主 题:A unified quantile framework reveals nonlinear heterogeneous transcriptome-wide associations揭示非线性转录组关联的统一分位数框架
主讲人:清华大学王天颖助理教授
主持人:统计学院林华珍教授
时间:2023年6月9日(周五)下午15:00-16:00
举办地点:柳林校区弘远楼408会议室
主办单位:统计研究中心和统计学院 科研处
主讲人简介:
王天颖,清华大学统计学研究中心助理教授。她于2018年获得Texas A&M University 统计学博士学位,在哥伦比亚大学生物统计系从事博士后工作,并在2020年加入清华大学。她的研究兴趣包括测量误差分析、分位数回归、高维数据分析,以及统计方法在遗传学和环境科学的应用。
内容简介:
Transcriptome-wide association studies (TWAS) are powerful tools for identifying putative causal genes by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that exist in biological systems. To address this limitation, we propose a novel quantile transcriptomics framework that takes into account the nonlinear and heterogeneous nature of gene-trait associations. Our approach leverages a quantile-based gene expression model into the TWAS model, which allows for the discovery of nonlinear and heterogeneous gene-trait associations. By conducting elaborate simulations and examining various psychiatric and neurodegenerative ailments, we demonstrated that the suggested model outperforms traditional techniques considerably in identifying gene-trait associations. Additionally, it uncovers crucial insights into non-linear relationships between gene expression levels and phenotypes, which complements the existing knowledge. The suggested approach was additionally implemented on 100 phenotypes from the UK Biobank, and the outcomes can be accessed through a publicly accessible repository.
转录组关联研究(TWAS)是通过整合全基因组关联研究和基因表达数据来识别推定的致病基因的有力工具。然而,大多数TWAS方法侧重于基因和性状之间的线性关联,忽略了生物系统中存在的复杂非线性关系。为了解决这一限制,主讲人提出了一个新的分位数转录组学框架,考虑到基因-性状关联的非线性和异质性。主讲人的方法在TWAS模型中利用了基于分位数的基因表达模型,该模型能够发现非线性和异质基因-性状关联。通过进行详细的模拟和验证各种精神和神经退行性疾病,主讲人证明了所推荐的模型在识别基因-性状关联方面明显优于传统技术。此外,它揭示了基因表达水平和表型之间非线性关系的重要见解,这补充了现有的知识。推荐的方法还被应用于英国生物银行的100种表型,结果可以通过公共访问的存储库访问。