Statistical Methods to detect and correct cryptic ascertainment in Biobank data collection

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Supervisor – Dr Loic

Project: Statistical Methods to detect and correct cryptic ascertainment in Biobank data collection.

Short Description. This project aims at developing new statistical methods to detect potential unmeasured confounders for epidemiological associations and correct their effects on estimation of risk. The methods explored in this project will be based on polygenic (risk) scores for a number of complex traits and disease and will quantify their distribution under various assumptions. The project will involve advanced modelling and statistical analyses of large volumes of data (genotyped and imputed SNP data, whole-exome sequencing. This research will be implied to better identify genetic risk factors for severe forms of COVID-19.

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).

Expected start. In 2021.

The Team. The successful candidate will be doing their research within the Statistical Genomics Group (SG2) led by Dr Loic Yengo, who are internationally recognized leader in the field of complex traits genetics. SG2 provides a stimulating and highly interdisciplinary environment for PhD candidates to form and develop their research.

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

Preferred educational background

Applications will be judged on a competitive basis taking into account the applicant's previous academic record, publication record, honours and awards, and employment history.

A working knowledge of programming in low-level languages such as C or C++ would be of benefit to someone working on this project.

The applicant will demonstrate academic achievement in the field(s) of statistics, (genetic) epidemiology, and/or computer ccience and the potential for scholastic success.

A background or knowledge of quantitative genetics is highly desirable.

*The successful candidate must commence by Research Quarter 1, 2022. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

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