10 research outputs found

    A Meta-analysis of Multiple Myeloma Risk Regions in African and European Ancestry Populations Identifies Putatively Functional Loci

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    Genome-wide association studies (GWAS) in European populations have identified genetic risk variants associated with multiple myeloma (MM)

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

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    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 \u3c 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 \u3c .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

    A Meta-analysis of Multiple Myeloma Risk Regions in African and European Ancestry Populations Identifies Putatively Functional Loci.

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    BACKGROUND: Genome-wide association studies (GWAS) in European populations have identified genetic risk variants associated with multiple myeloma. METHODS: We performed association testing of common variation in eight regions in 1,318 patients with multiple myeloma and 1,480 controls of European ancestry and 1,305 patients with multiple myeloma and 7,078 controls of African ancestry and conducted a meta-analysis to localize the signals, with epigenetic annotation used to predict functionality. RESULTS: We found that variants in 7p15.3, 17p11.2, 22q13.1 were statistically significantly (P \u3c 0.05) associated with multiple myeloma risk in persons of African ancestry and persons of European ancestry, and the variant in 3p22.1 was associated in European ancestry only. In a combined African ancestry-European ancestry meta-analysis, variation in five regions (2p23.3, 3p22.1, 7p15.3, 17p11.2, 22q13.1) was statistically significantly associated with multiple myeloma risk. In 3p22.1, the correlated variants clustered within the gene body of ULK4 Correlated variants in 7p15.3 clustered around an enhancer at the 3\u27 end of the CDCA7L transcription termination site. A missense variant at 17p11.2 (rs34562254, Pro251Leu, OR, 1.32; P = 2.93 x 10(-7)) in TNFRSF13B encodes a lymphocyte-specific protein in the TNF receptor family that interacts with the NF-kappaB pathway. SNPs correlated with the index signal in 22q13.1 cluster around the promoter and enhancer regions of CBX7. CONCLUSIONS: We found that reported multiple myeloma susceptibility regions contain risk variants important across populations, supporting the use of multiple racial/ethnic groups with different underlying genetic architecture to enhance the localization and identification of putatively functional alleles. IMPACT: A subset of reported risk loci for multiple myeloma has consistent effects across populations and is likely to be functional. Cancer Epidemiol Biomarkers Prev; 25(12); 1609-18. ©2016 AACR
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