Loic Yengo

Genetic and Molecular consequences of non-random mating in humans

Short Project description. This projects aims at utilising genetic and phenotypic data from ~500,000 participants of the UK Biobank to investigate phenotypic and genetic patterns induced by non-random mating in humans. Two forms of non-random mating will be investigated: assortative mating (resemblance between spouses) and inbreeding (mating between relatives). Findings from this project have implications in the analysis and interpretation of genome-wide association studies. The project will involve advanced modelling and statistical analyses of large volumes of data (genotyped and imputed SNP data, whole-exome sequencing, gene-expression, brain-imaging derived-traits).

Candidate. Candidates with a background in quantitative/population genetics, statistics, mathematics and other quantitative fields will be considered. Programming skills (R, python, C/C++) and prior experience in analysing genetic data (e.g. GWAS) is desirable. (Note: if required, lectures on fundamental concepts of quantitative and population genetics can be taken as part of the PhD training).

The Team. The successful candidate will be doing their research within the Program in Complex Traits Genomics (PCTG) Lab co-led by Professors Jian Yang, Naomi Wray and Peter Visscher, who are internationally recognized leaders in the field of complex traits genetics and have been recently listed among the world’s top one per cent most cited researchers of their field. PCTG provides a stimulating and highly interdisciplinary environment for PhD candidates to form and develop their research.

PhD advisor. Dr Loic Yengo is a senior research officer of the Institute of Molecular Bioscience at the University of Queensland, Australia; and the Statistical Genetics Team leader within PCTG. He did his PhD in applied mathematics and is an expert in statistical modelling and analysis of genetic data. His research interests intersect quantitative genetics, genetic epidemiology and sociogenomics.

Expected start. First semester of 2020.

Contact. If you’re interested, please send your CV and cover letter and two references to Dr Loic Yengo: l.yengo@imb.uq.edu.au

URLs

IMB: https://imb.uq.edu.au/

The team PCTG: http://cnsgenomics.com/

PhD advisor: https://scholar.google.fr/citations?user=iv8dxlIAAAAJ&hl=en

DNA sequence deep learning to map genome-wide genetic variants underlying complex traits and disease

Short Project description. This project aims to develop and apply new methods for identifying genetic variants that are causal for human traits and diseases. The primary approach will focus on leveraging DNA foundational models to improve the prioritisation of such variants. Training of DNA foundational models, especially when coupled with other sources of data (e.g., protein-level data), is notoriously computationally challenging. Throughout the project the successful candidate will, therefore, develop and optimise GPU parallelization and sub-network isolation to run inference across the entire genome. Beyond optimisation, the successful candidate will also develop new methods to quantify (prior to training) information content in a given dataset. This work will build nonlinear mixed models literature. Finally, the project will integrate predictions from DNA foundational models into various statistical genetics analyses such as polygenic scores and fine-mapping.

Candidate. Candidates with a background in machine learning, statistics, mathematics, ideally coupled with training in quantitative/population genetics and other quantitative fields will be considered. Programming skills (R, python, C/C++) and prior experience in analysing genetic data (e.g. GWAS) is desirable. (Note: if required, lectures on fundamental concepts of quantitative and population genetics can be taken as part of the PhD training).

The Team. The successful applicant will join the Statistical Genomics Laboratory led by Professor Yengo to conduct cutting-edge research at the intersection of data science and human genetics. The mission of the Yengo lab is to improve prevention and treatment of common disease by discovering genes and biological pathways involved in the etiology of human complex traits. The Yengo lab develops scalable analysis tools that can maximise the utility of genetics studies across all human populations. These tools are generally applied to analyse large scale biobank datasets available worldwide. This project will be a unique opportunity for an outstanding and curious mind to grow an international profile in statistical genetics.

PhD advisory team. The project will be co-supervised by Professor Loic Yengo (Snow Fellow, ARC Future Fellow and Group Leader at the Institute of Molecular Bioscience), and Dr Brad Balderson (Senior Research Associate within the Yengo Lab). The advisory team brings strong expertise in machine learning and statistical genetics applied to the analysis of large biobank and genomic datasets.

Expected start. First semester of 2027.

Contact. If you’re interested, please send your CV and cover letter and two references to Dr Brad Balderson: uqbbalde@uq.edu.au.