Cohorts

Cohort studies are essential for conducting large studies of multiple exposures and outcomes in humans. Recently, the ability to combine data from multiple cohorts in, for example, meta-analyses, and the willingness in the genetics community to collaborate to enable replication studies has led to many new insights into the genetic and environmental determinants of human health and behaviors. The contribution of Professor Nicholas Martin to the development of cohort studies, particularly of twin and twin-family studies, over a period of several decades is reviewed. He has contributed to the development and use of both Australian and international resources. The contributions of Australian twin studies to genomewide association projects are multiple, and across multiple domains, from biomarkers, lifestyle and behavior to disorders and disease.Peer reviewe

The use of vector bootstrapping to improve variable selection precision in Lasso models

The Lasso is a shrinkage regression method that is widely used for variable selection in statistical genetics. Commonly, K-fold cross-validation is used to fit a Lasso model. This is sometimes followed by using bootstrap confidence intervals to improve precision in the resulting variable selections. Nesting cross-validation within bootstrapping could provide further improvements in precision, but this has not been investigated systematically. We performed simulation studies of Lasso variable selection precision (VSP) with and without nesting cross-validation within bootstrapping. Data were simulated to represent genomic data under a polygenic model as well as under a model with effect sizes representative of typical GWAS results. We compared these approaches to each other as well as to software defaults for the Lasso. Nested cross-validation had the most precise variable selection at small effect sizes. At larger effect sizes, there was no advantage to nesting. We illustrated the nested approach with empirical data comprising SNPs and SNP-SNP interactions from the most significant SNPs in a GWAS of borderline personality symptoms. In the empirical example, we found that the default Lasso selected low-reliability SNPs and interactions which were excluded by bootstrapping

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