Population Genetics of Human Communication (PGHC)

Population Genetics of Human Communication (PGHC) research group

Human language is one of the most distinct and fascinating features of humankind. As a system of communication, language supports peer contact and interaction but also cognitive development, learning and knowledge transfer. The Population Genetics of Human Communication (PGHC) research group, led by Beate St Pourcain, aims to decipher the aetiological mechanisms shaping developing language and social skills during infancy to adolescence, as captured by common genetic variation. Utilising this knowledge, we seek to identify and characterise the trajectories that lead from mastering language and social abilities in early childhood to later-life mental health, wellbeing, educational and social outcomes. In particular, we develop statistical tools (GRM-SEM, grmsem r-package) to model structures of genomic and non-genomic factors in groups of unrelated individuals, utilising genetic information from large genotyping chips. Assuming shared dimensions of language and social development across the spectrum of mental health, our group also studies co-occurring features in autism, aiming to disentangle phenotypic heterogeneity, and investigates shared genetic links with neuropsychological and neuropsychiatric conditions. 

To find out more about our work, please look at our projects.

For information on co-creation of research with stakeholders, please visit our Neurodiversity and Mental Health focus group.

This research group is part of the Language and Genetics Department. The group is funded by the Max Planck Society, the Netherlands Organization for Scientific Research (NWO) and the European Commission.

Contact

Beate St Pourcain

Senior Investigator
Language and Genetics Department
+31 24 3521964
Beate [dot] StPourcain [at] mpi [dot] nl

Tools

Genetic-relationship-matrix structural equation modelling

We have developed multivariate genetic-relationship-matrix structural equation modelling (GRMSEM) techniques to model structures of genomic and non-genomic factors in groups of unrelated individuals. For this, we combined whole-genome genotyping information with structural equation modelling techniques, analogous to twin research methodologies. For the latest developments, please see our GRM-SEM project page and our grmsem r-package gitlab page. If your are interested in gaining experience with GRM-SEM, please register for our annual GENES & SEM course!

 

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