I am an NHMRC Australia Fellow and mathematical statistician by training, specializing in the design and analysis of family and twin studies. I lead or co-lead large international molecular, environmental, genetic and analytic epidemiology family resources for studying breast, bowel and other cancers. My vision is realise the full potential of these studies to improve the health and well-being people at increased familial or genetic risk of these diseases.
Novel Statistical Methods For The Analysis Of Meausred Genetic And Environmental Risk Factors In Twin Studies
Funder
National Health and Medical Research Council
Funding Amount
$478,314.00
Summary
Studies on twins are an important way to determine whether the risk of disease is likely to be influenced by genetic factors but have traditionally focussed on unmeasured factors. New epidemiological studies measure thousands of genetic variants on many participants. This project will extend methods for analysing data within and between twin pairs to determine whether risk factors are likely to be causal and therefore should be the subject of further designed studies based on intervention.
Statistical Analyses Of Breast Cancer Risks For Australian BRCA1 And BRCA2 Mutation Carriers
Funder
National Health and Medical Research Council
Funding Amount
$424,628.00
Summary
About 10 years ago two genes, called BRCA1 and BRCA2, were discovered. The normal function of these genes is to prevent breast and other cancers from developing. All people have two copies of each gene, one inherited from their mother and one from their father. Women who have inherited a fault in one copy are at increased risk of breast and ovarian cancer. There has been considerable controversy about what their actual cancer risks are, especially about how those risks might depend on their age. ....About 10 years ago two genes, called BRCA1 and BRCA2, were discovered. The normal function of these genes is to prevent breast and other cancers from developing. All people have two copies of each gene, one inherited from their mother and one from their father. Women who have inherited a fault in one copy are at increased risk of breast and ovarian cancer. There has been considerable controversy about what their actual cancer risks are, especially about how those risks might depend on their age. We have already conducted studies on this and have developed the necessary statistical methods to address these issues by analysing data from the families in which there are faulty genes. In this study we propose to use two large Australian studies, one of families with multiple-cases of breast cancer (Kathleen Cuningham Consortium for Research on Familial Breast Cancer; kConFab) and the other of the families of women with breast cancer chosen, irrespective of their family cancer histories, through the Victorian and NSW Cancer Registries (Australian Breast Cancer Family Study; ABCFS). A large amount of work has already been conducted to identify these families and test them for faults in BRCA1 and BRCA2. There are over 350 families who carry faults, making this one of the largest studies of its type in the world. We will check the cancer histories of these families and determine which members have, or are likely to have, inherited a faulty gene. We will then estimate the breast and ovarian cancer risks accurately, and with much more precision, than has been done previously. We will also use these large datasets to develop a simple method to identify which Australian women are most likely to carry a fault in BRCA1 or BRCA2, based on their personal and family cancer histories. This study will assist genetic counsellors inform Australian women who consider mutation testing for BRCA1 and BRCA2 about their cancer risks, and help make breast cancer genetics more cost effective.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