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

Jiyun Zhou; Qiang Chen; Patricia R. BraunKira A. Perzel MellAndrew E. JaffeHao Yang TanThomas M. HydeJoel E. KleinmanJames B. PotashGen ShinozakiDaniel R. WeinbergerShizhong Han

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

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

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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.

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《Proceedings of the National Academy of Sciences of the United States of America.》 |2022年第34期|e2206069119-e2206069119|共1页
作者
Jiyun Zhou; Qiang Chen; Patricia R. BraunKira A. Perzel MellAndrew E. JaffeHao Yang TanThomas M. HydeJoel E. KleinmanJames B. PotashGen ShinozakiDaniel R. WeinbergerShizhong Han;
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作者单位

Lieber Institute for Brain Development, The Johns Hopkins Medical Campus, Baltimore, MD 21287;

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收录信息美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类自然科学总论;
关键词
DNA methylation quantitative trait loci (mQTL); convolutional neural network (CNN); transformer; regulatory variants; GWAS;

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

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