This module covers state of the art methods for causal inference that use genetic data. In Part One, we will learn about Mendelian randomization (MR), an epidemiological method which uses genetic data to investigate potential causal relationships between variables in observational studies. In Part Two we will learn about structural equation modelling (SEM), an exceptionally flexible statistical method which can be used to fit pretty much any linear model (no matter how complicated), including those involving genetic data, causal relationships, feedback loops, and latent variables. Conceptual topics that will be covered include instrumental variables analysis, directed acyclic graphs and path analysis. Statistical methods that will be covered include Mendelian randomization, structural equation modelling, LD Score regression and genomic structural equation modelling. We will predominantly use the R statistics package to fit the statistical models described in this course.

Prerequisites:
• General understanding of basic statistics concepts, such as variance, covariance, and correlation.
• General understanding of genetic variants, such as single nucleotide polymorphisms (SNPs).
• Computer language R.

Goal:
• Students will have a general understanding of structural equation modelling (SEM) and how to use SEM to analyse complex relationships between variables.
• Students will have a general understanding of Mendelian randomization, including Mendal's laws and the core assumptions, and the ability to design and perform a Mendelian randomization analysis.

Primary instructor:
Dr Daniel Hwang
Dr Daniel Hwang is a Research Fellow at the Institute for Molecular Bioscience at The University of Queensland. He develops and applies statistical methods to large-scale high-dimensional data to understand the genetic and molecular mechanisms underlying human complex traits and to distinguish causal relationships from observational associations. His research interests lie in human perception of taste and smell, dietary behaviours, and their related disorders, such as the loss of smell in COVID-19.

Co-instructors:
Dr Gunn-Helen Moen
Dr Gunn-Helen Moen was awarded her PhD on the "Genetic and environmental etiology of glucose metabolism and cardiometabolic traits during pregnancy and in later life" in 2019 from the Faculty of Medicine at the University of Oslo. After finishing her PhD she was awarded a Mobility/Marie Sklodowska-Curie Fellowship from the Research Council of Norway and as part of that fellowship spent two years as a visiting academic at the University of Queensland. She is currently an ARC DECRA fellow at IMB. Her research focus is on using Mendelian randomization to investigate intergenerational effects.

Dr Christopher Flatley
Dr Flatley is a genetic and clinical epidemiologist at IMB. After completing his PhD in 2018 at the University of Queensland he undertook a post-doctoral position at the Folkehelseinstituttet in Norway to co-lead author on the first GWAS on placental weight. His research focuses on perinatal and early life growth genetics and their influence on cardio-metabolic outcomes.

Dr Geng Wang
Dr Geng Wang is a research fellow at the Institute for Molecular Bioscience at the University of Queensland. His current research interests include the developmental origins of health and diseases, the application of biometrical genetics approaches to large-scale datasets, and the refinement of statistical genetics methodologies.

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