Centre for Population and Disease Genomics

ALS variant interpretation

Generation of micro organospheres for patient-derived drug screening assays

Genetic Insights into Reproductive Health: Unravelling the Causal Links Between age at Menarche, BMI, Height, and Pregnancy Outcomes

Supervisors: Dr Christopher Flatley (c.flatley@uq.edu.au)

Menarche, the onset of a woman's reproductive capacity, typically occurs between the ages of 10 and 16 years. This pivotal stage, marked by the onset of menstruation, signifies a period of profound biological development that has lasting implications for later-life health outcomes. Studies suggest an earlier onset of menarche is associated with adverse pregnancy outcomes, including preterm birth and low birth weight. However, unravelling the intricate causal relationships between menarche and pregnancy outcomes remains a challenge, partly due to the complex relationships with body size. For example, there are established biological mechanisms linking earlier age at menarche with higher adult BMI and shorter stature. Additionally, both elevated BMI and shorter stature independently contribute to the adverse pregnancy outcomes. Using a genetic statistical method called Mendelian Randomization, this project will investigate the causal relationships between age of menarche, BMI, height and pregnancy outcomes. Insights gained from this research have the potential to inform public health initiatives and healthcare policies, ultimately fostering a better understanding of the factors influencing pregnancy complications.

Grey-matter atrophy in Alzheimer's disease

Supervisors: Dr Baptiste Couvy-Duchesne (b.couvy-duchesne@imb.uq.edu.au)

One project will involve the analysis of a large neuroimaging cohort, which contains thousands of elderly individuals imaged using MRI. Our lab develops statistical methods for the analysis of fine-grained brain images, which will be applied to analyse this cohort. Our approach allows refining the traditional neuroimaging analyses, that have been performed at a region of interest level, which can miss some of the fine-grained brain variation. The results will contribute to our current large-scale initiative that combines results from all continents, to create a high-resolution map of the brain regions associated with Alzheimer’s disease as well as with specific functional and cognitive domains. In particular, the student will perform neuroimaging analyses of one or several memory domains available in the cohort (e.g. WAIS-R Digit Symbol, WAIS-R Digit Span, WMS-R Logical Memory, Boston Naming Test). We expect the fine-grained brain map to refine our current understanding of the grey-matter regions associated with cognition domains.
The methods and software we use are shared with the field of genetics/genomics, meaning the student will acquire highly transferrable skills and knowledge. In addition, the dynamic environment of the program in complex trait genomics (PCTG), will support the student to to enrich their knowledge in the fast-paced / rapidly-evolving field of computational neuroimaging and human genetics.

We encourage applicants with scientific background, but most importantly with a strong inclination for problem solving and computational work.

Hypergraph-based integration of multi-omics data to prioritise candidate genes for drug repurposing

Supervisors: Dr Gagendeep Singh (g.singh@imb.uq.edu.au); Dr Sonia Shah (sonia.shah@imb.uq.edu.au)

Experimental discovery of a new drugs is time consuming and expensive process. However, several recent studies stated that drug repurposing which aims to identify novel indications from already existing drugs would be helpful with less risk and cost. Although, several methods are available for drug repurposing based on expression profiles. But selection of potential candidate genes for drug repurposing based on network approaches is still lacking. Thus, in this study, we aim to explore disease associated risk genes based on networks-based approaches such as gene-regulatory networks, drug-target interactions, drug-drug interactions, and pathway-based interactions using computational means. It is crucial to understand the cross-talks among the interacting genes regulating several pathways which ultimately results in side effects after drug dosages. This systemic analysis might be helpful in selection of key candidate genes for drug repurposing to deal with genetic associated diseases effectively.

Imputation of neuropsychological scores across multi-cohort data using machine learning

Supervisors: Dr Baptiste Couvy-Duchesne (b.couvy-duchesne@imb.uq.edu.au)

This project will focus on imputing neuropsychological scores using multi-cohort data. We have gathered 10+ neuroimaging cohorts of elderly individuals and each cohort has collected a (sub)set of neuropsychological batteries. Some scales have been very often collected (e.g. MMSE), and some have been only collected in a handful of cohorts (e.g. MoCA, or Boston Naming Test). A recent article from collaborators in Newcastle (https://doi.org/10.1002/dad2.12453), has shown that it is possible to impute some of the missing cognitive scores, by leveraging information nad items from the collected scores. Such imputation would be highly beneficial to boost power of downstream neuroimaging analyses. The student will perform and evaluate the neuropsychological score imputation, on some of our available cohorts. The project would include application of machine learning techniques, and interpretation of the prediction algorithms (i.e. which items are used in imputation). Beyond an efficient imputation, validation of the prediction algorithms based on the theory of cognitive processes, would increase confidence in the imputation process.

