GWAS and Polygenic Risk Score

Data Science Training Program

Michela Traglia

Since the human reference genome release, genome wide association studies (GWAS) have been performed to quantify the statistical association between common genetic variation and disease outcomes, quantitative traits, and risk factors for diseases. Thousands of single nucleotide polymorphisms (SNPs) associated with hundreds of risk factors and diseases have been published, but the biological mechanisms underlying most of the diseases are still unknown.

Are the identified variants actually causing the disease? What is the impact of the environment vs genetics in risk for diseases? What is the biological function of the variants mapping to the non-coding regions of the genome? Is it possible to predict the individual population-wide disease risk?

These are some of the challenges of the post-GWAS era that we will discuss during this 2-day workshop. The sessions will alternate foundational concepts of genomics and population genetics and practical analyses using genetic tools to perform genome-wide linear and logistic models, and to visualize the results using a Manhattan and quantile-quantile plots using online resources and R.

This is an introductory workshop in the Computational Genomics series. No prior experience or prerequisites are required. Understanding of simple statistical concepts and basics of R is required.