Evaluating The Genetic Contribution To Rheumatic Heart Disease Pathogenesis In Australian Aboriginal And Torres Strait Islander Communities
Funder
National Health and Medical Research Council
Funding Amount
$1,782,074.00
Summary
Rheumatic heart disease is highly prevalent in Aboriginal people in Australia and leads to early cardiac disease. Despite decades of research, the underlying genetic mechanisms for why it occurs are not well understood. We are conducting a genetic study to better understand why some people are susceptible to RHD and others are not. The study will involve substantial Aboriginal leadership and consultation and will be a model for the conduct of genetic studies in Aboriginal populations.
Methods to infer dense genomic information from sparsely genotyped populations. Prediction of phenotype based on DNA polymorphisms or sequence has important applications such as prediction of disease risk in human medicine and prediction of genetic value in plant or animal breeding. This project will enhance precision and lower the cost of association studies leading to substantial increase in accuracy of such predictions. This will allow more effective genetic improvement, particularly of diff ....Methods to infer dense genomic information from sparsely genotyped populations. Prediction of phenotype based on DNA polymorphisms or sequence has important applications such as prediction of disease risk in human medicine and prediction of genetic value in plant or animal breeding. This project will enhance precision and lower the cost of association studies leading to substantial increase in accuracy of such predictions. This will allow more effective genetic improvement, particularly of difficult but important traits such as disease resistance, reduced green-house gas emissions and product quality. The same methods can be extended to improve genetic improvement in plants and better prediction of human disease risk. Read moreRead less
Australian Laureate Fellowships - Grant ID: FL170100022
Funder
Australian Research Council
Funding Amount
$3,402,903.00
Summary
Redefining virus ecology and evolution. This project aims to employ novel genomic analyses of viruses from Australian fauna to resolve major questions in RNA virus ecology and evolution, and is expected to reveal the basic processes that shape the virosphere, determine how viruses jump species to emerge and cause disease in new hosts, and how viruses evolve new levels of virulence. The research will provide a new understanding of how viruses evolve and contribute to global ecosystems and develop ....Redefining virus ecology and evolution. This project aims to employ novel genomic analyses of viruses from Australian fauna to resolve major questions in RNA virus ecology and evolution, and is expected to reveal the basic processes that shape the virosphere, determine how viruses jump species to emerge and cause disease in new hosts, and how viruses evolve new levels of virulence. The research will provide a new understanding of how viruses evolve and contribute to global ecosystems and develop new bioinformatics tools to identify and analyse highly divergent genome sequences through studying meta-transcriptomic data from diverse animal phyla, from prokaryotes and basal eukaryotes, from iconic native mammalian species and their major invasive pests. The benefits provided will include determining the viromes of native and invasive species and enhancing the efforts to protect iconic Australian species from infectious disease.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100614
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Novel statistical algorithms and methods to quantify and partition pleiotropy between complex traits in populations. A fundamental question in biology is how common genetic effects are shared between traits or groups. For example, is cognition or human behaviour genetically identical across genders or across human population groups? This project will address these questions using multiple independent genome-wide association studies.
Estimating genotype-environment interaction using genomic information. This project aims to develop statistical methods that can explore genotype–environment interaction at the genomic level using genome-wide single nucleotide polymorphisms or sequence data. It plans to estimate how the effects of genetic variants change with changing environmental conditions and how overall genetic variance changes due to changing effects in specific gene regions. It plans to deliver statistical models and meth ....Estimating genotype-environment interaction using genomic information. This project aims to develop statistical methods that can explore genotype–environment interaction at the genomic level using genome-wide single nucleotide polymorphisms or sequence data. It plans to estimate how the effects of genetic variants change with changing environmental conditions and how overall genetic variance changes due to changing effects in specific gene regions. It plans to deliver statistical models and methods and an efficient algorithm implemented in software, which would broadly benefit the field of complex trait genetics. Methods to estimate genotype–environment interaction effects at the genomic level would help elucidate complex biological systems, including human genetic response to changing environmental factors and the potential adaptation of animals to changing environmental conditions.Read moreRead less
Whole-genome multivariate reaction norm model for complex traits. This project aims to develop a multivariate whole-genome genotype-covariate correlation and interaction model that can be applied to a wide range of existing genome-wide association study (GWAS) datasets. Genotype-covariate correlation and interaction (GCCI) are fundamental in biology but there is no standard approach to disentangle interaction from correlation in the whole-genome analyses. This project will address the key featur ....Whole-genome multivariate reaction norm model for complex traits. This project aims to develop a multivariate whole-genome genotype-covariate correlation and interaction model that can be applied to a wide range of existing genome-wide association study (GWAS) datasets. Genotype-covariate correlation and interaction (GCCI) are fundamental in biology but there is no standard approach to disentangle interaction from correlation in the whole-genome analyses. This project will address the key feature in biology, which relates to dissecting the complex mechanism of association and interaction. The proposed statistical model implemented in a context of a novel design based on multiple GWAS data sets is a paradigm shifting-tool with applications to multiple industries.Read moreRead less
Complex trait analyses based on genome-wide approaches. This project aims to develop whole genome approaches that can improve the estimation and prediction power by using information from the dynamic genetic architecture of complex traits (i.e. the changes of genetic characteristics and effects when varying effective population size and genetic backgrounds). The project intends to deliver advanced statistical models, efficient algorithms and design by combining data from close relatives, populat ....Complex trait analyses based on genome-wide approaches. This project aims to develop whole genome approaches that can improve the estimation and prediction power by using information from the dynamic genetic architecture of complex traits (i.e. the changes of genetic characteristics and effects when varying effective population size and genetic backgrounds). The project intends to deliver advanced statistical models, efficient algorithms and design by combining data from close relatives, population samples or from different populations (e.g. multi-ethnicities or multi-breeds). The expected outcome is to better understand the dynamic architecture of complex traits and develop methods with improved power, precision and accuracy in genomic analyses.Read moreRead less