Developing And Applying Biologically Plausible Statistical Models For Normal And Non-normal Family Data
Funder
National Health and Medical Research Council
Funding Amount
$339,700.00
Summary
Although molecular and computing advances have enabled more detailed investigations of inherited diseases and the ability to fit realistic statistical models to these data, limitations still exist when analysing family data. Often only basic statistical analyses are performed, due to the lack of understanding of complexities within the data and-or inability of researchers to fit appropriate statistical models. These factors have hampered the search for genes and environmental factors influencing ....Although molecular and computing advances have enabled more detailed investigations of inherited diseases and the ability to fit realistic statistical models to these data, limitations still exist when analysing family data. Often only basic statistical analyses are performed, due to the lack of understanding of complexities within the data and-or inability of researchers to fit appropriate statistical models. These factors have hampered the search for genes and environmental factors influencing common diseases. This project aims to develop novel, biologically realistic statistical models for investigation of common, complex diseases, such as heart disease and cancer, in families. These models will incorporate both measured and unmeasured genetic and environmental factors, and will be applicable to both normally distributed and non-normally distributed traits. Model fitting will use computer-intensive simulation techniques. Application of the models to data from two large pre-existing studies of international renown, the Victorian Family Heart Study and the Australian Prostate Cancer Family Study, will enable a better understanding of the genetic and environmental factors influencing heart disease and cancer. The models will also be applicable to many other studies of diseases which use data from families, and allow more accurate and useful information to be obtained from data. Software will also be made freely available to other researchers. This will ultimately translate into better outcomes from familial genetic research, and eventually, better prevention, detection, and treatment of the diseases.Read moreRead less
Analysing Genetic And Environmental Risk Factors And Their Interactions For Common Cancers And Cardiovascular Disease
Funder
National Health and Medical Research Council
Funding Amount
$129,937.00
Summary
The statistical models for analysing cancer and cardiovascular risk factor family data are important for understanding the genetic and environmental aetiology of these diseases, but complicated by the different levels of correlations between relatives in a family. The conventional assumption of independence in observations is invalid in these situations. We intend to develop, test, implement and distribute a comprehensive suite of new statistical methods designed specifically to assistant molecu ....The statistical models for analysing cancer and cardiovascular risk factor family data are important for understanding the genetic and environmental aetiology of these diseases, but complicated by the different levels of correlations between relatives in a family. The conventional assumption of independence in observations is invalid in these situations. We intend to develop, test, implement and distribute a comprehensive suite of new statistical methods designed specifically to assistant molecular geneticists and genetic epidemiologists undertake informative and meaningful analyses of the measured and latent genetic and environmental risk factors and their possible interactions. The two associate investigators, Prof John Hopper and Prof Stephen Harrap, will bring their respective genetic epidemiological and biometric statistical expertise and their prestigious family data resources to this project. With the suite of flexible statistical models and analyses, we will further our knowledge about genetic and environmental risk factors and their interactions of common cancers and major gene effects for cardiovascular phenotypes. Simulation studies will help us understand some phenomena accounted in the research but cannot be replicated in reality and assess the efficiency of the statistical methods and credibility of our analysis results independently. Statistical programs developed in this project can also be used in other genetic and epidemiological studies (e.g. diabetes, epilepsy) where such high-level statistical tools are not yet available.Read moreRead less