Centre for Population and Disease Genomics - Projects List

Principal Advisor: Dr Daniel Hwang (IMB)

This project requires candidates to commence no later than Research Quarter 3, 2025, which means you must apply no later than 29 February 2024.

Human perception of taste and smell plays a key role in food preferences and choices. There is a large and growing body of work suggesting that taste and smell (together known as "chemosensory perception") determine eating behaviour and dietary intake, a primary risk factor of chronic conditions such as obesity, cardiometabolic disorders, and cancer.  

However, evidence to date is largely based on observational studies that are susceptible to confounding and reverse causation, leaving the "causal effects" of chemosensory perception on food consumption unclear. If their relationship is truly causal, flavour modification may represent a tangible way of modifying food consumption in a way that benefits public health outcomes.  

This PhD project aims to: (i) elucidate the genetic architecture underlying individual differences in taste and smell perception, (ii) use this information to assess their causal effects on eating behaviour, and (iii) create a sensory-food causal network mapping individual sensory qualities (i.e. sweet taste, bitter taste, and more) to individual food items.  

The candidate will gain skills in big data analyses, computer programming, statistical method development and application (structural equation modelling, genome-wide association analysis, Mendelian randomisation), and writing and publishing scientific peer-reviewed papers. The candidate will also have opportunities to be involved and to lead national and international collaborative projects.  

Principal Advisor: Dr Allan McRae (IMB)

Associate Advisor: Prof Robert Parton (IMB)

Caveolae, small pits in the plasma membrane, are the most abundant surface subdomains of many mammalian cells.  Loss or mutation of genes involved in caveolae have shown to cause disease including lipodystrophy and pulmonary arterial hypertension.  This project will ustilise publicly available genomic data to further explore the role of genetic variation in caveole genes in disease.

Principal Advisor: Dr Allan McRae (IMB)

Associate Advisor: Dr Fleur Garton (IMB), A/Prof Robert Henderson (UQ Centre for Clinical Research)

Motor neuron disease results in the degeneration of the motor neurons leading to paralysis and death. There is limited knowledge on the underlying causes and no treatment can significantly change the fatal course of the disease. Slowing the discovery process has been the limited, clinic-based sample sizes. At least three large international biobank datasets, with matched genotype and phenotype data are now available and more are anticipated. The large sample provides a powerful opportunity to investigate this complex disease. Our group has expertise in harnessing large datasets such as the UK Biobank to answer questions about complex traits and diseases. This project will aim to integrate multiple international biobank datasets to better understand the disease and avenues for treatment.

Principal Advisor: Prof Ben Hankamer

Associate Advisor: Prof Ian Henderson

Algae cells have evolved over ~3 billion years of natural selection to yield a diverse array of highly efficient, self-assembling, light-responsive membranes. These act as Nature’s solar interfaces, via which plants tap into the power of the sun. These interfaces contain nano-machinery to drive the photosynthetic light reactions which convert light from the sun into food, fuel, and atmospheric oxygen to support life on Earth. However, microalgae can be used to produce foods/nutraceuticals, vaccines, peptide therapeutics, novel antibiotics, fuel, and bioremediation. While much successful work has been done to improve the use of algae, the genetics of the various species are not well understood. Here we will deploy a high through put genetic approach to identify essential and conditionally-essential genes in algae providing insight into the fundamental biology of these organisms. We will leverage this approach to forcibly evolve algae and improve recombinant protein production.

Principal Advisor: Associate Professor Nathan Palpant (n.palpant@uq.edu.au)

Associate Advisors: Dr Woo Jun Shim (w.shim@uq.edu.au), Dr Sonia Shah (sonia.shah@imb.uq.edu.au), Dr Bastien Llamas (University of Adelaide)

The billions of bases in the genome are shared among all cell types and tissues in the body. Understanding how regions of the genome control the diverse functions of cells is fundamental to understanding evolution, development, and disease. We recently identified approaches to define diverse biologically constrained regions of the genome that appear to control very specific cellular functions. This project will evaluate how these biologically constrained regions of the genome have influenced evolutionary processes, evaluate their regulatory basis in controlling the identity and function of cells, and analyse the promiscuity of cross-talk between different biologically constrained regions. The project will also study how these genomic regions impact disease mechanisms by evaluating how disease-associated variants in different regions influence survival of patients with cancer and assessing whether these regions are associated with identifying causal disease variants in human complex trait data. The project will involve significant collaborative work with industry partners and researchers across Australia with the goal of providing critical insights into fundamental mechanisms of genome regulation.

