Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses
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Functional brain network dynamics mediate the relationship between female reproductive aging and interpersonal adversity
Premature reproductive aging is linked to heightened stress sensitivity and psychological maladjustment across the life course. However, the brain dynamics underlying this relationship are poorly understood. Here, to address this issue, we analyzed multimodal data from female participants in the Adolescent Brain and Cognitive Development (longitudinal, N = 441; aged 9–12 years) and Human Connectome-Aging (cross-sectional, N = 130; aged 36–60 years) studies. Age-specific intrinsic functional brain network dynamics mediated the link between reproductive aging and perceptions of greater interpersonal adversity. The adolescent profile overlapped areas of greater glutamatergic and dopaminergic receptor density, and the middle-aged profile was concentrated in visual, attentional and default mode networks. The two profiles showed opposite relationships with patterns of functional neural network variability and cortical atrophy observed in psychosis versus major depressive disorder. Our findings underscore the divergent patterns of brain aging linked to reproductive maturation versus senescence, which may explain developmentally specific vulnerabilities to distinct disorders.
The evolution of preferred male traits, female preference and the G matrix: “Toto, I’ve a feeling we’re not in Kansas anymore”
Female preference exerts selection on male traits. How such preferences affect male traits, how female preferences change and the genetic correlation between male traits and female preference were examined by an experiment in which females were either mated to males they preferred (S lines) or to males chosen at random from the population (R lines). Female preference was predicted to increase the time spent calling by males. Thirteen other song components were measured. Preference for individual traits was greatest for time spent calling(CALL), volume(VOL) and chirp rate(CHIRP) but the major contributors in the multivariate function were CALL and CHIRP, the univariate influence of VOL arising from correlations to these traits. Estimation of β, the standardized selection differential, for CALL resulting from female preference showed that it was under strong direct selection. However, contrary to prediction, CALL did not change over the course of the experiment whereas VOL, CHIRP and other song components did. Simulation of the experiment using the estimated G matrix showed that lack of change in CALL resulted from indirect genetic effects negating direct effects. Changes in song components were largely due to indirect effects. This experiment showed that female preference may exert strong selection on traits but how they respond to such selection will depend greatly upon the G matrix. As predicted, female preference declined in the R lines. The genetic correlations between preference and preferred traits did not decline significantly more in the R lines, suggesting correlations resulted from both linkage disequilibrium and pleiotropy.
Chemical exposomics in biobanked plasma samples and associations with breast cancer risk factors
The chemical exposome includes exposure to numerous environmental and endogenous molecules, many of which have been linked to reproductive outcomes due to their endocrine-disrupting properties. As several breast cancer risk factors, including age and parity, are related to reproduction, it is imperative to investigate the interplay between such factors and the chemical exposome prior to conducting large scale exposome-based breast cancer studies.
Dynamic effects of psychiatric vulnerability, loneliness and isolation on distress during the first year of the COVID-19 pandemic
The COVID-19 pandemic’s impact on mental health is challenging to quantify because pre-existing risk, disease burden and public policy varied across individuals, time and regions. Longitudinal, within-person analyses can determine whether pandemic-related changes in social isolation impacted mental health. We analyzed time-varying associations between psychiatric vulnerability, loneliness, psychological distress and social distancing in a US-based study during the first year of the pandemic. We surveyed 3,655 participants about psychological health and COVID-19-related circumstances every 2 weeks for 6 months. We combined self-reports with regional social distancing estimates and a classifier that predicted probability of psychiatric diagnosis at enrollment. Loneliness and psychiatric vulnerability both impacted psychological distress. Loneliness and distress were also linked to social isolation and stress associated with distancing, and psychiatric vulnerability shaped how regional distancing affected loneliness across time. Public health policies should address loneliness when encouraging social distancing, particularly in those at risk for psychiatric conditions.
Associations between common genetic variants and income provide insights about the socio-economic health gradient
We conducted a genome-wide association study on income among individuals of European descent (N = 668,288) to investigate the relationship between socio-economic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes (the Income Factor). Our polygenic index captures 1–5% of income variance, with only one fourth due to direct genetic effects. A phenome-wide association study using this index showed reduced risks for diseases including hypertension, obesity, type 2 diabetes, depression, asthma and back pain. The Income Factor had a substantial genetic correlation (0.92, s.e. = 0.006) with educational attainment. Accounting for the genetic overlap of educational attainment with income revealed that the remaining genetic signal was linked to better mental health but reduced physical health and increased risky behaviours such as drinking and smoking. These findings highlight the complex genetic influences on income and health.
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