The Mercurio Project and Twins. How to use a registry

The Estimated Probability of Dizygotic Twins: A Comparison of Two Methods

This study presents a general model of two binary variables and applies it to twin sex pairing data from 21 twin data sources to estimate the frequency of dizygotic twins. The purpose of this study is to clarify the relationship between maximum likelihood and Weinberg's differential rule zygosity estimation methods. We explore the accuracy of these zygosity estimation measures in relation to twin ascertainment methods and the probability of a male. Twin sex pairing data from 21 twin data sources representing 15 countries was collected for use in this study. Maximum likelihood estimation of the probability of dizygotic twins is applied to describe the variation in the frequency of dizygotic twin births. The differences between maximum likelihood and Weinberg's differential rule zygosity estimation methods are presented as a function of twin data ascertainment method and the probability of a male. Maximum likelihood estimation of the probability of dizygotic twins ranges from 0.083 (95% approximate CI: 0.082, 0.085) to 0.750 (95% approximate CI: 0.749, 0.752) for voluntary ascertainment data sources and from 0.374 (95% approximate CI: 0.373, 0.375) to 0.987 (95% approximate CI: 0.959, 1.016) for active ascertainment data sources. In 17 of the 21 twin data sources differences of 0.01 or less occur between maximum likelihood and Weinberg zygosity estimation methods. The Weinberg and maximum likelihood estimates are negligibly different in most applications. Using the above general maximum likelihood estimate, the probability of a dizygotic twin is subject to substantial variation that is largely a function of twin data ascertainment method.

Genetic and environmental factors shape infant sleep patterns: a study of 18-month-old twins

OBJECTIVE

Between 25% and 30% of children and adolescents experience sleep disorders. These disorders are complex phenotypes that are regulated by many genes, the environment, and gene-environment interactions. The objective of this study was to evaluate the contribution of genetic and environmental factors to sleep behaviors in early childhood and to contribute to the knowledge on appropriate therapeutic approaches, using a twin design.

PATIENTS AND METHODS

Data on sleeping behavior were collected from 314 18-month-old twin pairs (127 monozygotic and 187 dizygotic)using a parent-rated questionnaire. We used structural equation modeling to estimate genetic and environmental variance components for different sleep behaviors (cosleeping, sleep duration, and night awakenings).

RESULTS

Shared environment explained almost all (98.3%) of the total variance in cosleeping. Sleep duration was substantially influenced by shared environmental factors (64.1% nocturnal sleep and 61.2% diurnal sleep), with a moderate contribution of additive genetic effects (30.8% and 36.3% for nocturnal and diurnal sleep, respectively). For nocturnal waking episodes, we found a shared environmental contribution of 63.2% and a heritability estimate of 35.3%.

CONCLUSIONS

Most sleep disturbances during early childhood are explained by common shared environmental factors, and behavioral interventions adopted by parents and focused on modifying sleep behavior could contribute to solving sleep disturbances in this age group. However, the influence of genetic factors should not be underestimated, and research in this area could clarify the physiologic architecture of sleeping and contribute to selecting appropriate personalized therapeutic approaches.