Comprehensive functional genomic resource and integrative model for the human brain
Authors: Wang Daifeng , Liu Shuang , Warrell Jonathan , Won Hyejung , Shi Xu , Navarro Fabio C. P. , Clarke Declan , Gu Mengting , Emani Prashant , Yang Yucheng T. , Xu Min , Gandal Michael J. , Lou Shaoke , Zhang Jing , Park Jonathan J. , Yan Chengfei , Rhie Suhn Kyong , Manakongtreecheep Kasidet , Zhou Holly , Nathan Aparna , Peters Mette , Mattei Eugenio , Fitzgerald Dominic , Brunetti Tonya , Moore Jill , Jiang Yan , Girdhar Kiran , Hoffman Gabriel E. , Kalayci Selim , Gümüş Zeynep H. , Crawford Gregory E. ,   PsychENCODE Consortium, Roussos Panos , Akbarian Schahram , Jaffe Andrew E. , White Kevin P. , Weng Zhiping , Sestan Nenad , Geschwind Daniel H. , Knowles James A. , Gerstein Mark B. , Ashley-Koch Allison E. , Crawford Gregory E. , Garrett Melanie E. , Song Lingyun , Safi Alexias , Johnson Graham D. , Wray Gregory A. , Reddy Timothy E , Goes Fernando S. , Zandi Peter , Bryois Julien , Jaffe Andrew E. , Price Amanda J. , Ivanov Nikolay A. , Collado-Torres Leonardo , Hyde Thomas M. , Burke Emily E. , Kleiman Joel E. , Tao Ran , Shin Joo Heon , Akbarian Schahram , Girdhar Kiran , Jiang Yan , Kundakovic Marija , Brown Leanne , Kassim Bibi S. , Park Royce B. , Wiseman Jennifer R , Zharovsky Elizabeth , Jacobov Rivka , Devillers Olivia , Flatow Elie , Hoffman Gabriel E. , Lipska Barbara K. , Lewis David A. , Haroutunian Vahram , Hahn Chang-Gyu , Charney Alexander W. , Dracheva Stella , Kozlenkov Alexey , Belmont Judson , DelValle Diane , Francoeur Nancy , Hadjimichael Evi , Pinto Dalila , van Bakel Harm , Roussos Panos , Fullard John F. , Bendl Jaroslav , Hauberg Mads E. , Mangravite Lara M , Peters Mette A. , Chae Yooree , Peng Junmin , Niu Mingming , Wang Xusheng , Webster Maree J. , Beach Thomas G. , Chen Chao , Jiang Yi , Dai Rujia , Shieh Annie W. , Liu Chunyu , Grennan Kay S. , Xia Yan , Vadukapuram Ramu , Wang Yongjun , Fitzgerald Dominic , Cheng Lijun , Brown Miguel , Brown Mimi , Brunetti Tonya , Goodman Thomas , Alsayed Majd , Gandal Michael J. , Geschwind Daniel H. , Won Hyejung , Polioudakis Damon , Wamsley Brie , Yin Jiani , Hadzic Tarik , De La Torre Ubieta Luis , Swarup Vivek , Sanders Stephan J. , State Matthew W. , Werling Donna M. , An Joon-Yong , Sheppard Brooke , Willsey A. Jeremy , White Kevin P. , Ray Mohana , Giase Gina , Kefi Amira , Mattei Eugenio , Purcaro Michael , Weng Zhiping , Moore Jill , Pratt Henry , Huey Jack , Borrman Tyler , Sullivan Patrick F. , Giusti-Rodriguez Paola , Kim Yunjung , Sullivan Patrick , Szatkiewicz Jin , Rhie Suhn Kyong , Armoskus Christoper , Camarena Adrian , Farnham Peggy J. , Spitsyna Valeria N. , Witt Heather , Schreiner Shannon , Evgrafov Oleg V. , Knowles James A. , Gerstein Mark , Liu Shuang , Wang Daifeng , Navarro Fabio C. P. , Warrell Jonathan , Clarke Declan , Emani Prashant S. , Gu Mengting , Shi Xu , Xu Min , Yang Yucheng T. , Kitchen Robert R. , Gürsoy Gamze , Zhang Jing , Carlyle Becky C. , Nairn Angus C. , Li Mingfeng , Pochareddy Sirisha , Sestan Nenad , Skarica Mario , Li Zhen , Sousa Andre M. M. , Santpere Gabriel , Choi Jinmyung , Zhu Ying , Gao Tianliuyun , Miller Daniel J. , Cherskov Adriana , Yang Mo , Amiri Anahita , Coppola Gianfilippo , Mariani Jessica , Scuderi Soraya , Szekely Anna , Vaccarino Flora M. , Wu Feinan , Weissman Sherman , Roychowdhury Tanmoy , Abyzov Alexej 
Published: December 2018

