Mega- or meta-analytic studies (e.g. genome-wide association studies) are increasingly used in behavior genetics. An issue in such studies is that phenotypes are often measured by different instruments across study cohorts, requiring harmonization of measures so that more powerful fixed effect meta-analyses can be employed. Within the Genetics of Personality Consortium, we demonstrate for two clinically relevant personality traits, Neuroticism and Extraversion, how Item-Response Theory (IRT) can be applied to map item data from different inventories to the same underlying constructs. Personality item data were analyzed in >160,000 individuals from 23 cohorts across Europe, USA and Australia in which Neuroticism and Extraversion were assessed by nine different personality inventories. Results showed that harmonization was very successful for most personality inventories and moderately successful for some. Neuroticism and Extraversion inventories were largely measurement invariant across cohorts, in particular when comparing cohorts from countries where the same language is spoken. The IRT-based scores for Neuroticism and Extraversion were heritable (48 and 49 %, respectively, based on a meta-analysis of six twin cohorts, total N = 29,496 and 29,501 twin pairs, respectively) with a significant part of the heritability due to non-additive genetic factors. For Extraversion, these genetic factors qualitatively differ across sexes. We showed that our IRT method can lead to a large increase in sample size and therefore statistical power. The IRT approach may be applied to any mega- or meta-analytic study in which item-based behavioral measures need to be harmonized.
The heritability of variety seeking in the food domain was estimated from a large sample (N?=?5,543) of middle age to elderly monozygotic and dizygotic twins from the "Virginia 30,000" twin study. Different dietary variety scores were calculated based on a semi-quantitative food choice questionnaire that assessed consumption frequencies and quantities for a list of 99 common foods. Results indicate that up to 30% of the observed variance in dietary variety was explained through heritable influences. Most of the differences between twins were due to environmental influences that are not shared between twins. Additional non-genetic analyses further revealed a weak relationship between dietary variety and particular demographic variables, including socioeconomic status, age, sex, religious faith, and the number of people living in the same household.
Considerable effort has been devoted to establish genotype by environment interaction (G x E) in case of unmeasured genetic and environmental influences. Although it has been outlined by various authors that the appearance of G x E can be dependent on properties of the given measurement scale, a non-biased method to assess G x E is still lacking. We show that the incorporation of an explicit measurement model can remedy potential bias due to ceiling and floor effects. By means of a simulation study it is shown that the use of sum scores can lead to biased estimates whereas the proposed method is unbiased. The power of the suggested method is illustrated by means of a second simulation study with different sample sizes and G x E effect sizes.
The multiple-look notion holds that the difference limen (DL) decreases with multiple observations. We investigated this notion for temporal discrimination in isochronous sound sequences. In Experiment 1, we established a multiple-look effect when sequences comprised nine standard time intervals (S) followed by an increasing number of comparison time intervals (C), but no multiple-look effect when one trailing C interval was preceded by an increasing number of S intervals. In Experiment 2, we extended the design. There were four sequential conditions: (a) 9 leading S intervals followed by 1, 2, …, or 9 C-intervals; (b) 9 leading C intervals followed by 1, 2, …, or 9 S intervals; (c) 9 trailing C-intervals preceded by 1, 2, …, or 9 S-intervals; and (d) 9 trailing S-intervals preceded by 1, 2, …, or 9 C-intervals. Both the interval accretions before and after the tempo change caused multiple-look effects, irrespective of the time order of S and C. Complete deconfounding of the number of intervals before and after the tempo change was accomplished in Experiment 3. The multiple-look effect of interval accretion before the tempo change was twice as big as that after the tempo change. The diminishing returns relation between the DL and interval accretion could be described well by a reciprocal function.
