Diurnal preference and sleep quality: same genes? A study of young adult twins

The aims of this study were to examine the genetic and environmental influences on diurnal preference and sleep quality, the association between these phenotypes, the genetic and environmental influences on this association, and the magnitude of overlap between these influences. Using a twin design, data on diurnal preference (measured by the Morningness-Eveningness Questionnaire) and sleep quality (measured by the Pittsburgh Sleep Quality Index) were collected from 420 monozygotic twins, 773 dizygotic twins, and 329 siblings (mode age = 20 yrs, range = 18–27 yrs) from a population-based twin registry across the UK. Univariate analyses indicated that dominance genetic influence accounted for 52% and non-shared environment 48% of variance in diurnal preference. For sleep quality, additive genetic influence explained 43% and non-shared environment 57% of the variance. The bivariate analysis indicated a significant association between greater eveningness preference and poorer sleep quality (r = .27). There was substantial overlap in the additive genetic influences on both phenotypes (rA = .57), and overlap in the dominance genetic influences common to both phenotypes was almost absolute (rD = .99). Overlap in non-shared environment was much smaller (rE = .02). Additive genetic influence accounted for 2% of the association, dominance genetic influence accounted for 94%, and non-shared environmental influences accounted for the remaining 4%. The substantial overlap in genetic influence between these phenotypes indicates that similar genes are important for diurnal preference and sleep quality. Therefore, those genes already known to influence one phenotype may be possible candidates to explore with regards to the other phenotype

A framework for power analysis using a structural equation modelling procedure

BACKGROUND: This paper demonstrates how structural equation modelling (SEM) can be used as a tool to aid in carrying out power analyses. For many complex multivariate designs that are increasingly being employed, power analyses can be difficult to carry out, because the software available lacks sufficient flexibility. Satorra and Saris developed a method for estimating the power of the likelihood ratio test for structural equation models. Whilst the Satorra and Saris approach is familiar to researchers who use the structural equation modelling approach, it is less well known amongst other researchers. The SEM approach can be equivalent to other multivariate statistical tests, and therefore the Satorra and Saris approach to power analysis can be used. METHODS: The covariance matrix, along with a vector of means, relating to the alternative hypothesis is generated. This represents the hypothesised population effects. A model (representing the null hypothesis) is then tested in a structural equation model, using the population parameters as input. An analysis based on the chi-square of this model can provide estimates of the sample size required for different levels of power to reject the null hypothesis. CONCLUSIONS: The SEM based power analysis approach may prove useful for researchers designing research in the health and medical spheres

Power calculations using exact data simulation: A useful tool for genetic study designs.

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes

The relative contribution of genes and environment to alcohol use in early adolescents: Are similar factors related to initiation of alcohol use and frequency of drinking?

Item does not contain fulltextBackground: The present study assessed the relative contribution of genes and environment to individual differences in initiation of alcohol use and frequency of drinking among early adolescents and examined the extent to which the same genetic and environmental factors influence both individual differences in initiation of alcohol use and frequency of drinking. Methods: Questionnaire data collected by the Netherlands Twin Register were available for 694 twin pairs aged of 12 to 15 years. Bivariate genetic model fitting analyses were conducted inmx. We modeled the variance of initiation of alcohol use and frequency of drinking as a function of three influences: genetic effects, common environmental effects, and unique environmental effects. Analyses were performed conditional on sex. Results: Findings indicated that genetic factors were most important for variation in early initiation of alcohol use (83% explained variance in males and 70% in females). There was a small contribution of common environment (2% in males, 19% in females). In contrast, common environmental factors explained most of the variation in frequency of drinking (82% in males and females). In males the association between initiation and frequency was explained by common environmental factors influencing both phenotypes. In females, there was a large contribution of common environmental factors that influenced frequency of drinking only. There was no evidence that different genetic or common environmental factors operated in males and females. Conclusion: Different factors were involved in individual differences in early initiation of alcohol use and frequency of drinking once adolescents have started to use alcohol

Co-opetition models for governing professional football

In recent years, models for co-creating value in a business-to-business context have often been examined with the aim of studying the strategies implemented by and among organisations for competitive and co-operative purposes. The traditional concepts of competition and co-operation between businesses have now evolved, both in terms of the sector in which the businesses operate and in terms of the type of goods they produce. Many researchers have, in recent times, investigated the determinants that can influence the way in which the model of co-opetition can be applied to the football world. Research interest lies in the particular features of what makes a good football. In this paper, the aim is to conduct an analysis of the rules governing the “football system”, while also looking at the determinants of the demand function within football entertainment. This entails applying to football match management the co-opetition model, a recognised model that combines competition and co-operation with the view of creating and distributing value. It can, therefore, be said that, for a spectator, watching sport is an experience of high suspense, and this suspense, in turn, depends upon the degree of uncertainty in the outcome. It follows that the rules ensuring that both these elements can be satisfied are a fertile ground for co-operation between clubs, as it is in the interest of all stakeholders to offer increasingly more attractive football, in comparison with other competing products. Our end purpose is to understand how co-opetition can be achieved within professional football

