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Using Genetic Similarity Quantified by Kinship Coefficients to Investigate Familial Contributions to Reading Disorder

利用亲属关系系数量化的遗传相似性来调查家庭对阅读障碍的影响

基本信息

批准号：
10712210
负责人：
Oliver H.M. Lasnick
金额：
$ 3.99万
依托单位：
UNIVERSITY OF CONNECTICUT STORRS
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-19 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10712210
关键词：
Achievement Adult Affect Age Area Behavior Behavioral Behavioral Mechanisms Biological Characteristics Child Classification Consensus DNA Data Data Set Developmental reading disorder Diagnosis Diagnostic Disease Early identification Environment Environmental Risk Factor Exhibits Extended Family Family Family Relationship Family history of Family member First Degree Relative Generations Genetic Genetic Materials Goals Grain Heritability Individual Knowledge Language Learning Disorders Linear Regressions Link Literature Machine Learning Measures Mediating Methods Modeling Morphology Neuroanatomy Neurobiology Neurocognitive Oral Outcome Parents Performance Phenotype Population Predisposition Prevalence Probability Proxy Public Policy Questionnaires Reader Reading Reading Disorder Recording of previous events Regression Analysis Reporting Research Risk Risk Factors Sampling Schools Severities Socioeconomic Status Speech Structural defect Surface Thick Training Variant behavior measurement comparison control disease classification family structure grandparent gray matter improved indexing individual variation intergenerational machine learning classifier machine learning model neuroimaging neuromechanism non-verbal novel novel strategies outcome prediction phonology predictive modeling prenatal proband reading ability reading difficulties risk prediction sex skills supervised learning transmission process

项目摘要

PROJECT SUMMARY Decoding-based reading disorder (RD, or developmental dyslexia) is one of the most prevalent specific learning disorders in the population. Previous literature suggests multiple familial and environmental factors play a role in the manifestations of RD. RD individuals often have a family history; children from families where at least one first-degree relative exhibits history of the disorder have up to a sixfold increase in RD occurrence compared to controls. However, studies on RD that use family history (FH) measures typically use it as a binary categorical (family history present/absent) variable to examine group differences based on whether at least one relative has an official or likely diagnosis of RD. This means the spectrum of variation in FH is not captured. FH is typically determined based on parents and often does not include information from extended family (aunts/uncles, grandparents, cousins, etc.); this means important familial data that is less subject to the confounds of shared environment is being discarded. In addition, neuroimaging is valuable when used jointly with behavioral and FH measures, which may together improve early identification of RD; my advisor has shown that neuroimaging data reflects non-redundant metrics and mechanisms for behavior. The goal of this project is to determine the predictive ability of (1) continuous FH, and (2) FH from extended families in relation to the likelihood/severity of RD characteristics in children. In a large group of children (N = 841) ages 5-12.5 with varying levels of reading ability, I construct a novel factor, known as the RD kinship index (RDKI) which describes how many biologically related family members have likely had RD and how genetically close they are to the children. I will first replicate prior findings using categorical FH as a predictor for reading ability and cortical morphology (gray matter [GM] thickness and cortical surface area [SA] in reading-related regions) (Aim 1A). I will then replicate this analysis using a continuous measure of FH (Adult Reading History Questionnaire [ARHQ] scores from parents) as a predictor (Aim 1B), then compare the predictive ability of categorical vs. continuous FH on all outcomes in a single regression model (Aim 1C). I also propose using the novel RDKI to measure FH inclusive of extended family members and examine its ability to predict reading ability and structural neuroanatomy using analogous methods from Aims 1A-B (Aim 2A). The goal is to examine how additional familial factors for which the RDKI serves as a proxy may account for variance in outcomes; the utility of the RDKI will be compared to the model predictors from Aim 1 (Aim 2B). Finally, I will construct a supervised machine learning classifier trained on RDKI and GM thickness / cortical SA (Aim 3) to predict binary RD diagnoses. This proposal therefore aims to characterize the multifaceted relationship between familial predisposition to RD, diagnostic risk, and phenotypic severity, and links to its neural mechanisms. Results from this research will directly compare categorical and continuous FH measures and indicate the utility of the RDKI as an FH measure/early identifier for RD that can inform public policy.

项目摘要基于解码的阅读障碍（RD，或发展性阅读障碍）是一种最普遍的具体学习障碍的人群。以往的文献表明，多个家庭和环境因素在RD的表现中发挥作用。RD个体通常有家族史;来自以下家庭的儿童：至少有一个一级亲属表现出疾病史，RD发生率增加了6倍与对照相比。然而，使用家族史（FH）测量的RD研究通常将其用作二元分类（家族史存在/不存在）变量，以检查组间差异，至少有一个亲属有官方或可能的RD诊断。这意味着FH的变化谱不是抓了FH通常是基于父母来确定的，并且通常不包括来自父母的信息。家庭（叔叔阿姨、祖父母、堂兄弟姐妹等）;这意味着重要的家庭数据，共享环境的混乱正在被丢弃。此外，神经影像学在联合使用时也很有价值与行为和FH措施，这可能会共同改善RD的早期识别;我的顾问已经表明神经影像学数据反映了行为的非冗余指标和机制。这个目标项目是确定预测能力（1）连续FH，（2）FH从大家庭的关系儿童RD特征的可能性/严重程度。在一个5-12.5岁的大组儿童（N = 841）中随着阅读能力水平的变化，我构建了一个新的因素，称为RD亲属关系指数（RDKI），描述了有多少生物学相关的家庭成员可能患有RD，以及他们在遗传上的接近程度。是给孩子们的我将首先使用分类FH作为阅读能力的预测因子，皮质形态学（阅读相关区域的灰质[GM]厚度和皮质表面积[SA]）（目的 1A）。然后，我将使用FH（成人阅读历史问卷）的连续测量重复此分析 [ARHQ]得分来自父母）作为预测因子（目标1B），然后比较分类与在单一回归模型（Aim 1C）中对所有结局进行连续FH。我还建议使用新的RDKI，测量FH，包括大家庭成员，并检查其预测阅读能力的能力，使用目标1A-B（目标2A）的类似方法进行结构神经解剖学。我们的目标是研究 RDKI作为替代的其他家族因素可能解释结局的差异; 将RDKI的效用与目标1（目标2B）的模型预测因子进行比较。最后，我将构建一个在RDKI和GM厚度/皮质SA（Aim 3）上训练的监督机器学习分类器，研发诊断。因此，这项建议旨在描述家庭暴力与暴力之间的多方面关系。 RD的易感性、诊断风险和表型严重程度，以及与其神经机制的联系。结果本研究将直接比较分类和连续FH测量，并指出RDKI的实用性作为可告知公共政策的RD的FH措施/早期标识符。