Search for multiple myeloma risk factors using Mendelian randomization

Author:

Went Molly1ORCID,Cornish Alex J.1ORCID,Law Philip J.1ORCID,Kinnersley Ben1ORCID,van Duin Mark2,Weinhold Niels3,Försti Asta456ORCID,Hansson Markus78ORCID,Sonneveld Pieter2ORCID,Goldschmidt Hartmut39ORCID,Morgan Gareth J.10ORCID,Hemminki Kari41112ORCID,Nilsson Björn713ORCID,Kaiser Martin14ORCID,Houlston Richard S.114ORCID

Affiliation:

1. Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom;

2. Department of Hematology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands;

3. Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany;

4. Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany;

5. Hopp Children’s Cancer Center, Heidelberg, Germany;

6. Division of Pediatric Neurooncology, DKFZ, DKTK, Heidelberg, Germany;

7. Hematology and Transfusion Medicine, Department of Laboratory Medicine, Biomedical Centre, Lund University, Lund, Sweden;

8. Hematology Clinic, Skåne University Hospital, Lund, Sweden;

9. National Centre of Tumor Diseases, Heidelberg, Germany;

10. NYU Langone Health Care, New York, NY;

11. Division of Cancer Epidemiology, DKFZ, DKTK, Heidelberg, Germany;

12. Biomedical Center, Faculty of Medicine in Pilsen, Charles University in Prague, Pilsen, Czech Republic;

13. Broad Institute, Cambridge, MA; and

14. Division of Molecular Pathology, The Institute of Cancer Research, London, United Kingdom

Abstract

AbstractThe etiology of multiple myeloma (MM) is poorly understood. Summary data from genome-wide association studies (GWASs) of multiple phenotypes can be exploited in a Mendelian randomization (MR) phenome-wide association study (PheWAS) to search for factors influencing MM risk. We performed an MR-PheWAS analyzing 249 phenotypes, proxied by 10 225 genetic variants, and summary genetic data from a GWAS of 7717 MM cases and 29 304 controls. Odds ratios (ORs) per 1 standard deviation increase in each phenotype were estimated under an inverse variance weighted random effects model. A Bonferroni-corrected threshold of P = 2 × 10−4 was considered significant, whereas P < .05 was considered suggestive of an association. Although no significant associations with MM risk were observed among the 249 phenotypes, 28 phenotypes showed evidence suggestive of association, including increased levels of serum vitamin B6 and blood carnitine (P = 1.1 × 10−3) with greater MM risk and ω-3 fatty acids (P = 5.4 × 10−4) with reduced MM risk. A suggestive association between increased telomere length and reduced MM risk was also noted; however, this association was primarily driven by the previously identified risk variant rs10936599 at 3q26 (TERC). Although not statistically significant, increased body mass index was associated with increased risk (OR, 1.10; 95% confidence interval, 0.99-1.22), supporting findings from a previous meta-analysis of prospective observational studies. Our study did not provide evidence supporting any modifiable factors examined as having a major influence on MM risk; however, it provides insight into factors for which the evidence has previously been mixed.

Publisher

American Society of Hematology

Subject

Hematology

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