Basic characteristics

In this study, we analyzed genetic data from 220,685 women who were part of the UK Biobank. The average age of the study participants at recruitment was 57, with a range from 40 to 71. The number of observations for each reproductive trait is provided in Table 1. The total sample of women was divided into five groups according to their AFB, AFS, AMC, AMP or NLB status (Table 1) to detect the mean difference of PRS across the five categories.

Mean difference of PRS across the five categories

First, we computed PRS of the six psychiatric disorders for the UK Biobank sample and estimated the mean of the PRS for each age category of each reproductive trait (Figs 1–5). Then, the mean difference of the PRS was tested across the five categories for each of the reproductive traits (Tables S2–S6). A statistically significant difference in the PRS was determined after Bonferroni correction for multiple testing (i.e. significance level divided by the number of tests, 0.05/300 = 1.7E-4) and the results that passed the significance threshold were highlighted in bold (see Tables S2–S6).

Figure 1 Means and standard errors of PRSs for the six psychiatric disorders, by age at first birth, in the UK Biobank sample. Full size image

Figure 2 Means and standard errors of PRS for the six psychiatric disorders, by age at first sexual intercourse, in the UK Biobank sample. Full size image

Figure 3 Means and standard errors of PRS for the six psychiatric disorders by age at menarche in the UK Biobank sample. Full size image

Figure 4 Means and standard errors of PRS for the six psychiatric disorders by age at menopause in the UK Biobank sample. Full size image

Figure 5 Means and standard errors of PRS for the six psychiatric disorders by number of live births in the UK Biobank sample. Full size image

In Fig. 1, the direction of association between the PRS and AFB was positive (the older the AFB, the higher the risk) for ED, ASD and BIP, but negative (the younger the AFB, the higher the risk) for ADHD, MDD and SCZ although some traits (MDD and SCZ) showed non-linear (U-shaped) associations. A U-shaped relationship was previously shown between AFB and SCZ in an independent study24. The mean difference in PRS was statistically significant for most pairwise comparisons of age classes for ADHD, ED and MDD (Table S2). The significance was particularly pronounced for ADHD, with a P-value of 2.0E-184, for example, for the difference between women with AFB < 20 and women with 30 ≤ AFB < 35 (Table S2). For SCZ, most significant signals came from younger AFB groups (Fig. 1 and Table S2).

The results for AFS were similar to those for AFB, consistent with the strong correlation between these traits (Fig. 6), although some signals were reduced and not significant after the Bonferroni correction (ASD, BIP and MDD) (Fig. 2 and Table S3).

Figure 6 Genetic correlations among the five reproductive traits estimated using the base model. In the base model, the reproductive traits were adjusted for age at interview, year of birth, study center, genotype batch, and the first 15 principal components. Error Bars are 95% confidence intervals. AFB: Age at first birth. AFS: Age at first sexual intercourse. AMC: Age at menarche. AMP: age at menopause. NLB: Number of live births. Full size image

For AMC and AMP, there were relatively few statistically significant differences between age groups for the six psychiatric disorders, with the exception of ADHD (Tables S4 and S5). The relationship between the PRS of ADHD and AMC was non-linear (Fig. 3), with earlier or later AMC tending to have significantly higher ADHD risk than intermediate AMC (Table S4). A similar relationship was observed between the ADHD PRS and AMP in that earlier or later AMP was associated with higher ADHD risk than moderate AMP (Fig. 4 and Table S5).

Since NLB was expected to be negatively correlated with AFB (see Fig. 6), the pattern of the mean PRS of ADHD was inversely correlated between the groups classified according to NLB and AFB (Figs 1 and 5). Table S6 shows that the ADHD PRS was strongly associated with NLB. In addition, there were a number of significant association signals of NLB for MDD and SCZ (Table S6).

Linear prediction

We used a regression method to assess the significance of genetic association between the reproductive behavior traits and six psychiatric disorders (see Methods).