Principal Advisor: Prof David Evans (IMB)

This project requires candidates to commence no later than Research Quarter 1, 2024, which means you must apply no later than 30 September, 2023.

There is a well-known mantra that correlation does not necessarily equal causation. This is why randomized controlled trials in which participants are physically randomized into treatment and placebo groups are the gold standard for assessing causality in epidemiological investigations. However, what is less appreciated is that strong evidence for causality can sometimes be obtained using observational data only. In particular, genotypes are randomly transmitted from parents to their offspring independent of the environment and other confounding factors, meaning that genotypes associated with particular traits can be used like natural “randomized controlled trials” to examine whether these traits causally affect risk of disease.

The aim of this PhD project is to develop statistical methods to assess causality using observational data alone. The successful candidate will gain experience across a wide range of advanced statistical genetics methodologies including Mendelian randomization (a way of using genetic variants to investigate putatively causal relationships), structural equation modelling, genome-wide association analysis (GWAS), genetic restricted maximum likelihood (G-REML) analysis of genome-wide data which can be used to partition variation in phenotypes into genetic and environmental sources of variation, and instrumental variables analysis (using natural “experiments” to obtain information on causality from observational data). The candidate will apply the new statistical methods that they develop to large genetically informative datasets like the UK Biobank (500,000 individuals with genome-wide SNP data).

Principal Advisor: Prof Denise Doolan (IMB)

Associate Advisor: Dr Carla Proietti (IMB); Dr Jessica Mar (AIBN)

We invite applications for a PhD position focused on identifying human host factors that predict immune control of malaria. The project will utilise systems-based immunology and multi-omics approaches to profile the host immune response in controlled infection models of malaria at molecular, cellular, transcriptome and proteome-wide scale. The overall aim will be to develop and apply computational approaches, including network theory and machine learning, which integrate systems biology and molecular immunology to understand host-pathogen immunity and predict immune responsiveness and parasite control. Modelling of largescale existing datasets, including those generated by single cell RNA-sequencing technologies, may also be a feature of this project. The opportunity to identify new knowledge and integrate this with experimental data produced by our laboratory will be instrumental to extending the impact of these bioinformatics analyses.  This project will provide an opportunity to be involved in cutting-edge advances integrating diverse fields of high dimensional omic datasets to inform the development of vaccines, immunotherapies or diagnostic biomarkers.

Methodologies: Bioinformatics, Machine Learning, Immunology, Systems Immunology, Systems Biology, Genomics/Proteomics/Transcriptomics, Molecular and Cell Biology, Statistics

Eligibility: Entry: BSc Honours Class I (or equivalent via outstanding record of professional or research achievements)
Experience/Background: Experience with programming languages, mathematics, statistics and/or background in immunology and molecular sciences, with an interest in integrating the fields of immunology and bioinformatics. Excellent computer, communication, and organisational skills are required. Forward thinking, innovation and creativity are encouraged. 

Principal Advisor: Dr Gunn-Helen Moen (IMB)

This project requires candidates to commence no later than Research Quarter 1, 2024, which means you must apply no later than 30 September, 2023.

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.

We are seeking a PhD candidate to join our research team in this exciting project funded by the Australian Research Council. The research group has conducted work within genetic epidemiology, focusing on pregnancy related exposures and outcomes.

Depending on the student’s level of experience and aptitude, they will help develop and/or apply statistical genetics approaches to investigate the possible existence of transgenerational epigenetic effects on human phenotypes.

A PhD is about learning new skills and learning how to do research. Our ideal candidate will have knowledge or keen interest in learning genetics, epidemiology, statistics, unix and shell scripting, and statistical software such as R. You will work closely with an experienced researcher on the project. There will also be possibility for a research stay in Norway during this PhD.

The main purpose of the fellowship is research training leading to the successful completion of a PhD degree.  

The advertised projects are fundamentally quantitative and computer-based, and so evidence of aptitude in these areas is essential. The candidate should also have the ability to design, plan, and execute experiments and be proficient in English, both written and oral. 

