期刊
PLOS ONE
卷 10, 期 6, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0126821
关键词
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资金
- NWO (Dutch National Science Organization) (VIDI) [452-10-012]
- European Research Council via an ERC Consolidator Grant SOCIOGENOME [615603]
- sciencestarter.de
- Wellcome Trust
- European Community's Seventh Framework Programme (FP7)
- National Institute for Health Research (NIHR)
- King's College London
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.
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