Leveraging Novel Multivariate Methods of Subphenotypes in Genetic Association Studies of Sjogren’s Syndrome

利用新的亚表型多变量方法进行干燥综合征的遗传关联研究

• 批准号: 9344575
• 负责人: Hugues Aschard
• 金额: $ 24.03万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-02 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9344575
• 关键词: Accounting; Address; Architecture; Autoantibodies; Autoimmune Diseases; Big Data; Biological Markers; Characteristics; Clinical; Clinical Data; Cohort Studies; Collection; Communities; Complex; Computer software; Coupled; Data; Data Analyses; Data Set; Development; Disease; Environmental Risk Factor; Etiology; Exhibits; Future; Genetic; Genetic Markers; Genetic Models; Genetic Risk; Genetic study; Genotype; Heterogeneity; Human; Human Genetics; Individual; International; Measures; Medical Genetics; Methodology; Methods; Modeling; Multivariate Analysis; National Institute of Dental and Craniofacial Research; Noise; Oral; Outcome; Phenotype; Principal Component Analysis; Production; Property; Public Health; Publications; Research Personnel; Research Project Grants; Research Proposals; Resolution; Resources; Sample Size; Signal Transduction; Sjogren' s Syndrome; Software Tools; Standardization; Structure; Techniques; Testing; Variant; Work; analytical method; base; case control; cohort; cost; design; disease heterogeneity; endophenotype; flexibility; genetic association; genetic epidemiology; genetic variant; genome wide association study; genome-wide; high dimensionality; human disease; improved; infancy; innovation; insight; novel; pleiotropism; success; systemic autoimmune disease; tool; trait

Abstract Genetic epidemiology has entered the big data era with many cohort studies having access to not only genome-wide genotyping data but also large number of disease-related traits and a variety of biomarkers. These extensive datasets hold great promise for increasing our understanding of human diseases and improving public health. However, statistical tools to leverage these data are severely lacking and the development of innovative methodological approaches remains a key component for future successes. Indeed, most genetic association studies still utilize standard univariate approach, testing each measured phenotype independently for association with each single genetic variant. Our recent work has shown that phenotypes sharing genetic and environmental underpinnings can be leveraged in multi-phenotype analyses to increase statistical power to detect associated genetic loci. Diseases showing heterogeneity and/or evidence for subtypes that can be partially characterized by endophenotypes and biomarkers, including several autoimmune diseases such as the Sjögren's syndrome (SS), are particularly good candidates for multi- phenotype methods. In this proposal we aim to apply two new multi-phenotype methods for the analysis of over 50 SS related phenotypes from the Sjögren's International Collaborative Clinical Alliance (SICCA) study. SICCA has generated a unique collection of SS case/control and SS related phenotypes along genome-wide genotypes data among more than 3,500 individuals. The first proposed approach is an extension of the multivariate method based on a principal component analysis framework that we recently developed. Unlike standard univariate approaches, our method is capable of detecting associations even when there exist multiple genetically heterogeneous subphenotypes of the disease. It is based on composite null hypothesis (all phenotypes are tested jointly), so that single phenotype-genotype association cannot be established. This limitation is the cost for dramatic increase in statistical power to identify genetic variant with positive and negative pleiotropic effect (concordant and discordant genetic effect respectively). The second approach relies on a new and innovative strategy that will be developed as part of this proposal. As oppose to multivariate methods, this approach keeps the univariate properties of determining association between a single outcome and a single genetic variant, but as in multivariate approaches, it leverages correlation with other available phenotypes. Using the proposed approaches we expect to dramatically increase our ability to identify genetic variants associated with SS phenotypes. We fully expect that these two methods will reveal important insights into the genetic basis of SS and will go on to serve the broader the genetics community.

抽象的 遗传流行病学已经进入大数据时代，许多队列研究不仅可以获得 全基因组基因分型数据，还有大量疾病相关性状和各种生物标志物。 这些广泛的数据集为增进我们对人类疾病和疾病的了解带来了巨大的希望。 然而，严重缺乏利用这些数据的统计工具。 事实上，创新方法的发展仍然是未来成功的关键组成部分。 大多数遗传关联研究仍然采用标准的单变量方法，测试每个测量的表型 我们最近的工作表明，表型与每个单一遗传变异的关联是独立的。 可以在多表型分析中利用共享遗传和环境基础来增加 检测相关遗传位点的疾病的统计能力和/或证据。 可以通过内表型和生物标志物部分表征的亚型，包括几种 自身免疫性疾病，例如干燥综合征（SS），是多方面治疗的特别好的候选者。 表型方法。 在本提案中，我们的目标是应用两种新的多表型方法来分析 50 多个 SS 相关的 来自 Sjögren 国际合作临床联盟 (SICCA) 研究的表型。 沿着全基因组基因型生成了 SS 病例/对照和 SS 相关表型的独特集合 第一个提出的方法是多变量的扩展。 与标准不同，该方法基于我们最近开发的主成分分析框架。 单变量方法，我们的方法能够检测关联，即使存在多个 该疾病的遗传异质亚表型基于复合零假设（全部）。 表型联合测试），因此无法建立单一表型-基因型关联。 限制是显着提高统计能力来识别具有阳性和阴性遗传变异的成本。 第二种方法依赖于负多效性效应（分别是一致和不一致的遗传效应）。 作为该提案的一部分，将制定一项新的创新战略，而不是多元。 方法，这种方法保留了确定单个结果之间关联的单变量属性 和单一遗传变异，但与多变量方法一样，它利用与其他可用的相关性 表型。 使用所提出的方法，我们预计将显着提高我们识别遗传变异的能力 我们完全期望这两种方法将揭示有关 SS 表型的重要见解。 SS 的遗传学基础，并将继续为更广泛的遗传学界服务。

项目成果

Multitrait GWAS to connect disease variants and biological mechanisms.

• DOI: 10.1371/journal.pgen.1009713
• 发表时间: 2021-08
• 期刊: PLoS genetics
• 影响因子: 4.5
• 作者: Julienne H;Laville V;McCaw ZR;He Z;Guillemot V;Lasry C;Ziyatdinov A;Nerin C;Vaysse A;Lechat P;Ménager H;Le Goff W;Dube MP;Kraft P;Ionita-Laza I;Vilhjálmsson BJ;Aschard H
• 通讯作者: Aschard H