Integrating genealogical and genomic data in studies of complexdisease

来自有限数量的个体并在一定程度上与其他种群保持隔离的种群，称为创始种群，有利于旨在寻找疾病基因的研究。创始种群的一个重要优势是可以获得家谱记录。目前正在研究世界各地的许多创始人群（其中包括冰岛人、阿米什人和赫特人、意大利几个地区、芬兰、魁北克和纽芬兰），近年来也在努力获得用于遗传研究的广泛家谱信息。然而，由于缺乏适当的分析方法来纳入家谱所提供的资料，这些家谱数据库迄今尚未得到充分利用。

Populations that originate from a limited number of individuals and remain isolated to some extent from other populations, called founder populations, are advantageous for studies aiming at finding disease genes. An important advantage of founder populations can be the availability of genealogical records. Many founder populations across the world are currently studied (among others Iceland, the Amish and Hutterites, several Italian regions, Finland, Quebec and Newfoundland) and efforts have been made in recent years to obtain extended genealogical information for use in genetic studies. However, these genealogical databases have been underutilized so far due to a lack of appropriate analytical methods to incorporate the information provided by genealogies. The Quebec founder population is a highly valuable resource because, in part, of the availability of extended genealogical records. Indeed, using the BALSAC population register, it is possible to reconstruct genealogies going back over four centuries. The optimal utilization of these genealogical resources can increase the ability of a study to efficiently identify disease genes. However, because of the complexity of large genealogical structures, new statistical methods are needed in order to fully incorporate them into data analysis. The goal of our study is to achieve optimal use of the available and extensive information from Quebec genealogical data by investigating, combining, and further developing methodological approaches. We will achieve our overall goal in two phases. Phase 1 will evaluate existing methods using simulation experiments based on existing data from Quebec. Specifically, we will: i) Compare genealogical and genomic methods to estimate distant relatedness; and ii) evaluate the feasibility and utility of incorporating distant relationships provided by the genealogical data in the analysis using existing methods. In Phase 2, guided by the phase 1 results, we will iii) develop, evaluate, and apply a new analytical method to efficiently utilize genealogical information. We propose to extend a recently developed method in order to accommodate larger genealogies such as those observed in Quebec. Despite recent progress in the search for disease genes, new approaches are needed to detect the wide range of genetic factors involved in complex disease risk. Our study results will provide novel statistical tools to increase study efficiency and power in founder populations. In addition, our application to an ongoing breast cancer study will aid in the search for breast cancer genes. Our study is not only an essential contribution to the optimal integration of the BALSAC register in genetic epidemiology and population genetics research, but our results will also benefit genetic research in other Canadian and international founder populations. Overall, our results will advance the search for genes involved in disease risk, especially for diseases thought to be influenced by many genetic and environmental factors, such as cardiovascular disease or cancer. This will in turn yield better prevention and/or therapeutic interventions.