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.

项目概要基于解码的阅读障碍（RD，或发展性阅读障碍）是最普遍的特定阅读障碍之一人群中的学习障碍。先前的文献表明多种家庭和环境因素在RD的表现中发挥作用。 RD 个体通常有家族史；来自哪里的家庭的孩子至少一名一级亲属有该病史 RD 发生率增加六倍与对照相比。然而，使用家族史 (FH) 测量的 RD 研究通常将其用作二元分类（存在/不存在家族史）变量，用于根据是否存在来检查群体差异至少一名亲属被正式诊断或可能诊断为 RD。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，通常不包括来自扩展的信息家庭（阿姨/叔叔、祖父母、表兄弟姐妹等）；这意味着重要的家庭数据较少受到共享环境的混乱正在被丢弃。此外，联合使用神经影像学也很有价值结合行为和 FH 测量，可以共同改善 RD 的早期识别；我的顾问有研究表明，神经影像数据反映了行为的非冗余指标和机制。此举的目标项目旨在确定 (1) 连续 FH 和 (2) 来自大家庭的 FH 的预测能力儿童 RD 特征的可能性/严重性。在一大群 5-12.5 岁的儿童中 (N = 841) 针对不同水平的阅读能力，我构建了一个新的因素，称为 RD 亲属关系指数（RDKI），描述了有多少具有生物学相关性的家庭成员可能患有 RD，以及他们在基因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来复制先前的发现皮质形态（阅读相关区域的灰质 [GM] 厚度和皮质表面积 [SA]）（目标 1A）。然后，我将使用 FH（成人阅读历史调查问卷）的连续测量来复制此分析 [ARHQ] 父母的分数）作为预测因子（目标 1B），然后比较分类与分类的预测能力。在单一回归模型中对所有结果进行连续 FH（目标 1C）。我还建议使用新颖的 RDKI 测量包括大家庭成员在内的 FH，并检查其预测阅读能力和能力的能力使用目标 1A-B（目标 2A）中的类似方法进行结构神经解剖学。目标是检查如何 RDKI 作为替代因素的其他家庭因素可能会导致结果的差异；这 RDKI 的效用将与目标 1（目标 2B）的模型预测变量进行比较。最后，我将构建一个在 RDKI 和 GM 厚度/皮质 SA（目标 3）上训练的监督机器学习分类器来预测二进制 RD 诊断。因此，该提案旨在描述家庭之间的多方面关系。 RD 的易感性、诊断风险和表型严重程度及其神经机制的联系。结果来自这项研究将直接比较分类和连续 FH 测量并表明 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。