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Meta-Analysis
. 2020 Jan 14;4(1):181-190.
doi: 10.1182/bloodadvances.2019000491.

A meta-analysis of genome-wide association studies of multiple myeloma among men and women of African ancestry

Zhaohui Du  1 , Niels Weinhold  2 , Gregory Chi Song  3 , Kristin A Rand  3 , David J Van Den Berg  1 , Amie E Hwang  3 , Xin Sheng  1 , Victor Hom  3 , Sikander Ailawadhi  4 , Ajay K Nooka  5 , Seema Singhal  6 , Karen Pawlish  7 , Edward S Peters  8 , Cathryn Bock  9 , Ann Mohrbacher  10 , Alexander Stram  3 , Sonja I Berndt  11 , William J Blot  12 , Graham Casey  13 , Victoria L Stevens  14 , Rick Kittles  15 , Phyllis J Goodman  16 , W Ryan Diver  14 , Anselm Hennis  17 , Barbara Nemesure  17 , Eric A Klein  18 , Benjamin A Rybicki  19 , Janet L Stanford  20 , John S Witte  21 , Lisa Signorello  11 , Esther M John  22 , Leslie Bernstein  15 , Antoinette M Stroup  7   23 , Owen W Stephens  2 , Maurizio Zangari  2 , Frits Van Rhee  2 , Andrew Olshan  24 , Wei Zheng  12 , Jennifer J Hu  25 , Regina Ziegler  11 , Sarah J Nyante  26 , Sue Ann Ingles  3 , Michael F Press  27 , John David Carpten  28 , Stephen J Chanock  11 , Jayesh Mehta  6 , Graham A Colditz  29 , Jeffrey Wolf  21 , Thomas G Martin  21 , Michael Tomasson  30 , Mark A Fiala  29 , Howard Terebelo  31 , Nalini Janakiraman  32 , Laurence Kolonel  33 , Kenneth C Anderson  34 , Loic Le Marchand  33 , Daniel Auclair  35 , Brian C-H Chiu  36 , Elad Ziv  21 , Daniel Stram  3 , Ravi Vij  29 , Leon Bernal-Mizrachi  37 , Gareth J Morgan  38 , Jeffrey A Zonder  9 , Carol Ann Huff  39 , Sagar Lonial  5 , Robert Z Orlowski  40 , David V Conti  1 , Christopher A Haiman  1 , Wendy Cozen  1   27
Affiliations
Meta-Analysis

A meta-analysis of genome-wide association studies of multiple myeloma among men and women of African ancestry

Zhaohui Du et al. Blood Adv. .

Abstract

Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10-6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10-12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.

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Conflict of interest statement

Conflict-of-interest disclosure: C. A. Huff has acted as a consultant for Karyopharm Therapeutics, Sanofi, and miDiagnostics and is a member of the Safety Monitoring Board for Johnson and Johnson. T.G.M. has acted as a consultant for Roche and Juno Therapeutics and has received research funding from Amgen, Sanofi, and Seattle Genetics. J.M. is a member of the Speakers Bureau for Takeda Pharmacauticals and Celgene and owns stock in Celgene, Bristol-Myers Squibb, and bluebird bio. S.S. is a member of the Speakers Bureau for Takeda Pharmaceuticals, Celgene, and Janssen Pharmaceuticals and owns stock in Celgene, Bristol-Myers Squibb, and bluebird bio. S.L. has acted as a consultant for Janssen Pharmaceuticals, Takeda Pharmaceuticals, Celgene, Novartis, Bristol-Myers Squibb, Merck, and GlaxoSmithKline and has received research funding from Celgene, Takeda Pharmaceuticals, and Janssen Pharmaceuticals. K.C.A. is a consultant for Celgene, Jansen, Bristol-Myers Squibb and Sanofi, and a scientific founder of OncoPep and C4 Therapeutics. S.A. has acted as a consultant for Novartis, Amgen, and Takeda Pharmaceuticals, and has received research funding from Pharmacyclics. A.K.N. has acted as a consultant for Amgen, Novartis, Spectrum Pharmaceuticals, and Adaptive Biotechnologies. R.V. has received honoraria and research funding from Takeda Pharmaceuticals and Amgen and received honoraria from Celgene, Bristol-Myers Squibb, Janssen Pharmaceuticals, AbbVie, Jazz Pharmaceuticals, and Konypharma. J.A.Z. has acted as a consultant for Prothena and Janssen Pharmaceuticals; has acted as a consultant and received research funding from Bristol-Myers Squibb, Celgene, and Takeda Pharmaceuticals; and is a member of the Data Safety Monitoring Committee for Pharmacyclics. G.J.M. has acted as a consultant for Celgene, Takeda Phamaceuticals, and Bristol-Myers Squibb; has received research funding from Celgene; and has received honoraria from Celgene, Takeda Pharmaceuticals, and Bristol-Myers Squibb. R.Z.O. is a member of the Advisory Board for Amgen, Celgene, Forma Therapeutics, GlaxoSmithKline Biologicals, Ionis Pharmaceuticals, Janssen Biotech, Juno Therapeutics, Kite Pharma, Legend Biotech, Sanofi, Servier, and Takeda Pharmaceuticals; has acted as a consultant for Molecular Partners; and has received research funding from BioTheryX. The remaining authors declare no competing financial interests.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
RAF of the 23 known risk alleles in 8871 unaffected controls of AA and in the EA population from the phase 3 1KGP.
Figure 2.
Figure 2.
Regional association plot of the 2p23.3 risk region (25.4-25.9 Mb) in persons of AA. SNPs are plotted by position (x-axis) and −log10P value (y-axis). Linkage disequilibriums are estimated from the EUR population in phase 3 1KGP using r2 statistics. The index SNP (bottom red arrow to purple circle) is rs6746082. The surrounding SNPs are colored to indicate pairwise correlation with the index SNP. The most associated SNP in the AA population in this region is rs10180663 (top red arrow).
See this image and copyright information in PMC

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