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

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易感基因，阅读障碍定义为意外的低准确度和/或比率神经生物学起源的阅读或拼写。这种复杂的疾病影响 5-12% 的学龄儿童，并且，尽管采取了代价高昂且强度大的补救措施，但在长期的教育、经济、和社会反响。双胞胎和家庭研究一致认为遗传因素在其中发挥着作用阅读障碍和连锁分析的有力证据表明存在可发现的风险等位基因/基因。遗传性范式为发现和描述潜在的生化和神经发育途径。鉴于缺乏替代的非人类物质，这一点尤其重要。用于研究这种特殊的人类交流形式的模型系统。多个基因和位点已被与阅读障碍有关，但正如对常见的复杂疾病所预期的那样，没有一个占大多数病例和致病 DNA 变异尚未得到证实。我们将利用规模庞大、特征鲜明的数据集我们收集的家庭和联系数据，再加上来自一家新跨国公司的大量样本联盟，识别与组件相关的基因和非编码调控元件（基因单元）阅读障碍的表型。这一目标将通过三个具体目标来实现： 1. 综合评价并鉴定已提出的强阅读障碍候选基因座的基因单元中的变异； 2. 发现、完善和在最有希望的感兴趣区域中优先考虑阅读障碍成分表型的候选基因单元通过之前的基因组扫描在我们的家庭队列中发现； 3. 验证最有前途的基因单元额外的主题样本。我们提出的项目有多个主要的新颖组成部分。首先，我们将测试特定型号的通过综合分析阅读障碍重点区域的 DNA 序列数据，了解阅读障碍的遗传结构对基于家庭的样本的兴趣（ROI）。这将包括评估来自分子的基因组序列数据大型数据集中基因单元的倒置探针（MIP）捕获。我们将使用我们开发的分析工具小组选择最少数量的信息丰富的家庭成员进行排序，并结合基于家庭的基于群体的插补，通过将变体填充到其余的序列中来增强序列数据谱系。该策略将减少多重测试问题并提高功效。其次，这些分析将纳入 ENCODE 注释的调控元件，这些元件可能包含影响基因表达的变体或比蛋白质编码变体以更微妙的方式发挥作用。关注转录 DNA 和孟德尔特征这在编码外显子数据的人类遗传分析中是典型的，可能会错过影响影响的重要变异数量上的变化，而不是质量上的变化。第三，我们将采用生物信息学方法来确定优先顺序解释 ROI 中观察到的表型变异的基因和调控区域。这些方法包括基因负担和基于家族的关联方法，同时适应罕见和作为成分表型贡献者的常见变异。我们的多代受试者样本和来自该联盟的大量主题样本为相关的发现和验证提供了极好的资源变体和基因。