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Science. 2018 Dec 14;362(6420). pii: eaat8464. doi: 10.1126/science.aat8464.

Comprehensive functional genomic resource and integrative model for the human brain.

Author information

1
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
2
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
3
Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA.
4
Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
5
UNC Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599, USA.
6
Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California-Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA.
7
Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90007, USA.
8
Sage Bionetworks, Seattle, WA 98109, USA.
9
Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
10
Institute for Genomics and Systems Biology, Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
11
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
12
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
13
Center for Genomic and Computational Biology, Department of Pediatrics, Duke University, Durham, NC 27708, USA.
14
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
15
Lieber Institute for Brain Development, Johns Hopkins Medical Campus, and Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
16
Tempus Labs, Chicago, IL 60654, USA.
17
Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA.
18
Department of Human Genetics, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA. dhg@mednet.ucla.edu james.knowles@downstate.edu mark@gersteinlab.org.
19
Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA.
20
Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA 90095, USA.
21
SUNY Downstate Medical Center College of Medicine, Brooklyn, NY 11203, USA. dhg@mednet.ucla.edu james.knowles@downstate.edu mark@gersteinlab.org.
22
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. dhg@mednet.ucla.edu james.knowles@downstate.edu mark@gersteinlab.org.
23
Department of Computer Science, Yale University, New Haven, CT 06520, USA.
24
Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA.

Abstract

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.

PMID:
30545857
DOI:
10.1126/science.aat8464
[Indexed for MEDLINE]

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