To what extent do genes determine when you have your first child and the total number of children that you have? Until now, social science research on fertility has largely ignored genetic explanations and instead attributed our fertility behaviour almost exclusively to the social environment and upbringing, postponement of having children in lieu of educational attainment and labour force participation and value change. Yet a growing number of studies within biology, demography, and genetics have shown that genetic factors can explain up to 40–50 % of our fertility behaviour.
Until now, the majority of genetic research on human fertility has relied on twin or other family designs to determine whether genes matter. Monozygotic twins are genetically identical, sharing almost 100 percent of the same genetic material while dizygotic twins – just as any brother and sister – share on average around 50%. If monozygotic pairs are more similar in their fertility behavior in comparison to dizygotic twins, this is interpreted as a reflection of genetic effects. A certain behavior or disease is then said to be 20 or 80 % ‘heritable’ or attributed to genetic determinants.
The last years has brought a revolution in the ability to access and analyze molecular genetic data, which for the first time in history now allows us to go beyond drawing indirect inferences about the impact of genes by comparing twins to a more direct estimate using single genetic variants across the entire genome for unrelated individuals. These genetic variants are called single nucleotide polymorphisms (SNPs) and allow us to explore the data in new ways by applying new statistical tools.
In our study just published in PLOS ONE as part of the Sociogenome project funded by the European Research Council, we exploited the latest advances in molecular and quantitative genetics by applying the genomic-relationship-matrix based restricted maximum likelihood (GREML) method. It allowed us to quantify for the first time the extent to which common genetic variants influence the age at first birth and total number of children women have. Simply put, the new GREML method calculates the genetic similarity between unrelated individuals based on their genetic material (i.e., their SNPs). This genetic similarity matrix is then related to an outcome across all individuals, which in our case is age at first birth and number of children ever born. For example, if you share what we call your ‘segregating genetic material’ (i.e., what makes you genetically you) at a level of 0.05% with one group and 2.5% with another, we would say that you have a higher similarity in your fertility behaviour with the second group. Since we are looking at genetic similarity, we are then able to draw the conclusion that genes explain the variation in fertility.
The genetic overlap is very small between unrelated people, so large numbers of people are required for this type of analysis. In our study, we pooled data from the LifeLines Cohort Study in the Netherlands and TwinsUK. Using the TwinsUK data, which indeed contains just as the name sounds ‘twins’, might sound a bit misleading. However, we used this data since it had the molecular genetic level SNPs that we needed and we selected only one twin per pair for our analysis. We therefore estimated the genetic relatedness matrix for only unrelated individuals from both samples.
Our main finding is that for the first time we were able to quantify the extent to which common genetic variants (SNPs) influence fertility. We found that the differences in women’s age at first birth and the number of children ever born were associated with genetic differences. For the age at first birth, 15 % of the observed variance was explained by genetic variation in common genes; for the number of children it was 10 % (see Figure 1).
Figure 1. Estimates of the genetic variance explanation from common genes for the age at first birth (AFB) and the number of children ever born (NEB). (The genetic variance component is called heritability).
In demography and sociology, it is well established that the age at first birth and the number of children are strongly correlated (see Figure 2 which shows this correlation in our data). By ‘strong correlation’ we mean that if you have your first child later, you will have fewer children. We found that the genetic effects for both outcomes overlap, which partly explains the association between the age at first birth and the number of children (see Figure 3). In other words, the overlap means that it appears that the same genes that are related when women have their first child appear to also influence the number of children they ultimately have.
Figure 2. The observed correlation between the age at first birth and the number children ever born in the TwinsUK and the Lifelines Cohort studies from the Netherlands.
Figure 3. The correlation of genetic and environmental factors for the age at first birth and the number children ever born in the pooled samples and their contribution to the observed correlation between both outcomes.
Our study also contributes to the controversial debate about whether humans still evolve via natural selection. The question of whether humans are still evolving has been a hotly debated and contested topic. One camp argues that selection pressure on humans has halted due to the decline in mortality in modern societies before and during our reproductive lifespan. The other camp contends that natural selection is still very much occurring in modern populations. They argue that this is attributed to the fact that there is still considerable variation in the number of children people have. In other words, if particular genes are related to higher reproductive success (i.e., having more children), these genes will be passed on with a higher frequency to future generations.
Additive genetic variance in the number of children is seen as a proxy for ‘fitness’, which indicates that natural selection occurs within these modern populations: genes that lead to higher reproductive success will have a higher frequency in future generations. In this study, women from the UK and the Netherlands born in the twentieth century who had a genetic predisposition for an earlier age at first birth have had a reproductive advantage across the generations. Genes associated with an earlier age at first birth have been passed on more frequently to the next generation, meaning that natural selection acts not only in historical, but also contemporary populations.
But what are the implications of this finding? If genes associated with an earlier age at first birth are more likely to be passed down to the next generation why is it that younger generations are not having their children at an even earlier age? What we see in fact, is that women are doing exactly the opposite in most industrialized nations. Since the 1970s, women are having their first child around 4-5 years later, which is now on average at age 28-29 years. This massive postponement in the age at first birth suggests that the socio-environmental influences predicted as important by social scientists, such as women’s educational expansion and entry into the labour market and the widespread use of effective contraception, has had a much stronger influence on fertility trends than natural selection. However, given the fact that both genes and the socio-environment can be shown to empirically matter for fertility, the study also emphasizes the need for an integrative research design of genetics and the social sciences to better understand and predict human fertility.
This post has been jointly written by Felix C. Tropf and Melinda Mills, Nuffield Professor of Sociology at the Department of Sociology and Nuffield College, University of Oxford.
‘Human Fertility, molecular genetics, and natural selection in modern societies’ by Felix C. Tropf, Gert Stulp, Nicola Barban, Peter M. Visscher , Jian Yang, Harold Snieder and Melinda C. Mills has been published in the journal, PLOS ONE, on 3 June 2015.
The research leading to these results has received funding from NWO (Dutch National Science Organization) (VIDI grant 452-10-012 to M. Mills) and the European Research Council via an ERC Consolidator Grant SOCIOGENOME (615603 awarded to M. Mills).