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

> **NIH NIH F31** · UNIVERSITY OF CONNECTICUT STORRS · 2022 · $43,490

## Abstract

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...

## Key facts

- **NIH application ID:** 10537060
- **Project number:** 1F31HD107944-01A1
- **Recipient organization:** UNIVERSITY OF CONNECTICUT STORRS
- **Principal Investigator:** Oliver H.M. Lasnick
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $43,490
- **Award type:** 1
- **Project period:** 2022-08-19 → 2024-08-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10537060

## Citation

> US National Institutes of Health, RePORTER application 10537060, Using Genetic Similarity Quantified by Kinship Coefficients to Investigate Familial Contributions to Reading Disorder (1F31HD107944-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10537060. Licensed CC0.

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