Mia Grasso
Class of 2024
Class of 2024
People with developmental dyslexia, a learning disability affecting 5-12% of English speakers, have difficulty learning to read and interpret words, but those factors do not affect their intelligence. Dyslexia is diagnosed via a neuropsychological evaluation, which tests problem-solving, memory, language, and intelligence, costing around $6,000. The high cost might be why about 40 million adults in the U.S. have dyslexia, but only 2 million know it. Neuropsychological evaluations can be given only once signs of reading difficulties appear. This is an issue because children’s skills develop more rapidly during the early ages of school so a later diagnosis increases the chance of struggle during their future years of life. As a result, researchers are exploring genetic and neurological abnormalities in dyslexia, hoping to find a cheaper screening to identify those who would most likely have dyslexia before the start of reading. Then the child would get a neuropsychological evaluation, allowing for help to be implemented at a younger age.
However, genetic (genome-wide association studies, or GWAS) and neurological studies show inconsistent results. For instance, for a certain gene, some studies find an association between its variants and dyslexia, while other studies do not. Since errors in our DNA lead to changes in genes and affect gene expression, leading to changes in brain imaging phenotypes, leading to behavioral presentation, we should be able to discover causal variants (variants that cause a trait to be expressed) by comparing the GWAS and eQTL data (variants that affect the expression of a trait). The eQTL data I will use represents the variants expressed to a certain tissue or brain region, selected from the results of neuroimaging studies, which show evidence that this tissue or brain region is involved in dyslexia.
Then I will conduct Bayesian Colocalization analysis to determine what causal variants eQTL and GWAS variants share. In other words, comparing the eQTL data, which has all variants that affected a relevant brain region, with variants identified in genetic studies will result in a list of variants that are most likely to cause dyslexia rather than being merely associated with it.