Sex-specific genetic effects across biomarkers


Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian Mixture Model, Sex Effects Mixture Model (SEMM), to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 445 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist and hip circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.

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Fig. 1: Schematic overview of Sex-Effect Mixture Model.
Fig. 2: Heritability and genetic correlations of biomarkers between females and males and related to menopausal status.
Fig. 3: Identification of genetic variants with sex-specific effects on testosterone levels.
Fig. 4: Results of Mendelian randomization tests with sex-specific testosterone variants as instruments.

Data availability

The sex-specific biomarker variants are in the supplement and in the Global Biobank Engine: [45]. The polygenic risk score coefficients are available at figshare (

Code availability

SEMM is publicly available as an R package on GitHub at; additional scripts used in the analysis are included at


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This research has been conducted using the UK Biobank resource. We thank all the participants in the UK Biobank study. The primary and processed data used to generate the analyses presented here are available in the UK Biobank access management system ( for application 24983, “generating effective therapeutic hypotheses from genomic and hospital linkage data” ( We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results.


EF is supported by the NIH NLM F31 Fellowship F31LM013053 and the Stanford Data Science program. RBA is supported by the NIH GM 102365, LM 005652, and the Chan Zuckerberg Biohub. YT is supported by a Funai Overseas Scholarship from the Funai Foundation for Information Technology and School of Medicine at Stanford University. FR is supported by a career development award from the National Heart, Lung, and Blood Institute, National Institutes of Health (1K01HL144607). NS-A is supported by a Stanford Graduate Fellowship. MAR is supported by National Human Genome Research Institute of the National Institutes of Health (R01HG010140) and the National Institute of Health Center for Multi- and Trans-Ethnic Mapping of Mendelian and Complex Diseases Grant (5U01 HG009080).

Author information




MAR conceived and designed the study. EF and MAR designed and carried out the statistical and computational analyses. YT performed PRS analysis. EF, YT, NS-A, and MAR carried out quality control of the data. The manuscript was written by EF and MAR. All authors provided feedback. MAR supervised all aspects of the study.

Corresponding authors

Correspondence to Emily Flynn or Manuel A. Rivas.

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Conflict of interest

The authors filed a provisional patent application No. 62/925,133 titled “Genetic Determination of Hormone Levels and Applications Thereof.” The authors declare that they have no conflict of interest.

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Flynn, E., Tanigawa, Y., Rodriguez, F. et al. Sex-specific genetic effects across biomarkers. Eur J Hum Genet (2020).

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