A computational genomics approach to identify roles of rare genetic variants in psychiatric disorders and gene expression

一种计算基因组学方法，用于识别罕见遗传变异在精神疾病和基因表达中的作用

• 批准号: 9752584
• 负责人: Jae Hoon Sul
• 金额: $ 20.48万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9752584
• 关键词: Address; Affect; Award; Awareness; Biological; Biology; Bipolar Disorder; Case-Control Studies; Clinical; Clinical Research; Complex; Computers; Copy Number Polymorphism; Costa Rica; Data; Data Analyses; Data Science; Data Set; Disease; Educational workshop; Family; Frequencies; Gene Expression; Gene Expression Profiling; Gene Frequency; Genes; Genetic; Genetic Variation; Genetic study; Genome; Genomic approach; Genomics; Genotype; Goals; Health; Heritability; Human; Individual; Institution; K-Series Research Career Programs; Knowledge; Laws; Lead; Mental disorders; Mentored Research Scientist Development Award; Mentors; Methods; Mutation; Nucleotides; Population; Predisposition; Quantitative Trait Loci; Research; Research Design; Research Training; Role; Sample Size; Sampling; Schizophrenia; Science; Software Tools; Structure; Technology; Testing; Tissues; Training; Training and Education; Variant; Work; base; big biomedical data; care outcomes; career; case control; clinical care; computer science; cost; design; disease phenotype; genetic pedigree; genetic variant; genome sequencing; genome wide association study; genomic data; human tissue; improved; neuropsychiatric disorder; next generation sequencing; novel; personalized medicine; phenotypic data; rare variant; skills training; statistics; success; therapy development; trait; transcriptome sequencing; whole genome

Project Summary This is a proposal for the K01 Mentored Career Development Award in Biomedical Big Data Science. The goal of this proposal is to obtain training in biomedical science with focus on psychiatric disorders, and perform research to discover rare genetic variants that influence human complex traits including two psychiatric disorders, bipolar disorder (BP) and schizophrenia (SCZ). Identifying those rare variants is critical for both biology and human health as it will elucidate the genetic basis of those disorders and facilitate development of treatment. Recently, as the cost of next-generation sequencing decreases at a rate faster than that described by Moore's law for computer chips, many genetic studies are utilizing whole-genome sequencing (WGS) to identify roles of rare variants in human complex traits. However, these studies have had limited success most likely due to the small sample size. In this proposal, I will analyze three WGS data sets that provide unique opportunities to find effect of rare variants. The first is WGS data of large pedigrees with BP in which rare variants may be enriched in a certain large family, increasing our chance to detect their effect. The second is expression quantitative trait loci data that contain WGS and RNA-Seq from Genotype-Tissue Expression (GTEx) initiative. GTEx collected gene expression from multiple human tissues, which would enable discovery of functional effects of rare variants on different tissues. The third is WGS data of 4,000 BP and SCZ case-control samples from two recently bottlenecked populations. Deleterious rare variants may have elevated allele frequency in these populations, which increases statistical power to detect their effect. To effectively analyze the three WGS data sets, I will develop a new statistical approach and also utilize methods that I already developed. These methods combine effects of multiple rare variants in a gene to increase statistical power. I will apply these methods to the three WGS data sets to identify rare variants that influence psychiatric disorders (BP and SCZ) and gene expression. Although I have considerable knowledge and expertise in computer science and statistics, I seek to obtain additional training in biomedical science, especially in psychiatric disorders and clinical research to better interpret results of the rare variant analyses and extract biologically meaningful information from results. I will participate in several courses and workshops offered at UCLA and other institutions to obtain this training. This training will enable me to design and lead genomic studies for psychiatric disorders and to develop a niche as a statistical geneticist. These immediate goals will be the basis for my long-term career goal, which is to enhance understanding of how genome sequences influence one's susceptibility to diseases and to develop personalized treatments. I will be mentored by Drs. Nelson Freimer, Jonathan Flint, and Giovanni Coppola who are experts in neuropsychiatric disorders and genomics. They will provide guidance on my education and research training throughout the award period.

项目概要 这是生物医学大数据科学 K01 指导职业发展奖的提案。目标是 该提案旨在获得以精神疾病为重点的生物医学科学培训，并进行研究 发现影响人类复杂特征的罕见遗传变异，包括两种精神疾病，双相情感障碍 精神障碍（BP）和精神分裂症（SCZ）。识别这些罕见的变异对于生物学和人类都至关重要 健康，因为它将阐明这些疾病的遗传基础并促进治疗的发展。最近， 因为下一代测序的成本下降速度比摩尔定律描述的速度更快 计算机芯片，许多遗传学研究正在利用全基因组测序（WGS）来识别罕见基因的作用 人类复杂特征的变异。然而，这些研究取得的成功很有限，很可能是由于规模较小 样本大小。在本提案中，我将分析三个 WGS 数据集，这些数据集提供了独特的机会来发现 罕见的变种。第一个是具有 BP 的大谱系的 WGS 数据，其中稀有变异可能在某些方面得到富集。 大家庭，增加了我们发现其影响的机会。第二个是表达数量性状基因座数据 包含来自基因型组织表达 (GTEx) 计划的 WGS 和 RNA-Seq。 GTEx收集基因 从多个人体组织中表达，这将有助于发现罕见变异的功能效应 不同的组织。第三个是最近两个国家的 4,000 个 BP 和 SCZ 病例对照样本的 WGS 数据 人口瓶颈。有害的罕见变异可能在这些人群中具有升高的等位基因频率， 这增加了检测其效果的统计能力。为了有效地分析三个WGS数据集，我将 开发一种新的统计方法并利用我已经开发的方法。这些方法结合起来 基因中多个罕见变异的影响以提高统计功效。我将把这些方法应用到这三个 WGS 数据集可识别影响精神疾病（BP 和 SCZ）和基因表达的罕见变异。 尽管我在计算机科学和统计学方面拥有丰富的知识和专业知识，但我力求 获得生物医学科学方面的额外培训，特别是精神疾病和临床研究方面的培训，以便更好地 解释罕见变异分析的结果并从结果中提取具有生物学意义的信息。我会 参加加州大学洛杉矶分校和其他机构提供的多个课程和研讨会以获得此培训。这 培训将使我能够设计和领导精神疾病的基因组研究，并开发一个利基市场 统计遗传学家。这些近期目标将成为我长期职业目标的基础，即增强 了解基因组序列如何影响一个人对疾病的易感性并制定个性化的 治疗。我将受到博士的指导。纳尔逊·弗雷默 (Nelson Freimer)、乔纳森·弗林特 (Jonathan Flint) 和乔瓦尼·科波拉 (Giovanni Coppola) 是以下领域的专家 神经精神疾病和基因组学。他们将为我的教育和研究培训提供指导 整个奖励期间。