This module will introduce basic concepts and methods for mapping genetic variants associated with a trait or disease in the context of genome-wide association studies (GWAS). Topics covered include introduction to GWAS for quantitative and disease traits, procedures of data cleaning and quality control, methods to consider population structure and relatedness, GWAS meta-analysis, using summary statistics, fine-mapping and annotation. Computer practicals will involve the use of real genotype data and popular programs to conduct GWAS such as PLINK and GCTA.

Prerequisites: This is a foundation module aimed at new RHD students or researchers with little prior experience in the area. The module assumes introductory statistics (e.g. correlation, simple linear regression) and genetics knowledge at an undergraduate level, plus basic computer programming in R and Unix. We focus on human traits and data, however similar principles could be applied to any species.

Goal: Students will understand the requirements and limitations of GWAS and be able to perform a GWAS on a trait of interest. They will be introduced to post-GWAS analysis such as meta-analysis, fine mapping & annotation.

Primary Instructor:
Dr Kathryn Kemper
Dr Kemper is a senior post-doctoral fellow in quantitative genetics at the Institute for Molecular Bioscience (IMB) at the University of Queensland (UQ). Her research involves understanding the sources of variation in human populations for traits such as height and body mass index (BMI).

Co-instructors:
Dr Alesha Hatton
Alesha recently completed her PhD investigating the trans-ancestral genetic control of DNA methylation. She holds a Bachelor of Medical Mathematics (Hons) from the University of Wollongong and was previously employed as a statistician at the South Australian Health and Medical Research Institute.

Dr Fleur Garton
Fleur Garton is the Scott Sullivan Research Fellow with a special interest in Motor Neuron Disease. She is a molecular geneticist with experience in the analysis of genetic and genomic data. Garton will provide detailed insight into variant calling, filtering, functional and clinical annotation in a human health/disease context.

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