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Epigenetic age acceleration (EAA) and epigenetic gestational age acceleration (EGAA) are biomarkers of physiological development and may be affected by the perinatal environment. The aim of this study was to evaluate performance of epigenetic clocks and to identify biological and sociodemographic correlates of EGAA and EAA at birth and in childhood. In the Project Viva pre-birth cohort, DNA methylation was measured in nucleated cells in cord blood (leukocytes and nucleated red blood cells, N = 485) and leukocytes in early (N = 120, median age = 3.2 years) and mid-childhood (N = 460, median age = 7.7 years). We calculated epigenetic gestational age (EGA; Bohlin and Knight clocks) and epigenetic age (EA; Horvath and skin & blood clocks), and respective measures of EGAA and EAA. We evaluated the performance of clocks relative to chronological age using correlations and median absolute error. We tested for associations of maternal-child characteristics with EGAA and EAA using mutually adjusted linear models controlling for estimated cell type proportions. We also tested associations of Horvath EA at birth with childhood EAA.

Bohlin EGA was strongly correlated with chronological gestational age (Bohlin EGAr = 0.82,p < 0.001). Horvath and skin & blood EA were weakly correlated with gestational age, but moderately correlated with chronological age in childhood (r = 0.45–0.65). Maternal smoking during pregnancy was associated with higher skin & blood EAA at birth [B(95% CI) = 1.17 weeks (− 0.09, 2.42)] and in early childhood [0.34 years (0.03, 0.64)]. Female newborns and children had lower Bohlin EGAA [− 0.17 weeks (− 0.30, − 0.04)] and Horvath EAA at birth [B(95% CI) = − 2.88 weeks (− 4.41, − 1.35)] and in childhood [early childhood: − 0.3 years (− 0.60, 0.01); mid-childhood: − 0.48 years (− 0.77, − 0.18)] than males. When comparing self-reported Asian, Black, Hispanic, and more than one race or other racial/ethnic groups to White, we identified significant differences in EGAA and EAA at birth and in mid-childhood, but associations varied across clocks. Horvath EA at birth was positively associated with childhood Horvath and skin & blood EAA.

Maternal smoking during pregnancy and child sex were associated with EGAA and EAA at multiple timepoints. Further research may provide insight into the relationship between perinatal factors, pediatric epigenetic aging, and health and development across the lifespan.

 

Reproduction and self-maintenance are energetically costly activities involved in classic life history trade-offs. However, few studies have measured the responses of wild organisms to simultaneous changes in reproductive and self-maintenance costs, which may have interactive effects. In free-living female Barn Swallows (Hirundo rustica), we simultaneously manipulated reproductive costs (by adding or removing two nestlings) and self-maintenance costs (by attaching a ∼1 g weight in the form of a GPS tag to half of our study birds) and measured mass, immune status, blood glucose, feather growth, and reproductive output (likelihood of a second clutch, number of eggs, and time between clutches). GPS tags allowed us to analyze how movement range size affected response to brood size manipulation. Tagging altered females’ immune function as evidenced by an elevated heterophil to lymphocyte (H:L) ratio, but all females were equally likely to lay more eggs. There was no evidence of interactive effects of the tagging and brood size treatment. Range size was highly variable, and birds with large ranges grew feathers more slowly, but analyzing the effect of brood size manipulation while accounting for variation in range size did not result in any physiological response. Our results support the theoretical prediction that short-lived vertebrates do face a trade-off between reproduction and self-maintenance and, when faced with increased costs, tend to preserve investment in reproduction at the expense of parental condition. This experiment also helps us to understand how movement patterns may be relevant to life history trade-offs in wild birds.

 

Introduction Dominance relationships in which females dominate males are rare among mammals. Mechanistic hypotheses explaining the occurrence of female dominance suggest that females dominate males because (1) they are intrinsically more aggressive or less submissive than males, and/or (2) they have access to more social support than males. Methods Here, we examine the determinants of female dominance across ontogenetic development in spotted hyenas ( Crocuta crocuta ) using 30 years of detailed behavioral observations from the Mara Hyena Project to evaluate these two hypotheses. Results Among adult hyenas, we find that females spontaneously aggress at higher rates than males, whereas males spontaneously submit at higher rates than females. Once an aggressive interaction has been initiated, adult females are more likely than immigrant males to elicit submission from members of the opposite sex, and both adult natal and immigrant males are more likely than adult females to offer submission in response to an aggressive act. We also find that adult male aggressors are more likely to receive social support than are adult female aggressors, and that both adult natal and immigrant males are 2–3 times more likely to receive support when attacking a female than when attacking another male. Across all age classes, females are more likely than males to be targets of aggressive acts that occur with support. Further, receiving social support does slightly help immigrant males elicit submission from adult females compared to immigrant males acting alone, and it also helps females elicit submission from other females. However, adult females can dominate immigrant males with or without support far more often than immigrant males can dominate females, even when the immigrants are supported against females. Discussion Overall, we find evidence for both mechanisms hypothesized to mediate female dominance in this species: (1) male and female hyenas clearly differ in their aggressive and submissive tendencies, and (2) realized social support plays an important role in shaping dominance relationships within a clan. Nevertheless, our results suggest that social support alone cannot explain sex-biased dominance in spotted hyenas. Although realized social support can certainly influence fight outcomes among females, adult females can easily dominate immigrant males without any support at all. 

In ecology and evolutionary biology (EEB), the study of developmental plasticity seeks to understand ontogenetic processes underlying the phenotypes upon which natural selection acts. A central challenge to this inquiry is ascertaining a causal effect of the exposure on the manifestation of later-life phenotype due to the time elapsed between the two events. The exposure is a potential cause of the outcome—i.e. an environmental stimulus or experience. The later phenotype might be a behaviour, physiological condition, morphology or life-history trait. The latency period between the exposure and outcome complicates causal inference due to the inevitable occurrence of additional events that may affect the relationship of interest. Here, we describe six distinct but non-mutually exclusive conceptual models from the field of lifecourse epidemiology and discuss their applications to EEB research. The models include Critical Period with No Later Modifiers, Critical Period with Later Modifiers, Accumulation of Risk with Independent Risk Exposures, Accumulation of Risk with Risk Clustering, Accumulation of Risk with Chains of Risk and Accumulation of Risk with Trigger Effect. These models, which have been widely used to test causal hypotheses regarding the early origins of adult-onset disease in humans, are directly relevant to research on developmental plasticity in EEB. 

A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data. 

Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B₁₂concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.