Historical Patterns in the Intergenerational Transmission of Lifespan and Longevity: A Research Note on U.S. Cohorts Born Between 1700 and 1900.

This research note examines historical trends in lifespan inequality and the intergenerational transmission of lifespan and longevity in the United States over the eighteenth, nineteenth, and twentieth centuries. We contribute to the literature by expanding the estimates of the familial component beyond parent-child associations to include multigenerational and horizontal classes of relatives of different sexes. We also examine how lifespan inequality and the role of the family in lifespan and longevity changed over time. We address the challenge of studying extended family networks in historical times by leveraging recent online crowdsourced genealogical data. Results confirm the presence of a familial component for all classes of relatives considered and highlight a stronger association for horizontal than for vertical relationships. Despite decreasing lifespan inequality, we find no evidence of decreased familial lifespan stratification throughout history. If anything, the results suggest a strengthening of the parent-child association. Finally, the results contribute to the debate on the representativeness and usability of crowdsourced genealogical data by emphasizing the importance of sample selection based on the quality of the information collected.

Enduring maternal brain changes and their role in mediating motherhood's impact on well-being.

Parenthood, particularly motherhood, is known to impact the structure and function of the brain in the short term, but the long-term effects of parenthood and their impacts on well-being are still poorly understood. This study explores the potential longer-term associations between parenthood and the brain, parenthood and well-being, and the potential role of brain modifications in influencing mothers' well-being. Using data from the UK Biobank, which includes brain imaging information from individuals aged 45-82 at the MRI scanning, we discovered differences in brain structure between mothers and non-mothers, with mothers exhibiting widely distributed higher gray matter density, particularly strong in frontal and occipital regions. No brain changes were observed in fathers. Parents reported a higher sense of life's meaning compared to their childless counterparts. Gray matter changes did not mediate the relationship between motherhood and well-being. This suggests that the alterations in gray matter associated with motherhood do not play a deterministic role in shaping long-term changes in well-being.

Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus.

Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identified 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology, including puberty timing, age at first birth, sex hormone regulation, endometriosis and age at menopause. Missense variants in ARHGAP27 were associated with higher NEB but shorter reproductive lifespan, suggesting a trade-off at this locus between reproductive ageing and intensity. Other genes implicated by coding variants include PIK3IP1, ZFP82 and LRP4, and our results suggest a new role for the melanocortin 1 receptor (MC1R) in reproductive biology. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Integration with data from historical selection scans highlighted an allele in the FADS1/2 gene locus that has been under selection for thousands of years and remains so today. Collectively, our findings demonstrate that a broad range of biological mechanisms contribute to reproductive success.

Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour.

Age at first sexual intercourse and age at first birth have implications for health and evolutionary fitness. In this genome-wide association study (age at first sexual intercourse, N = 387,338; age at first birth, N = 542,901), we identify 371 single-nucleotide polymorphisms, 11 sex-specific, with a 5-6% polygenic score prediction. Heritability of age at first birth shifted from 9% [CI = 4-14%] for women born in 1940 to 22% [CI = 19-25%] for those born in 1965. Signals are driven by the genetics of reproductive biology and externalising behaviour, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility and spermatid differentiation. Our findings suggest that polycystic ovarian syndrome may lead to later age at first birth, linking with infertility. Late age at first birth is associated with parental longevity and reduced incidence of type 2 diabetes and cardiovascular disease. Higher childhood socioeconomic circumstances and those in the highest polygenic score decile (90%+) experience markedly later reproductive onset. Results are relevant for improving teenage and late-life health, understanding longevity and guiding experimentation into mechanisms of infertility.

A Sequence-Analysis Approach to the Study of the Transition to Adulthood in Low- and Middle-Income Countries.

