Comprehensive functional genomic resource and integrative model for the human brain

Daifeng Wang; Shuang Liu; Jonathan Warrell; Hyejung Won; Xu Shi; Fabio C.P. Navarro; Declan Clarke; Mengting Gu; Prashant Emani; Yucheng T. Yang; X. Min; Michael J. Gandal; Shaoke Lou; Jing Zhang; Jonathan J. Park; Chengfei Yan; Suhn KyongRhie; Kasidet Manakongtreecheep; Holly Zhou; A. Aparna Natha; Mette Peters; Eugenio Mattei; Dominic Fitzgerald; Tonya Brunetti; Jill Moore; Yan Jiang; Kiran Girdhar; Gabriel E. Hoffman; Selim Kalayci; Zeynep H. Gümüş; Gregory E. Crawford; Panos Roussos; Schahram Akbarian; Andrew E. Jaffe; Kevin P. White; Zhiping Weng; Nenad Sestan; Daniel H. Geschwind; James A. Knowles; Mark B. Gerstein

doi:10.1126/science.aat8464

Comprehensive functional genomic resource and integrative model for the human brain

Daifeng Wang, Shuang Liu, Jonathan Warrell, Hyejung Won, Xu Shi, Fabio C.P. Navarro, Declan Clarke, Mengting Gu, Prashant Emani, Yucheng T. Yang, X. Min, Michael J. Gandal, Shaoke Lou, Jing Zhang, Jonathan J. Park, Chengfei Yan, Suhn KyongRhie, Kasidet Manakongtreecheep, Holly Zhou, A. Aparna NathaMette Peters, Eugenio Mattei, Dominic Fitzgerald, Tonya Brunetti, Jill Moore, Yan Jiang, Kiran Girdhar, Gabriel E. Hoffman, Selim Kalayci, Zeynep H. Gümüş, Gregory E. Crawford, Panos Roussos, Schahram Akbarian, Andrew E. Jaffe, Kevin P. White, Zhiping Weng, Nenad Sestan, Daniel H. Geschwind, James A. Knowles, Mark B. Gerstein

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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.

Original language	English
Article number	1266
Journal	Science
Volume	362
Issue number	6420
DOIs	https://doi.org/10.1126/science.aat8464
State	Published - 14 Dec 2018

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Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., Min, X., Gandal, M. J., Lou, S., Zhang, J., Park, J. J., Yan, C., KyongRhie, S., Manakongtreecheep, K., Zhou, H., ... Gerstein, M. B. (2018). Comprehensive functional genomic resource and integrative model for the human brain. Science, 362(6420), Article 1266. https://doi.org/10.1126/science.aat8464

@article{df0897fa587a44df98848f153744773a,

title = "Comprehensive functional genomic resource and integrative model for the human brain",

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.",

author = "Daifeng Wang and Shuang Liu and Jonathan Warrell and Hyejung Won and Xu Shi and Navarro, {Fabio C.P.} and Declan Clarke and Mengting Gu and Prashant Emani and Yang, {Yucheng T.} and X. Min and Gandal, {Michael J.} and Shaoke Lou and Jing Zhang and Park, {Jonathan J.} and Chengfei Yan and Suhn KyongRhie and Kasidet Manakongtreecheep and Holly Zhou and {Aparna Natha}, A. and Mette Peters and Eugenio Mattei and Dominic Fitzgerald and Tonya Brunetti and Jill Moore and Yan Jiang and Kiran Girdhar and Hoffman, {Gabriel E.} and Selim Kalayci and G{\"u}m{\"u}{\c s}, {Zeynep H.} and Crawford, {Gregory E.} and Panos Roussos and Schahram Akbarian and Jaffe, {Andrew E.} and White, {Kevin P.} and Zhiping Weng and Nenad Sestan and Geschwind, {Daniel H.} and Knowles, {James A.} and Gerstein, {Mark B.}",

year = "2018",

month = dec,

day = "14",

doi = "10.1126/science.aat8464",

language = "English",

volume = "362",

journal = "Science",

issn = "0036-8075",

publisher = "American Association for the Advancement of Science",

number = "6420",

}

Wang, D, Liu, S, Warrell, J, Won, H, Shi, X, Navarro, FCP, Clarke, D, Gu, M, Emani, P, Yang, YT, Min, X, Gandal, MJ, Lou, S, Zhang, J, Park, JJ, Yan, C, KyongRhie, S, Manakongtreecheep, K, Zhou, H, Aparna Natha, A, Peters, M, Mattei, E, Fitzgerald, D, Brunetti, T, Moore, J, Jiang, Y, Girdhar, K , Hoffman, GE, Kalayci, S, Gümüş, ZH, Crawford, GE, Roussos, P , Akbarian, S, Jaffe, AE, White, KP, Weng, Z, Sestan, N, Geschwind, DH, Knowles, JA & Gerstein, MB 2018, 'Comprehensive functional genomic resource and integrative model for the human brain', Science, vol. 362, no. 6420, 1266. https://doi.org/10.1126/science.aat8464

TY - JOUR

T1 - Comprehensive functional genomic resource and integrative model for the human brain

AU - Wang, Daifeng

AU - Liu, Shuang

AU - Warrell, Jonathan

AU - Won, Hyejung

AU - Shi, Xu

AU - Navarro, Fabio C.P.

AU - Clarke, Declan

AU - Gu, Mengting

AU - Emani, Prashant

AU - Yang, Yucheng T.

AU - Min, X.

AU - Gandal, Michael J.

AU - Lou, Shaoke

AU - Zhang, Jing

AU - Park, Jonathan J.

AU - Yan, Chengfei

AU - KyongRhie, Suhn

AU - Manakongtreecheep, Kasidet

AU - Zhou, Holly

AU - Aparna Natha, A.

AU - Peters, Mette

AU - Mattei, Eugenio

AU - Fitzgerald, Dominic

AU - Brunetti, Tonya

AU - Moore, Jill

AU - Jiang, Yan

AU - Girdhar, Kiran

AU - Hoffman, Gabriel E.

AU - Kalayci, Selim

AU - Gümüş, Zeynep H.

AU - Crawford, Gregory E.

AU - Roussos, Panos

AU - Akbarian, Schahram

AU - Jaffe, Andrew E.

AU - White, Kevin P.

AU - Weng, Zhiping

AU - Sestan, Nenad

AU - Geschwind, Daniel H.

AU - Knowles, James A.

AU - Gerstein, Mark B.

PY - 2018/12/14

Y1 - 2018/12/14

N2 - 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.

AB - 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.

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U2 - 10.1126/science.aat8464

DO - 10.1126/science.aat8464

M3 - Article

C2 - 30545857

AN - SCOPUS:85058375534

SN - 0036-8075

VL - 362

JO - Science

JF - Science

IS - 6420

M1 - 1266

ER -

Comprehensive functional genomic resource and integrative model for the human brain

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