Genetic and demographic predictors of general reading ability in two cohorts

PurposeReading ability is a complex skill utilizing multiple proficiencies and that develops through interactions between genetic and environmental factors. This study presents an alternative analytic pipeline to identify key genetic and demographic contributors to reading ability. MethodsWe analyzed data from the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 3 232) using a multi-step analytical pipeline. To reduce measurement error, we generated a latent reading ability score. We selected single nucleotide polymorphisms (SNPs) based on existing literature and genome-wide association studies (GWAS). We applied elastic net regression to identify informative predictors in two models, a SNP-only model and a SNP-plus demographic, environmental, and behavioral variables model. We compared the SNP-based heritability estimates and R2 from the fitted models. We also performed pathway enrichment analysis on the informative SNPs. ResultsThe traditional GWAS identified one genome-wide significant SNP on chromosome X and produced a moderate heritability estimate of .23 (SE = 0.07). We included 148 SNPs in the elastic net models. The SNP-only model identified 61 informative SNPs (R2 = .12), whereas the SNP-plus model identified 96 informative SNPs (R2 = .32). The SNP-plus model also showed that several behavioral characteristics positively predicted latent reading ability. Enrichment analysis revealed overrepresentation of several biological pathways among the informative SNPs. ConclusionsThis study shows that our analytic pipeline can identify important genetic and demographic predictors of reading ability, providing a powerful alternative to traditional methods and contributing to a deeper understanding of the factors that drive reading development.