研究揭示多組織轉錄組關聯和孟德爾隨機分析的整合網絡
作者：
小柯機器人發布時間：2020/10/8 22:12:31
美國範德堡大學Eric R. Gamazon、Dan Zhou研究小組近日取得一項新成果。經過不懈努力，他們揭示了多組織聯合轉錄組關聯和孟德爾隨機分析的整合網絡。這一研究成果發表在2020年10月5日出版的國際學術期刊《自然-遺傳學》上。
在本研究中，研究人員研發了整合多個相似組織(（JTI）和孟德爾隨機化因果推斷算法MR-JTI。JTI利用共享的遺傳調控，借用不同組織轉錄組中的信息，以組織依賴性的方式來提高預測性能。
值得注意的是，在特殊情況下JTI包含了單組織插補方法PrediXcan，並且優於其他單組織方法（貝葉斯稀疏線性混合模型和Dirichlet過程回歸）。MR-JTI模擬了變體異質性（主要是由水平多效性造成、解決了轉錄組關聯研究注釋的難題），並通過I型錯誤控制進行因果推理。
研究人員明確了基因表達和複雜性狀的遺傳結構並證實了孟德爾隨機化作為轉錄組範圍關聯研究因果推斷的適用性。研究人員提供了從GTEx和PsychENCODE平臺生成的估算模型資源。對生物庫和薈萃分析數據的分析以及廣泛的模擬表明，相比於現有的方法，JTI的計算能力、重複和因果映射率顯著提高。
附：英文原文
Title: A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis
Author: Dan Zhou, Yi Jiang, Xue Zhong, Nancy J. Cox, Chunyu Liu, Eric R. Gamazon
Issue&Volume: 2020-10-05
Abstract: Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches. MR-JTI, a unified framework for joint-tissue imputation and Mendelian randomization, improves prediction performance in a tissue-dependent manner when applied to large-scale biobanks and meta-analysis data.
DOI: 10.1038/s41588-020-0706-2
Source: https://www.nature.com/articles/s41588-020-0706-2