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

项目总结 基于解码的阅读障碍(RD，或发育性阅读障碍)是最普遍的特殊类型之一 人群中的学习障碍。以前的文献表明有多种家庭和环境因素。 在RD的表现中起一定作用。研发人员通常有家族病史；来自以下家庭的孩子 至少有一名一级亲属表现出这种疾病的病史，RD的发生率增加了六倍 与对照组相比。然而，使用家族史(FH)测量的RD研究通常将其用作 二进制分类(有/无家族病史)变量，用于根据是否有以下情况检查组差异 至少有一个亲属有正式或可能的RD诊断。这意味着跳频的变化光谱不是 被抓了。FH通常基于父项来确定，并且通常不包括来自扩展的信息 家庭(阿姨/叔叔、祖父母、堂兄弟姐妹等)；这意味着重要的家庭数据不太受 共享环境的混乱正在被抛弃。此外，神经成像在联合使用时也很有价值。 使用行为和FH措施，这些措施可能共同改善RD的早期识别；我的顾问 研究表明，神经成像数据反映了行为的非冗余指标和机制。这样做的目的是 项目是确定(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(N=841)中 根据阅读能力的不同水平，我构建了一个新的因素，称为RD亲属关系指数(RDKI)，它 描述有多少生物上有血缘关系的家庭成员可能患有RD，以及他们在基因上有多么接近 是给孩子们的。我将首先复制以前的发现，使用分类FH作为阅读能力的预测因子，并 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(成人阅读历史问卷)的连续测量来重复这一分析 [ARHQ]来自父母的分数)作为预测指标(目标1B)，然后比较分类预测能力与 在单一回归模型(目标1C)中对所有结果进行连续FH。我还建议使用小说RDKI来 测量包括大家庭成员在内的FH，并检查其预测阅读能力和 使用目标1A-B(目标2A)的类似方法的结构神经解剖学。目标是研究如何 RDKI作为替代指标的其他家族因素可能会解释结果的差异； RDKI的效用将与目标1(目标2B)的模型预测值进行比较。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以预测二进制 研发诊断。因此，这一建议旨在刻画家庭和家庭之间的多方面关系 RD的易感性、诊断风险和表型严重性，以及与其神经机制的联系。结果来自 这项研究将直接比较直接式和连续式跳频指标，并指出RDKI的实用性 作为FH措施/RD的早期标识，可以为公共政策提供信息。

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.