2024-02-26

078_ Phenome-Wide Association Study (PheWAS) of Non-Cognitive Phenotypes using Polygenic Risk Scores for a Common Executive Function Factor and P-Factor

Research Question and Aims

Executive functioning encompasses metacognitive processes such as planning, decision-making, and behavioral regulation. It can be quantitatively measured through a “common executive functioning” (cEF) score, which aggregates results from various neuropsychological tests using dimension-reduction techniques, including some of which were also administered in the PsyCourse Study. Hatoum et al. (2023) recently conducted a GWAS on cEF scores using the UK Biobank, identifying genetic variants linked to this Common Executive functioning.
In a similar way, different psychiatric disorders also share a common genomic and neural basis, called the P-Factor (e.g. Sprooten et al. 2022), for which GWAS summary statistics calculated with GenomicSEM (Grotzinger et al. 2019) are available.
Our study aims to use Polygenic Scores (PGS) for Executive Functioning derived from the summary statistics of Hatoum et al.’s GWAS (similar to PsyCourse Proposals #68 and #55) as well as for the P-Factor. We want to conduct Phenome-Wide Association Studies (pheWAS) to investigate cross-sectional associations between the cEF PGS/the P-Factor PGS and non-cognitive traits measured in PsyCourse. We will account for demographic (age, sex, recruitment center) and ancestry components (see below).
We aim to identify non-cognitive consequence of cEF and P-factor genetics associations with other traits. Any significant associations will be considered for validation using a different study, if available, such as the FOR2107.
We plan to conduct the study cross-sectionally for the neurotypic and clinical participants separately. For non-psychiatric phenotypes (e.g., personality) we will use all participants.

Analytic Plan

Participants
Data from all participants in PsyCourse who have genotype data available will be included in this study.

Polygenic risk scores
PRS calculated using the PRS-CS method, using the summary statistics of the UK Biobank based GWAS of Hatoum et al. 2023 resp. Grotzinger 2019, will be used. This method was also used in PsyCourse projects #55 and #68, and we want our methods to be consistent with those for comparability. During the project we will observe the PRS literature for competing methods for quality.

Imputation of missing phenotypes
We will also investigate whether the imputation of missing phenotypes using state-of-the-art methods is worthwhile and sensible. (This will depend on how much data are missing and how well they could be imputed.)

Power Calculations and QC
Before performing statistical testing, we will perform power analysis and QC for the traits in the PsyCourse dataset to exclude traits with insufficient power while at the same time lowering the multiple testing burden. We will use appropriate statistical software (for example QUANTO or CLARITE) for the power analysis.

The results of the power analysis could also be made available to other PsyCourse researchers.

Statistical Analysis
The association of the PGS with phenotypes in the PsyCourse dataset will be studied using linear/logistic models, adjusting for sex, age, ancestry components and recruitment site.

(For the ancestry components, we will use the first 4 principal components of genetic variation, again to stay consistent with previous PsyCourse projects #55 and #68.)

Resources needed

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psyc_id
gsa_id