Twin Registries Moving Forward and Meeting the Future: A Review

Longitudinal Retinal and Choroidal Image Analysis in a Set of Monozygotic Twins

We analyzed multimodal retinal and choroidal imaging, including optical coherence tomography (OCT) and OCT angiography (OCTA), to assess differences and characterize variations in the retinal and choroidal structure and microvasculature between healthy monozygotic twins without ocular or systemic pathology over a five-year period. Retinal imaging of both subjects revealed normal age-related changes. There was up to an 11% difference in OCT and OCTA variables within the subjects, both at baseline and at five years, and there was up to an 18% difference in OCT and OCTA parameters between the subjects for both time points. Larger changes in subfoveal choroidal thickness and foveal avascular zone area were observed. Our observations suggest that the parafoveal superficial capillary plexus, choroidal vascularity index, central subfield thickness, retinal nerve fiber layer thickness, and ganglion cell-inner plexiform layer thickness may be more heavily influenced by genetic, rather than environmental, factors. In contrast, subfoveal choroidal thickness and the foveal avascular zone area may be more heavily influenced by environmental factors. The environmental impact on retinal and choroidal structure and microvasculature is increasingly important to characterize, as such imaging parameters are being explored as potential biomarkers of systemic disease. These differences, as seen in these identical twin subjects, may be important considerations in supporting the security of biometric identifiers.

Maximizing the value of twin studies in health and behaviour

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22-36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m^-2). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.