Integrating genealogical and genomic data in studies of complex disease

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。魁北克的创始人人口是一个非常有价值的资源，部分原因是扩展的家谱记录的可用性。事实上，使用BALSAC人口登记簿，可以重建四个多世纪以来的家谱。这些家谱资源的最佳利用可以提高研究有效识别疾病基因的能力。然而，由于大型家谱结构的复杂性，需要新的统计方法，以便将它们充分纳入数据分析。我们研究的目标是通过调查、结合和进一步发展方法学方法，实现对魁北克家谱数据中可用和广泛信息的最佳利用。我们将分两个阶段实现总体目标。第一阶段将使用基于魁北克现有数据的模拟实验来评估现有方法。具体来说，我们将：i)比较家谱和基因组方法来估计远亲关系；ii)评估利用现有方法将家谱数据提供的远亲关系纳入分析的可行性和实用性。在第二阶段，在第一阶段结果的指导下，我们将iii)开发，评估和应用一种新的分析方法来有效地利用家谱信息。我们建议扩展最近开发的方法，以适应较大的谱系，如在魁北克观察到的。尽管最近在寻找疾病基因方面取得了进展，但需要新的方法来检测涉及复杂疾病风险的广泛遗传因素。我们的研究结果将提供新的统计工具，以提高研究的效率和力量在创始人群。此外，我们在一项正在进行的乳腺癌研究中的应用将有助于寻找乳腺癌基因。我们的研究不仅对遗传流行病学和群体遗传学研究中BALSAC登记的最佳整合做出了重要贡献，而且我们的结果也将有益于其他加拿大和国际创始人群的遗传研究。总的来说，我们的研究结果将推动对疾病风险相关基因的研究，特别是那些被认为受许多遗传和环境因素影响的疾病，如心血管疾病或癌症。这将反过来产生更好的预防和/或治疗干预措施。

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.