Relatively little is known about how genetic influences on alcohol abuse and dependence (AAD) change with age. We examined the change in influence of genetic and environmental factors which explain symptoms of AAD from adolescence into early adulthood. Symptoms of AAD were assessed using the four AAD screening questions of the CAGE inventory. Data were obtained up to six times by self-report questionnaires for 8,398 twins from the Netherlands Twin Register aged between 15 and 32 years. Longitudinal genetic simplex modeling was performed with Mx. Results showed that shared environmental influences were present for age 15-17 (57%) and age 18-20 (18%). Unique environmental influences gained importance over time, contributing 15% of the variance at age 15-17 and 48% at age 30-32. At younger ages, unique environmental influences were largely age-specific, while at later ages, age-specific influences became less important. Genetic influences on AAD symptoms over age could be accounted for by one factor, with the relative influence of this factor differing across ages. Genetic influences increased from 28% at age 15-17 to 58% at age 21-23 and remained high in magnitude thereafter. These results are in line with a developmentally stable hypothesis that predicts that a single set of genetic risk factors acts on symptoms of AAD from adolescence into young adulthood.
The extent to which verbal (VM) and visuospatial memory (VSM) tests measure the same or multiple constructs is unclear. Likewise the relationship between VM and VSM across development is not known. These questions are addressed using genetically informative data, studying two age cohorts (young adults and children) of twins and siblings. VM and VSM were measured in the working memory and short-term memory domain. Multivariate genetic analyses revealed that two highly correlated common genetic factors, one for VM and one for VSM, gave the best description of the covariance structure among the measures. Only in children, specific genetic factors were also present. This led to the following conclusions: In children, one genetic factor is responsible for linking VM and VSM. Specific genetic factors create differences between these two domains. During the course of development, the influence of genetic factors unique to each of these domains disappears and the genetic factor develops into two highly correlated factors, which are specific to VM and VSM respectively. At the environmental level, in both age cohorts, environmental factors create differences between these domains.
Sex steroids exert important organizational effects on brain structure. Early in life, they are involved in brain sexual differentiation. During puberty, sex steroid levels increase considerably. However, to which extent sex steroid production is involved in structural brain development during human puberty remains unknown. The relationship between pubertal rises in testosterone and estradiol levels and brain structure was assessed in 37 boys and 41 girls (10-15 years). Global brain volumes were measured using volumetric-MRI. Regional gray and white matter were quantified with voxel-based morphometry (VBM), a technique which measures relative concentrations (density) of gray and white matter after individual global differences in size and shape of brains have been removed. Results showed that, corrected for age, global gray matter volume was negatively associated with estradiol levels in girls, and positively with testosterone levels in boys. Regionally, a higher estradiol level in girls was associated with decreases within prefrontal, parietal and middle temporal areas (corrected for age), and with increases in middle frontal-, inferior temporal- and middle occipital gyri. In boys, estradiol and testosterone levels were not related to regional brain structures, nor were testosterone levels in girls. Pubertal sex steroid levels could not explain regional sex differences in regional gray matter density. Boys were significantly younger than girls, which may explain part of the results. In conclusion, in girls, with the progression of puberty, gray matter development is at least in part directly associated with increased levels of estradiol, whereas in boys, who are in a less advanced pubertal stage, such steroid-related development could not (yet) be found. We suggest that in pubertal girls, estradiol may be implicated in neuronal changes in the cerebral cortex during this important period of brain development.
Puberty represents the phase of sexual maturity, signaling the change from childhood into adulthood. During childhood and adolescence, prominent changes take place in the brain. Recently, variation in frontal, temporal, and parietal areas was found to be under varying genetic control between 5 and 19 years of age. However, at the onset of puberty, the extent to which variation in brain structures is influenced by genetic factors (heritability) is not known. Moreover, whether a direct link between human pubertal development and brain structure exists has not been studied. Here, we studied the heritability of brain structures at 9 years of age in 107 monozygotic and dizygotic twin pairs (N = 210 individuals) using volumetric MRI and voxel-based morphometry. Children showing the first signs of secondary sexual characteristics (N = 47 individuals) were compared with children without these signs, based on Tanner-stages. High heritabilities of intracranial, total brain, cerebellum, and gray and white matter volumes (up to 91%) were found. Regionally, the posterior fronto-occipital, corpus callosum, and superior longitudinal fascicles (up to 93%), and the amygdala, superior frontal and middle temporal cortices (up to 83%) were significantly heritable. The onset of secondary sexual characteristics of puberty was associated with decreased frontal and parietal gray matter densities. Thus, in 9-year-old children, global brain volumes, white matter density in fronto-occipital and superior longitudinal fascicles, and gray matter density of (pre-)frontal and temporal areas are highly heritable. Pubertal development may be directly involved in the decreases in gray matter areas that accompany the transition of our brains from childhood into adulthood.