Runs of homozygosity implicate autozygosity as a schizophrenia risk factor

Autozygosity occurs when two chromosomal segments that are identical from a common ancestor are inherited from each parent. This occurs at high rates in the offspring of mates who are closely related (inbreeding), but also occurs at lower levels among the offspring of distantly related mates. Here, we use runs of homozygosity in genome-wide SNP data to estimate the proportion of the autosome that exists in autozygous tracts in 9,388 cases with schizophrenia and 12,456 controls. We estimate that the odds of schizophrenia increase by ~17% for every 1% increase in genome-wide autozygosity. This association is not due to one or a few regions, but results from many autozygous segments spread throughout the genome, and is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia. Such a bias towards recessivity suggests that alleles that increase the risk of schizophrenia have been selected against over evolutionary time

High loading of polygenic risk for ADHD in children with comorbid aggression

Objective: Although attention deficit hyperactivity disorder (ADHD) is highly heritable, genome-wide association studies (GWAS) have not yet identified any common genetic variants that contribute to risk. There is evidence that aggression or conduct disorder in children with ADHD indexes higher genetic loading and clinical severity. The authors examine whether common genetic variants considered en masse as polygenic scores for ADHD are especially enriched in children with comorbid conduct disorder. Method: Polygenic scores derived from an ADHD GWAS meta-analysis were calculated in an independent ADHD sample (452 case subjects, 5,081 comparison subjects). Multivariate logistic regression analyses were employed to compare polygenic scores in the ADHD and comparison groups and test for higher scores in ADHD case subjects with comorbid conduct disorder relative to comparison subjects and relative to those without comorbid conduct disorder. Association with symptom scores was tested using linear regression. Results: Polygenic risk for ADD, derived from the meta-analysis, was higher in the independent ADHD group than in the comparison group. Polygenic score was significantly higher in ADHD case subjects with conduct disorder relative to ADHD case subjects without conduct disorder. ADHD polygenic score showed significant association with comorbid conduct disorder symptoms. This relationship was explained by,the aggression items. Conclusions: Common genetic variation is relevant to ADHD, especially in individuals with comorbid aggression. The findings suggest that the previously published ADHD GWAS meta-analysis contains weak but true associations with common variants, support for which falls below genome-wide significance levels. The findings also highlight the fact that aggression in ADHD indexes genetic as well as clinical severity

Heritability of non-speech auditory processing skills

Recent insight into the genetic bases for autism spectrum disorder, dyslexia, stuttering, and language disorders suggest that neurogenetic approaches may also reveal at least one etiology of auditory processing disorder (APD). A person with an APD typically has difficulty understanding speech in background noise despite having normal pure-tone hearing sensitivity. The estimated prevalence of APD may be as high as 10% in the pediatric population, yet the causes are unknown and have not been explored by molecular or genetic approaches. The aim of our study was to determine the heritability of frequency and temporal resolution for auditory signals and speech recognition in noise in 96 identical or fraternal twin pairs, aged 6–11 years. Measures of auditory processing (AP) of non-speech sounds included backward masking (temporal resolution), notched noise masking (spectral resolution), pure-tone frequency discrimination (temporal fine structure sensitivity), and nonsense syllable recognition in noise. We provide evidence of significant heritability, ranging from 0.32 to 0.74, for individual measures of these non-speech-based AP skills that are crucial for understanding spoken language. Identification of specific heritable AP traits such as these serve as a basis to pursue the genetic underpinnings of APD by identifying genetic variants associated with common AP disorders in children and adults

Phenotypic Complexity, Measurement Bias, and Poor Phenotypic Resolution Contribute to the Missing Heritability Problem in Genetic Association Studies

Background The variance explained by genetic variants as identified in (genome-wide) genetic association studies is typically small compared to family-based heritability estimates. Explanations of this ‘missing heritability’ have been mainly genetic, such as genetic heterogeneity and complex (epi-)genetic mechanisms. Methodology We used comprehensive simulation studies to show that three phenotypic measurement issues also provide viable explanations of the missing heritability: phenotypic complexity, measurement bias, and phenotypic resolution. We identify the circumstances in which the use of phenotypic sum-scores and the presence of measurement bias lower the power to detect genetic variants. In addition, we show how the differential resolution of psychometric instruments (i.e., whether the instrument includes items that resolve individual differences in the normal range or in the clinical range of a phenotype) affects the power to detect genetic variants. Conclusion We conclude that careful phenotypic data modelling can improve the genetic signal, and thus the statistical power to identify genetic variants by 20-99