We regressed each of pre-adjusted reproductive traits (AFB, AFS, AMC, AMP and NLB) on the PRS of each of the psychiatric disorders. Using the linear regression prediction modeling, we showed that the PRS of each six psychiatric disorder were significantly associated with at least one of the five reproductive traits, confirming some of the robust associations from the earlier analyses of mean difference of PRS across the five age categories (Tables S2–S6). Of all the five reproductive traits, AFB was the trait best predicted by the PRS of the six psychiatric disorders (Fig. 7, the corresponding R-squared and P-values are in Table S7). Because AFB and AFS are highly correlated traits, the results for AFS were similar to those for AFB except that there was no significant association between PRS of AFS and BIP, which is consistent with the analyses of mean difference of PRS above (Tables S2 and S3). NLB was significantly predicted by the PRS of ADHD, ASD, MDD, and SCZ. In addition, AMC and AMP were only associated with the PRS of ASD and ADHD, respectively. We noted that the majority of significance was explained by linear predictions, but not by quadratic polynomial predictions for all of the associations (Table S8). There were marginal significances for quadratic polynomial associations only for a few pairwise comparisons (AFB vs SCZ and AFS vs ED).

Figure 7 Coefficient of determination (R2) and p-values for its significance based on a linear prediction model. Color of each box represents the level of R-squared, and the size of squares represents its significance (p-value). R-squared that are significantly different from zero after Bonferroni correction (0.05/30) are marked with an asterisk. Dependent variables were adjusted for age at interview, year of birth, assessment centre at which the participant consented, genotype batch, and the first 15 principal components. The number of records used for the analyses was 121,544 for AFB, 156,143 for AFS, 102,386 for AMP, and 172,856 for AMC and 177,744 for NLB. AFB: age at first firth. AFS: age at first sexual intercourse. AMP: age at menopause. AMC: age at menarche. NLB: number of live births. ADHD: Attention-Deficit/Hyperactivity Disorder. ASD: Autism spectrum disorder. ED: Eating disorder. BIP: Bipolar disorder. MDD: Major depressive disorder. SCZ: Schizophrenia. Full size image

We conducted a sensitivity analysis in which dependent variables were further adjusted for educational attainment, income level, smoking and alcohol consumption status (Fig. S1 and Table S9). Most of the association signals remained significant with some exceptions. For example, the association of AFB, AFS, and NLB with ASD disappeared, as did the association between AFB and BIP. Conversely, ADHD became significantly associated with AMC after correcting for the additional covariates.

When using GWAS p-value thresholds to filter SNPs, the significance of linear prediction decreased for most of the association analyses between the reproductive traits and six psychiatric disorders (Table S10). This indicates that the associations between the reproductive traits and six psychiatric disorders were probably due to many genes, not due to a few major genes.

Genetic correlations

We used LDSC to estimate genetic correlations between the reproductive behavior traits and six psychiatric disorders (see Methods). We estimated genetic correlations between the five reproductive traits to reveal the shared genetic architecture of the traits (Fig. 6). As expected, the genetic correlation between AFB and AFS was very high (0.821 ± 0.018) and that between AFB and NLB was high (−0.594 ± 0.032). However, the genetic correlation between AMC and AMP was relatively low and they also have moderate or low genetic correlations with other reproductive traits, e.g. 0.346 ± 0.036 between AFB and AMP, 0.303 ± 0.039 between AFS and AMP and ~ 0.1 between AMC and other traits (Fig. 6). These results explain the observations in the analyses of mean difference in PRS (Figs 1–5) and linear predictors (Fig. 7), where the results between AFB and AFS were similar, and those between AFB (AFS) and NLB were reciprocally similar for the association with ADHD PRS. The estimated genetic correlations among those five reproductive traits remained similar after additional adjustment of the dependent variables for educational attainment, income levels, and smoking and alcohol consumption status (Fig. 6 vs. Fig. S2).

