055_ Contributions of Polygenic Risk Scores for Genomic Common EF- and P-Factors to Cognitive Performance
Research Question and Aims
Executive Functions (EFs) are meta-cognitive functions that control and coordinate mental processes (e.g., PMID: 34408280). Albeit lacking a concise definition, EFs include an array of meta-cognitive processes such as inhibition, set-shifting, and working memory. During the past decades, evidence has accumulated that these higher-level cognitive functions are best assessed on a latent level, since any single EF test also captures non-executive processes (PMID: 27251123). It is well known that latent EFs factors are related to but distinct from general intelligence, highly polygenic, and, in functional neuroimaging studies, associated with activation of the frontal lobes. In an earlier twin-study, a latent "common EF" (cEF) factor was found to be highly heritable (96%, PMID: 27251123), making it an attractive target for further molecular studies. A recent large-scale UK Biobank GWAS (currently available as a preprint) has recently examined the genomics of this common EF factor in over 427,000 individuals (Hatoum et al, 2020, doi.org/10.1101/674515), and has identified 112 distinct genomic loci associated with it. Furthermore, based on analyses using LDSC, the cEF factor is genetically correlated to many psychiatric disorders (e.g., about -0.3 to -0.4 with schizophrenia, bipolar disorder, and major depression). Paralleling this line of research, it has also been shown that different psychiatric disorders share a common phenotypic, neural, and genomic basis (e.g., PMID: 33526822). GWAS summary statistics of this latent "p-factor" were calculated with GenomicSEM (PMID: 30962613) and are readily available. Moreover, in a genomic structural equation model, the p- and cEF factors are related (Hatoum et al, 2020, doi.org/10.1101/674515, -0.50).
This proposal seeks to assess influences of polygenic risk scores (PRS) for both the cEF as well as the p-factor on all (phenotypic) cognitive variables collected in PsyCourse.
1. We hypothesize that PRS for cEF and the p-factor are significantly correlated. To systematically assess this, we will calculate PRS at different p-value thresholds.
2. We hypothesize that PRS for cEF and the p-factor explain variability in at least some neuropsychological test results (both PRS assessed separately).
3. We aim to calculate a phenotypic common EF factor score for the PsyCourse participants, by performing a confirmatory factor analysis, and assess the relationship of PRS for cEF and the p-factor with phenotypic factor scores for the cEF factor. The following neuropsychological tests from Visit 1 will be used to calculate the phenotypic factor score, and were selected to ensure maximum overlap with the test used by Hatoum et al. (2020): TMT-A [reaction time], TMT-B [reaction time], Digit-Span Forward, Digit-Span Backward, and the Digit-Symbol-Test.
PRS will be calculated with both PRS-CS, and the clumping-and-thresholding approach, using GSA data.
The analyses will include covariates, such as age, sex, study center, diagnosis, severity of current psychiatric symptoms, cumulative medication, and the first four principal components of an ancestry PCA (or MDS).
Secondary analyses will be performed separately for cases and controls, including broad diagnostic groups.
Phenotype data (PsyCourse 5.0):
Imputed and best guess genotypes (already available from other proposals)