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：
10537060
负责人：
Oliver H.M. Lasnick
金额：
$ 4.35万
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 2024-08-18
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10537060
关键词：
Achievement Adult Affect Age Area Behavior Behavioral Behavioral Mechanisms Biological Categories 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 Play Population Predisposition Prevalence Proxy Public Policy Questionnaires Reader Reading Reading Disorder Recording of previous events Regression Analysis Reporting Research Risk Risk Factors Role Sampling Schools Severities Socioeconomic Status Speech Structural defect Surface Thick Training Variant base behavior measurement disease classification family structure grandparent gray matter improved indexing individual variation intergenerational machine learning classifier machine learning model neuroimaging neuromechanism novel novel strategies outcome prediction phonology predictive modeling prenatal proband reading ability reading difficulties 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或发育阅读障碍）是最普遍的特异性之一人口中的学习障碍。以前的文献提出了多个家庭和环境因素在RD的表现中发挥作用。 RD个人经常有家族史；来自家庭的孩子至少一个一级相对展示了该疾病的历史，RD的发生率最高为六倍与对照组相比。但是，对使用家族史（FH）衡量的RD的研究通常将其用作二进制分类（家族史出席/不存在）变量可根据AT检查组差异至少有一个亲戚有正式或可能的RD诊断。这意味着FH的变化频谱不是被捕获。 FH通常是根据父母确定的，通常不包括扩展的信息家庭（阿姨/叔叔，祖父母，堂兄等）；这意味着重要的家族数据，而不受任何限制的家庭数据共享环境的混杂被丢弃。此外，当共同使用时，神经影像学很有价值采用行为和FH措施，可以共同改善RD的早期识别；我的顾问有表明神经成像数据反映了行为的非冗余指标和机制。目标的目标项目是为了确定（1）连续FH的预测能力，以及（2）来自大家庭的FH 儿童RD特征的可能性/严重程度。在一大批5-12.5岁的儿童中（n = 841）具有不同水平的阅读能力，我构建了一个新的因素，称为RD亲属指数（RDKI）描述有多少与生物学相关的家庭成员可能有RD以及他们如何在遗传上关闭是给孩子们的。我将首先使用分类FH作为阅读能力和皮质形态（灰质[GM]厚度和皮质表面积[SA]与阅读相关区域）（AIM 1A）。然后，我将使用连续的FH（成人阅读历史问卷调查表）复制此分析 [ARHQ]来自父母的分数）作为预测因子（AIM 1B），然后比较分类VS的预测能力。在单个回归模型中的所有结果（AIM 1C）中连续FH。我还建议使用小说rdki 衡量包括扩展家庭成员的FH，并检查其预测阅读能力和使用AIMS 1A-B（AIM 2A）的类似方法的结构神经解剖学。目标是检查如何 RDKI用作代理的其他家族因素可能解释了结果的差异；这 RDKI的效用将与AIM 1（AIM 2B）的模型预测变量进行比较。最后，我将建造一个在RDKI和GM厚度 /皮层SA（AIM 3）培训的监督机学习分类器（AIM 3）以预测二进制 RD诊断。因此，该建议旨在表征家族性之间的多方面关系 RD，诊断风险和表型严重程度以及与其神经机制的联系。结果这项研究将直接比较分类和连续的FH措施，并指示RDKI的实用性作为RD的FH措施/早期标识符，可以为公共政策提供信息。