Constructing Control Samples For The Australian And Other Populations: Improving Power And False Positive Rates In The Next Generation Of Genetic Association Studies With A Focus On Controlling For Fine-scale Population Structure In DNA Sequence Data
Funder
National Health and Medical Research Council
Funding Amount
$283,447.00
Summary
Individuals who live near each other tend to be more similar genetically than individuals who live in different parts of the world. One reason is that they share more of their genetic ancestry. There can be very subtle differences in patterns of genetic variation even within countries. Accounting for these subtle differences can be important for studies of the genetic basis of diseases. We will develop novel statistical methods to control for these genetic differences in disease studies.
Implementing Multiple Imputation With Sensitivity Analysis In Large-scale Longitudinal Studies
Funder
National Health and Medical Research Council
Funding Amount
$473,507.00
Summary
Missing data arise in most research studies and if not handled appropriately can mean the study results are not correct. With researchers now conducting larger and longer studies the challenges posed by missing data are increasing. In this grant we study a powerful technique for handling missing data, which in its current form often cannot be applied effectively in large studies. By developing this approach we will improve the accuracy of results from large-scale epidemiological studies.
Complex Statistical Analyses Of Genome-wide Association Studies Related To Breast And Prostate Cancers Using High Performance Supercomputing
Funder
National Health and Medical Research Council
Funding Amount
$656,073.00
Summary
Breast and prostate cancers are the most common cancers in Australian women and men. Simple analyses of genome-wide association (GWAS) studies explain only a fraction of why these cancers run in families. The University of Melbourne now has a supercomputer that can conduct much more complex analyses. We will apply these to the world’s GWAS data for breast and prostate cancers. We hope to learn more about the causes of these cancers, and expand an expert Australian workforce in supercomputing.