This study investigates whether young people in low- and middle-income countries (LMICs) have experienced processes of destandardization of the life course similar to those observed in high-income societies. We provide two contributions to the relevant literature. First, we use data from 263 Demographic and Health Surveys (DHS) across 69 LMICs, offering the richest comparative account to date of women's transition to adulthood (TTA) patterns in the developing world. In so doing, we adopt sequence analysis and shift the focus from individual life-course events-namely first sexual intercourse, first union, and first birth-to a visually appealing approach that allows us to describe interrelations among events. By focusing on the analysis of trajectories rather than the occurrence of single events, the study provides an in-depth focus on the timing of events, time intervals between events, and how experiencing (or not) one event might have consequences for subsequent markers in the TTA in cross-national comparative perspective. Second, we identify clusters of TTA and explore their changes across cohorts by region and household location of residence (rural vs. urban). We document significant differences by macro-regions, yet relative stability across cohorts. We interpret the latter as suggestive of cultural specificities that make the TTA resistant to change and slow to converge across regions, if converging at all. Also, we find that much of the difference across cluster typologies ensues from variation related to when the transition begins (early vs. late), rather than from the duration between events, which tends to be uniformly quick across three out of four clusters.

Educational attainment and allostatic load in later life: Evidence using genetic markers.

Education is strongly correlated with health outcomes in older adulthood. Whether the impact of education expansion improves health remains unclear due to a lack of clarity over the causal relationship. Previous health research within the social sciences has tended to use specific activities of daily living or self-reported health status. This study uses a broader and objective health measure - allostatic load (AL) - to take into consideration the exposures that accumulate throughout the life course. This paper applies a Mendelian Randomization (MR) approach to identify causality in relation to education on health as measured by AL. Using the Health and Retirement Study 2008 (N=3935), we adopt a polygenic score built from genetic variants associated with years of education. To test whether our analyses violate the exclusion assumption, we further run MR Egger regressions to test for bias from pleiotropy. We also explore the potential pathways between education and AL, including smoking, drinking, marital length, health insurance, etc. Using this genetic instrument, we find a 0.3 unit (19% of a standard deviation) reduction in AL per year of schooling. The effect is mainly driven by BMI and Hba1c. Smoking and marital stability are two potential pathways that also causally influenced by education. If our main and sensitivity analyses are valid, the results find support that a higher level of education is causally related to better health in older adulthood.

The relationship between cognitive decline and a genetic predictor of educational attainment.

Genetic and environmental factors both make substantial contributions to the heterogeneity in individuals' levels of cognitive ability. Many studies have examined the relationship between educational attainment and cognitive performance and its rate of change. Yet there remains a gap in knowledge regarding whether the effect of genetic predictors on individual differences in cognition becomes more or less prominent over the life course. In this analysis of over 5000 older adults from the Health and Retirement Study (HRS) in the U.S., we measured the change in performance on global cognition, episodic memory, attention & concentration, and mental status over 14 years. Growth curve models are used to evaluate the association between a polygenic risk score for education (education PGS) and cognitive change. Using the most recent education PGS, we find that individuals with higher scores perform better across all measures of cognition in later life. Education PGS is associated with a faster decline in episodic memory in old age. The relationships are robust even after controlling for phenotypic educational attainment, and are unlikely to be driven by mortality bias. Future research should consider genetic effects when examining non-genetic factors in cognitive decline. Our findings represent a need to understand the mechanisms between genetic endowment of educational attainment and cognitive decline from a biological angle.

Using Polygenic Scores in Social Science Research: Unraveling Childlessness.