Figure 8 shows the estimated genetic correlation from LDSC for each pair of five reproductive traits and six psychiatric disorders. The detailed genetic correlations and P-value are in Table S11. Nine pairs of genetic correlations out of 30 were significantly different from zero after Bonferroni correction. For AFB analyses, the estimated genetic correlations were greater than zero (positive association) between AFB and ED (0.349 ± 0.061), and lower than zero (negative association) between AFB and ADHD (−0.677 ± 0.034) and AFB and MDD (−0.273 ± 0.069). Similarly, AFS was inversely correlated with ADHD (−0.563 ± 0.034), MDD (−0.265 ± 0.066) and SCZ (−0.100 ± 0.030) and positively correlated with ED (0.189 ± 0.055). For AMC and AMP, there was no significant genetic correlation except that between AMP and ADHD (−0.272 ± 0.038). NLB showed positive genetic correlation with ADHD (0.356 ± 0.042) and was non-significant for other pairs of traits. These results agreed with the analyses of mean difference of PRS (Figs 1–5) and linear prediction above (Fig. 7).

Figure 8 Genetic correlations between the five reproductive traits and the six psychiatric disorders estimated using the base model. Color of each box represents the level of estimated genetic correlation (blue for positive and red for negative correlation), and the size of squares represents its significance (p-value). Estimated genetic correlations that are significantly different from zero after Bonferroni correction (0.05/30) are marked with an asterisk. AFB: Age at first birth. AFS: Age at first sexual intercourse. AMC: Age at menarche. AMP: age at menopause. NLB: Number of live births. ADHD: Attention-Deficit/Hyperactivity Disorder. ASD: Autism spectrum disorder. ED: Eating disorder. BIP: Bipolar disorder. MDD: Major depressive disorder. SCZ: Schizophrenia. In the base model, the reproductive traits were adjusted for age at interview, year of birth, study center, genotype batch, and the first 15 principal components. Full size image

In the analyses where dependent variables were further adjusted for education, income levels, smoking and alcohol consumption status, the estimated genetic correlations between reproductive traits and psychiatric disorders were not substantially changed (Fig. S3 and Table S11), compared to those depicted in Fig. 8 and Table S11.

Analysis of overlapping samples between PGC and UK biobank data

The intercepts between the reproductive traits and six psychiatric disorders were not significantly different from zero, indicating little overlaps between UK biobank and PGC samples (Figs S4 and S5). The sole exception was that the intercept between AFB and ED was significantly lower than zero (−0.017 ± SE 0.006), which may be due to sampling errors or excessive heterogeneity40,41. Most of the intercepts from the cross-trait LDSC analyses of the five reproductive traits were significantly different from zero, as expected (Figs S6 and S7). We note that the intercepts of AMP-AMC and AMC-NLB were not different from zero, which was because of the small phenotypic correlation between these pairs of traits.

Causal effects of psychiatric disorder PRS on female reproduction traits

In the analysis using IVW regression, we found weak evidence (not significant after multiple testing correction) for a causal relationship between ADHD and AFB (β = −0.521, 95% confidence interval: −0.969 to −0.072, p-value = 0.031), and between ADHD and AFS (β = −0.407, 95% CI: −0.667 to −0.147, p-value = 0.010). None of the sensitivity analyses confirmed the significance except the weighted median approach, i.e. p-value = 0.042 for the causal relationship between ADHD and AFB and p-value = 0.016 for that between ADHD and AFS (which was also not significant after multiple testing correction) (Table 2). It was noted that the MR-Egger intercept estimate for the causal relationship between ADHD and AFS was significantly different from zero (p-value = 0.041), indicating that the signal of causal relationship might be due to pleiotropic effects. There was no notable evidence for causal relationship between any pair of psychiatric disorder PRS and reproduction traits in the analyses. The I2 statistics48 of MR-Egger were 0.96, 0.69 and 0.97 for ADHD, BIP and SCZ, mostly satisfying the ‘no measurement error’ assumption48 except BIP that had a weaker association signal than ADHD or SCZ (Figs 7 and 8).