TwinsUK is the largest cohort of community-dwelling adult twins in the UK. The registry comprises over 14,000 volunteer twins (14,838 including mixed, single and triplets); it is predominantly female (82%) and middle-aged (mean age 59). In addition, over 1800 parents and siblings of twins are registered volunteers. During the last 27 years, TwinsUK has collected numerous questionnaire responses, physical/cognitive measures and biological measures on over 8500 subjects. Data were collected alongside four comprehensive phenotyping clinical visits to the Department of Twin Research and Genetic Epidemiology, King’s College London. Such collection methods have resulted in very detailed longitudinal clinical, biochemical, behavioral, dietary and socioeconomic cohort characterization; it provides a multidisciplinary platform for the study of complex disease during the adult life course, including the process of healthy aging. The major strength of TwinsUK is the availability of several ‘omic’ technologies for a range of sample types from participants, which includes genomewide scans of single-nucleotide variants, next-generation sequencing, metabolomic profiles, microbiomics, exome sequencing, epigenetic markers, gene expression arrays, RNA sequencing and telomere length measures. TwinsUK facilitates and actively encourages sharing the ‘TwinsUK’ resource with the scientific community — interested researchers may request data via the TwinsUK website (http://twinsuk.ac.uk/resources-for-researchers/access-our-data/) for their own use or future collaboration with the study team. In addition, further cohort data collection is planned via the Wellcome Open Research gateway (https://wellcomeopenresearch.org/gateways). The current article presents an up-to-date report on the application of technological advances, new study procedures in the cohort and future direction of TwinsUK.

Twin studies have found that ~50% of variance in electrocardiogram (ECG) traits can be explained by genetic factors. However, genetic variants identified through genome-wide association studies explain less than 10% of the total trait variability. Some have argued that the equal environment assumption for the classical twin model might be invalid, resulting in inflated narrow-sense heritability (h2) estimates, thus explaining part of the ‘missing h2’. Genomic relatedness restricted maximum likelihood (GREML) estimation overcomes this issue. This method uses both family data and genome-wide coverage of common SNPs to determine the degree of relatedness between individuals to estimate both h2 explained by common SNPs and total h2. The aim of the current study is to characterize more reliably than previously possible ECG trait h2 using GREML estimation, and to compare these outcomes to those of the classical twin model. We analyzed ECG traits (heart rate, PR interval, QRS duration, RV5+SV1, QTc interval, Sokolow-Lyon product, and Cornell product) in up to 3,133 twins from the TwinsUK cohort and derived h2 estimates by both methods. GREML yielded h2 estimates between 47% and 68%. Classical twin modeling provided similar h2 estimates, except for the Cornell product, for which the best fit included no genetic factors. We found no evidence that the classical twin model leads to inflated h2 estimates. Therefore, our study confirms the validity of the equal environment assumption for monozygotic and dizygotic twins and supports the robust basis for future studies exploring genetic variants responsible for the variance of ECG traits.

Our study examines the contribution of genetic and environmental factors (both shared and unique) to frailty, measured using the Rockwood Frailty Index (FI) in a sample of twins from the St Thomas’ UK Adult Twin Registry. The FI was based on 39 items of potential health deficit. Study participants were 3,375 volunteer adult twins (840 monozygotic and 802 dizygotic twin-pairs) 40.0–84.5 years old. First, we used structural equation modeling to estimate the relative contribution of genetics and of the shared and unique environment to variance in FI adjusted for age. In a second analysis, multiple linear regression was used to examine variance in FI as a function of father's occupational class (a component of shared environment and a measure of childhood socioeconomic status [SES]), adjusting for age, birth weight, marital status, and health behaviors (smoking, alcohol consumption, and physical activity). Statistical analyses were conducted using IBM SPSS® Version 22 software and Mx open source software. Findings showed that 45% (95% confidence intervals [CIs] 30–53%) of the inter-individual variation in FI was heritable and 52% (95% CIs 47–57%) was due to the individual's unique environment. Multiple linear regression also showed a small but statistically significant inverse association between father's occupational class and FI, mediated by one's own educational attainment and birth weight. Our results indicate that frailty is both genetically and environmentally determined. Thus, its prevention and management call for a multifaceted approach that includes addressing deleterious environmental factors, some of which, like childhood SES, may act across the life course.

