The Genomics of Dyslexia and its Component Phenotypes

阅读障碍的基因组学及其组成表型

基本信息

批准号：
10207697
负责人：
WENDY H RASKIND
金额：
$ 58.34万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-05 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10207697
关键词：
Adult Affect Amino Acids Architecture Biochemical Bioinformatics Biological Biological Models Brain Candidate Disease Gene Child Code Cognitive Collaborations Color Communications Media Complex Consensus Copy Number Polymorphism Coupled DNA DNA Sequence DNA Sequence Analysis Data Data Linkages Data Set Deoxyribonucleases Disease Dyslexia Early Diagnosis Economics Education Evaluation Exons Family Family Study Family member Gene Expression Genes Genetic Genetic Heterogeneity Genetic Models Genetic study Genome Scan Genomic Segment Genomics Genotype Goals Human Human Genetics Hypersensitivity Individual Instruction Intelligence Intervention Investigation Language Lead Learning Letters Markov Chains Methods Minority Modeling Molecular Molecular Probes Molecular Sequence Data Names Neurobiology Neurodevelopmental Disorder Nucleic Acid Regulatory Sequences Oral Pathway interactions Phenotype Play Predisposition Process Proteins Quality Control Quantitative Reverse Transcriptase PCR Reading Regulatory Element Research Personnel Resources Rest Reverse Transcriptase Polymerase Chain Reaction Risk Risk Factors Role Sampling School-Age Population Schools Sensory Short Tandem Repeat Polymorphism Single Nucleotide Polymorphism Site Susceptibility Gene Technology Testing Twin Studies Universities Untranslated RNA Validation Variant Washington Writing base cohort cost disorder risk early detection biomarkers exome gene function genetic analysis genetic architecture genetic linkage analysis genetic pedigree genetic risk factor genome wide association study genomic locus interest large datasets motor impairment nervous system disorder next generation sequencing novel personalized intervention population based proband psychologic reading difficulties remediation risk variant social sound spelling tool trait transcription factor vector whole genome

Adult Affect Amino Acids Architecture Biochemical Bioinformatics Biological Biological Models Brain Candidate Disease Gene Child Code Cognitive Collaborations Color Communications Media Complex Consensus Copy Number Polymorphism Coupled DNA DNA Sequence DNA Sequence Analysis Data Data Linkages Data Set Deoxyribonucleases Disease Dyslexia Early Diagnosis Economics Education Evaluation Exons Family Family Study Family member Gene Expression Genes Genetic Genetic Heterogeneity Genetic Models Genetic study Genome Scan Genomic Segment Genomics Genotype Goals Human Human Genetics Hypersensitivity Individual Instruction Intelligence Intervention Investigation Language Lead Learning Letters Markov Chains Methods Minority Modeling Molecular Molecular Probes Molecular Sequence Data Names Neurobiology Neurodevelopmental Disorder Nucleic Acid Regulatory Sequences Oral Pathway interactions Phenotype Play Predisposition Process Proteins Quality Control Quantitative Reverse Transcriptase PCR Reading Regulatory Element Research Personnel Resources Rest Reverse Transcriptase Polymerase Chain Reaction Risk Risk Factors Role Sampling School-Age Population Schools Sensory Short Tandem Repeat Polymorphism Single Nucleotide Polymorphism Site Susceptibility Gene Technology Testing Twin Studies Universities Untranslated RNA Validation Variant Washington Writing base cohort cost disorder risk early detection biomarkers exome gene function genetic analysis genetic architecture genetic linkage analysis genetic pedigree genetic risk factor genome wide association study genomic locus interest large datasets motor impairment nervous system disorder next generation sequencing novel personalized intervention population based proband psychologic reading difficulties remediation risk variant social sound spelling tool trait transcription factor vector whole genome

