Multivariate Modelling and Inference in Genetic Studies

遗传研究中的多变量建模和推理

• 批准号: 433266-2013
• 负责人: Oualkacha, Karim
• 金额: $ 1.09万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636044
• 关键词: Multivariate; Modelling; Inference; Genetic; Studies

Whole genome sequencing is becoming a standard means of genome scanning for human complex diseases. Thus, almost all genetic variability in each person will be measured. However, statistical power to detect genes of interest is a challenge due to the number and frequency of much of the genetic variation. Many human complex diseases manifest themselves through correlated quantitative phenotypes (traits). For instance, obesity is associated with weight, waist circumference and waist to hip ratio. However, direct measured trait along (e. g. weight, height) may not be informative enough to detect susceptibility variants. For instance, body mass index (BMI), defined as weight divided by squared height, is a typical example of a very informative composite phenotype that has shown to be linked with genetic variants not associated with weight nor height. Consequently, given the comprehensive data on DNA variation that will be provided by whole genome sequencing, we need to optimize other aspects of the study design, such as the choice of phenotypes to be studied. This ''phenotype optimization'' problem is believed to be part of the way that genotypes map to the phenotypes for complex diseases. Statistical genetic methods that take advantage of optimizing phenotypes may improve power to localize genes of interest by exploiting correlational structures to identify genes jointly influencing suites of related phenotypes or by using new phenotypes more proximal to gene action, which are designed to highlight independent measures underlying the correlations among a set of related traits.The research program I propose deals with the phenotype optimization, one of the most interesting problems in modern genetic/genomic analysis of complex diseases. Within this proposal, I suggest new multivariate approaches that take advantage of multiple related phenotypes to improve statistical power to identify susceptible genes in the whole genome sequencing era.

全基因组测序正在成为人类复杂疾病基因组扫描的标准手段。因此，每个人的几乎所有遗传变异都将被测量。然而，由于许多遗传变异的数量和频率，检测感兴趣基因的统计能力是一个挑战。许多人类复杂疾病通过相关的数量表型（性状）表现出来。例如，肥胖与体重、腰围和腰臀比有关。然而，直接测量的性状沿着（e. G.体重、身高）可能不足以提供检测易感性变体的信息。例如，身体质量指数（BMI），定义为体重除以身高的平方，是一个典型的例子，一个非常丰富的复合表型，已被证明与遗传变异与体重和身高无关。因此，鉴于全基因组测序将提供有关DNA变异的全面数据，我们需要优化研究设计的其他方面，例如待研究表型的选择。这种“表型优化”问题被认为是基因型映射到复杂疾病表型的方式的一部分。利用优化表型的统计遗传学方法可以通过利用相关结构来识别共同影响相关表型的基因，或者通过使用更接近基因作用的新表型来提高定位感兴趣基因的能力，这些表型被设计为突出一组相关性状之间相关性的独立度量。复杂疾病的现代遗传/基因组分析中最有趣的问题之一。在这个提议中，我提出了新的多变量方法，利用多个相关的表型，以提高统计能力，以确定在全基因组测序时代的易感基因。