Dataset Information

Genetic and environmental influences on the relationship between flow proneness, locus of control and behavioral inhibition.

ABSTRACT: Flow is a psychological state of high but subjectively effortless attention that typically occurs during active performance of challenging tasks and is accompanied by a sense of automaticity, high control, low self-awareness, and enjoyment. Flow proneness is associated with traits and behaviors related to low neuroticism such as emotional stability, conscientiousness, active coping, self-esteem and life satisfaction. Little is known about the genetic architecture of flow proneness, behavioral inhibition and locus of control--traits also associated with neuroticism--and their interrelation. Here, we hypothesized that individuals low in behavioral inhibition and with an internal locus of control would be more likely to experience flow and explored the genetic and environmental architecture of the relationship between the three variables. Behavioral inhibition and locus of control was measured in a large population sample of 3,375 full twin pairs and 4,527 single twins, about 26% of whom also scored the flow proneness questionnaire. Findings revealed significant but relatively low correlations between the three traits and moderate heritability estimates of .41, .45, and .30 for flow proneness, behavioral inhibition, and locus of control, respectively, with some indication of non-additive genetic influences. For behavioral inhibition we found significant sex differences in heritability, with females showing a higher estimate including significant non-additive genetic influences, while in males the entire heritability was due to additive genetic variance. We also found a mainly genetically mediated relationship between the three traits, suggesting that individuals who are genetically predisposed to experience flow, show less behavioral inhibition (less anxious) and feel that they are in control of their own destiny (internal locus of control). We discuss that some of the genes underlying this relationship may include those influencing the function of dopaminergic neural systems.

SUBMITTER: Mosing MA

PROVIDER: S-EPMC3487896 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic and environmental influences on the relationship between flow proneness, locus of control and behavioral inhibition.

Mosing Miriam A MA Pedersen Nancy L NL Cesarini David D Johannesson Magnus M Magnusson Patrik K E PK Nakamura Jeanne J Madison Guy G Ullén Fredrik F

PloS one 20121102 11

Flow is a psychological state of high but subjectively effortless attention that typically occurs during active performance of challenging tasks and is accompanied by a sense of automaticity, high control, low self-awareness, and enjoyment. Flow proneness is associated with traits and behaviors related to low neuroticism such as emotional stability, conscientiousness, active coping, self-esteem and life satisfaction. Little is known about the genetic architecture of flow proneness, behavioral in ...[more]

PMID: 23133606

Similar Datasets

Project description:Esophageal squamous cell carcinoma (ESCC) has a high prevalence in several countries in Africa and Asia. Previous genome-wide association studies (GWAS) in Chinese populations have identified several ESCC susceptibility loci, including variants on chromosome 2q33 and 6p21, but the contribution of these loci to risk in African populations is unknown. In this study we tested the association of 10 genetic variants at these two risk loci on susceptibility to ESCC in two South African ethnic groups. Variants at 2q33 (rs3769823, rs10931936, rs13016963, rs7578456, rs2244438) and 6p21 (rs911178, rs3763338, rs2844695, rs17533090, rs1536501) were genotyped in a set of Black Xhosa (463 cases and 480 controls) and Mixed Ancestry (269 cases and 288 controls) individuals. Genotyping was performed using TaqMan allelic discrimination assays. The Pearson's chi-squared test was used to compare the allele frequency between cases and controls. Gene-environment interactions with tobacco smoking and alcohol consumption were investigated in a case-control analysis. A logistic regression analysis was further performed to elucidate the independent effect of each association signal on the risk of ESCC. The 2q33 variants rs10931936, rs7578456, and rs2244438 were marginally associated with higher risk of ESCC in the Mixed Ancestry population (ORs = 1.39-1.58, p ≤ 0.035), of which rs7578456 and rs2244438 remained significant after multiple correction (p < 0.005). The associations with rs7578456 and rs2244438 were also observed across strata of tobacco smoking (ORs = 1.47-2.75, p ≤ 0.035) and alcohol consumption (ORs = 1.45-2.06, p ≤ 0.085) status. However, only the association with rs2244438, which lies within an exon of TRAK2, remained significant after adjustment for the other variants in the region. Interestingly, none of the variants tested were significantly associated with ESCC in the Black South African population. These finding implicate TRAK2 as a casual gene for ESCC risk in the Mixed Ancestry population of South Africa and confirm prior evidence of population-specific differences in the genetic contribution to ESCC, which may reflect differences in genetic architecture and environmental exposure across ethnic groups.

