New Computational Tools for Advanced Analytics in Genome-wide Association Studies

用于全基因组关联研究高级分析的新计算工具

• 批准号: 10582852
• 负责人: Xiang Zhou
• 金额: $ 31.92万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-14 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10582852
• 关键词: Advanced Development; African American population; Algorithms; American; Architecture; Biological; Biology; Calibration; Clinical; Communities; Complex; Computer software; Computing Methodologies; Data; Dependence; Development; Disease; Etiology; European; Foundations; Funding; Gene Expression; Gene Frequency; Genes; Genetic; Genetic study; Genomics; Goals; Health; Heritability; Human; Individual; Investigation; Joints; Knowledge; Linkage Disequilibrium; Maps; Mendelian randomization; Methodology; Methods; Minor; Modeling; Molecular; Phenotype; Research; Risk Factors; Sampling; Scoring Method; Signal Transduction; Software Tools; Solid; Uncertainty; Variant; advanced analytics; biobank; clinical application; clinical translation; computational suite; computerized tools; data complexity; disease diagnosis; disorder risk; experience; flexibility; genetic architecture; genetic association; genetic predictors; genetic variant; genome wide association study; genome-wide analysis; improved; insight; instrument; interest; method development; novel; novel diagnostics; novel therapeutics; open source; pleiotropism; polygenic risk score; precision medicine; predictive tools; screening; simulation; statistics; success; tool; trait; transcriptome; transcriptomics; user-friendly

Project Summary Many genome-wide association studies (GWAS) have been successfully carried out, identifying numerous genetic variants associated with common diseases and disease related complex traits. The identified genetic associations can now explain a large fraction of the genetic contribution and trait heritability, revealing the genetic architecture underlying common diseases. The success of GWAS in the past few years have laid down a solid foundation both for pursuing the mechanistic insights towards the biology of disease and for transitioning towards new diagnostics and therapeutics in clinical settings. Advancing GWAS towards mechanistic insights and clinical translations, however, urgently requires the development of advanced computational methods that can take advantage of the unique data features and increased data complexity of GWAS as well as the data available from parallel genomics studies. Here, we propose to develop a set of new computational methods to advance GWAS analytics beyond simple variant association analysis and move towards the understanding of the biology of disease and enable potential clinical translations. Specifically, in Aim 1, we will develop integrative methods to integrate GWAS with multiple gene expression mapping studies of distinct genetic ancestries to investigate the molecular mechanisms underlying the variant-trait associations and interrogate the contribution of ancestry specific genetic architecture underlying expression towards gene-trait associations. In Aim 2, we will develop causal inference methods to leverage the genetic associations to improve our understanding of the causal relationship among complex traits and to identify causal risk factors that underlie disease etiology. In Aim 3, we will develop prediction methods to make use of the genetic associations and take advantage of the genetic and environmental correlation among multiple complex traits to facilitate the genetic prediction of disease risk, aiding disease diagnosis and clinical applications. All methods will be implemented in user-friendly open- source software and disseminated to the scientific community. At its conclusion, the proposed study will provide a comprehensive suite of computational methods and software tools for advanced analytics in GWAS. These methods are essential for understanding the transcriptomic and causal mechanism underlying disease etiology, enabling accurate and robust genetic prediction of disease risks, and facilitating biological discoveries and insights.

项目摘要 许多全基因组关联研究（GWAS）已经成功地进行，确定了许多 与常见疾病和疾病相关的复杂性状相关的遗传变异。所述已鉴别基因 关联现在可以解释很大一部分的遗传贡献和性状遗传力，揭示了 常见疾病的遗传结构GWAS在过去几年的成功奠定了 为追求对疾病生物学的机械见解和 在临床环境中向新的诊断和治疗方法过渡。将GWAS推向 然而，机械的见解和临床翻译迫切需要开发先进的 计算方法，可以利用独特的数据特征和增加的数据复杂性， GWAS以及来自平行基因组学研究的数据。 在这里，我们建议开发一套新的计算方法来推进GWAS分析， 简单的变异关联分析，并走向疾病的生物学的理解，使 潜在的临床翻译具体来说，在目标1中，我们将开发集成GWAS的集成方法 通过对不同遗传祖先的多基因表达图谱研究， 变异性状关联的潜在机制，并询问祖先特异性的贡献。 遗传结构潜在的表达对基因性状协会。在目标2中，我们将发展因果关系 利用遗传关联的推理方法来提高我们对因果关系的理解 在复杂的性状中，并确定疾病病因学的因果风险因素。在目标3中，我们将开发 预测方法，利用遗传协会，并利用遗传和 多个复杂性状之间的环境相关性，以促进疾病风险的遗传预测， 有助于疾病诊断和临床应用。所有的方法都将在用户友好的开放- 源软件，并分发给科学界。拟议的研究结束时， 提供一套全面的计算方法和软件工具，用于GWAS中的高级分析。 这些方法对于理解疾病的转录组学和因果机制至关重要 病因学，能够对疾病风险进行准确和可靠的遗传预测，并促进生物学发现 和洞察力