@article {111,
	title = {A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.},
	journal = {Nat Genet},
	volume = {52},
	year = {2020},
	month = {2020 11},
	pages = {1239-1246},
	abstract = {<p>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.</p>},
	keywords = {Animals, Gene Expression Profiling, Genetic Association Studies, Humans, Lipoproteins, LDL, Mendelian Randomization Analysis, Mice, Models, Genetic, Multifactorial Inheritance, Predictive Value of Tests},
	issn = {1546-1718},
	doi = {10.1038/s41588-020-0706-2},
	author = {Zhou, Dan and Jiang, Yi and Zhong, Xue and Cox, Nancy J and Liu, Chunyu and Gamazon, Eric R}
}
@article {64,
	title = {A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data.},
	journal = {Nat Neurosci},
	volume = {22},
	year = {2019},
	month = {2019 05},
	pages = {691-699},
	abstract = {<p>Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.</p>},
	keywords = {Animals, Bayes Theorem, Disease Models, Animal, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Mice, Risk Factors, Schizophrenia},
	issn = {1546-1726},
	doi = {10.1038/s41593-019-0382-7},
	author = {Wang, Quan and Chen, Rui and Cheng, Feixiong and Wei, Qiang and Ji, Ying and Yang, Hai and Zhong, Xue and Tao, Ran and Wen, Zhexing and Sutcliffe, James S and Liu, Chunyu and Cook, Edwin H and Cox, Nancy J and Li, Bingshan}
}