This study investigates the genetic relationship among reading performance, IQ, verbal and visuospatial working memory (WM) and short-term memory (STM) in a sample of 112, 9-year-old twin pairs and their older siblings. The relationship between reading performance and the other traits was explained by a common genetic factor for reading performance, IQ, WM and STM and a genetic factor that only influenced reading performance and verbal memory. Genetic variation explained 83% of the variation in reading performance; most of this genetic variance was explained by variation in IQ and memory performance. We hypothesize, based on these results, that children with reading problems possibly can be divided into three groups: (1) children low in IQ and with reading problems; (2) children with average IQ but a STM deficit and with reading problems; (3) children with low IQ and STM deficits; this group may experience more reading problems than the other two.
Modeling both genetic and cultural transmission in parent-offspring data in the presence of phenotypic assortment requires the imposition of nonlinear constraints. This article reports a simulation study that determined how well the structural equation modeling software package Mx and the Bayesian-oriented BUGS software package can handle such nonlinear constraints under various conditions. Results generally showed good and comparable results for Mx and BUGS, although BUGS was much slower than Mx. However, since BUGS uses Markov-chain Monte Carlo estimation it could be used for parent-offspring models with non-normal data and/or item-response theory models.
The ultimate goal of QTL studies is to find causative mutations, which requires additional expression studies. Given the limited amount of time and funds, the smart option is to identify the most important QTL with minimal effort. A cost-effective solution is to genotype only those animals with high or low phenotypic values or DNA-pools of these individuals. A two-stage genotyping strategy was applied on samples in the tails of the distribution of breeding values.
This study investigated psychometric properties of two widely used instruments to measure subclinical levels of psychosis, the Community Assessment of Psychic Experiences (CAPE) and the Structured Interview for Schizotypy-Revised (SIS-R), and aimed to enhance measurements through the use of multidimensional measurement models. Data were collected in 747 siblings of schizophrenia patients and 341 healthy controls. Multidimensional Item-Response Theory, Mokken Scale and ordinal factor analyses were performed. Both instruments showed good psychometric properties and were measurement invariant across siblings and controls. The latent traits measured by the instruments show a correlation of 0.62 in siblings and 0.47 in controls. Multidimensional modeling resulted in smaller standard errors for SIS-R scores. By exploiting correlations among related traits through multidimensional models, scores from one diagnostic instrument can be estimated more reliably by making use of information from instruments that measure related traits.
As data from sequencing studies in humans accumulate, rare genetic variants influencing liability to disease and disorders are expected to be identified. Three simulation studies show that characteristics and properties of diagnostic instruments interact with risk allele frequency to affect the power to detect a quantitative trait locus (QTL) based on a test score derived from symptom counts or questionnaire items. Clinical tests, that is, tests that show a positively skewed phenotypic sum score distribution in the general population, are optimal to find rare risk alleles of large effect. Tests that show a negatively skewed sum score distribution are optimal to find rare protective alleles of large effect. For alleles of small effect, tests with normally distributed item parameters give best power for a wide range of allele frequencies. The item-response theory framework can help understand why an existing measurement instrument has more power to detect risk alleles with either low or high frequency, or both kinds.
Twin concordance rates provide insight into the possibility of a genetic background for a disease. These concordance rates are usually estimated within a frequentistic framework. Here we take a Bayesian approach. For rare diseases, estimation methods based on asymptotic theory cannot be applied due to very low cell probabilities. Moreover, a Bayesian approach allows a straightforward incorporation of prior information on disease prevalence coming from non-twin studies that is often available. An MCMC estimation procedure is tested using simulation and contrasted with frequentistic analyses. The Bayesian method is able to include prior information on both concordance rates and prevalence rates at the same time and is illustrated using twin data on cleft lip and rheumatoid arthritis.
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