# The Genomics of Dyslexia and its Component Phenotypes

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2021 · $583,365

## Abstract

Our goal is to identify susceptibility genes for dyslexia, defined as unexpectedly low accuracy and/or rate of
reading or spelling of neurobiological origin. This complex disorder affects 5-12% of school-aged children and,
despite costly and intense remediation, aspects persist into adulthood with long-term educational, economic,
and social repercussions. There is consensus from twin and family studies that genetic factors play a role in
dyslexia and strong evidence from linkage analyses that there are discoverable risk alleles/genes. The genetic
paradigm provides a powerful approach for discovery and delineation of underlying biochemical and
neurodevelopmental pathways. This is particularly important given the absence of alternative non-human
model systems for studying this specifically human form of communication. Multiple genes and loci have been
associated with dyslexia but, as expected for a common complex disorder, none accounts for a majority of
cases and causative DNA variants have not been confirmed. We will leverage the large, well-characterized set
of families and linkage data we have amassed, coupled with large samples from a new multinational
consortium, to identify genes and non-coding regulatory elements (gene-units) associated with component
phenotypes of dyslexia. This goal will be accomplished with three specific aims: 1. Comprehensively evaluate
and identify variants in gene-units of strong already-proposed dyslexia candidate loci; 2. Discover, refine and
prioritize candidate gene-units for dyslexia component phenotypes in the most promising regions of interest
identified in our cohort of families by prior genome scans; and 3. Validate the most promising gene-units in
additional subject samples.
 Our proposed project has multiple major novel components. First, we will test a particular model of the
genetic architecture of dyslexia by comprehensively analyzing DNA sequence data in focused regions of
interest (ROIs) in family-based samples. This will include evaluation of genomic sequence data from molecular
inversion probe (MIP) capture of gene-units in large datasets. We will use analysis tools developed in our
group to select the minimal number of informative family members to sequence, and combine family-based
and population-based imputation to augment the sequence data by populating variants into the rest of the
pedigree. This strategy will reduce the multiple test problem and increase power. Second, these analyses will
incorporate ENCODE-annotated regulatory elements that may harbor variants that affect gene expression or
function in more subtle ways than protein coding variants. The focus on transcribed DNA and Mendelian traits
that is typical in human genetic analysis of coding-exon data may miss important variants that affect
quantitative, rather than qualitative, variation. Third, we will employ bioinformatics approaches to prioritize
genes and regulatory regions that explain the observed phenotypic variation in ROIs. T...

## Key facts

- **NIH application ID:** 10207697
- **Project number:** 5R01HD088431-05
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** WENDY H RASKIND
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $583,365
- **Award type:** 5
- **Project period:** 2017-07-05 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10207697

## Citation

> US National Institutes of Health, RePORTER application 10207697, The Genomics of Dyslexia and its Component Phenotypes (5R01HD088431-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10207697. Licensed CC0.

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