Children with poor reading comprehension despite typical word reading skills were examined using neuropsychological, genetic, and neuroimaging data collected from the Genes, Reading and Dyslexia Study of 1432 Hispanic American and African American children. This unexpected poor comprehension was associated with profound deficits in vocabulary, when compared to children with comprehension skills consistent with their word reading. Those with specific comprehension difficulties were also more likely to have RU2Short alleles of READ1 regulatory variants of DCDC2, strongly associated with reading and language difficulties. Subjects with RU2Short alleles showed stronger resting state functional connectivity between the right insula/inferior frontal gyrus and the right supramarginal gyrus, even after controlling for potentially confounding variables including genetic ancestry and socioeconomic status. This multi-disciplinary approach advances the current understanding of specific reading comprehension difficulties, and suggests the need for interventions that are more appropriately tailored to the specific comprehension deficits of this group of children.

SignificanceLanguages evolve rapidly due to an interaction between sociocultural factors and underlying phonological processes that are influenced by genetic factors. DCDC2 has been strongly associated with core components of the phonological processing system in animal models and multiple independent studies of populations and languages. To characterize subtle language differences arising from genetic variants associated with phonological processes, we examined the relationship between READ1, a regulatory element in DCDC2, and phonemes in languages of 43 populations across five continents. Variation in READ1 was significantly correlated with the number of consonants. Our results suggest that subtle cognitive biases conferred by different READ1 alleles are amplified through cultural transmission that shape consonant use by populations over time.

Specific learning disorders (SLD) are an archetypal example of how clinical neuropsychological traits can differ from underlying genetic and neurobiological risk factors. Disparate environmental influences and pathologies impact learning performance assessed through cognitive exams and clinical evaluations, the primary diagnostic tools for SLD. We propose a neurobiological risk for SLD with neuroimaging biomarkers which is integrated into a genomewide association study (GWAS) of learning performance in a cohort of 479 European individuals between 8 and 21 years of age. We first identified six regions of interest (ROIs) in temporal and anterior cingulate regions where the group diagnosed with learning disability has the least overall variation, relative to the other group, in thickness, area, and volume measurements. Although we used the three imaging measures, the thickness was the leading contributor. Hence, we calculated the Euclidean distances between any two individuals based on their thickness measures in the six ROIs. Then, we defined the relative similarity of one individual according to the averaged ranking of pairwise distances from the individuals to those in the SLD group. The inverse of this relative similarity is called the neurobiological risk for the individual. Single nucleotide polymorphisms in the AGBL1 gene on chromosome 15 had a significant association with learning performance at a genomewide level. This finding was supported in an independent cohort of 2,327 individuals of the same demographic profile. Our statistical approach for integrating genetic and neuroimaging biomarkers can be extended into studying the biological basis of other neuropsychological traits.

Neuropsychological disorders have a biological basis rooted in brain function, and neuroimaging data are expected to better illuminate the complex genetic basis of neuropsychological disorders. Because they are biological measures, neuroimaging data avoid biases arising from clinical diagnostic criteria that are subject to human understanding and interpretation. A challenge with analyzing neuroimaging data is their high dimensionality and complex spatial relationships. To tackle this challenge, we introduced a novel distance covariance tests that can assess the association between genetic markers and multivariate diffusion tensor imaging measurements, and analyzed a genome-wide association study (GWAS) dataset collected by the Pediatric Imaging, Neurocognition, and Genetics (PING) study. We also considered existing approaches as comparisons. Our results showed that, after correcting for multiplicity, distance covariance tests of the multivariate phenotype yield significantly greater power at detecting genetic markers affecting brain structure than standard mass univariate GWAS of individual neuroimaging biomarkers. Our results underscore the usefulness of utilizing the distance covariance to incorporate neuroimaging data in GWAS.

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