Improved statistical methods for identifying generic risk factors underlying complex diseases

糖尿病、哮喘和癌症等复杂疾病可能涉及许多基因，这些基因与生活方式的选择和环境暴露相一致，增加了个体患病的风险。通过研究家族遗传来寻找潜在基因的努力受到了低功率的阻碍，因此只取得了有限的成功。因此，生物医学研究人员现在转向人口研究，将疾病或其他疾病相关性状的存在与DNA序列的变异相关联。收集这种关联数据是昂贵的。我们如何才能最好地利用这些数据资源？对数据进行有效的统计分析是关键。例如，研究人员如何最佳地提取有关基因是否与环境相互作用的信息，以确定一个人的疾病风险？另一个问题是如何处理被称为单倍型的基因组数据，由于所有个体都是远亲，这些数据无法完全观察到，并且具有复杂的潜在依赖性。最后，我们如何才能有效地将以前从其他类型的研究（如动物或细胞研究）中获得的信息纳入到人类群体的研究中？这项研究将调查所有这三个统计问题，并开发和传播作为免费软件的分析工具，供更广泛的生物医学界使用。在本研究过程中开发的统计方法和软件将使用生物医学合作者正在进行的研究数据进行验证和改进。在国际上和加拿大国内，严重缺乏遗传流行病学和统计和人口遗传学等相关研究领域的能力。因此，加拿大卫生研究所遗传学研究所宣布遗传流行病学是国家培训的战略优先事项。拟议的研究将为这一应用战略领域的统计基础提供培训机会。具有这种统计基础的高素质人员将能够为跨学科的健康研究做出贡献，使下一代加拿大人受益。

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.