Ovarian reserve and anti-Mullerian hormone (AMH) in mothers of dizygotic twins

This study aimed to explore if natural dizygotic (DZ) twinning is associated with earlier menopause and lower anti-Mullerian hormone (AMH) values. We investigated if advanced biological reproductive aging, which can be responsible for the multiple follicle growth in familial twinning, is similar to mechanisms that occur in normal ovarian aging, reflected by earlier menopause in mothers of DZ twins and lower levels of AMH. A total of 16 mothers of DZ twins enrolled with the Netherlands Twin Register (average age at first assessment: 35.9 +/- 3.0 years) and 14 control mothers (35.1 +/- 3 years) took part in a prospective study. Fifteen years after entry into the study, which included follicle-stimulating hormone (FSH) assessment, AMH was measured in stored serum samples and menopause status was evaluated. Average AMH levels were not significantly different between DZ twin mothers and controls (2.1 +/- 2.4 mu g/L vs. 1.9 +/- 1.9 mu g/L). Among the 16 mothers of twins, 7 had an elevated (FSH) value over 10 U/L at first assessment. Their AMH levels were lower than the nine twin mothers with normal FSH values: 0.6 +/- 0.4 versus 3.4 +/- 2.6 mu g/L (p = .01). Of the mothers of twins, eight mothers had entered menopause at the second assessment compared with only one control mother (p = .07). Thus, slightly more DZ mothers were in menopause than the control mothers, although this difference was not significant. The subgroup of DZ twin mothers who had an increased FSH concentration 15 years ago had a limited ovarian reserve as reflected by lower AMH levels. These data indicate that advanced ovarian aging can be a feature in familial DZ twinning, particularly with elevated early follicular phase FSH

Genetic and environmental covariation between autistic traits and behavioral problems

Objective: To examine the overlap between autistic traits and other behavioral problems in a general population sample, and explore the extent to which this overlap is due to genetic or environmental factors. Method: Youth Self Report (YSR) data were collected in a general population sample of 424 twin pairs at 18 years of age, and their non twin siblings. In 197 of these twin families, self-report ratings on the Autism-spectrum Quotient (AQ) were collected. Results: Stepwise backward regression analyses revealed that of all 8 YSR syndrome scales, the Withdrawn Behavior (WB) and Social Problems (SOC) scale were the most important predictors of AQ scores, and together with sex, explained 23% of the variance in AQ scores. Genetic structural equation modeling showed that the overlap between AQ and WB and SOC was mainly due to genetic effects. About half of the genetic variance in AQ scores was specific to the AQ, with the remaining half shared with genetic variance in WB and SOC. Conclusions: Endorsement of autistic traits in a general population sample is associated with social and withdrawn behavioral problems and these problems partly share a common genetic etiology with autistic traits. However, most of the variance in AQ scores remains unexplained by YSR scores, and half of the genetic variance in AQ is unshared with WB and SOC. These results indicate that autistic traits have specific characteristics that are substantially genetically independent from other common but related behavioral domains such as social problems and withdrawn behavior

Variance Decomposition Using an IRT Measurement Model

Large scale research projects in behaviour genetics and genetic epidemiology are often based on questionnaire or interview data. Typically, a number of items is presented to a number of subjects, the subjects’ sum scores on the items are computed, and the variance of sum scores is decomposed into a number of variance components. This paper discusses several disadvantages of the approach of analysing sum scores, such as the attenuation of correlations amongst sum scores due to their unreliability. It is shown that the framework of Item Response Theory (IRT) offers a solution to most of these problems. We argue that an IRT approach in combination with Markov chain Monte Carlo (MCMC) estimation provides a flexible and efficient framework for modelling behavioural phenotypes. Next, we use data simulation to illustrate the potentially huge bias in estimating variance components on the basis of sum scores. We then apply the IRT approach with an analysis of attention problems in young adult twins where the variance decomposition model is extended with an IRT measurement model. We show that when estimating an IRT measurement model and a variance decomposition model simultaneously, the estimate for the heritability of attention problems increases from 40% (based on sum scores) to 73%