Biological, genetic, and socio-demographic factors are all important in explaining reproductive behavior, yet these factors are typically studied in isolation. In this study, we explore an innovative sociogenomic approach, which entails including key socio-demographic (marriage, education, occupation, religion, cohort) and genetic factors related to both behavioral [age at first birth (AFB), number of children ever born (NEB)] and biological fecundity-related outcomes (endometriosis, age at menopause and menarche, polycystic ovary syndrome, azoospermia, testicular dysgenesis syndrome) to explain childlessness. We examine the association of all sets of factors with childlessness as well as the interplay between them. We derive polygenic scores (PGS) from recent genome-wide association studies (GWAS) and apply these in the Health and Retirement Study (N = 10,686) and Wisconsin Longitudinal Study (N = 8,284). Both socio-demographic and genetic factors were associated with childlessness. Whilst socio-demographic factors explain 19-46% in childlessness, the current PGS explains <1% of the variance, and only PGSs from large GWASs are related to childlessness. Our findings also indicate that genetic and socio-demographic factors are not independent, with PGSs for AFB and NEB related to education and age at marriage. The explained variance by polygenic scores on childlessness is limited since it is largely a behavioral trait, with genetic explanations expected to increase somewhat in the future with better-powered GWASs. As genotyping of individuals in social science surveys becomes more prevalent, the method described in this study can be applied to other outcomes.

Hidden heritability due to heterogeneity across seven populations.

Meta-analyses of genome-wide association studies (GWAS), which dominate genetic discovery are based on data from diverse historical time periods and populations. Genetic scores derived from GWAS explain only a fraction of the heritability estimates obtained from whole-genome studies on single populations, known as the 'hidden heritability' puzzle. Using seven sampling populations (N=35,062), we test whether hidden heritability is attributed to heterogeneity across sampling populations and time, showing that estimates are substantially smaller from across compared to within populations. We show that the hidden heritability varies substantially: from zero (height), to 20% for BMI, 37% for education, 40% for age at first birth and up to 75% for number of children. Simulations demonstrate that our results more likely reflect heterogeneity in phenotypic measurement or gene-environment interaction than genetic heterogeneity. These findings have substantial implications for genetic discovery, suggesting that large homogenous datasets are required for behavioural phenotypes and that gene-environment interaction may be a central challenge for genetic discovery.

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.

Evidence for Genetic Overlap Between Schizophrenia and Age at First Birth in Women.

IMPORTANCE: A recently published study of national data by McGrath et al in 2014 showed increased risk of schizophrenia (SCZ) in offspring associated with both early and delayed parental age, consistent with a U-shaped relationship. However, it remains unclear if the risk to the child is due to psychosocial factors associated with parental age or if those at higher risk for SCZ tend to have children at an earlier or later age. OBJECTIVE: To determine if there is a genetic association between SCZ and age at first birth (AFB) using genetically informative but independently ascertained data sets. DESIGN, SETTING, AND PARTICIPANTS: This investigation used multiple independent genome-wide association study data sets. The SCZ sample comprised 18 957 SCZ cases and 22 673 controls in a genome-wide association study from the second phase of the Psychiatric Genomics Consortium, and the AFB sample comprised 12 247 genotyped women measured for AFB from the following 4 community cohorts: Estonia (Estonian Genome Center Biobank, University of Tartu), the Netherlands (LifeLines Cohort Study), Sweden (Swedish Twin Registry), and the United Kingdom (TwinsUK). Schizophrenia genetic risk for each woman in the AFB community sample was estimated using genetic effects inferred from the SCZ genome-wide association study. MAIN OUTCOMES AND MEASURES: We tested if SCZ genetic risk was a significant predictor of response variables based on published polynomial functions that described the relationship between maternal age and SCZ risk in offspring in Denmark. We substituted AFB for maternal age in these functions, one of which was corrected for the age of the father, and found that the fit was superior for the model without adjustment for the father's age. RESULTS: We observed a U-shaped relationship between SCZ risk and AFB in the community cohorts, consistent with the previously reported relationship between SCZ risk in offspring and maternal age when not adjusted for the age of the father. We confirmed that SCZ risk profile scores significantly predicted the response variables (coefficient of determination R2 = 1.1E-03, P = 4.1E-04), reflecting the published relationship between maternal age and SCZ risk in offspring by McGrath et al in 2014. CONCLUSIONS AND RELEVANCE: This study provides evidence for a significant overlap between genetic factors associated with risk of SCZ and genetic factors associated with AFB. It has been reported that SCZ risk associated with increased maternal age is explained by the age of the father and that de novo mutations that occur more frequently in the germline of older men are the underlying causal mechanism. This explanation may need to be revised if, as suggested herein and if replicated in future studies, there is also increased genetic risk of SCZ in older mothers.

