Integrating linkage information in tests of association for rare variants in ILD

描述（由申请方提供）：纤维化间质性肺病（fILD）是指一组由于肺泡上皮的进行性瘢痕形成而导致显著发病率和死亡率的肺部疾病。没有有效的治疗方法来可靠地缓解症状或延长生命。FILD是一种复杂的疾病，可能是由多种遗传和环境因素相互作用引起的，其中大部分是未知的。这个项目的动机是我们正在完成的两个大的全基因组fILD研究：一个家族连锁研究和一个病例对照关联研究。我们将对来自家族研究的病例以及其他病例和对照进行测序，以确定这些研究中高优先级区域内的风险位点。测序研究的优先事项将是罕见（<1%）和不常见（<5%）变异。已经在病例对照设置中开发了几种罕见和不常见变异的关联性测试，其中将变异聚合以提高统计功效。然而，这些统计检验都没有使用家族特异性连锁信息，这些信息对于遗传异质性来说是非常有用的。因此，没有现有的方法整合家庭特定的连锁信息的关联性的聚合测试。的目标 该项目旨在填补统计学方法的这一空白，将家族特异性连锁信息与其他病例对照序列数据直接结合，以识别fILD的遗传风险变异。该项目的中心假设是，通过使用家族特异性等位基因共享信息，可以提高罕见变异的遗传关联测试的能力， 更重视来自有证据表明与感兴趣的基因座有关联的家庭的家族病例。我们建议扩展罕见/不常见变异的关联性检验，a）每个家族只需要一个病例，B）使用连锁研究中等位基因共享的外部测量，以及c）允许对具有连锁信息的家族病例和其他病例进行联合分析。第一个目标是评估一个 一组罕见/不常见的遗传变异和二分性状之间的关联加权测试的新框架。每个家族病例的贡献根据病例的家族特异性证据在感兴趣的基因座处共享等位基因来加权。第二个目标是将新方法应用于fILD，以识别增加fILD风险的新变体，并将结果与使用现有方法获得的结果进行比较。第三个目标是开发和传播一种免费使用的目标1中开发的测试方法，这将有助于将这些方法应用于其他疾病。虽然两项fILD研究激发了这项工作，但有数百项类似的研究具有可用的连锁数据，这些研究将受益于这些测试检测风险变异的能力增加。这门科学的成功完成将导致适用于重新排序研究的关联和软件的测试，这些研究通过利用家族联系队列中包含的信息和投入的资源来提高功率。这些方法自然可以扩展到数量性状和其他复杂的测试策略，以提高检测功能变体的能力。

DESCRIPTION (provided by applicant): Fibrosing interstitial lung disease (fILD) refers to a group of lung diseases that result in significant morbidity and mortality due to progressive scarring of the alveolar interstitium. There are no effective treatments to reliably relieve symptoms or prolong life. FILD is a complex disease likely caused by the interplay among multiple genetic and environmental factors, most of which are unknown. This project is motivated by two large genome-wide studies of fILD that we are completing: one familial linkage study and one case-control association study. We will sequence cases from the familial study in addition to other cases and controls to identify the risk loci within the high priority regions fro these studies. A priority for the sequencing study will be rare (<1%) and uncommon (<5%) variants. Several tests of association for rare and uncommon variants have been developed in the case-control setting, where the variants are aggregated to improve statistical power. However, none of these statistical tests use family-specific linkage information, information that is highly informative for genetic heterogeneity. Hence, there are no existing methods for integration of family-specific linkage information into aggregate tests of association. The goal of this project is to fill this gap in statistical methodology for directly combining family-specific linkage information with other case-control sequence data in order to identify genetic risk variants for fILD. The central hypothesis of this project is that the power of genetic association tests for rare variants can be improved by using family-specific allele sharing information to give more weight to familial cases from families with evidence for linkage at the locus of interest. We propose extensions of rare/uncommon variant tests of association that a) require only one case per family, b) use external measures of allele sharing from linkage studies, and c) allow joint analysis of familial cases with linkage information and other cases. The first aim is to evaluate a new framework for weighted tests of association between a group of rare/uncommon genetic variants and a dichotomous trait. The contribution of each familial case is weighted according to the case's family-specific evidence for allele sharing at the locus of interest. The second aim is to apply the new methods to fILD to identify novel variants that increase the risk of fILD and compare the results to those obtained using existing methods. The third aim is to develop and disseminate a freely-available implementation of the tests developed in aim 1, which will facilitate the application of these methods to other disorders. While two studies of fILD motivate this work, there are hundreds of similar studies with available linkage data which would benefit from the increased power to detect risk variants with these tests. The successful completion of this science will result in tests of association and software applicable to resequencing studies that improve power by leveraging the information contained in, and the resources that have been devoted to, familial linkage cohorts. The methods are naturally extendable to quantitative traits and other complex testing strategies to increase power to detect functional variants.