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Genetics and Genomics

Identifying causality, genetic correlation, priority and pathways of large-scale complex exposures of breast and ovarian cancers

British Journal of Cancer volume 125, pages 1570–1581 (2021)Cite this article

Subjects

Abstract

Background

Genetic correlations, causalities and pathways between large-scale complex exposures and ovarian and breast cancers need systematic exploration.

Methods

Mendelian randomisation (MR) and genetic correlation (GC) were used to identify causal biomarkers from 95 cancer-related exposures for risk of breast cancer [BC: oestrogen receptor-positive (ER + BC) and oestrogen receptor-negative (ER − BC) subtypes] and ovarian cancer [OC: high-grade serous (HGSOC), low-grade serous, invasive mucinous (IMOC), endometrioid (EOC) and clear cell (CCOC) subtypes].

Results

Of 31 identified robust risk factors, 16 were new causal biomarkers for BC and OC. Body mass index (BMI), body fat mass (BFM), comparative body size at age 10 (CBS-10), waist circumference (WC) and education attainment were shared risk factors for overall BC and OC. Childhood obesity, BMI, CBS-10, WC, schizophrenia and age at menopause were significantly associated with ER + BC and ER − BC. Omega-6:omega-3 fatty acids, body fat-free mass and basal metabolic rate were positively associated with CCOC and EOC; BFM, linoleic acid, omega-6 fatty acids, CBS-10 and birth weight were significantly associated with IMOC; and body fat percentage, BFM and adiponectin were significantly associated with HGSOC. Both GC and MR identified 13 shared factors. Factors were stratified into five priority levels, and visual causal networks were constructed for future interventions.

Conclusions

With analysis of large-scale exposures for breast and ovarian cancers, causalities, genetic correlations, shared or specific factors, risk factor priority and causal pathways and networks were identified.

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Fig. 1: Study design and framework of this research.
Fig. 2: Association of 95 genetically determined risk factors with breast cancer.
Fig. 3: Association of 95 genetically determined risk factors with ovarian cancer.
Fig. 4: Summarised robust associations for overall BC, OC and their subtypes.
Fig. 5: Summarised results for MR, GC and the levels of identified risk factors.
Fig. 6: Causal pathways and networks of identified risk factors for breast and ovarian cancer.

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Data availability

Researchers may have access to this data from the original researches shown in the supplement material.

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Acknowledgements

We thank to the MR Base platform, Integrative Epidemiology Unit (IEU) project, Neale Lab (Neale Lab analysis of UK Biobank phenotypes, round 2), MRC-IEU (MRC-IEU UK Biobank GWAS pipeline v 2), Borges et al. (metabolic biomarkers in the UK Biobank measured by Nightingale Health 2020), all related consortiums including BCAC, OCAC, GIANT, EGG, MRC-IEU, GUGC, Neale Lab, DIAGRAM, IMSGC, PGC, SSGAC, UK Biobank, GSCAN, GLGC, MAGIC, ADIPOGen, GIS, CHARGE and ReproGen, and original research by Astle et al., Jiang et al., Sun et al., Kettunen et al., Clarke et al., Doherty et al., Ferreira et al., Demenais et al., Klimentidis et al., Strawbridge et al., Manning et al., Dupuis et al., Evans et al. and Borges et al. for providing open access summary-level data resources. For a full description of acknowledgements concerning these studies, cohorts and consortiums, as well as funding information and other details, see Supplementary Text 3. We also thank the Charlesworth Group (https://www.cwauthors.com.cn/) for its linguistic assistance during the round 2 revision.

Funding

This research was funded by the National Key Research and Development Program (2020YFC2003500), the National Natural Science Foundation of China (81773547) and the Natural Science Foundation of Shandong Province (ZR2019ZD02). The corresponding author FX obtained the funding. The funders had no role in this work.

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Authors and Affiliations

  1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 250012, Jinan, P.R. China

    Shucheng Si, Jiqing Li, Hongkai Li, Xinhui Liu, Yunxia Li, Xiaolu Chen, Congcong Liu, Tonghui Yuan, Wenchao Li, Bojie Wang & Fuzhong Xue

  2. Institute for Medical Dataology, Shandong University, 250002, Jinan, P.R. China

    Shucheng Si, Jiqing Li, Hongkai Li, Xinhui Liu & Fuzhong Xue

  3. National Institute of Health Data Science of China, 250002, Jinan, P.R. China

    Shucheng Si, Jiqing Li, Hongkai Li, Xinhui Liu & Fuzhong Xue

  4. The Bamenda Center for Health Promotion and Research (Former Tuberculosis Reference Lab Bamenda), P.O Box 586, Bamenda, Cameroon

    Marlvin Anemey Tewara

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  1. Shucheng Si
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Contributions

SS and FX have the conception. SS did the statistical analyses and drafted the initial manuscript. MAT completed the revision of English grammar. All authors participated in the interpretation of the results, edited and reviewed the manuscript.

Corresponding author

Correspondence to Fuzhong Xue.

Ethics declarations

Ethics approval and consent to participate

Our research is only based on publicly available summarised data and no individual data were involved. These genome-wide association studies (including the GIANT, EGG, MRC-IEU, GUGC, Neale Lab, DIAGRAM, IMSGC, PGC, SSGAC, UK Biobank, GSCAN, GLGC, MAGIC, ADIPOGen, GIS, CHARGE and ReproGen consortium, and the individual research by Astle WJ et al, Jiang X et al, Sun BB et al, Kettunen et al, T-K Clarke et al, Aiden Doherty et al, Ferreira MA et al, Demenais F et al, Klimentidis YC et al, Strawbridge RJ et al, Manning AK et al, Dupuis J et al, Evans et al and Borges CM et al) had declared the ethic approval from the relevant institutional review board and in accordance with the declaration of Helsinki, or other (see reference [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50]). This secondary analysis is based on summary-level statistics that are not applicable for the clauses of ethics, the Ethics Committee of the School of Public Health of Shandong University ruled that ethics approval was not required in this particular case.

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The authors declare no competing interests.

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Si, S., Li, J., Tewara, M.A. et al. Identifying causality, genetic correlation, priority and pathways of large-scale complex exposures of breast and ovarian cancers. Br J Cancer 125, 1570–1581 (2021). https://doi.org/10.1038/s41416-021-01576-7

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