Background
Waldenström macroglobulinemia (WM) is a rare, indolent B-cell malignancy with a poorly understood aetiology. Its low incidence and prolonged preclinical phase have limited the identification of established environmental or clinical risk factors. As a result, traditional observational epidemiologic studies are challenging, motivating the use of genetic susceptibility to investigate disease-related risk factors.
Objective
We developed an innovative genetics-driven framework to identify novel traits and risk factors associated with WM that are difficult to characterize using conventional observational approaches.
Methods
We conducted a genome-wide association study (GWAS) of WM including 2,200 cases and approximately 830,000 controls from the International Lymphoma Epidemiology Consortium (InterLymph). Using these data, we implemented a two-stage analytic framework. In the first stage, we performed a systematic pleiotropic scan of approximately 6,500 traits to identify phenotypes genetically associated with WM susceptibility loci. In the second stage, prioritised traits were evaluated in independent observational analyses to assess their association with WM risk.
Results
We identified 11 susceptibility loci associated with WM risk. Pleiotropic analyses highlighted several trait domains related to WM susceptibility, including circulating IgM-related proteins, autoimmune diseases, telomere length, and blood pressure. The strongest genetically prioritised trait was circulating JCHAIN protein level, which plays a key role in immunoglobulin stabilisation in the circulation. In the UK Biobank, circulating JCHAIN levels were markedly higher among incident WM cases than among controls (median, 2.18 vs 0.08; Wilcoxon P = 4.2 × 10??), and remained elevated even more than seven years prior to diagnosis. These findings were further supported by Mendelian randomization analyses (OR = 11.42; 95% CI, 3.41–38.18). Ongoing analyses extend this framework to additional prioritised traits, integrating correlated immune and biomarker signals to refine hypotheses related to immune dysregulation and early disease processes in WM.
Conclusion
This integrative, genetics-driven approach identifies novel traits linked to WM susceptibility and provides insights into biological pathways underlying disease development, including early dysregulation of antibody production and handling reflected by genetically prioritized circulating markers such as JCHAIN. By bridging GWAS findings with phenome-wide and observational analyses, this framework offers a scalable strategy for uncovering early risk markers and generating testable hypotheses in cancers with limited established risk factors.