Module 5: Statistical Genomics 3 – Polygenic Prediction
This module covers critical concepts and statistical methods for polygenic prediction of complex traits and diseases. We will learn about the utility and evaluation of polygenic risk scores (PRS), the factors affecting prediction accuracy, the methodology to construct PRS using individual-level data or GWAS summary statistics, data quality controls, and challenges and pitfalls in the application. Additionally, participants will gain insights into Bayesian methods, a methodology widely used in the area, and we will introduce our in-house pipeline for polygenic prediction analysis. In practical sessions, we will use R, HPC, and command line software, including PLINK, GCTA and GCTB.
Prerequisites:
- Recommends attending Module 1 Genetic Mapping if unfamiliar with GWAS
- Requires basic knowledge of statistics
- Requires programming skills in R and Linux
Goal:
- Acquire the skills to compute polygenic risk scores for the trait of interest from scratch
- Develop a general understanding of the complex statistical methods involved in polygenic prediction
Primary instructor:
Dr Jian Zeng
Dr Jian Zeng is a statistical geneticist and NHMRC Emerging Leadership Fellow at the Institute for Molecular Bioscience (IMB) at the University of Queensland (UQ). His research focuses on the development and application of innovative statistical methods for estimating the genetic architecture of complex traits, mapping trait-associated genetic variants and genes, and predicting trait phenotypes using genomic data.