Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.
Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.
复制标题
DOI：
10.1371/journal.pcbi.1010011
复制
发表时间：
2022-05
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
Genomewide association studies (GWAS) have identified a large number of loci associated with neuropsychiatric traits, however, understanding the molecular mechanisms underlying these loci remains difficult. To help prioritize causal variants and interpret their functions, computational methods have been developed to predict regulatory effects of non-coding variants. An emerging approach to variant annotation is deep learning models that predict regulatory functions from DNA sequences alone. While such models have been trained on large publicly available dataset such as ENCODE, neuropsychiatric trait-related cell types are under-represented in these datasets, thus there is an urgent need of better tools and resources to annotate variant functions in such cellular contexts. To fill this gap, we collected a large collection of neurodevelopment-related cell/tissue types, and trained deep Convolutional Neural Networks (ResNet) using such data. Furthermore, our model, called MetaChrom, borrows information from public epigenomic consortium to improve the accuracy via transfer learning. We show that MetaChrom is substantially better in predicting experimentally determined chromatin accessibility variants than popular variant annotation tools such as CADD and delta-SVM. By combining GWAS data with MetaChrom predictions, we prioritized 31 SNPs for Schizophrenia, suggesting potential risk genes and the biological contexts where they act. In summary, MetaChrom provides functional annotations of any DNA variants in the neuro-development context and the general method of MetaChrom can also be extended to other disease-related cell or tissue types. A large number of genetic variants have been statistically associated with the risks of common diseases. However, whether such variants are actual risk variants and when and where they function are often unknown. To address this challenge, machine learning methods have been developed to predict functional variants in specific cellular contexts. These methods correlate DNA sequences with their biological functions, e.g. enhancer activities, and can predict effects of single base mutations. Nevertheless, the training data used by existing methods often lack neurodevelopment-related cell types, thus annotating variant effects in neuropsychiatric genetics remains difficult. In this work, we fill this gap by collecting a large set of regulatory genomic datasets from fetal and adult brain, from iPSC-based cellular models and brain organoids. We trained deep learning models on this data, and further improved its performance by borrowing information from large external datasets, a strategy known as transfer learning. Our tool, MetaChrom, is substantially better at predicting experimentally determined regulatory variants than current methods, and helps us identify candidate risk variants of Schizophrenia. We believe MetaChrom provides a valuable tool for the neuropsychiatric genetic community, and the software can be of interest to researchers in other fields as well.
登录
查看更多内容
影响因子：
64.5
作者：
de la Torre-Ubieta L;Stein JL;Won H;Opland CK;Liang D;Lu D;Geschwind DH
通讯作者：
Geschwind DH
遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
64.8
作者：
Zoonomia Consortium
通讯作者：
Zoonomia Consortium
影响因子：
7
作者：
Arnold P;Schöler A;Pachkov M;Balwierz PJ;Jørgensen H;Stadler MB;van Nimwegen E;Schübeler D
通讯作者：
Schübeler D
影响因子：
30.8
作者：
Calderon, Diego;Nguyen, Michelle L. T.;Pritchard, Jonathan K.
通讯作者：
Pritchard, Jonathan K.