Centre for Population and Disease Genomics - Projects List
Gender matters: Using genomic data to understand sex-specific risk in heart disease
Principal Advisor: Dr Sonia Shah (IMB)
Associate Advisor: Prof Gita Mishra (UQ School of Public Health)
The 2019 Women and Heart Disease forum highlighted clear disparities in CVD outcomes between males and females. The report (Arnott et al 2019 Heart, Lung and Circulation), highlighted a need to increase our understanding of sex-specific pathophysiology driving susceptibility to common diseases, and identification of sex-specific risk factors to improve early detection and prevention of CVD in women. Until recently, sex-specific research was underpowered and most studies on heart disease included a much smaller number of female participants. But this is beginning to change with the availability of large biobank data.
This project will require statistical analysis of very large datasets with health records linked to genomic data to address these gaps in our understanding of heart disease in women. This includes data from the UK Biobank cohort in ~500,000 individuals (54% women) and data from the Australian Women’s Longitudinal Study (led by Prof Gita Mishra), a study looking at the factors contributing to the health and wellbeing of over 57,000 Australian women, and is the largest, longest-running project of its kind ever conducted in Australia.
This project will lead to a better understanding of sex-specific risk factors for CVD, which will inform better CVD prevention strategies in women.
Genomics of Caveolae Disease
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.
Harnessing biobank information to understand Motor Neuron 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.
Leveraging high-throughput genetic screens to evolve the power of algae in biotechnology
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.
Parsing the genome into functional units to understand the genetic basis of cell identity and function
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.
Systems immunology and multi-omics approaches to understand protective immunity to human malaria
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.
Understanding the genetic and phenotypic basis of rare disease variants
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.
Using genetic adaptation to high altitude to discover mechanisms regulating acute responses to ischemia
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.