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 the largest genome-wide association study to date of both sexes including 251,151 individuals for AFB and 343,072 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 four additional loci in a gene-based effort. These loci harbor genes that are likely to play a role – either directly or by affecting non-local gene expression – in human reproduction and infertility, thereby increasing our understanding of these complex traits.

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.

By integrating insights from economic and sociological theories, this article investigates whether and through which mechanisms friends’ fertility behavior affects an individual’s transition to parenthood. By exploiting the survey design of the Add Health data, our strategy allows us to properly identify interaction effects and distinguish them from selection and contextual effects. We use a series of discrete-time event history models with random effects at the dyadic level. Results show that, net of confounding effects, a friend’s childbearing increases an individual’s risk of becoming a parent. We find a short-term, curvilinear effect: an individual’s risk of childbearing starts increasing after a friend’s childbearing, reaches its peak approximately two years later, and then decreases.

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.

Summary.  We compare two techniques that are widely used in the analysis of life course trajectories: latent class analysis and sequence analysis. In particular, we focus on the use of these techniques as devices to obtain classes of individual life course trajectories. We first compare the consistency of the classification that is obtained via the two techniques by using a data set on the life course trajectories of young adults. Then, we adopt a simulation approach to measure the ability of these two methods to classify groups of life course trajectories correctly when specific forms of ‘random’ variability are introduced within prespecified classes in an artificial data set. To do so, we introduce simulation operators that have a life course and/or observational meaning. Our results contribute on the one hand to outline the usefulness and robustness of findings based on the classification of life course trajectories through latent class analysis and sequence analysis and on the other hand to illuminate the potential pitfalls in applications of these techniques.