| Title: | The shared genetic architecture and evolution of human language and musical rhythm |
| Journal: | Nature Human Behaviour |
| Published: | 21 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39572686/ |
| DOI: | https://doi.org/10.1038/s41562-024-02051-y |
Publication 14307
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Abstract
This study aimed to test theoretical predictions over biological underpinnings of previously documented phenotypic correlations between human language-related and musical rhythm traits. Here, after identifying significant genetic correlations between rhythm, dyslexia and various language-related traits, we adapted multivariate methods to capture genetic signals common to genome-wide association studies of rhythm (N = 606,825) and dyslexia (N = 1,138,870). The results revealed 16 pleiotropic loci (P < 5 × 10−8) jointly associated with rhythm impairment and dyslexia, and intricate shared genetic and neurobiological architectures. The joint genetic signal was enriched for foetal and adult brain cell-specific regulatory regions, highlighting complex cellular composition in their shared underpinnings. Local genetic correlation with a key white matter tract (the left superior longitudinal fasciculus-I) substantiated hypotheses about auditory-motor connectivity as a genetically influenced, evolutionarily relevant neural endophenotype common to rhythm and language processing. Overall, we provide empirical evidence of multiple aspects of shared biology linking language and musical rhythm, contributing novel insight into the evolutionary relationships between human musicality and linguistic communication traits.</p>
10 Authors
- Gökberk Alagöz
- Else Eising
- Yasmina Mekki
- Giacomo Bignardi
- Pierre Fontanillas
- Michel G. Nivard
- Michelle Luciano
- Nancy J. Cox
- Simon E. Fisher
- Reyna L. Gordon
1 Application
| Application ID | Title |
|---|---|
| 79683 | Machine learning-based phenotyping and methods development for the identification, characterization, and validation of disease susceptibility loci from high-throughput sequencing and microarray data. |
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