Modeling Dimensionality and Genetic Heterogeneity in Schizophrenia

精神分裂症的维度和遗传异质性建模

• 批准号: 9088679
• 负责人: Anna R. Docherty
• 金额: $ 2.75万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9088679
• 关键词: Accounting; Address; Age of Onset; Alcohol consumption; Applied Genetics; Architecture; Autopsy; Bioinformatics; Biological; Brain; Chromosome Mapping; Classification; Clinical; Collaborations; Complex; Data; Dimensions; Disease; Environment; Gene Expression; Genes; Genetic; Genetic Heterogeneity; Genetic Polymorphism; Genetic Risk; Genomics; Goals; Heritability; Heterogeneity; Human; Individual; Interview; Lead; Machine Learning; Malignant neoplasm of lung; Malignant neoplasm of ovary; Maps; Measurement; Measures; Mental disorders; Mentors; Meta-Analysis; Methods; Mission; Modeling; Molecular; Molecular Genetics; National Institute of Mental Health; Nicotine; Outcome; Performance; Personality; Phenotype; Population; Prefrontal Cortex; Preventive Intervention; Process; Psychotic Disorders; Public Health; Questionnaires; Research; Research Domain Criteria; Research Training; Risk; Risk Assessment; Risk Factors; Sample Size; Sampling; Schizophrenia; Schizotypal Personality Disorder; Series; Signal Transduction; Statistical Methods; Stratification; Structure; Students; Subgroup; Suicide attempt; Symptoms; Techniques; Testing; Time; Tissue-Specific Gene Expression; Training; Transcript; Translational Research; Trees; Validation; Variant; Wisconsin; aged; base; brain tissue; burden of illness; case control; clinical risk; cocaine use; density; differential expression; disorder subtype; endophenotype; gene function; genetic pedigree; genetic variant; genome wide association study; genome-wide; genomic data; improved; innovation; insight; malignant breast neoplasm; marijuana use; mind control; novel; programs; public health relevance; response; severe mental illness; sex; substance misuse; symptomatology; theories; training opportunity; trait; transmission process

 DESCRIPTION (provided by applicant): Schizophrenia (SZ) represents a significant and costly public health burden. Recently, we have witnessed the emergence of the first molecular insights into the etiopathogenic mechanisms of SZ, via the first genome-wide association studies (GWAS) with sufficient case-control sample sizes to detect allelic effects. Importantly, a polygenic signature, which includes genome-wide significant common genetic variants of small effect, has been clearly demonstrated to influence SZ risk. The availability of well-defined risk factors makes it possible, for the first time, to address several critical questions about the genetic architecture of SZ and its component phenotypes and endophenotypes. The overarching goals of this K01 proposal are 1) to test polygenic risk score prediction of dimensional SZ and schizotypy phenotypes across large case, high-density SZ pedigree, and prodromal-aged GWAS samples, and 2) to develop and test, with leaders of the Psychiatric Genomics Consortium, innovative statistical methods to identify and characterize "genetic subtypes" of SZ. This proposal delineates a series of training and research goals for the candidate that incorporates strengths from phenotypic assessment, statistical genetics, and molecular genetics and combines samples reflecting a broad range of genetic risk. The candidate will capitalize on previously established expertise in schizotypy and clinical risk assessment, as well as expertise in the familial transmission of dimensional traits, to establish a program of translational research wherein the application of statistical genetic/bioinformatic techniques to genetic subtyping analyses will be used to generate promising candidates for further exploration in human genomic data. Empirically-based genetic subtyping methods will be developed and tested in the largest SZ case sample to date, and subtypes will be characterized with respect to dimensional phenotypic traits. Top loci hits in subtype analyses of dimensional symptoms will be validated in secondary analyses of differential gene expression in post-mortem SZ brain. This will allow for a detailed analysis of function at both the SNP and gene levels, and will provide the candidate with substantive training in both statistical and molecular genetics methods. The institutional environment is ideal for the candidate's goal of developing a comprehensive program in SZ research, and the proposed research represents an important contribution toward advancing the understanding of SZ through a combination of clinical, statistical, molecular, and translational methods, consistent with the mission of the NIMH.

 描述（由申请人提供）：精神分裂症（SZ）是一个重大和昂贵的公共卫生负担。最近，我们通过第一个全基因组关联研究（GWAS），利用足够的病例对照样本量来检测等位基因效应，见证了对SZ发病机制的第一个分子见解的出现。重要的是，多基因签名，其中包括全基因组显着的常见遗传变异的影响小，已被明确证明影响SZ的风险。定义明确的风险因素的可用性使得有可能，第一次，以解决几个关键问题的遗传结构的SZ及其组成表型和内表型。该K 01提案的总体目标是1）在大病例、高密度SZ谱系和前驱期年龄GWAS样本中测试维度SZ和单体型表型的多基因风险评分预测，以及2）与精神病基因组学联盟的领导人一起开发和测试创新的统计方法，以识别和表征SZ的“遗传亚型”。 该提案为候选人描绘了一系列培训和研究目标，这些目标结合了表型评估，统计遗传学和分子遗传学的优势，并结合了反映广泛遗传风险的样本。候选人将利用先前建立的在体型和临床风险评估方面的专业知识，以及在尺寸性状家族传播方面的专业知识，建立一个 翻译研究计划，其中应用统计遗传学/生物信息学技术进行遗传分型分析，将用于产生有前途的候选人，以进一步探索人类基因组数据。基于经验的遗传分型方法将在迄今为止最大的SZ病例样本中开发和测试，亚型将根据尺寸表型特征进行表征。将在死后SZ脑中差异基因表达的二次分析中验证维度症状亚型分析中的顶级基因座命中。这将允许在SNP和基因水平上对功能进行详细分析，并将为候选人提供统计和分子遗传学方法的实质性培训。机构环境非常适合候选人制定全面的深圳研究计划的目标，并且拟议的研究代表了通过临床、统计、分子和转化方法相结合来促进对深圳的理解的重要贡献，与NIMH的使命一致。