The Genetic Architecture of Active Music Engagement and its Relationship to Neurobiological Function and Health
Bibliographic record
Abstract
Active music engagement (AME), i.e., playing a musical instrument or singing in everyday life, is an underappreciated epidemiological trait and neurobiological marker of health, especially in aging populations. Although clinical studies have shown that playing a musical instrument or singing can be harnessed to rehabilitate neurological functioning, few studies have examined AME within a genetic epidemiological framework. Prior genetic studies have shown that AME is moderately heritable in adults but have not explored its molecular underpinnings. This dissertation comprises two works that investigate the genetic architecture of AME, its relationships to neurobiological function, and shared genetic etiology with human health. In Chapter 1, I introduce background on prior approaches to studying AME, the current genetic epidemiological approach, and the motivations for studying AME’s connection to cognition, motor, speech and language, and psychiatric risk/mental health resilience. In Chapter 2, I conducted a genome-wide association study (GWAS) that was designed to examine the function of the common genetic variation associated with AME and its shared genetic variation with aging-related health traits and musical rhythm abilities. The primary findings were that the top independent genomic loci associated with AME were also implicated in affecting gene expression in the cerebellum, the heritability (h2SNP=10%) was enriched for regulatory function in brain-cell types, and AME showed significant positive genetic correlations with cognition, language, motor function, social engagement, and mental health resilience, albeit increased mood disorder risk. Additionally, two-sample Mendelian randomization analyses revealed potential evidence for a directional influence of musical rhythm abilities on AME. Chapter 3 is a pre-registered study in which I investigated the shared genetic architecture between AME and motor behaviour and neuromotor traits by testing associations with 24 polygenic scores (PGSs) in four distinct cohorts (total N=32,198). After multiple testing corrections, higher PGSs for walking pace were associated with greater AME within the Canadian Longitudinal Study on Aging (CLSA) and in the meta-analysis across four cohorts. Chapter 4 synthesizes these findings and discusses future directions. Together, this dissertation provides novel insights into the genetic architecture of AME and its functional relevance to brain structure. Cross-trait analyses from Chapters 2 and 3 illustrate how the genetic architecture of AME is a transdiagnostic marker of health, especially in aging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".