Search for multiple myeloma risk factors using Mendelian randomization

Abstract

The 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.

Assessing the Relationship Between Leukocyte Telomere Length and Cancer Risk/Mortality in UK Biobank and TCGA Datasets With the Genetic Risk Score and Mendelian Randomization Approaches

“…We found that the observed associations between leukocyte telomere length and cancers in the present study (i.e., multiple myeloma, chronic lymphocytic leukemia, kidney/renal cell cancer, bladder cancer, malignant melanoma, basal cell carcinoma, and prostate cancer) are greatly consistent with prior findings obtained in terms of MR ( Supplementary Table S1; Zhang et al, 2015;Ojha et al, 2016;Haycock et al, 2017;Machiela et al, 2017;Li et al, 2020;Went et al, 2020). Particularly, several previous studies demonstrated that a shorter telomere length was associated with a decreased lung cancer risk or mortality and that the association was present in adenocarcinoma while absent in squamous cell carcinoma ( Supplementary Table S1; Zhang et al, 2015;Haycock et al, 2017;Kachuri et al, 2018;Yuan et al, 2018), which may be attributed to the discrepancy in the biological characteristics of various subtypes of lung cancer.…”

Metabolic Disorders in Multiple Myeloma

“…Mendelian randomization studies have shown contradictory results. Although the two prior studies have not shown a significant correlation between obesity/adiposity and risk of MM [ 114 , 115 ], a recent study revealed a possible causal relationship between MM and greater genetically instrumented unfavorable adiposity according to single nucleotide polymorphisms [ 116 ].…”

Search for AL amyloidosis risk factors using Mendelian randomization

“…Unfortunately, our previous MR study on MM did not include exposures, such as monocyte count, TNFRSF protein, or fibrinogen levels, which does not allow conclusions about the specificity of these associations with AL amyloidosis compared with MM or MGUS. 18…”

“…Mendelian randomization (MR) is an analytical method that exploits genetic variants as instrumental variables to infer the causal relevance of an exposure to disease risk. 17,18 Because the genetic variants are randomly assigned at conception, they are not influenced by reverse causation; in the absence of pleiotropy (ie, genetic variants being associated with a disease through alternative pathways), they can provide unconfounded estimates of disease risk (Figure 1). GWASs have identified associations between SNPs and thousands of traits/phenotypes, offering the prospect of identifying causal relationships for diseases such as AL amyloidosis through MR-based analyses.…”