SMARTbiomed Workshop: Bayesian Methods for Complex Trait Genomic Analysis

This SMARTbiomed workshop introduces current Bayesian methods for genomic analysis using genome-wide association study (GWAS) data. Participants will gain a foundation in Bayesian modelling, including the principles of Bayesian inference and parameter estimation. Building on this, we will cover widely used Bayesian approaches for estimating genetic architecture, predicting polygenic risk scores, and identifying likely causal variants (genetic fine-mapping) of complex traits and diseases.

The workshop emphasizes hands-on practice with 30-60 minute practical session following lectures to consolidate learning. Practical exercises will be conducted in R or Rstudio. The workshop is designed to help participants understand Bayesian methods conceptually, interpret results effectively, and gain insights into how new Bayesian methods can be developed.

Please see key publications by the presenters:

• Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries | Nature Genetics 
• Enhanced genetic fine mapping accuracy with Bayesian Linear Regression models in diverse genetic architectures | PLOS Genetics

[REGISTRATION IS NOW CLOSED]

Prerequisites:

Participants are expected to have experience with genetic data analysis, as well as basic knowledge of linear algebra, probability distributions, and coding in R.

The course is provided free because it is sponsored bythe Pioneer Centre for SMARTbiomed.
Please be respectful and responsible and only sign up if you will attend.

Instructors:

Jian Zeng (University of Queensland, Brisbane, Australia)
Peter Sørensen (Aarhus University)
Palle D Rohde (Aalborg University)
Bjarni J Vilhjálmsson (Aarhus University)

Schedule:

8:45-9:00: Arrival 

9:00-10:00: Introduction to Bayesian linear regression, posterior inference and Markov chain Monte Carlo (MCMC)

10:00-10:30: Coffee break

10:30-11:15 : Bayesian estimation of genetic architecture for complex traits

11:15-12:15: Practical exercise: estimating SNP-based heritability, polygenicity and selection signature using SBayesS and LDpred2-auto

12:15-13:00: Lunch  (provided)

13:00-13:45: Bayesian prediction of polygenic risk scores for common diseases

13:45-14:15: Bayesian approaches for genetic fine-mapping

14:15-15:00: Practical exercise: polygenic prediction and fine-mapping using SBayesRC

15:00-15:30: Coffee break

15:30-16:15: Bayesian gene-set analyses

16:15-16:45: Practical exercise: Bayesian gene-set analyses

16:45-17:00: Discussion