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项目摘要

Our goal is to identify susceptibility genes for dyslexia, defined as unexpectedly low accuracy and/or rate of reading or spelling of neurobiological origin. This complex disorder affects 5-12% of school-aged children and, despite costly and intense remediation, aspects persist into adulthood with long-term educational, economic, and social repercussions. There is consensus from twin and family studies that genetic factors play a role in dyslexia and strong evidence from linkage analyses that there are discoverable risk alleles/genes. The genetic paradigm provides a powerful approach for discovery and delineation of underlying biochemical and neurodevelopmental pathways. This is particularly important given the absence of alternative non-human model systems for studying this specifically human form of communication. Multiple genes and loci have been associated with dyslexia but, as expected for a common complex disorder, none accounts for a majority of cases and causative DNA variants have not been confirmed. We will leverage the large, well-characterized set of families and linkage data we have amassed, coupled with large samples from a new multinational consortium, to identify genes and non-coding regulatory elements (gene-units) associated with component phenotypes of dyslexia. This goal will be accomplished with three specific aims: 1. Comprehensively evaluate and identify variants in gene-units of strong already-proposed dyslexia candidate loci; 2. Discover, refine and prioritize candidate gene-units for dyslexia component phenotypes in the most promising regions of interest identified in our cohort of families by prior genome scans; and 3. Validate the most promising gene-units in additional subject samples. Our proposed project has multiple major novel components. First, we will test a particular model of the genetic architecture of dyslexia by comprehensively analyzing DNA sequence data in focused regions of interest (ROIs) in family-based samples. This will include evaluation of genomic sequence data from molecular inversion probe (MIP) capture of gene-units in large datasets. We will use analysis tools developed in our group to select the minimal number of informative family members to sequence, and combine family-based and population-based imputation to augment the sequence data by populating variants into the rest of the pedigree. This strategy will reduce the multiple test problem and increase power. Second, these analyses will incorporate ENCODE-annotated regulatory elements that may harbor variants that affect gene expression or function in more subtle ways than protein coding variants. The focus on transcribed DNA and Mendelian traits that is typical in human genetic analysis of coding-exon data may miss important variants that affect quantitative, rather than qualitative, variation. Third, we will employ bioinformatics approaches to prioritize genes and regulatory regions that explain the observed phenotypic variation in ROIs. These approaches include gene burden and family-based association methods that simultaneously accommodate rare and common variants as contributors to the component phenotypes. Our multigenerational subject sample and a large subject sample from the consortium provide excellent resources for discovery and validation of relevant variants and genes.

我们的目标是确定阅读障碍的敏感性基因，定义为出乎意料的低精度和/或速率。读或拼写神经生物学起源。这种复杂疾病影响了5-12％的学龄儿童，并且尽管昂贵和强烈的补救措施，但在长期的教育，经济，和社会影响。双胞胎和家庭研究达成共识，遗传因素在连锁分析中的阅读障碍和强有力的证据表明有可发现的风险等位基因/基因。遗传范式提供了一种强大的方法，用于发现和描述基础生化和神经发育途径。鉴于没有替代性非人类，这一点尤其重要用于研究这种特殊人类交流形式的模型系统。多个基因和基因座与阅读障碍有关，但是，正如常见的复杂疾病所期望病例和病因DNA变异尚未证实。我们将利用大型，良好的套装我们已经积累的家庭和链接数据，再加上新的跨国公司的大型样本财团，识别与成分相关的基因和非编码调节元件（基因单元）阅读障碍的表型。该目标将通过三个具体目标来实现：1。全面评估并鉴定出强大的本已有阅读障碍候选基因座基因单元的变体； 2。发现，完善和优先考虑最有前途的感兴趣区域中的候选基因单位的阅读障碍分量表型通过先前的基因组扫描在我们的家庭中确定；和3。验证最有前途的基因单位其他主题样本。我们提出的项目具有多个主要的新颖组成部分。首先，我们将测试一个特定模型通过全面分析重点区域的DNA序列数据的遗传结构基于家庭样本的兴趣（ROI）。这将包括评估分子的基因组序列数据反转探针（MIP）捕获大型数据集中的基因单元。我们将使用我们开发的分析工具小组选择最少的信息次数以序列，并结合家庭成员和基于人群的插补以增加序列数据，通过将变体列入其余部分来增加序列数据谱系。该策略将减少多重测试问题并增加功率。其次，这些分析将结合可能具有影响基因表达或比蛋白质编码变体以更微妙的方式发挥作用。关注转录的DNA和门德利特征这是对编码 - 外观数据的人类遗传分析的典型代表，可能会错过影响影响的重要变体定量，而不是定性变化。第三，我们将采用生物信息学方法来优先解释ROI中观察到的表型变异的基因和调节区域。这些方法包括基因负担和基于家庭的关联方法，这些方法同时适应稀有和常见变体是组件表型的贡献者。我们的多代主题样本和来自财团的大型主题样本为发现和验证提供了极好的资源变体和基因。