Increasing drug success rate in human clinical trials using genomics

Supervisors: Dr Sonia Shah (sonia.shah@imb.uq.edu.au)

Around 90% of drug candidates fail in human clinical trials largely due to lack of efficacy or safety concerns. This partly reflects the limitations of using in vitro and animal studies to predict the effect of compounds in humans. Recent studies highlight that drug targets backed by evidence from human genetic studies are 2 times more likely to make it to market. Human genetic data can also identify potential adverse side effects. Such information prior to embarking on human clinical trials could improve the success rate of a compound in human clinical trials and help avoid adverse outcomes for participants.

This project will use statistical genomics analyses using publicly available human genomic data to predict efficacy as well as any safety concerns of compounds that are currently in the drug development pipeline.,

Skills: Familiarity with computational analyses (e.g using R or python etc) is needed for this project

Project significance: Findings from this project could potentially identify new therapeutic applications for these compounds or unknown side effects, and ultimately informing future human clinical trials.

Supervisors: You will be working with a multidisciplinary team of supervisors Prof Dave Evans, Dr Sonia Shah, Prof Glenn King, Assoc/Prof Nathan Palpant

Investigating the genetic basis of left-handedness

Supervisor: Professor David Evans (d.evans1@uq.edu.au)

This project will involve latent class analysis of handedness, footedness and ocular dominance data in 10,000 children from the Avon Longitudinal Study of Parents and Children. The student will then investigate the genetic aetiology of these latent classes including how known variants for left handedness and ambidexterity relate to them.

Investigation of cell types found in patient-derived organoids

Large scale neuroimaging study of Alzheimers’ disease

Supervisors: Dr Baptiste Couvy-Duchesne (uqbcouvy@uq.edu.au)

This project will involve the analysis of a large neuroimaging cohort from the US, which contains more than 10,000 individuals imaged using MRI. Our lab develops statistical methods for the analysis of fine-grained brain images, which will be applied to analyse this cohort. The results will contribute to our current large-scale initiative that combines results from all continents, to create a high-resolution map of the brain regions associated with Alzheimer’s disease status and risk. The methods and software we use are shared with the field of genetics/genomics, meaning the student will acquire highly transferrable skills and knowledge. In addition, the dynamic environment of the program in complex trait genomics (PCTG), will support the student to to enrich theirknowledge in the fast-paced / rapidly-evolving field of neuroimaging and human genetics.

We encourage applicants with a scientific background, but most importantly with a strong inclination for problem solving and computational work. We recognise the richness of Indigenous cultures and the unique knowledge Aboriginal and Torres Strait Islander employees bring to our workplace. We welcome and encourage applications from Aboriginal and Torres Strait Islander people. We encourage applications from individuals with disabilities, culturally and linguistically diverse individuals, and individuals from the LGBTIQA+ community. If you have accessibility requirements, please note them in your application and we will endeavour to make any reasonable adjustments.

Navigating the genetic landscape of neurodegenerative disease: Evaluating Variant Prioritisation Tools for Precision Medicine

Supervisor: Dr Fleur Garton (f.garton@imb.uq.edu.au)

Amyotrophic Lateral Sclerosis is a fatal neurodegenerative condition with a complex genetic architecture. Whole genome and exome sequencing supports the identification of both common and rare variants contributing to disease. Rare variants in known ALS genes have often not been seen before and are labelled as variants of uncertain significance. As more samples are analysed this number becomes larger and prioritising the variants to follow-up is necessary. In-silico prediction tools exist for this purpose. They use empirical data to predict their likelihood to be deleterious but their sensitivity for ALS has not yet been explored.

The project will test the sensitivity known pathogenic ALS and benign ALS variants across a range of in-silico tools. We hypothesise that certain tools have better sensitivity at detecting pathogenicity, and these are the tools that the community should be used to prioritise variants of unknown significance.

This is a computational project that will require you to understand human genome nomenclature. It will involve variant annotation and analysis. You will be involved in comparing tools using a range of software tools and packages with analyses performed in R. You will use a variety of statistical methods to make conclusions. This may reveal future opportunities for variant interpretation (i.e. critical assessment of proposed oligogenic genetic architecture) and/or sensitivity testing for other conditions.

Within the dynamic environment of the program in complex trait genomics (PCTG), you will be supported and encouraged to enrich your knowledge in the fast-paced / rapidly-evolving field of human genetics.