A genome-wide association study of the longitudinal course of executive functions

GenomicSEM Modelling of Diverse Executive Function GWAS Improves Gene Discovery

Previous research has supported the use of latent variables as the gold-standard in measuring executive function. However, for logistical reasons genome-wide association studies (GWAS) of executive function have largely eschewed latent variables 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 eleven GWAS of EF taken from five studies, using GenomicSEM. Models demonstrated a bifactor structure consistent with previous research, with factors capturing common EF and working memory- specific variance. Furthermore, the GWAS performed on this model identified 20 new genomic risk loci for common EF and 4 for working memory reaching genome-wide significance beyond what was found in the constituent GWAS, together resulting in 29 newly mapped EF genes. 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.

Gut microbiotas, inflammatory factors, and mental-behavioral disorders: A mendelian randomization study

BACKGROUND

The Mendelian randomization approach has emerged as a powerful tool, leveraging genetic variations as natural random experiments to minimize confounding and infer causality with unique advantages. Previous research has highlighted the crucial roles of gut microbiotas and inflammatory factors in mental-behavioral disorders, albeit to varying degrees. However, the precise causal relationship between gut microbiotas and mental-behavioral disorders remains elusive, and the potential role of inflammatory factors as mediators in this process is unclear.

METHODS

To investigate the associations between gut microbiotas, inflammatory factors, and mental-behavioral disorders, we pooled data from large-scale genome-wide association studies (GWAS). Our study screened 27 diseases, encompassing nine subtypes of mental-behavioral disorders: neurodevelopmental disorders, eating disorders, sleep disorders, schizophrenia spectrum disorders, stress- and trauma-related disorders, mood and affective disorders, cognitive and executive function disorders, personality and somatization disorders, and addiction disorders. Mendelian randomization(MR) was employed to assess causal relationships between gut microbiotas, inflammatory factors, and these mental-behavioral disorders, with inverse variance weighting (IVW) as the primary statistical method. Furthermore, we explored whether inflammatory factors mediate the relationship between gut microbiotas and mental-behavioral disorders.

RESULTS

Having investigated the intricate interplay among gut microbiota, inflammatory factors, and mental-behavioral disorders, we have identified nine pivotal inflammatory factors that intricately regulate the progression of eight distinct disease subtypes. Noteworthy among these findings, levels of CC motif chemokine ligand 28 (CCL28) and CC motif chemokine ligand 25 (CCL25) are associated with the progression of attention-deficit/hyperactivity disorder (ADHD), interleukin-18 (IL-18) levels are linked to anorexia, IL-12β levels are related to schizophrenia (SZ) progression, IL-8 levels are associated with manic episodes, and IL-10 and monocyte chemoattractant protein-2 (MCP-2) levels are closely related to enduring personality changes(EPC). Additionally, fibroblast growth factor 19 (FGF19) levels are associated with cognitive disorders, while C-X-C motif chemokine ligand 1 (CXCL1) levels are related to executive functioning.

CONCLUSION

Gut microbiotas and mental-behavioral disorders have causal relationships, with inflammatory factors mediating the pathway from gut microbiotas to these disorders.

Relationship Between Executive Function Subdomains and Postural Balance in Community-Dwelling Older Adults

BACKGROUND

Executive function (EF) deficits are a significant risk factor for falls among older adults (OAs). However, relationship between EF subdomains (shifting, updating, and inhibition), postural balance (PB), and fall risk in healthy OAs, remains poorly understood.

OBJECTIVE

This study aimed to investigate the relationship between EF subdomains (shifting, updating, and inhibition) and PB, and to assess their impact on risk of falls in community-dwelling OAs.

METHODS

A cross-sectional study involving 50 OAs aged over 60 years (average age of 72 years) was conducted. Participants underwent assessments of EF subdomains and PB using validated tests. A correlation analysis was employed to examine the relationships between EF and PB.

RESULTS

The study revealed significant correlations between subdomains and PB. Mental set shifting (r = -.539; p < .001) and inhibition (r = -.395; p = .050) exhibited inverse relationships with PB. Stepwise multiple linear regression showed that Trail Making Test Part B was associated with the PB (R2 = .42, p < .001).

CONCLUSION

These findings highlight the importance of assessing EF subdomains, particularly shifting and inhibition, to identify risk of falls. Trail Making Test Part B largely explains the variability of the PB. Integrating PB assessments and EF training, such as the Mini-BESTest, into routine care can be vital for fall prevention strategies. Significance/Implications: This knowledge underscores the need for cognitive training interventions focusing on shifting and inhibition to enhance PB and potentially reduce falls. Additionally, incorporation of EF assessment tools as Trail Making Test Part B and the Mini-BESTest into routine clinical practice for community-dwelling OAs is recommended to address fall prevention strategies.

A framework for conducting GWAS using repeated measures data with an application to childhood BMI

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS of the 12 estimated phenotypes identified 28 genome-wide significant variants at 13 loci, one of which (in DAOA) has not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover unique biological mechanisms influencing quantitative traits.

Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

Computer gaming alters resting-state brain networks, enhancing cognitive and fluid intelligence in players: evidence from brain imaging-derived phenotypes-wide Mendelian randomization

The debate on whether computer gaming enhances players' cognitive function is an ongoing and contentious issue. Aiming to delve into the potential impacts of computer gaming on the players' cognitive function, we embarked on a brain imaging-derived phenotypes (IDPs)-wide Mendelian randomization (MR) study, utilizing publicly available data from a European population. Our findings indicate that computer gaming has a positive impact on fluid intelligence (odds ratio [OR] = 6.264, P = 4.361 × 10-10, 95% confidence interval [CI] 3.520-11.147) and cognitive function (OR = 3.322, P = 0.002, 95% CI 1.563-7.062). Out of the 3062 brain IDPs analyzed, only one phenotype, IDP NET100 0378, was significantly influenced by computer gaming (OR = 4.697, P = 1.10 × 10-5, 95% CI 2.357-9.361). Further MR analysis suggested that alterations in the IDP NET100 0378 caused by computer gaming may be a potential factor affecting fluid intelligence (OR = 1.076, P = 0.041, 95% CI 1.003-1.153). Our MR study lends support to the notion that computer gaming can facilitate the development of players' fluid intelligence by enhancing the connectivity between the motor cortex in the resting-state brain and key regions such as the left dorsolateral prefrontal cortex and the language center.

Investigation of genetic determinants of cognitive change in later life

Cognitive decline is a major health concern and identification of genes that may serve as drug targets to slow decline is important to adequately support an aging population. Whilst genetic studies of cross-sectional cognition have been carried out, cognitive change is less well-understood. Here, using data from the TOMMORROW trial, we investigate genetic associations with cognitive change in a cognitively normal older cohort. We conducted a genome-wide association study of trajectories of repeated cognitive measures (using generalised estimating equation (GEE) modelling) and tested associations with polygenic risk scores (PRS) of potential risk factors. We identified two genetic variants associated with change in attention domain scores, rs534221751 (p = 1 × 10-8 with slope 1) and rs34743896 (p = 5 × 10-10 with slope 2), implicating NCAM2 and CRIPT/ATP6V1E2 genes, respectively. We also found evidence for the association between an education PRS and baseline cognition (at >65 years of age), particularly in the language domain. We demonstrate the feasibility of conducting GWAS of cognitive change using GEE modelling and our results suggest that there may be novel genetic associations for cognitive change that have not previously been associated with cross-sectional cognition. We also show the importance of the education PRS on cognition much later in life. These findings warrant further investigation and demonstrate the potential value of using trial data and trajectory modelling to identify genetic variants associated with cognitive change.

Factors related to the development of executive functions: A cumulative dopamine genetic score and environmental factors predict performance of kindergarten children in a go/nogo task

BACKGROUND

This study aimed at investigating the interaction between genetic and environmental factors in predicting executive function in children aged four to six years.

METHODS

Response inhibition as index of EF was assessed in 197 children using a go/nogo task. A cumulative dopamine (DA) genetic score was calculated, indexing predisposition of low DA activity. Dimensions of parenting behavior and parental education were assessed.

RESULTS

Parental education was positively related to accuracy in nogo trials. An interaction between the cumulative genetic score and the parenting dimension Responsiveness predicted go RT indicating that children with a high cumulative genetic score and high parental responsiveness exhibited a careful response mode.

CONCLUSION

The development of EF in kindergarten children is related to parental education as well as to an interaction between the molecular-genetics of the DA system and parenting behavior.

Genome-wide association study of executive function in a multi-ethnic cohort implicates LINC01362: Results from the northern Manhattan study

Executive function is a cognitive domain with sizable heritability representing higher-order cognitive abilities. Genome-wide association studies (GWAS) of executive function are sparse, particularly in populations underrepresented in medical research. We performed a GWAS on a composite measure of executive function that included measures of mental flexibility and reasoning using data from the Northern Manhattan Study, a racially and ethnically diverse cohort (N = 1077, 69% Hispanic, 17% non-Hispanic Black and 14% non-Hispanic White). Four SNPs located in the long intergenic non-protein coding RNA 1362 gene, LINC01362, on chromosome 1p31.1, were significantly associated with the composite measure of executive function in this cohort (top SNP rs2788328, ß = 0.22, p = 3.1 × 10-10). The associated SNPs have been shown to influence expression of the tubulin tyrosine ligase like 7 gene, TTLL7 and the protein kinase CAMP-activated catalytic subunit beta gene, PRKACB, in several regions of the brain involved in executive function. Together, these findings present new insight into the genetic underpinnings of executive function in an understudied population.

Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study

A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.

The roles of familial transmission and smoking during pregnancy on executive function skills: A sibling-comparison study

This research examines maternal smoking during pregnancy and risk for poorer executive function in siblings discordant for exposure. Data (N = 173 families) were drawn from the Missouri Mothers and Their Children study, a sample, identified using birth records (years 1998-2005), in which mothers changed smoking behavior between two pregnancies (Child 1 [older sibling]: M age = 12.99; Child 2 [younger sibling]: M age = 10.19). A sibling comparison approach was used, providing a robust test for the association between maternal smoking during pregnancy and different aspects of executive function in early-mid adolescence. Results suggested within-family (i.e., potentially causal) associations between maternal smoking during pregnancy and one working memory task (visual working memory) and one response inhibition task (color-word interference), with increased exposure associated with decreased performance. Maternal smoking during pregnancy was not associated with stop-signal reaction time, cognitive flexibility/set-shifting, or auditory working memory. Initial within-family associations between maternal smoking during pregnancy and visual working memory as well as color-word interference were fully attenuated in a model including child and familial covariates. These findings indicate that exposure to maternal smoking during pregnancy may be associated with poorer performance on some, but not all skills assessed; however, familial transmission of risk for low executive function appears more important.