Worldwide distribution of the DCDC2 READ1 regulatory element and its relationship with phoneme variation across languages.

DCDC2 is a gene strongly associated with components of the phonological processing system in animal models and in multiple independent studies of populations and languages. We propose that it may also influence population-level variation in language component usage. To test this hypothesis, we investigated the evolution and worldwide distribution of the READ1 regulatory element within DCDC2, and compared its distribution with variation in different language properties. The mutational history of READ1 was estimated by examining primate and archaic hominin sequences. This identified duplication and expansion events, which created a large number of polymorphic alleles based on internal repeat units (RU1 and RU2). Association of READ1 alleles was studied with respect to the numbers of consonants and vowels for languages in 43 human populations distributed across five continents. Using population-based approaches with multivariate ANCOVA and linear mixed effects analyses, we found that the RU1-1 allele group of READ1 is significantly associated with the number of consonants within languages independent of genetic relatedness, geographic proximity, and language family. We propose that allelic variation in READ1 helped create a subtle cognitive bias that was amplified by cultural transmission, and ultimately shaped consonant use by different populations over time.

Whole genome association study of brain-wide imaging phenotypes: A study of the ping cohort.

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.

A method for integrating neuroimaging into genetic models of learning performance.

Specific learning disorders (SLD) are an archetypal example of how clinical neuropsychological (NP) traits can differ from underlying genetic and neurobiological risk factors. Disparate environmental influences and pathologies impact learning performance assessed through cognitive examinations and clinical evaluations, the primary diagnostic tools for SLD. We propose a neurobiological risk for SLD with neuroimaging biomarkers, which is integrated into a genome-wide 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 genome-wide 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 NP traits.