Matthew Bryan
The study of genetic association is a rapidly growing area of statistical research. Due to the massive amounts of data generated by these studies, many problems arise in analyzing such data such as multiple comparisons, computational issues, and missing data. This presentation will discuss portions of an ongoing research project on missing data in genetic association studies. The goal of the project is to study genetic association methods, understand how these methods account for missing data, and assess whether these methods can be improved by further adjustment for missing data. This presentation will focus on a method suggested by Timothy Thornton and Mary McPeek that uses a Quasi-Likelihood Score Test approach. The method incorporates information from subjects that are missing phenotype and genotype information. Further discussion will look into extending these methods to better account for missingness through multiple imputation.
