While they obviously depend on environmental input, much of the variation in language-related traits within human populations is explained by genetic differences between individuals. Besides rare cases of severe disorders, such inter-individual variability mainly involves complex genetic underpinnings, involving interactions of a large number of common genetic variants, each of which has only a tiny effect on the trait. To successfully investigate the genetic architecture underlying these complex traits, we need to study large cohorts comprising thousands of participants in order to have sufficient statistical power to detect the small effects involved. Participants are assessed on behavioural and cognitive measures related to speech, language, reading, and/or other communication skills. They are also characterised using DNA chips that assess hundreds of thousands of variable genetic markers across the genome. Using such datasets, we can test whether any of the genetic markers are associated with variation in the behavioural/cognitive skills of interest. In this work, we not only investigate large samples of families and cases with impairments such as developmental language disorder (DLD) or reading disability (developmental dyslexia), but also general population cohorts (cross-sectional and longitudinal designs, including twin registries and birth cohorts), in which speech-, language-, and reading-related indicators have been obtained.

We are the driving force behind an international effort, the GenLang Consortium, to facilitate large-scale meta-analyses of multiple cohorts from around the world, to achieve sample sizes of tens of thousands of people. We are also part of the Dutch Language in Interaction Consortium, which is developing a comprehensive new test battery for the systematic evaluation of natural variation in language skills. The findings from genetic association studies are used to generate insights into the biology of human language, by integrating findings with those from other sources, including neuroimaging and evolutionary datasets.

Example publications:
Gialluisi, A., et al. (2019). Genome-wide association scan identifies new variants associated with a cognitive predictor of dyslexia. Translational Psychiatry, 9(1), 77. doi:10.1038/s41398-019-0402-0.

Deriziotis, P., & Fisher, S. E. (2017). Speech and Language: Translating the Genome. Trends in Genetics, 33(9), 642-656. doi:10.1016/j.tig.2017.07.002.

Carrion Castillo, A., van Bergen, E., Vino, A., van Zuijen, T., de Jong, P. F., Francks, C., & Fisher, S. E. (2016). Evaluation of results from genome-wide studies of language and reading in a novel independent dataset. Genes, Brain and Behaviour, 15(6), 531-541. doi:10.1111/gbb.12299. [pdf]

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