PhD; MSc; BSc (Hons)
Professor of Statistical Genetics
- Visiting Professor
The primary aim of my research has been the development and evaluation of novel statistical methodology for the analysis of genome-wide association studies (GWAS) of complex human traits. My research group has considered genetic variation from diverse ethnic groups, interrogated through traditional GWAS genotyping arrays and via whole-exome or whole-genome sequencing studies. We have focused on methods to enable discovery of novel loci through improved modelling of traits, and fine-mapping through aggregation of GWAS from multiple ethnic groups and integration with genomic annotation. The analytical tools have been implemented in user friendly software and have been widely utilised in GWAS of complex human traits. Some of our innovations include: 1. Methodology and software for the analysis of rare genetic variation. 2. Approaches for detecting association in multi-ethnic GWAS, allowing for population structure and heterogeneity. 3. Exploration of the utility of linear mixed models in the context of meta-analysis of GWAS of binary phenotypes. 4. Methodology and software for the analysis of time-to-event data in pharmacogenetic GWAS. 5.
Software to enable GWAS analysis of multiple correlated traits. In parallel, my research group have made substantial contributions to international consortia investigating the genetic basis of a wide range of complex human traits, thereby maximising the impact of our methodological development. This research has identified hundreds of loci associated with T2D (DIAGRAM and T2D-GENES), metabolic phenotypes (MAGIC and AAGILE), kidney function (COGENT-Kidney), anthropometric measures (GIANT), blood pressure (ICBP) and birth weight (EGG). These studies have provided insight into biological pathways underlying these phenotypes, enabled fine-mapping through trans-ethnic meta-analysis, and highlighted molecular mechanisms through which variants underlying association signals exert their effects on disease. As a consequence, my research group have attained joint first and senior authorship in high-impact publications, including in Nature, Nature Genetics, the American Journal of Human Genetics, and PLoS Genetics.
A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure.
Sung YJ. et al, (2018), Am J Hum Genet, 102, 375 - 400
Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.
Flannick J. et al, (2018), Sci Data, 5
Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.
Jun G. et al, (2018), Proc Natl Acad Sci U S A, 115, 379 - 384
Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.
Turcot V. et al, (2018), Nat Genet, 50, 26 - 41
Exome-wide association study of plasma lipids in >300,000 individuals.
Liu DJ. et al, (2017), Nat Genet, 49, 1758 - 1766