Novel statistical methods for genome-wide association summary statistics
At the moment we live in an era where it is easier to generate data than to interpret them. The focus of Zheng Ning’s thesis is on methodology and analysis to exploit novel biological knowledge from published results of genome-wide association study (GWAS).
GWAS is the most widely used strategy to detect the associations between genetic variants and a complex trait. In most cases, large samples are needed in GWAS. Unfortunately, given the restrictions on sharing individual-level data, it is often not feasible to pool data from different cohorts. Despite that, in each cohort, it is possible to report and share GWAS results (summary statistics). Zheng's objective was to develop novel statistical genetics methods based on GWAS summary statistics and to apply these methods to better understand the genetic architecture underlying complex traits.
On Sep 11, Zheng will defend his thesis “Novel statistical methods for genome-wide association summary statistics”. His opponent is Professor Jian Yang from the Institute for Molecular Bioscience at the University of Queensland. Zheng’s supervisors are Xia Shen and Yudi Pawitan.
Location: Lecture hall Atrium, Nobels väg 12B, Karolinska Institutet, Solna
Join by Zoom: https://ki-se.zoom.us/j/63276277555
Read the thesis in KI's open archive.