Cargando…

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Jiyun, Chen, Qiang, Braun, Patricia R., Perzel Mandell, Kira A., Jaffe, Andrew E., Tan, Hao Yang, Hyde, Thomas M., Kleinman, Joel E., Potash, James B., Shinozaki, Gen, Weinberger, Daniel R., Han, Shizhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407663/
https://www.ncbi.nlm.nih.gov/pubmed/35969790
http://dx.doi.org/10.1073/pnas.2206069119
_version_ 1784774418231197696
author Zhou, Jiyun
Chen, Qiang
Braun, Patricia R.
Perzel Mandell, Kira A.
Jaffe, Andrew E.
Tan, Hao Yang
Hyde, Thomas M.
Kleinman, Joel E.
Potash, James B.
Shinozaki, Gen
Weinberger, Daniel R.
Han, Shizhong
author_facet Zhou, Jiyun
Chen, Qiang
Braun, Patricia R.
Perzel Mandell, Kira A.
Jaffe, Andrew E.
Tan, Hao Yang
Hyde, Thomas M.
Kleinman, Joel E.
Potash, James B.
Shinozaki, Gen
Weinberger, Daniel R.
Han, Shizhong
author_sort Zhou, Jiyun
collection PubMed
description There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.
format Online
Article
Text
id pubmed-9407663
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher National Academy of Sciences
record_format MEDLINE/PubMed
spelling pubmed-94076632023-02-15 Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders Zhou, Jiyun Chen, Qiang Braun, Patricia R. Perzel Mandell, Kira A. Jaffe, Andrew E. Tan, Hao Yang Hyde, Thomas M. Kleinman, Joel E. Potash, James B. Shinozaki, Gen Weinberger, Daniel R. Han, Shizhong Proc Natl Acad Sci U S A Biological Sciences There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits. National Academy of Sciences 2022-08-15 2022-08-23 /pmc/articles/PMC9407663/ /pubmed/35969790 http://dx.doi.org/10.1073/pnas.2206069119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Biological Sciences
Zhou, Jiyun
Chen, Qiang
Braun, Patricia R.
Perzel Mandell, Kira A.
Jaffe, Andrew E.
Tan, Hao Yang
Hyde, Thomas M.
Kleinman, Joel E.
Potash, James B.
Shinozaki, Gen
Weinberger, Daniel R.
Han, Shizhong
Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders
title Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders
title_full Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders
title_fullStr Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders
title_full_unstemmed Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders
title_short Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders
title_sort deep learning predicts dna methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407663/
https://www.ncbi.nlm.nih.gov/pubmed/35969790
http://dx.doi.org/10.1073/pnas.2206069119
work_keys_str_mv AT zhoujiyun deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT chenqiang deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT braunpatriciar deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT perzelmandellkiraa deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT jaffeandrewe deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT tanhaoyang deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT hydethomasm deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT kleinmanjoele deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT potashjamesb deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT shinozakigen deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT weinbergerdanielr deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders
AT hanshizhong deeplearningpredictsdnamethylationregulatoryvariantsinthehumanbrainandelucidatesthegeneticsofpsychiatricdisorders