Schizophrenia is a debilitating and heritable mental disorder associated with lower reproductive success. However, the prevalence of schizophrenia is stable over populations and time, resulting in an evolutionary puzzle: how is schizophrenia maintained in the population given its apparent fitness costs? One possibility is that increased genetic liability for schizophrenia, in the absence of the disorder itself, may confer some reproductive advantage. We assessed the correlation and causal effect of genetic liability for schizophrenia with number of children, age at first birth and number of sexual partners using data from the Psychiatric Genomics Consortium and UK Biobank. Linkage disequilibrium score regression showed little evidence of genetic correlation between genetic liability for schizophrenia and number of children (rg=0.002, p=0.84), age at first birth (rg=-0.007, p=0.45) or number of sexual partners (rg=0.007, p=0.42). Mendelian randomization indicated no robust evidence of a causal effect of genetic liability for schizophrenia on number of children (mean difference: 0.003 increase in number of children per doubling in the natural log odds ratio of schizophrenia risk, 95% CI: -0.003 to 0.009, p=0.39) or age at first birth (-0.004 years lower age at first birth, 95% CI: -0.043 to 0.034, p=0.82). We find some evidence of a positive effect of genetic liability for schizophrenia on number of sexual partners (0.165 increase in number of sexual partners, 95% CI: 0.117 to 0.212, p =5.30×10-10). These results suggest that increased genetic liability for schizophrenia does not confer a fitness advantage but does increase mating success.
Original languageEnglish
Article number181049
Number of pages17
JournalRoyal Society Open Science
Issue number3
Early online date6 Mar 2019
Publication statusPublished - 6 Mar 2019

Structured keywords

  • Brain and Behaviour
  • Cognitive Science
  • Social Cognition
  • Physical and Mental Health


  • Schizophrenia
  • Reproductive success
  • Cliff-edge fitness
  • Stabilizing selection
  • Mendelian randomization


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