Overview
Background
Dr Loic Yengo is a Professor of Statistical Genomics at The University of Queensland (UQ) and Group Leader of the Statistical Genomics Laboratory within UQ’s Institute for Molecular Bioscience. He was awarded a prestigious Snow Medical Research Fellowship in 2024 to dramatically advance the use of genomics to prevent chronic disease such as type 2 diabetes, heart disease and Alzheimer’s, with a particular focus on increasing participation of people with diverse ancestries. After completing a PhD in applied mathematics and statistics at the University of Lille (France) in 2014, he joined UQ in 2016 for postdoctoral training in Quantitative and Statistical Genetics. Loic started his own lab in 2020 to investigate the causes and consequences of genetic variation within and between human populations. His group develops and applies novel statistical methods to analyse large volumes of genomic data. Loic’s research has contributed to improving understanding of the genetic and phenotypic consequences of non-random mating (inbreeding and assortative mating) in human populations and has led to identifying novel genetic variants associated with complex traits and diseases. Loic was named among the top 40 rising stars of research by The Australian newspaper in 2021 and received the UQ Foundation research excellence award the same year. Loic is the 2022 recipient of the Ruth Stephens Gani Medal of the Australian Academy of Science recognizing outstanding contributions to research in human genetics, and was named in Nature Medicine’s 2022 Yearbook among 11 early-career researchers “to watch”.
In 2024, he was the recipient of the American Society of Human Genetics Early Career Award and a Snow Medical Research Foundation Fellowship to accelerate the deployment of genomic risk prediction in the clinic and improve the benefit of genomic medicine in all populations.
Availability
- Professor Loic Yengo is:
- Available for supervision
Qualifications
- Doctor of Philosophy, Université Lille 1 - Sciences et Technologies
Research impacts
The research in the Yengo Lab contributes to the discovery of genes and biological pathways involved in the etiology of multifactorial diseases such as obesity and type 2 diabetes. The ultimate goal of our research is to better understand what genes underlie inter-individual variation in disease susceptibility and help translate that knowledge into new and personalised therapies.
Works
Search Professor Loic Yengo’s works on UQ eSpace
2010
Conference Publication
Combined global sensitivity analysis and population PBPK modeling for assessing consistency of TCDD toxicokinetics data in mice
Yengo, Loïc, Brochot, Céline and Péry, Alexandre (2010). Combined global sensitivity analysis and population PBPK modeling for assessing consistency of TCDD toxicokinetics data in mice. doi: 10.1016/j.sbspro.2010.05.220
Funding
Current funding
Past funding
Supervision
Availability
- Professor Loic Yengo is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
Available projects
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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
The team PCTG: http://cnsgenomics.com/
PhD advisor: https://scholar.google.fr/citations?user=iv8dxlIAAAAJ&hl=en
Supervision history
Current supervision
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Doctor Philosophy
Investigating the time and tissue dependent genetic architecture of complex traits
Principal Advisor
Other advisors: Dr Nicole Warrington
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Doctor Philosophy
Genomic Selection for Bull Fertility Traits
Associate Advisor
Other advisors: Associate Professor Marina Fortes
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Doctor Philosophy
New approaches to quantify the genetic cause of disease
Associate Advisor
Other advisors: Professor Nathan Palpant
Completed supervision
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2024
Doctor Philosophy
Genomic signature of non-random mating in human complex traits
Principal Advisor
Other advisors: Professor Peter Visscher
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2021
Doctor Philosophy
Within and Across Populations Complex Traits and Diseases Prediction Using Summary Statistics from Large-scale Genome-wide Association Studies
Principal Advisor
Other advisors: Professor Peter Visscher
Media
Enquiries
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