Preschool musicality is associated with school-age communication abilities through genes related to rhythmicity

Abstract

Early-life musical engagement is an understudied but developmentally important and heritable precursor of later (social) communication and language abilities. This study aims to uncover the aetiological mechanisms linking musical to communication abilities. We derived polygenic scores (PGS) for self-reported beat synchronisation abilities (PGS_rhythmicity) in children (N≤6,737) from the Avon Longitudinal Study of Parents and Children and tested their association with preschool musical (0.5-5 years) and school-age (social) communication and cognition-related abilities (9-12 years). We further assessed whether relationships between preschool musicality and school-age communication are shared through PGS_rhythmicity, using structural equation modelling techniques. PGS_rhythmicity were associated with preschool musicality (Nagelkerke-R²=0.70-0.79%), and school-age communication and cognition-related abilities (R²=0.08-0.41%), but not social communication. We identified links between preschool musicality and school-age speech- and syntax-related communication abilities as captured by known genetic influences underlying rhythmicity (shared effect β=0.0065(SE=0.0021), p=0.0016), above and beyond general cognition, strengthening support for early music intervention programmes.

Keywords: beat synchronisation; communication; musical engagement; polygenic scoring analyses; structural equation modelling.

Figure 1.. Study design.

Analyses were carried out in 6,737 unrelated children of European descent from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort adopting a two-stage research design. In Stage 1, we constructed polygenic scores (PGS) for self-reported beat synchronisation (PGS_rhythmicity) on ALSPAC individuals. Within Stage 2, we explored the phenotypic structure across phenotypes with PGS_rhythmicity p<0.05. To do so, we computed principal component analysis (PCA) and exploratory and confirmatory factor analysis, as described in the Methods. Finally, we mapped the PGS_rhythmicity to the factor structure, using methods analogous to mediation analysis.

Figure 2.. Effects of polygenic association with rhythmicity on ALSPAC measures.

Beta estimates are shown as circles with their corresponding 95% confidence intervals. Variance explained for each phenotype is shown as bars and expressed as the regression R² for continuous traits (represented by an empty grey diamond) and, in analogy, by Nagelkerke-R² for binary traits (represented by an empty grey square). Filled circles/bars and empty circles/bars represent phenotypes with an association with PGS_rhythmicity of p<0.05 and p≥0.05, respectively. Estimates are shown in dark purple if they were included in subsequent factor analysis and in light purple otherwise. If a phenotype passed multiple-testing threshold of 2.5×10⁻³ this was indicated with an asterisk in the beta coefficient. A full table with the values for this analysis is available in Supplementary Table 1. Abbreviations: SCDC (Social Communication Difficulties Checklist), CCC (Children’s Communication Checklist).

Figure 3.. Phenotypic relationships between preschool musicality, school-age cognition-related skills and communication skills.

a) Confirmatory factor model of phenotypes sharing genetic links with PGS_rhythmicity. Estimates are shown with their corresponding SEs. Observed measures are represented by squares and latent variables by circles. Coloured single-headed arrows define factor loadings with p≤0.05. Double-headed black arrows represent the variance of each phenotype and factor correlations. The CFA model provided an optimal model fit (CFI=0.97, TLI=0.95, RMSEA=0.03, SRMR=0.02). b) Genetic characterisation of phenotypic relationships between F1 on F2 and F3 explained by shared genetic links with PGS_rhythmicity. Estimates are shown with their corresponding SEs. Observed measures are represented by squares and latent variables by circles. Coloured single-headed arrows define factor loadings with p≤0.05. Double-headed black arrows represent the variance of each phenotype and factor correlations. Grey dotted and black solid single-headed arrows define relationships between factors and with PGS_rhythmicity with p>0.05 and p≤0.05, respectively. The shared genetic effect between F1 and F3, as captured by PGS_rhythmicity, is estimated as a*b_F3 and the total effect between F1 and F3 as a*b_F3 + c_F1-F3. The shared genetic effect between F1 and F2, as captured by PGS_rhythmicity, is estimated as a* b_F2 and the total effect as a* b_F2 + c_F1-F2.