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
• 负责人: SHIZHONG HAN
• 金额: $ 59.57万
• 依托单位: LIEBER INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-10 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10413142
• 关键词: 3' Untranslated Regions; 5' Untranslated Regions; Address; Affect; Bioinformatics; Biological; Bipolar Disorder; Brain; Brain region; Cell Nucleus; Cells; DNA; DNA Methylation; DNA sequencing; Data; Development; Disease; Enhancers; Gene Expression Regulation; Gene Targeting; Genes; Genetic; Genetic Risk; Genome; Genomics; Goals; Heritability; Hippocampus (Brain); Human Genome; Link; Machine Learning; Major Depressive Disorder; Maps; Mendelian randomization; Mental disorders; Methylation; Modeling; Mood Disorders; Neurons; Prefrontal Cortex; Prevention; RNA Splicing; Resolution; Resources; Sample Size; Sampling; Scheme; Schizophrenia; Short-Term Memory; Signal Transduction; Site; Statistical Methods; Technology; Testing; Tissue-Specific Gene Expression; Tissues; Transcriptional Regulation; Translating; United States; Untranslated RNA; Variant; Weight; Work; base; bisulfite sequencing; brain cell; brain tissue; burden of illness; causal variant; cell type; cognitive function; data resource; deep learning; deep neural network; design; frontal lobe; functional genomics; gene discovery; gene regulatory network; genetic association; genetic variant; genome wide association study; improved; innovation; insight; methylome; multidisciplinary; neuroimaging; novel; polygenic risk score; postnatal; promoter; psychiatric genomics; psychogenetics; psychosis risk; risk variant; statistics; success; supervised learning; trait; transcriptome sequencing; whole genome

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， 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。