GenomicSEM Modelling of Diverse Executive Function GWAS Improves Gene Discovery

Abstract

AbstractPrevious research has supported the use of factor scores as the gold-standard in measuring executive function. However, for logistical reasons genome-wide association studies (GWAS) of executive function have largely eschewed factor scores in favour of singular task measures. As low correlations have traditionally been found between individual executive function (EF) tests, it is unclear whether these GWAS have truly been measuring the same construct. In this study, we addressed this question by performing a factor analysis on summary statistics from nine GWAS of EF taken from four studies, using GenomicSEM. Models were able to capture the classic three factors of inhibition, working memory, and set-shifting, although set-shifting and inhibition could be merged without negatively impacting model fit. Furthermore, the GWAS performed on a common factor model identified 63 new genomic risk loci beyond what was found in the constituent GWAS reaching genome-wide significance, resulting in 18 newly mapped EF genes. The majority of these have previous associations with brain morphology but no established associations with cognitive performance. These results help to clarify the underlying genetic structure of EF and support the idea that EF GWAS are capable of measuring genetic variance related to latent EF constructs even when not using factor scores. Furthermore, they demonstrate that GenomicSEM can combine GWAS with divergent and non-ideal measures of the same phenotype to improve statistical power.