INTRODUCTION
Strong genetic associations have been found for a number of psychiatric disorders. However, understanding the underlying molecular mechanisms remains challenging.

RATIONALE
To address this challenge, the PsychENCODE Consortium has developed a comprehensive online resource and integrative models for the functional genomics of the human brain.

RESULTS
The base of the pyramidal resource is the datasets generated by PsychENCODE, including bulk transcriptome, chromatin, genotype, and Hi-C datasets and single-cell transcriptomic data from ~32,000 cells for major brain regions. We have merged these with data from Genotype-Tissue Expression (GTEx), ENCODE, Roadmap Epigenomics, and single-cell analyses. Via uniform processing, we created a harmonized resource, allowing us to survey functional genomics data on the brain over a sample size of 1866 individuals.
From this uniformly processed dataset, we created derived data products. These include lists of brain-expressed genes, coexpression modules, and single-cell expression profiles for many brain cell types; ~79,000 brain-active enhancers with associated Hi-C loops and topologically associating domains; and ~2.5 million expression quantitative-trait loci (QTLs) comprising ~238,000 linkage-disequilibrium–independent single-nucleotide polymorphisms and of other types of QTLs associated with splice isoforms, cell fractions, and chromatin activity. By using these, we found that >88% of the cross-population variation in brain gene expression can be accounted for by cell fraction changes. Furthermore, a number of disorders and aging are associated with changes in cell-type proportions. The derived data also enable comparison between the brain and other tissues. In particular, by using spectral analyses, we found that the brain has distinct expression and epigenetic patterns, including a greater extent of noncoding transcription than other tissues.
The top level of the resource consists of integrative networks for regulation and machine-learning models for disease prediction. The networks include a full gene regulatory network (GRN) for the brain, linking transcription factors, enhancers, and target genes from merging of the QTLs, generalized element-activity correlations, and Hi-C data. By using this network, we link disease genes to genome-wide association study (GWAS) variants for psychiatric disorders. For schizophrenia, we linked 321 genes to the 142 reported GWAS loci. We then embedded the regulatory network into a deep-learning model to predict psychiatric phenotypes from genotype and expression. Our model gives a ~6-fold improvement in prediction over additive polygenic risk scores. Moreover, it achieves a ~3-fold improvement over additive models, even when the gene expression data are imputed, highlighting the value of having just a small amount of transcriptome data for disease prediction. Lastly, it highlights key genes and pathways associated with disorder prediction, including immunological, synaptic, and metabolic pathways, recapitulating de novo results from more targeted analyses.

CONCLUSION
Our resource and integrative analyses have uncovered genomic elements and networks in the brain, which in turn have provided insight into the molecular mechanisms underlying psychiatric disorders. Our deep-learning model improves disease risk prediction over traditional approaches and can be extended with additional data types (e.g., microRNA and neuroimaging).

A comprehensive functional genomic resource for the adult human brain.
The resource forms a three-layer pyramid. The bottom layer includes sequencing datasets for traits, such as schizophrenia. The middle layer represents derived datasets, including functional genomic elements and QTLs. The top layer contains integrated models, which link genotypes to phenotypes. DSPN, Deep Structured Phenotype Network; PC1 and PC2, principal components 1 and 2; ref, reference; alt, alternate; H3K27ac, histone H3 acetylation at lysine 27.