METHOD: Genome-wide association studies (GWASs) were conducted in Australian (between 1988 and 1990 and between 2010 and 2013) and Amish (between May 2010 and December 2011) samples in whom the Seasonal Pattern Assessment Questionnaire (SPAQ) had been administered, and the results were meta-analyzed in a total sample of 4,156 individuals. Genetic risk scores based on results from prior large GWAS studies of bipolar disorder, major depressive disorder (MDD), and schizophrenia were calculated to test for overlap in risk between psychiatric disorders and seasonality.
RESULTS: The most significant association was with rs11825064 (P = 1.7 × 10⁻⁶, β = 0.64, standard error = 0.13), an intergenic single nucleotide polymorphism (SNP) found on chromosome 11. The evidence for overlap in risk factors was strongest for schizophrenia and seasonality, with the schizophrenia genetic profile scores explaining 3% of the variance in log-transformed global seasonality scores. Bipolar disorder genetic profile scores were also associated with seasonality, although at much weaker levels (minimum P value = 3.4 × 10⁻³), and no evidence for overlap in risk was detected between MDD and seasonality.
CONCLUSIONS: Common SNPs of large effect most likely do not exist for seasonality in the populations examined. As expected, there were overlapping genetic risk factors for bipolar disorder (but not MDD) with seasonality. Unexpectedly, the risk for schizophrenia and seasonality had the largest overlap, an unprecedented finding that requires replication in other populations and has potential clinical implications considering overlapping cognitive deficits in seasonal affective disorders and schizophrenia.
MATERIALS AND METHODS: A retrospective cross-sectional study was carried out on 645 women with DC twins, excluding pregnancies complicated by one or both fetuses with demise (n = 22) or congenital anomalies (n = 9), who gave birth after 28 complete gestational weeks between 1 January 2001 and 31 December 2018. Univariable and multiple logistic regression analyses were carried out.
RESULTS: Maternal age >34 years (adjusted odds ratio 2.52; 95% confidence interval 1.25-5.07) and pre-pregnancy body mass index >24.9 kg/m2 (adjusted odds ratio 2.83, 95% confidence interval 1.47-5.46) were independent risk factors for GDM in women with DC twins. Newborns from women with GDM DC twins were more likely to be admitted to the neonatal intensive care unit (adjusted odds ratio 1.70, 95% confidence interval 1.06-2.72) than newborns from women with non-GDM DC twins. Other pregnancy and neonatal outcomes were similar between the two groups.
CONCLUSIONS: Advanced maternal age and pre-pregnancy overweight or obesity are risk factors for GDM in women with DC twins. Except for a nearly twofold increased risk of neonatal intensive care unit admission of newborns, the pregnancy and neonatal outcomes for women with GDM DC twins are similar to those for women with non-GDM DC twins.
OBJECTIVE: Our study objectives were (a) to investigate the heritability of age of menarche in twins, (b) to obtain the association between age of menarche and childhood factors, and reproductive events/behavior, (c) to examine whether or not having a male co-twin affects early/late menarche.
METHODOLOGY: A group of female-female identical (n = 108, 54 pairs), non-identical twins (n = 68, 34 pairs) and 17 females from opposite-sex twin sets were identified from twin registries of Malaysia and Iran. Genetic analysis was performed via two methods of Falconers' formula and maximum likelihood.
RESULTS: Heritability was found to be 66% using Falconers' formula and 15% using univariate twin analysis. Model analysis revealed that shared environmental factors have a major contribution in determining the age of menarche (82%) followed by non-shared environment (18%).
DISCUSSION: Result of this study is consistent with that of the literature. Timing of menarche could be under the influence of shared and non-shared environmental effects. Hirsutism was found to have a higher frequency among subjects with late menarche. There was no significant difference in age of menarche between females of opposite-sex twins and females of same-sex twins.
CONCLUSION: It is concluded that twin models provide a powerful means of examining the total genetic contribution to age of menarche. Longitudinal studies of twins may clarify the type of environmental effects that determine the age of menarche.
AIMS: (1) To investigate the association between birth weight and anthropometric measurements during adulthood; (2) to study the genetic and environmental influences on body measures including birth weight, weight and height among twins; and (3) to assess the variation in heritability versus environment among two cohorts of twins who lived in different geographical areas.
SUBJECTS AND METHODS: Twins were collected from two twin registers. Data on birth weight, adult weight and height in 430 MZ and 170 DZ twins living in two geographically distinct parts of the world were collected. A genetic analysis was performed using MX software.
RESULTS: Birth weight was associated with weight, height and BMI. Both MZ and DZ twins with low birth weight had shorter height during their adult life (p = 0.001), but only MZ twins with lower birth weight were lighter at adulthood (p = 0.001). Intra-pair differences in birth weight were not associated with differences in adult height (p = 0.366) or weight (p = 0.796). Additive genetic effects accounted for 53% of the variance in weight, 43% in height and 55% in birth weight. The remaining variance was attributed to unique environmental effects (15% for weight, 13% for height and 45% for birth weight and only 16% for BMI). Variability was found to be different in the two cohorts. The best fitting model for birth weight and BMI was additive genetic and non-shared environment and for weight and height was additive genetic, non-shared environment (plus common Environment).
CONCLUSIONS: Data suggests that the association between weight at birth and anthropometric measures in later life is influenced by both genetic and environmental factors. Living in different environments can potentially relate to variation found in the environment.