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Research Topic : Bioinformatics Software
Field of Research : Genetics
Australian State/Territory : NSW
Australian State/Territory : SA
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  • Funded Activity

    Discovery Projects - Grant ID: DP160102575

    Funder
    Australian Research Council
    Funding Amount
    $313,600.00
    Summary
    Diet influences the selective advantage of mitochondrial DNA mutations. This project aims to examine critical mechanisms that affect mitochondrial DNA variation within species. It aims to test the hypothesis that mitochondrial DNA haplotypes have the potential to be under nutritionally induced balancing selection as a consequence of cellular signalling and/or Adenosine triphosphate (ATP) production by mitochondria. Diet can vary both seasonally and geographically and is a key environmental param .... Diet influences the selective advantage of mitochondrial DNA mutations. This project aims to examine critical mechanisms that affect mitochondrial DNA variation within species. It aims to test the hypothesis that mitochondrial DNA haplotypes have the potential to be under nutritionally induced balancing selection as a consequence of cellular signalling and/or Adenosine triphosphate (ATP) production by mitochondria. Diet can vary both seasonally and geographically and is a key environmental parameter that influences the ability of a species to colonise new habitats. The project plans to characterise the functional links between specific mitochondrial DNA haplotypes, mitochondrial functions and organismal traits. The expected outcome is a more precise grasp of the processes influencing genetic variation within and among species, which would inform current issues in ecology and genetics.
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    Funded Activity

    Discovery Projects - Grant ID: DP160102126

    Funder
    Australian Research Council
    Funding Amount
    $331,600.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190100766

    Funder
    Australian Research Council
    Funding Amount
    $404,000.00
    Summary
    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.
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