Hair diameter and curvature are two characteristics of human scalp hair used in forensic contexts. While previous data show that subjective categorization of hair curvature is highly heritable, the heritability of objectively measured curvature and diameter, and variability of hair characteristics within each individual have not yet been studied. The present study measured hair diameter and curvature using an optical fiber diameter analyzer in a sample of 2,332 twins and siblings. Heritability was estimated using maximum likelihood structural equation modeling. Results show sex differences in the magnitude of genetic influence for mean diameter and curvature, with the vast majority of the variance accounted for by genetic effects in males (diameter = 86%, curvature = 53%) and females (diameter = 77%, curvature = 61%). The consistency of diameter (variance within an individual) was also highly heritable, but did not show sex limitation, with 68% of the variance accounted for by genetic factors. Moderate phenotypic correlations were seen between diameter and consistency (r = 0.3) but there was little correlation between diameter and curvature (r = -0.13). A bivariate Cholesky analysis was used to estimate the genetic and environmental correlations between hair diameter and consistency, yielding genetic correlations of rgF = 0.27 for females and rgM = 0.25 for males.

Little is known about the extent to which aging trajectories of different body systems share common sources of variance. We here present a large twin study investigating the trajectories of change in five systems: cardiovascular, respiratory, skeletal, morphometric, and metabolic. Longitudinal clinical data were collected on 3,508 female twins in the TwinsUK registry (complete pairs:740 monozygotic (MZ), 986 dizygotic (DZ), mean age at entry 48.9 ± 10.4, range 18–75 years; mean follow-up 10.2 ± 2.8 years, range 4–17.8 years). Panel data on multiple age-related variables were used to estimate biological ages for each individual at each time point, in linear mixed effects models. A weighted average approach was used to combine variables within predefined body system groups. Aging trajectories for each system in each individual were then constructed using linear modeling. Multivariate structural equation modeling of these aging trajectories showed low genetic effects (heritability), ranging from 2% in metabolic aging to 22% in cardiovascular aging. However, we found a significant effect of shared environmental factors on the variations in aging trajectories in cardiovascular (54%), skeletal (34%), morphometric (53%), and metabolic systems (53%). The remainder was due to environmental factors unique to each individual plus error. Multivariate Cholesky decomposition showed that among aging trajectories for various body systems there were significant and substantial correlations between the unique environmental latent factors as well as shared environmental factors. However, there was no evidence for a single common factor for aging. This study, the first of its kind in aging, suggests that diverse organ systems share non-genetic sources of variance for aging trajectories. Confirmatory studies are needed using population-based twin cohorts and alternative methods of handling missing data.

A trend toward greater body size in dizygotic (DZ) than in monozygotic (MZ) twins has been suggested by some but not all studies, and this difference may also vary by age. We analyzed zygosity differences in mean values and variances of height and body mass index (BMI) among male and female twins from infancy to old age. Data were derived from an international database of 54 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins), and included 842,951 height and BMI measurements from twins aged 1 to 102 years. The results showed that DZ twins were consistently taller than MZ twins, with differences of up to 2.0 cm in childhood and adolescence and up to 0.9 cm in adulthood. Similarly, a greater mean BMI of up to 0.3 kg/m2 in childhood and adolescence and up to 0.2 kg/m2 in adulthood was observed in DZ twins, although the pattern was less consistent. DZ twins presented up to 1.7% greater height and 1.9% greater BMI than MZ twins; these percentage differences were largest in middle and late childhood and decreased with age in both sexes. The variance of height was similar in MZ and DZ twins at most ages. In contrast, the variance of BMI was significantly higher in DZ than in MZ twins, particularly in childhood. In conclusion, DZ twins were generally taller and had greater BMI than MZ twins, but the differences decreased with age in both sexes.

