Revealing How Interactions And Mutation Patterns Among Genes Change In Different Human Tissues By Bioinformatics Tools
Funder
National Health and Medical Research Council
Funding Amount
$334,884.00
Summary
Our understanding of common disease is hampered by the complexity of the human system. The DNA variations found in genome wide association studies of common disease are rarely in the gene coding region. I aim to develop statistical bioinformatic tools to find how the DNA variations affect human disease by taking gene expression as the quantitative phenotype. The results will explain the genetic risk of human common disease, so that better personalized prevention and therapy can be achieved.
Developing And Applying Quantitative Methods For Obtaining New Insights Into Children's Health Inequalities From Longitudinal Cohort Studies
Funder
National Health and Medical Research Council
Funding Amount
$307,946.00
Summary
Children who live in more deprived family circumstances are more likely to experience poorer health including asthma, obesity, and difficulties in language, learning and behaviour. This research uses large scale population surveys of Australian children to investigate how difficulties such as experiencing poverty may affect childrenÍs health. This research also investigates how families and communities experience barriers to receiving health services and how this affects childrenÍs health.
Improved models to understand the genomic architecture of complex traits. This project aims to improve modelling of the genetics underlying complex traits. The project will develop and test models for using genome-wide genetic data to investigate how much heritability (genetic effect) underlies traits of interest, where it lies in the genome, and how much of it is shared across traits. The new models will be implemented in statistical algorithms in a freely-available software package. This proj ....Improved models to understand the genomic architecture of complex traits. This project aims to improve modelling of the genetics underlying complex traits. The project will develop and test models for using genome-wide genetic data to investigate how much heritability (genetic effect) underlies traits of interest, where it lies in the genome, and how much of it is shared across traits. The new models will be implemented in statistical algorithms in a freely-available software package. This project expects to increase understanding of biological mechanisms, the efficiency of genetic association analyses and the accuracy of genomic prediction, including the effects of interventions. The project will adapt human models to a wider range of organisms, in particular bacteria.Read moreRead less