Integrative approaches to identification and interpretation of genes underlying psychiatric disorders
识别和解释精神疾病基因的综合方法
基本信息
- 批准号:10630276
- 负责人:
- 金额:$ 59.57万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-08-10 至 2025-05-31
- 项目状态:未结题
- 来源:
- 关键词:3&apos Untranslated Regions5&apos Untranslated RegionsAddressAffectBioinformaticsBiologicalBipolar DisorderBrainBrain regionCell NucleusCellsChromosome MappingDNADNA MethylationDNA sequencingDataDevelopmentDinucleoside PhosphatesDiseaseElasticityEnhancersGene Expression RegulationGene TargetingGenesGeneticGenetic RiskGenomeGenomicsGoalsHeritabilityHippocampusHuman GenomeLinkMachine LearningMajor Depressive DisorderMapsMendelian randomizationMental disordersMethylationModelingMood DisordersNeuronsPrefrontal CortexPreventionRNA SplicingResolutionSample SizeSamplingSchemeSchizophreniaShort-Term MemorySignal TransductionSiteStatistical MethodsTechnologyTestingTissue-Specific Gene ExpressionTissuesTranscriptional RegulationTranslatingUnited StatesUntranslated RNAVariantWeightWorkbisulfite sequencingbrain cellbrain tissueburden of illnesscausal variantcell typecognitive functiondata resourcedeep learningdeep neural networkdesignfrontal lobefunctional genomicsgene discoverygene regulatory networkgenetic associationgenome resourcegenome wide association studyimprovedinnovationinsightmethylomemultidisciplinaryneuroimagingnovelpolygenic risk scorepostnatalpromoterpsychiatric genomicspsychogeneticspsychosis riskrisk variantsingle nucleus RNA-sequencingstatisticssuccesssupervised learningtraitwhole genome
项目摘要
Psychiatric disorders contribute substantially to the disease burden in the United States and worldwide. There
is strong evidence for a genetic contribution to many psychiatric illnesses. In recent years, with the
advancement of high throughput genomic technologies and the availability of large samples, remarkable
success has been made in risk gene discovery for major psychiatric disorders [e.g., schizophrenia (SCZ),
bipolar disorder (BD) and major depressive disorder (MDD)] through genome-wide association studies
(GWAS). However, due to the high complexity of the human genome, few causal genes or variants have been
identified within GWAS risk loci, thus, to date, limiting the potential of translating these genetic findings into
biological mechanisms. There is now a great need to pinpoint causal genes/variants at the known GWAS risk
loci and to understand their causal mechanisms, as well as to discover novel genes from novel risk loci. There
is also growing evidence that risk variants from GWAS tend to be located in regulatory DNA regions in
disease-relevant tissues or cell types, suggesting that risk variants may act through regulation of gene
expression. Studies leveraging diverse functional genomic resources may benefit psychiatric risk gene
discovery and result in better prediction of their biological relevance. This proposal aims to employ highly
integrative approaches to identify causal genes and regulatory noncoding variants underlying SCZ, BD, and
MDD. Our specific aims are: 1) Integrate GWAS with brain methylome for risk gene discovery, by leveraging a
dense high-resolution reference panel of DNAm from whole genome bisulfite sequencing of DNA from three
different brain regions (frontal cortex, hippocampus, and caudate) and an enlarged array-based reference
panel; 2) Apply a deep learning approach to predicting disease-relevant regulatory variants, by employing
features from disease-relevant gene regulatory networks and functional genomic annotations within brain
tissues and neural cell types; and 3) Map prioritized genes and variants to specific brain cell types and brain
function. We have assembled an outstanding multidisciplinary team with expertise in psychiatric genetics,
bioinformatics, machine learning, and neuroimaging. Our goal is to apply multidisciplinary and cutting-edge
analytical strategies to help address the challenges arising in the post-GWAS era. The identification and
characterization of risk genes and noncoding regulatory variants would help improve our understanding of the
biological mechanisms that underlie psychiatric illnesses, moving us closer to designing effective prevention
and treatment for these disorders.
