Improved statistical methods for identifying generic risk factors underlying complex diseases

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Complex disorders such as diabetes, asthma and cancer likely involve a number of genes acting in concert with lifestyle choices and environmental exposures to increase an individual's risk to develop disease. Efforts to find the underlying genes by studying inheritance in families have been hampered by low power and, consequently, have met with only limited success. As a result, biomedical investigators are now turning to population studies that correlate presence of the disease or other disease-related traits to variation in DNA sequences. Collecting such association data is expensive. How can we make the best use of these data resources? Efficient statistical analyses of the data are key. For example, how can investigators optimally extract information about whether or not genes interact with the environment in determining a person's disease risk? Another question is how to handle genomic data, known as haplotypes, that are incompletely observed and have complex underlying dependencies due to the fact that all individuals are distantly related. Finally, how can we effectively incorporate into studies of human populations previously available information from other types of studies such as those on animals or cells? This research will investigate all three of these statistical questions and develop and disseminate, as freely available software, analytical tools accessible to the wider biomedical community. Statistical methods and software developed during the course of this research will be validated and refined using data from ongoing studies of biomedical collaborators. Internationally and within Canada, there is a critical lack of capacity in genetic epidemiology and related research areas such as statistical and population genetics. As a result, the Institute of Genetics at the Canadian Institutes for Health Research has declared genetic epidemiology a national strategic priority for training. The proposed research will provide training opportunities in the statistical underpinnings of this applied strategic area. Highly qualified personnel with such a statistical foundation  will be well-placed to contribute to interdisciplinary health research benefitting the next generation of Canadians.