For over 100 years, the genetics of human anthropometric traits has attracted scientific interest. In particular, height and body mass index (BMI, calculated as kg/m2) have been under intensive genetic research. However, it is still largely unknown whether and how heritability estimates vary between human populations. Opportunities to address this question have increased recently because of the establishment of many new twin cohorts and the increasing accumulation of data in established twin cohorts. We started a new research project to analyze systematically (1) the variation of heritability estimates of height, BMI and their trajectories over the life course between birth cohorts, ethnicities and countries, and (2) to study the effects of birth-related factors, education and smoking on these anthropometric traits and whether these effects vary between twin cohorts. We identified 67 twin projects, including both monozygotic (MZ) and dizygotic (DZ) twins, using various sources. We asked for individual level data on height and weight including repeated measurements, birth related traits, background variables, education and smoking. By the end of 2014, 48 projects participated. Together, we have 893,458 height and weight measures (52% females) from 434,723 twin individuals, including 201,192 complete twin pairs (40% monozygotic, 40% same-sex dizygotic and 20% opposite-sex dizygotic) representing 22 countries. This project demonstrates that large-scale international twin studies are feasible and can promote the use of existing data for novel research purposes.

TwinsUK is a nation-wide registry of volunteer twins in the United Kingdom, with about 12,000 registered twins (83% female, equal number of monozygotic and dizygotic twins, predominantly middle-aged and older). Over the last 20 years, questionnaire and blood/urine/tissue samples have been collected on over 7,000 subjects, as well as three comprehensive phenotyping assessments in the clinical facilities of the Department of Twin Research and Genetic Epidemiology, King's College London. The primary focus of study has been the genetic basis of healthy aging process and complex diseases, including cardiovascular, metabolic, musculoskeletal, and ophthalmologic disorders. Alongside the detailed clinical, biochemical, behavioral, and socio-economic characterization of the study population, the major strength of TwinsUK is availability of several ‘omics’ technologies for the participants. These include genome-wide scans of single nucleotide variants, next-generation sequencing, exome sequencing, epigenetic markers (MeDIP sequencing), gene expression arrays and RNA sequencing, telomere length measures, metabolomic profiles, and gut flora microbiomics. The scientific community now can freely access parts of the phenotype data from the ‘TwinsUK’, and interested researchers are encouraged to contact us via our Web site (www.twinsuk.ac.uk) for future collaborations.

Miscarriage is the most common type of pregnancy loss, occurring in up to 15% of clinically recognized pregnancies. Our understanding of the etiology is still limited but is believed to be multifactorial, including endocrine and anatomical abnormalities, immunologic, genetic and lifestyle factors. The aim of this study was to explore whether genetic variability in miscarriage is under any genetic influence. 3234 MZ and DZ female twins completed postal self-completion questionnaires on pregnancies. Rates were adjusted for total number of pregnancies. The relative contribution of genetic and environmental factors to variation in miscarriage was assessed using twin intra-pair correlations and quantified using a variance components model fitting approach. We found 22.7% of our twins reporting having suffered at least one miscarriage. Current age, age at first pregnancy and higher number of pregnancies all had a significant influence on reported miscarriage. The concordance of miscarriage was similar in identical and non-identical twins, 26% and 27%, respectively. Shared environment and predominantly random error and unique environment rather than genetic factors best explained the total variation of miscarriage. To our knowledge, this is the first large twin study exploring heritability of miscarriage which unlike the vast majority of common variable traits, shows no significant genetic influence. In the absence of clear environmental factors, these results suggest the influence of random factors.

Osteoporosis, which is characterised by reduced bone mineral density (BMD) and an increased risk of fragility fractures, is the result of a complex interaction between environmental factors and genetic variants that confer susceptibility. Heritability studies have shown that BMD and other osteoporosis-related traits such as ultrasound properties of bone, skeletal geometry and bone turnover have significant inheritable components. Although previous linkage and candidate gene studies have provided few replicated loci for osteoporosis, genome-wide association approaches have produced clear and reproducible findings. To date, 20 genome-wide association studies (GWASs) for osteoporosis and related traits have been conducted, identifying dozens of genes. Further meta-analyses of GWAS data and deep resequencing of rare variants will uncover more novel susceptibility loci and ultimately provide possible therapeutic targets for fracture prevention.