精神疾病在美国和全世界的疾病负担中占很大比例。那里
是许多精神疾病遗传因素的有力证据。近年来随着
高通量基因组技术的进步和大样本的可用性,令人瞩目
在发现主要精神疾病的风险基因方面已经取得了成功[例如,精神分裂症(SCZ),
双相情感障碍(BD)和重度抑郁症(MDD)]通过全基因组关联研究
(GWAS)。然而,由于人类基因组的高度复杂性,很少有致病基因或变体被发现。
因此,迄今为止,限制了将这些遗传发现转化为
生物机制。现在非常需要在已知的GWAS风险中精确定位致病基因/变异
基因座,并了解其因果机制,以及发现新的基因从新的风险基因座。那里
越来越多的证据表明,来自GWAS的风险变异倾向于位于基因组中的调控DNA区域,
疾病相关的组织或细胞类型,这表明风险变异可能通过基因调控起作用,
表情利用不同功能基因组资源的研究可能有利于精神病风险基因
发现并导致更好地预测其生物学相关性。该提案旨在高度利用
综合方法,以确定致病基因和调控非编码变异的基础SCZ,BD,
MDD。我们的具体目标是:1)将GWAS与脑甲基化组整合用于风险基因发现,
来自全基因组的DNA m的高密度高分辨率参考组
不同的大脑区域(额叶皮层、海马和尾状核)和放大的基于阵列的参考
2)应用深度学习方法来预测疾病相关的调节变体,
脑内疾病相关基因调控网络和功能基因组注释的特征
组织和神经细胞类型;以及3)将优先化的基因和变体映射到特定的脑细胞类型和脑
功能我们组建了一支优秀的多学科团队,他们在精神病遗传学方面具有专长,
生物信息学、机器学习和神经成像。我们的目标是应用多学科和尖端的
分析战略,以帮助解决在后GWAS时代出现的挑战。确定和
风险基因和非编码调控变异的特征将有助于我们更好地理解
精神疾病背后的生物机制,使我们更接近设计有效的预防措施,
和治疗这些疾病的方法。
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare.
一种自检自适应 SMOTE 算法 (SASMOTE),用于医疗保健中高度不平衡的数据分类。
- DOI:10.1186/s13040-023-00330-4
- 发表时间:2023-04-25
- 期刊:
- 影响因子:4.5
- 作者:
- 通讯作者:
Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.
深度学习可预测特定脑细胞类型中的 DNA 甲基化调控变异,并增强大脑疾病的精细定位。
- DOI:10.1101/2024.01.18.576319
- 发表时间:2024
- 期刊:
- 影响因子:0
- 作者:Zhou,Jiyun;Weinberger,DanielR;Han,Shizhong
- 通讯作者:Han,Shizhong
scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia.
scMeFormer:一种基于 Transformer 的深度学习模型,用于估算单细胞中的 DNA 甲基化状态,增强了精神分裂症表观遗传改变的检测。
- DOI:10.1101/2024.01.25.577200
- 发表时间:2024
- 期刊:
- 影响因子:0
- 作者:Zhou,Jiyun;Luo,Chongyuan;Liu,Hanqing;Heffel,MatthewG;Straub,RichardE;Kleinman,JoelE;Hyde,ThomasM;Ecker,JosephR;Weinberger,DanielR;Han,Shizhong
- 通讯作者:Han,Shizhong
Molecular phenotypes associated with antipsychotic drugs in the human caudate nucleus.
- DOI:10.1038/s41380-022-01453-6
- 发表时间:2022-04
- 期刊:
- 影响因子:11
- 作者:Mandell, Kira A. Perzel;Eagles, Nicholas J.;Deep-Soboslay, Amy;Tao, Ran;Han, Shizhong;Wilton, Richard;Szalay, Alexander S.;Hyde, Thomas M.;Kleinman, Joel E.;Jaffe, Andrew E.;Weinberger, Daniel R.
- 通讯作者:Weinberger, Daniel R.
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SHIZHONG HAN其他文献
SHIZHONG HAN的其他文献
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{{ truncateString('SHIZHONG HAN', 18)}}的其他基金
Integrative approaches to identification and interpretation of genes underlying psychiatric disorders
识别和解释精神疾病基因的综合方法
- 批准号:
10413142 - 财政年份:2020
- 资助金额:
$ 59.57万 - 项目类别:
A systems approach to the genetic study of alcohol dependence
酒精依赖遗传研究的系统方法
- 批准号:
9237365 - 财政年份:2017
- 资助金额:
$ 59.57万 - 项目类别:
Functional methylomics approaches for schizophrenia in the frontal cortex and hippocampus
额叶皮层和海马区精神分裂症的功能甲基组学方法
- 批准号:
9891106 - 财政年份:2017
- 资助金额:
$ 59.57万 - 项目类别:
A SYSTEMS APPROACH TO THE GENETIC STUDY OF ALCOHOL DEPENDENCE
酒精依赖性遗传研究的系统方法
- 批准号:
10187881 - 财政年份:2017
- 资助金额:
$ 59.57万 - 项目类别:
Fine mapping a gene sub-network underlying alcohol dependence
精细绘制酒精依赖背后的基因子网络
- 批准号:
9696026 - 财政年份:2014
- 资助金额:
$ 59.57万 - 项目类别:
Fine mapping a gene sub-network underlying alcohol dependence
精细绘制酒精依赖背后的基因子网络
- 批准号:
8674963 - 财政年份:2014
- 资助金额:
$ 59.57万 - 项目类别:
Fine mapping a gene sub-network underlying alcohol dependence
精细绘制酒精依赖背后的基因子网络
- 批准号:
8887090 - 财政年份:2014
- 资助金额:
$ 59.57万 - 项目类别:
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