Annual Report 2020
Project Title: Developing Effective Bayesian Variable Selection Methods for Detecting Causal Mutations in Genome-wide Association Studies
Variations in human DNA are responsible for many common diseases, from asthma to heart disease as well as numerous forms of cancer. These variations often occur as differences in a single unit of DNA, called single-nucleotide polymorphisms or SNPs. Genome-wide association studies (GWAS) are a type of experiment developed to search the genome for SNPs that occur more frequently in people with a disease than those without, by looking at hundreds of thousands of SNPs at the same time. GWAS have already helped in identifying genetic mutations underlying diseases like diabetes, Parkinson’s disease and some cancers. However, there still are huge statistical challenges in accurately detecting SNPs underlying several complex diseases, due to their spread over the genome, interactions between SNPs and sometimes external environmental factors, and the high cost of sequencing individual genomes, leading to relatively small sample sizes.
My project will focus on developing powerful and efficient new statistical approaches for the detection of SNPs, or their combinations, associated with specific human diseases. In particular, I will construct a comprehensive Bayesian model framework for SNP detection, incorporating biological and genomic information, and develop a robust computational methodology to detect DNA variations of importance.
Awarded: Carnegie PhD Scholarship
Field: Mathematics & Statistics
University: University of Glasgow