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

项目摘要 许多全基因组关联研究(GWAS)已经成功地进行了，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。已识别的基因 关联现在可以解释很大一部分遗传贡献和性状遗传力，揭示了 常见疾病背后的遗传结构。在过去的几年里，全球气候变化网络的成功奠定了 为追求对疾病生物学的机械论见解和 在临床环境中向新的诊断和治疗过渡。推动全球气候变化网络走向 然而，机械论的洞察和临床翻译迫切需要先进的 可以利用的独特数据功能和增加的数据复杂性的计算方法 以及从平行基因组学研究中获得的数据。 在这里，我们建议开发一套新的计算方法，以推动GWAS分析超越 简单的变异关联分析和走向对疾病生物学的理解，并使 潜在的临床翻译。具体地说，在目标1中，我们将开发综合方法来整合全球气候变化 用多基因表达图谱研究不同的遗传祖先来研究分子 变异性状关联的潜在机制，并询问祖先特有的贡献 基因结构是基因-性状关联表达的基础。在目标2中，我们将发展因果关系 利用遗传关联来提高我们对因果关系的理解的推理方法 在复杂的特征之间，并确定疾病病因学基础上的因果风险因素。在目标3中，我们将开发 利用遗传关联并利用遗传和遗传优势的预测方法 多个复杂性状之间的环境相关性有助于疾病风险的遗传预测， 辅助疾病诊断和临床应用。所有方法都将在用户友好的开放式环境中实现- 来源软件，并向科学界传播。在结束时，拟议的研究将 提供一套全面的计算方法和软件工具，用于全球气候变化分析的高级分析。 这些方法对于理解疾病的转录和致病机制是必不可少的。 病因学，使疾病风险的准确和可靠的遗传预测成为可能，并促进生物发现 和洞察力。

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.