小柯机器人

美国范德堡大学Eric R. Gamazon、Dan Zhou研究小组近日取得一项新成果。经过不懈努力，他们揭示了多组织联合转录组关联和孟德尔随机分析的整合网络。这一研究成果发表在2020年10月5日出版的国际学术期刊《自然-遗传学》上。

在本研究中，研究人员研发了整合多个相似组织(（JTI）和孟德尔随机化因果推断算法MR-JTI。JTI利用共享的遗传调控，借用不同组织转录组中的信息，以组织依赖性的方式来提高预测性能。

值得注意的是，在特殊情况下JTI包含了单组织插补方法PrediXcan，并且优于其他单组织方法（贝叶斯稀疏线性混合模型和Dirichlet过程回归）。MR-JTI模拟了变体异质性（主要是由水平多效性造成、解决了转录组关联研究注释的难题），并通过I型错误控制进行因果推理。

研究人员明确了基因表达和复杂性状的遗传结构并证实了孟德尔随机化作为转录组范围关联研究因果推断的适用性。研究人员提供了从GTEx和PsychENCODE平台生成的估算模型资源。对生物库和荟萃分析数据的分析以及广泛的模拟表明，相比于现有的方法，JTI的计算能力、重复和因果映射率显著提高。

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

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
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