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（由应用程序提供）：精神分裂症（SZ）代表着重要且昂贵的公共卫生伯恩。最近，我们通过第一个全基因组关联研究（GWAS）的第一个分子见解对SZ的疗法机制的出现，具有足够的病例对照样本量，以检测shillic效应。重要的是，已清楚地证明，多基因签名包括范围内基因组的重要遗传变异，以影响SZ风险。明确定义的风险因素的可用性使得第一次解决了有关SZ遗传结构及其组成型表型和内型型的几个关键问题。该K01提案的总体目标是1）测试大案例，高密度SZ谱系和高密度SZ谱系和原始GWAS样本的多基因风险评分预测预测多基因的SZ和精神分裂型表型，以及2），以开发和测试精神基因组的领导者，以识别统计和统计学的统计学分析和识别统计学的精神群体和识别。该提案描述了候选人的一系列培训和研究目标，其中结合了表型评估，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精神病型和临床风险评估的专业知识，以及在家庭传播方面的专业知识，以建立一个 转化研究计划，其中将使用统计遗传/生物信息学技术在遗传亚型分析中的应用来生成有望候选者，以在人类基因组数据中进一步探索。迄今为止，将在最大的SZ病例样本中开发和测试基于经验的遗传亚型方法，并将亚型在维表型性状方面进行表征。尺寸符号的亚型分析中的最高基因座命中将在验证后SZ脑中差异基因表达的次级分析中得到验证。这将允许对SNP和基因水平的功能进行详细的分析，并将为候选人提供统计和分子遗传学方法的实质性训练。制度环境是候选人在SZ研究中制定全面计划的目标的理想选择，而拟议的研究代表了通过临床，统计，分子和翻译方法的结合，与NIMH的使命相结合，为推进SZ的理解做出了重要贡献。