Project description:Magnetic resonance imaging (MRI) has become an important tool in the early detection of age-related and neuropathological brain changes. Recent studies suggest that changes in mean diffusivity (MD) of cortical gray matter derived from diffusion MRI scans may be useful in detecting early effects of Alzheimer's disease (AD), and that these changes may be detected earlier than alterations associated with standard structural MRI measures such as cortical thickness. Thus, due to its potential clinical relevance, we examined the genetic and environmental influences on cortical MD in middle-aged men to provide support for the biological relevance of this measure and to guide future gene association studies. It is not clear whether individual differences in cortical MD reflect neuroanatomical variability similarly detected by other MRI measures, or whether unique features are captured. For instance, variability in cortical MD may reflect morphological variability more commonly measured by cortical thickness. Differences among individuals in cortical MD may also arise from breakdowns in myelinated fibers running through the cortical mantle. Thus, we investigated whether genetic influences on variation in cortical MD are the same or different from those influencing cortical thickness and MD of white matter (WM) subjacent to the cortical ribbon. Univariate twin analyses indicated that cortical MD is heritable in the majority of brain regions; the average of regional heritability estimates ranged from 0.38 in the cingulate cortex to 0.66 in the occipital cortex, consistent with the heritability of other MRI measures of the brain. Trivariate analyses found that, while there was some shared genetic variance between cortical MD and each of the other two measures, this overlap was not complete (i.e., the correlation was statistically different from 1). A significant amount of distinct genetic variance influences inter-individual variability in cortical MD; therefore, this measure could be useful for further investigation in studies of neurodegenerative diseases and gene association studies.

Project description:Population growth models are integral to ecological studies by providing estimates of population performance across space and time. Several models have been developed that estimate population growth through correlates of demographic traits, as measuring each parameter of the model can be prohibitive in experimental studies. Since differences in female size can accurately reflect changes in fecundity for many taxa, Livdahl and Sugihara developed a population growth index that incorporates size-fecundity relationships as a proxy for fecundity. To investigate the extent to which this model is robust to variation of this proxy, we tested if genetic (source population), temperature and resource treatments affect the size-fecundity relationship in Aedes albopictus (Skuse), the Asian tiger mosquito. We then determined if variation in the size-fecundity relationship alters the population growth estimates, lambda (?'), when applied to Livdahl and Sugihara's model. We performed 2 laboratory experiments in which we reared cohorts of four different geographic populations of A. albopictus across 5 temperature treatments (18, 21, 25, 18, 31°C) and three resource treatments (low, medium, high larval resources). We determined if the slope of the size-fecundity relationship varied by source population, temperature, or resource; and if variation in this relationship affects lambda (?') estimates in a competition study between A. albopictus and Culex pipiens (Linnaeus), the northern house mosquito. Temperature treatments significantly affected the size-fecundity relationship, resource level marginally affected the relationship, while source population had no effect. We found positive relationships between size and fecundity when mosquito larvae were reared at high temperatures and low resource levels but the relationship disappeared when mosquitoes were reared at a low temperature or with high levels of resources. The variation in the size-fecundity relationship produced from different temperatures resulted in statistically different lambda (?') estimates. However, these changes in lambda (?') did not alter the trends in the population performance across treatments or conclusions of the competition study. This study provides evidence that the population growth model is sensitive to variation in size-fecundity relationships and we recommend biologists apply the most compatible size-fecundity relationship to the models to obtain the most accurate estimates of population performance.

Dataset Information

Genetic and environmental influences on the relationship between flow proneness, locus of control and behavioral inhibition.

Publications

Genetic and environmental influences on the relationship between flow proneness, locus of control and behavioral inhibition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets