Journal Article

Comprehensive functional genomic resource and integrative model for the human brain

Hadjimichael, Evi, Niu, Mingming, Kundakovic, Marija, Xu, Min, Polioudakis, Damon, Wu, Feinan, Pochareddy, Sirisha, Fitzgerald, Dominic, Li, Mingfeng, Zhang, Jing, Won, Hyejung, Park, Jonathan J., Hadzic, Tarik, Armoskus, Christoper, Moore, Jill, Borrman, Tyler, Warrell, Jonathan, Miller, Daniel J., Navarro, Fabio C. P., Kitchen, Robert R., Sanders, Stephan J., Giusti-Rodriguez, Paola, DelValle, Diane, Hoffman, Gabriel E., Zandi, Peter, Peng, Junmin, State, Matthew W., Brown, Miguel, Weng, Zhiping, Chae, Yooree, Xia, Yan, Ray, Mohana, Francoeur, Nancy, Abyzov, Alexej, Hahn, Chang-Gyu, Reddy, Timothy E, Evgrafov, Oleg V., Nathan, Aparna, Szekely, Anna, Gürsoy, Gamze, Geschwind, Daniel H., Zharovsky, Elizabeth, Sheppard, Brooke, White, Kevin P., Devillers, Olivia, Hauberg, Mads E., Clarke, Declan, Akbarian, Schahram, Amiri, Anahita, Wang, Xusheng, Mariani, Jessica, van Bakel, Harm, Wang, Daifeng, Vadukapuram, Ramu, Zhou, Holly, Alsayed, Majd, Goodman, Thomas, Mattei, Eugenio, Gao, Tianliuyun, Pratt, Henry, Shin, Joo Heon, Giase, Gina, Yang, Yucheng T., Dai, Rujia, Liu, Chunyu, Yang, Mo, Coppola, Gianfilippo, Cherskov,

2018

Overview

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.

• Publisher: The American Association for the Advancement of Science
• Subject: Aging / Behavior disorders / Brain / Brain - metabolism / Chromatin / Consortia / Datasets / Datasets as Topic / Deep Learning / Disorders / Domains / Enhancer Elements, Genetic / Enhancers / Epigenesis, Genetic / Epigenetics / Epigenomics / Fractions / Gene expression / Gene Expression Regulation / Gene Regulatory Networks / Genes / Genome-wide association studies / Genome-Wide Association Study / Genomes / Genomics / Genotype & phenotype / Health risks / Humans / Immunology / Internet / Internet resources / Isoforms / Learning algorithms / Machine learning / Mathematical models / Medical imaging / Mental disorders / Mental Disorders - genetics / Metabolic pathways / miRNA / Molecular modelling / Networks / Neuroimaging / Neurology / Phenotypes / Polygenic inheritance / Psychiatry / Quantitative Trait Loci / Ribonucleic acid / Risk / RNA / Schizophrenia / Single-Cell Analysis / Single-nucleotide polymorphism / Splicing / Transcription factors / Transcriptome