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Australian State/Territory : QLD
Field of Research : Genetics
Status : Active
Research Topic : Health programs
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  • Active Funded Activity

    Australian Laureate Fellowships - Grant ID: FL180100072

    Funder
    Australian Research Council
    Funding Amount
    $3,460,832.00
    Summary
    Causes and consequence of human trait variation. This project aims to exploit the availability of Big Data from the genomics revolution to understand the relationship between the genome, the environment and complex human traits. New statistical methods and user-friendly software tools will be developed and applied to datasets on millions of individuals to generate new knowledge on human life history variation and healthy ageing. This project will position Australia to benefit from rapid advances .... Causes and consequence of human trait variation. This project aims to exploit the availability of Big Data from the genomics revolution to understand the relationship between the genome, the environment and complex human traits. New statistical methods and user-friendly software tools will be developed and applied to datasets on millions of individuals to generate new knowledge on human life history variation and healthy ageing. This project will position Australia to benefit from rapid advances in genomic technologies, to build and sustain critical capacity in statistical genetics, and better understand the causes and consequence of individual differences in human traits from genetic and environmental factors across the entire human lifespan.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP190103600

    Funder
    Australian Research Council
    Funding Amount
    $443,000.00
    Summary
    Investigating differences in decision-making ability in older adults. This project aims to investigate how healthy ageing impacts decision making and its associated neural circuits using computation modelling and neurogenetic methods. Decision-making is a fundamental cognitive ability, allowing us to choose the best course of action. This project will investigate the relationship between genes and decision-making performance across the adult lifespan. Expected outcomes include a deeper understan .... Investigating differences in decision-making ability in older adults. This project aims to investigate how healthy ageing impacts decision making and its associated neural circuits using computation modelling and neurogenetic methods. Decision-making is a fundamental cognitive ability, allowing us to choose the best course of action. This project will investigate the relationship between genes and decision-making performance across the adult lifespan. Expected outcomes include a deeper understanding of how decision-making evolves in healthy ageing, and a tool based on genetic scores and computational modelling to predict an individual's trajectory of cognitive function. This could help identify individuals who are at risk for cognitive decline, which could then inform better interventions.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200100499

    Funder
    Australian Research Council
    Funding Amount
    $415,000.00
    Summary
    Prediction of phenotype for multiple traits from multi-omic data. This project aims to develop better methods for predicting traits in an individual based on their genome sequence. This method will be tested in agricultural animals and plants and in humans. The prediction formula is derived from a training dataset that has information on the traits and genome sequence of a sample of individuals. The prediction formula can then be applied to predict the trait in individuals where the trait is un .... Prediction of phenotype for multiple traits from multi-omic data. This project aims to develop better methods for predicting traits in an individual based on their genome sequence. This method will be tested in agricultural animals and plants and in humans. The prediction formula is derived from a training dataset that has information on the traits and genome sequence of a sample of individuals. The prediction formula can then be applied to predict the trait in individuals where the trait is unknown. This is useful for selecting the best parents for breeding in agriculture and for predicting the future phenotype of animals, crops and people. The proposed method uses data on very many traits to identify sequence variants that have a function and to predict the traits affected by each variant.
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