On the etiology of aesthetic chills: a behavioral genetic study

Aesthetic chills, broadly defined as a somatic marker of peak emotional-hedonic responses, are experienced by individuals across a variety of human cultures. Yet individuals vary widely in the propensity of feeling them. These individual differences have been studied in relation to demographics, personality, and neurobiological and physiological factors, but no study to date has explored the genetic etiological sources of variation. To partition genetic and environmental sources of variation in the propensity of feeling aesthetic chills, we fitted a biometrical genetic model to data from 14,127 twins (from 8995 pairs), collected by the Netherlands Twin Register. Both genetic and unique environmental factors accounted for variance in aesthetic chills, with heritability estimated at 0.36 ([0.33, 0.39] 95% CI). We found females more prone than males to report feeling aesthetic chills. However, a test for genotype x sex interaction did not show evidence that heritability differs between sexes. We thus show that the propensity of feeling aesthetic chills is not shaped by nurture alone, but it also reflects underlying genetic propensities

The genetics of neuroticism: Insights from the Maudsley rat model and human studies

We examine some of the genetic features of neuroticism (N) taking as an animal model the Maudsley Reactive (MR) and Maudsley Nonreactive (MNR) rat strains which were selectively bred, respectively, for high and low open-field defecation (OFD) starting in the late 1950s. To draw analogies with human genetic studies, we explore the genetic correlation of N with irritable bowel syndrome (IBS). We review progress with the rat model and developments in the field of human complex trait genetics, including genetic association studies that relate to current understanding of the genetics of N. The widespread differences in the tone of the peripheral sympathetic nervous system that have been found between the Maudsley strains, particularly those observed in the colon, may underly the differences in OFD (MNR, higher sympathetic tone and zero defecation). In humans, a large genome-wide association study (GWAS) reported six genes contributing to IBS, four of which were implicated in mood and anxiety disorders or were expressed in the brain, with three of the four also expressed in the nerve fibers and ganglia of the gut. Heritability of N is estimated at around 50% in twin and family studies, and GWASs identified hundreds of loci, enabling estimation of genome-wide correlations (rg) with other traits. Significantly, the estimate for rg between risk of IBS, anxiety, N, and depression was >0.5 and suggested genetic pleiotropy without evidence for causal mechanisms. Findings on the adrenergic pharmacology of the colon, coupled with new understanding of the role of the locus ceruleus in modifying afferent information from this organ, generate hypotheses that challenge traditional cause/effect notions about the relationship of the central nervous system to peripheral events in response to stress, suggest specific targets for gene action in the Maudsley model and emphasize the value of reciprocal evaluation of genetic architecture underlying N in rodents and humans

Statistical power to detect genetic and environmental influences in the presence of data missing at random.

We study the situation in which a cheap measure (X) is observed in a large, representative twin sample, and a more expensive measure (Y) is observed in a selected subsample. The aim of this study is to investigate the optimal selection design in terms of the statistical power to detect genetic and environmental influences on the variance of Y and on the covariance of X and Y. Data were simulated for 4000 dizygotic and 2000 monozygotic twins. Missingness (87% vs. 97%) was then introduced in accordance with 7 selection designs: (i) concordant low + individual high design; (ii) extreme concordant design; (iii) extreme concordant and discordant design (EDAC); (iv) extreme discordant design; (v) individual score selection design; (vi) selection of an optimal number of MZ and DZ twins; and (vii) missing completely at random. The statistical power to detect the influence of additive and dominant genetic and shared environmental effects on the variance of Y and on the covariance between X and Y was investigated. The best selection design is the individual score selection design. The power to detect additive genetic effects is high irrespective of the percentage of missingness or selection design. The power to detect shared environmental effects is acceptable when the percentage of missingness is 87%, but is low when the percentage of missingness is 97%, except for the individual score selection design, in which the power remains acceptable. The power to detect D is low, irrespective of selection design or percentage of missingness. The individual score selection design is therefore the best design for detecting genetic and environmental influences on the variance of Y and on the covariance of X and Y. However, the EDAC design may be preferred when an additional purpose of a study is to detect quantitative trait loci effects

Estimation of individual genetic and environmental profiles in longitudinal designs

Parameter estimates obtained in the genetic analysis of longitudinal data can be used to construct individual genetic and environmental profiles across time. Such individual profiles enable the attribution of individual phenotypic change to changes in the underlying genetic or environmental processes and may lead to practical applications in genetic counseling and epidemiology. Simulations show that individual estimates of factor scores can be reliably obtained. Decomposition of univariate, and to a lesser extent of bivariate, phenotypic time series may yield estimates of independent individual G(t) and E(t), however, that are intercorrelated. The magnitude of these correlations depends somewhat on the autocorrelation structure of the underlying series, but to obtain completely independent estimates of genetic and environmental individual profiles, at least three measured indicators are needed at each point in time. KEY WORDS: longitudinal genetic analysis; environmental profiles; genetic profiles; factor scores; Kalman filter