His research interests include statistical methodological developments in bioinformatics, statistical genetics, and biostatistics.
Detecting the association between a set of variants and a given phenotype has attracted a large amount of attention in the scientific community, although it is a difficult task. Recently, several related statistical approaches have been proposed in the literature; powerful statistical tests are still highly desired and yet to be developed in this area. In this talk, I will present one such powerful test that we recently developed, which combines information from each individual single nucleotide polymorphism (SNP) based on principal component analysis without relying on the eigenvalues associated with the principal components. We compare the proposed approach with some existing popular tests through a simulation study and real data applications. Our results show that in general the new test is more powerful than its competitors considered in this study; the gain in detecting power can be substantial in many situations.
All are welcome！