Volume 4 Issue 1
Jun.  2020
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Yan Cheng, Fumou Sun, Xing Cui, Siegfried Janz. Genetic predisposition to multiple myeloma[J]. Blood&Genomics, 2020, 4(1): 9-18. doi: 10.46701/BG2020012020103
Citation: Yan Cheng, Fumou Sun, Xing Cui, Siegfried Janz. Genetic predisposition to multiple myeloma[J]. Blood&Genomics, 2020, 4(1): 9-18. doi: 10.46701/BG2020012020103

Genetic predisposition to multiple myeloma

doi: 10.46701/BG2020012020103
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  • Corresponding author: Siegfried Janz, Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee 53226, WI, USA. TEL: +1- 414-955-5784, E-mail: sjanz@mcw.edu
  • Received Date: 2020-02-29
  • Accepted Date: 2020-04-21
  • Rev Recd Date: 2020-04-08
  • Available Online: 2021-07-01
  • Publish Date: 2020-06-30
  • Genetic myeloma risk research relied on genome-wide association studies to identify 24 common but low-impact germline predisposition alleles that account for an estimated one eighth of the heritable myeloma risk in Caucasians. Next-generation sequencing, particularly whole-exome sequencing, uncovered a handful of rare but high-impact myeloma risk loci that convey intriguing clues about etiology. The recent discovery of NCOA1 as a myeloma susceptibility gene in Han Chinese has set the stage for the more complete elucidation of the genetic myeloma risk across ethnic barriers. Validating individual myeloma risk loci at the functional level and integrating predisposition genes in genetic networks and biological pathways are important research tasks going forward. Candidate pathways that are currently emerging include plasma cell development, autophagy, telomere maintenance, and cell cycle regulation. An outstanding knowledge gap in the area of gene-environment interaction concerns the possibility that tumor-promoting effects of myeloma susceptibility alleles depend on specific environmental or occupational exposures. An implicit promise of myeloma risk research is the detection of new molecular targets for myeloma treatments and preventions. A related outcome is new biomarkers for patient stratification, prognostication, and development of individualized treatment plans.


  • Abbreviations: GWAS, genome-wide association study; MGUS, monoclonal gammopathy of undetermined significance; MM, multiple myeloma; NGS, next-generation sequencing; OR, odds ratio; RAF, relative allele frequency; RR, relative risk; TWAS, transcriptome-wide association study; WES, whole exome sequencing
    The authors declared no conflict of interests.
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