We are looking for someone who is:

• Excellent communication and team working skills
• Organized and structured
• Flexible
• Enthusiastic and willing to learn new methods and techniques

Principal Advisor: Associate Professor Nathan Palpant (n.palpant@uq.edu.au)

Associate Advisors: Dr Sonia Shah (sonia.shah@imb.uq.edu.au) and Dr Mikael Boden (SCMB)

Genome sequencing is a powerful tool for studying the biological basis of disease, yet out of millions of data points, finding the underlying cause of disease can be difficult. Current protocols for classifying variants from patient DNA data largely rely on prior knowledge about normal and abnormal gene variation contained in large public databases, known disease-causing gene panels, or identifying variants causing amino acid changes in proteins (which only comprise 2% of the genome). Despite these powerful approaches, studies indicate that classifying variants as pathogenic occurs in only a minority of cases and among variants reported in ClinVar, a public archive of relationships between human variation and phenotype, wherein a large proportion (37%) are classified as variants of unknown significance (VUS). This project aims to address this key gap in knowledge, involving work in computational and/or cell biology studies, depending on the student skills and interests. For computational studies, this project aims to develop methods that integrate predictive, genome-wide identifiers of pathogenicity. We will use machine learning to build non-linear prediction methods that outperform individual prediction tools in identifying genetic causes of disease and accelerating clinical diagnosis of genetic diseases. For cell biology studies, we aim to use clinical genetics data (from the Australian Functional Genomics Network) to determine pathogenicity of variants from patients with inherited cardiovascular diseases. The approaches will include: 1) cell modelling with human pluripotent stem cells (hPSCs), a disease-agnostic and scalable platform for high-throughput hPSC variant screening. To study variants in genes such as transcription factors that are known to cause genetic diseases, we will use molecular phenotyping by genome-wide proximity labelling with DNA adenine methyltransferase (DamID) to study how disease-causing variants alter regulatory control of the genome. Collectively, this aim implements computational predictions with disease modelling as an efficient, scalable, and disease agnostic pipeline to increase the diagnostic rate of unresolved cases.

Principal Advisor: Associate Professor Nathan Palpant (n.palpant@uq.edu.au)

Associate Advisors: Professor Glenn King (glenn.king@imb.uq.edu.au), Dr Sonia Shah (sonia.shah@imb.uq.edu.au) and Dr Toby Passioura (University of Sydey)

Human populations living in high-altitude hypoxic environments have shown generational gene adaptations compared to lowland cohorts. These extreme stresses result in adaptive changes in the genome to maintain cell viability and function. We hypothesise that genes adapted to high altitude provide a unique approach for discovering novel mechanisms to protect organs from acute ischemic stresses like heart attacks. My laboratory is studying the genetics of lowland versus highland populations in China and Central America and using human pluripotent stem cells (hiPSCs) to study genes selected for high-altitude survival. Preliminary single-cell RNA-seq analysis of differentiated European vs. Han Chinese iPSCs revealed a unique gene expression signature for hypoxia pathways shared by the Han Chinese iPSCs with high altitude-associated haplotypes. We have also identified the gene encoding TMEM206, an acid-sensing ion channel, as a candidate “high-altitude gene”. Genetic knockout of TMEM206 reduces cardiomyocyte sensitivity to ischemia. These data and cell tools are a rich resource for discovering genes under adaptive pressure that could in turn reveal mechanisms and drug targets for protecting the heart against acute injury. This project will use iPSCs selected by known high-altitude haplotypes and compared using in vitro ischemia assays to measure cardiomyocyte cell death. We will analyse haplotype differences in differentiated cardiomyocytes by RNA-seq to identify gene expression programs associated with high altitude-adapted genotypes. We will then use the Broad Institute Connectivity MAP, which links drug perturbations to gene expression changes, to identify novel drugs that induce a “high altitude” gene expression profile in cardiomyocytes. Candidate drugs will be tested in wildtype cells (lacking the high-altitude haplotypes) to assess efficacy in reducing cell death during acute ischemic stress. Using our CRISPR gene methods, we will also knockout candidate “high-altitude genes” identified from statistical genetic studies and assay them using in vitro acidosis/ischemia models. For genes such as TMEM206 that show a role in mediating cardiomyocyte cell death, we will work with associate supervisors Glenn King (UQ) and Toby Passioura (U Sydney) in using the RaPID screen to discover cyclic peptides that inhibit stress-sensitive ion channels.