@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}
}
@article {57,
	title = {Opportunities and challenges for transcriptome-wide association studies.},
	journal = {Nat Genet},
	volume = {51},
	year = {2019},
	month = {2019 04},
	pages = {592-599},
	abstract = {<p>Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn{\textquoteright}s disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.</p>},
	keywords = {Crohn Disease, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Lipoproteins, LDL, Quantitative Trait Loci, Schizophrenia, Transcriptome},
	issn = {1546-1718},
	doi = {10.1038/s41588-019-0385-z},
	author = {Wainberg, Michael and Sinnott-Armstrong, Nasa and Mancuso, Nicholas and Barbeira, Alvaro N and Knowles, David A and Golan, David and Ermel, Raili and Ruusalepp, Arno and Quertermous, Thomas and Hao, Ke and Bj{\"o}rkegren, Johan L M and Im, Hae Kyung and Pasaniuc, Bogdan and Rivas, Manuel A and Kundaje, Anshul}
}
