Hierarchical investigation of genetic influences on response inhibition in healthy young adults.

Abstract

Poor inhibitory control is a known risk factor for substance use disorders, making it a priority to identify the determinants of these deficits. The aim of the current study was to identify genetic associations with inhibitory control using the stop signal task in a large sample (n=934) of healthy young adults of European ancestry. We genotyped the subjects genome-wide and then used a hierarchical approach in which we tested: 1) 7 a priori single nucleotide polymorphisms (SNPs) previously associated with stop signal task performance; 2) ~9,000 SNPs designated as high-value addiction (HVA) markers by the SmokeScreen© array; and 3) ~5M genotyped and imputed SNPs, followed by a gene-based association analysis utilizing the resultant p-values. A priori SNP analyses revealed nominally significant associations between response inhibition and one locus in HTR2A (rs6313; p = 0.04, dominance model, uncorrected) in the same direction as prior findings. A nominally significant association was also found in one locus in ANKK1 (rs1800497; p = 0.03, uncorrected), although this was in the opposite direction of previous reports. After accounting for multiple comparisons, the HVA, genome-wide, and gene-based analyses yielded no significant findings. This study implicates variation in serotonergic and dopaminergic genes, but also underscores the difficulty of detecting the influence of individual SNPs, even when biological information is used to prioritize testing. While such small effect sizes suggest limited utility of individual SNPs in predicting risk for addiction or other impulse control disorders, they may nonetheless shed light on complex biological processes underlying poor inhibitory control.

Cross-Disorder Genomics Data Analysis Elucidates a Shared Genetic Basis Between Major Depression and Osteoarthritis Pain

“…Similarly, STRA6 is a shared OA/MD risk gene ( P meta = 1.20 × 10 –9 , P OA = 2.96 × 10 –7 , P MD = 5.35 × 10 –5 ) and involved in “mechanosensory behavior.” This gene encodes a membrane protein that transports retinol across the cell membrane as part of vitamin A metabolism ( Noy, 2016 ). The STRA6 gene has been associated with behavioral inhibitory control ( Weafer et al, 2017 ), as well as knee and hip osteoarthritis ( Tachmazidou et al, 2019 ).…”

The effect of ANKK1 Taq1A and DRD2 C957T polymorphisms on executive function: A systematic review and meta-analysis

“…A third study not included in the meta-analysis, conducted bySundararajan (2012) found better performance in Taq1A A1+ group in a spatial working memory task, but only when age was included as a betweensubjects variable. Two other studies mentioned above, which could not be integrated into the meta-analyses due to incomparability of the outcome measures(Weafer et al, 2017;White et al, 2008) showed better performance in A1+ carriers compared to A-carriers. On the other hand, some studies have noted that C957T CC homozygotes or C carriers were more successful in inhibiting responses on a stop signal task(Colzato et al, 2013; Wildenberg, Van der Does, & Hommel, 2010).…”

Latency and amplitude of the stop-signal P3 event-related potential are related to inhibitory GABAaactivity in primary motor cortex

“…This would indicate a superior functioning of the distributed inhibitory motor control network in these subjects, either as a result of practice, due to genetic factors, or as a result of both. Indeed, previous research has shown that the ability to exert motor inhibition can both be improved through repeated practice (Manuel, Bernasconi, & Spierer, 2013) and is partially attributable to genetic variables (Colzato, Wildenberg, Does, & Hommel, 2010; Weafer et al, 2017). Since it is broadly agreed upon that the precise timing of inhibitory control is paramount to action-stopping – especially in tasks like the stop-signal task, which purportedly reflect a race between response preparation and inhibitory control – it is likely that the individual components of the neural network underlying inhibitory control (cortical, subcortical, and motor system level) work in tight accord with one another, resulting in subjects with superior stopping abilities showing indicators of better neural functioning on several levels of measurement.…”