Genetic influence on age at first birth of female twins born in the UK, 1919-68.

Using a sample of monozygotic (945, 42 per cent) and dizygotic (1,329, 58 per cent) twin pairs born 1919-68 in the UK, we applied innovative tobit models to investigate genetic and environmental influences on age at first birth (AFB). We found that a substantial part (40 per cent) of the variation in AFB is caused by latent family characteristics. Genetic dispositions (26 per cent) play a more important role than the shared environment of siblings (14 per cent), with the non-shared environment/measurement error having the strongest influence (60 per cent). Like previous studies, this study reveals marked changes in estimates over time, and supports the idea that environmental constraints (war or economic crisis) suppress and normative freedom (sexual revolution) promotes the activation of genetic predispositions that affect fertility. We show that the exclusion of censored information (i.e., on the childless) by previous studies biased their results.

Human fertility, molecular genetics, and natural selection in modern societies.

Research on genetic influences on human fertility outcomes such as number of children ever born (NEB) or the age at first childbirth (AFB) has been solely based on twin and family-designs that suffer from problematic assumptions and practical limitations. The current study exploits recent advances in the field of molecular genetics by applying the genomic-relationship-matrix based restricted maximum likelihood (GREML) methods to quantify for the first time the extent to which common genetic variants influence the NEB and the AFB of women. Using data from the UK and the Netherlands (N = 6,758), results show significant additive genetic effects on both traits explaining 10% (SE = 5) of the variance in the NEB and 15% (SE = 4) in the AFB. We further find a significant negative genetic correlation between AFB and NEB in the pooled sample of -0.62 (SE = 0.27, p-value = 0.02). This finding implies that individuals with genetic predispositions for an earlier AFB had a reproductive advantage and that natural selection operated not only in historical, but also in contemporary populations. The observed postponement in the AFB across the past century in Europe contrasts with these findings, suggesting an evolutionary override by environmental effects and underscoring that evolutionary predictions in modern human societies are not straight forward. It emphasizes the necessity for an integrative research design from the fields of genetics and social sciences in order to understand and predict fertility outcomes. Finally, our results suggest that we may be able to find genetic variants associated with human fertility when conducting GWAS-meta analyses with sufficient sample size.

Mathematical modeling in life course research.

Approaching (chronic) diseases within a 'life course' framework provides primarily answers to two main questions: What are the long-term effects on health of life events and of previous exposures to risk (or protecting) factors during sensitive periods (e.g. childhood, adolescence) of life? What are the effects of the onset (and course) of a chronic disease on all other variables (such as social and economic pathway, marital status, etc.) that define a life trajectory and how can this influence be modulated by medical and social interventions? In order to tackle such ambitious questions, to collect epidemiological data related to them and to attempt to provide forecasts concerning the effect of interventions, it will be necessary to represent life course trajectories by formal means within a mathematical model in order to render the trajectories classifiable and 'measurable'. We outline briefly some important concepts and techniques that can be applied to the purpose of modeling life course, such as multistate models, Markov models, latent class analysis and sequence analysis.

Immigrants' children's transition to secondary school in Italy.

Choosing a secondary school represents an important step in the lives of students in Italy, in that it has a strong bearing on their ultimate educational achievement and labor force trajectory. In this paper, we analyze the effect of generational status and length of residence on the transition to secondary school among immigrants living in Italy. Using data from the ITAGEN2 follow-up, we analyze scholastic results from the middle school final exam and the choice of secondary school among the adolescents in Italy. Children of immigrants are more likely to have inferior outcomes on the middle school exam and to enroll in vocational and polytechnic schools. Our multivariate results indicate that, after controlling for the family's human capital and other key background factors, immigrant students show greater propensity to choose a vocational path. Differences between immigrants and natives in secondary school tracks are also manifest when previous scholastic results are taken into account.