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

Multiple myeloma, a neoplasm of malignant plasma cells arising in bone marrow, comprises 1.9% of all cancer deaths and 20% of all hematologic cancer deaths (www.seer.ca.gov; ref. 1). Multiple myeloma is uncommon, with an age-adjusted incidence rate of 7.9 per 100,000 in males and 5.1 per 100,000 in females in the United States in 2012 (www.seer.cancer.gov; ref. 1). Clinical manifestations range from asymptomatic (smoldering) myeloma to active symptomatic disease (2). There is a 2- to 3-fold higher risk of disease in African Americans compared with individuals of European origin and a 2-fold increased risk in relatives of multiple myeloma cases (3, 4), suggesting a heritable component to this cancer.

A genome-wide association study (GWAS) of 1,675 cases and 5,903 controls from a Northern European population identified 2 genome-wide significant novel loci associated with multiple myeloma risk at 3p22.1 (rs1052501) and 7p15.3 (rs4487645), as well as a suggestive association (P ∼ 10⁻⁷) at 2p23.3 (rs6746082; ref. 5). In a second GWAS of 4,692 cases and 10,990 controls from the United Kingdom and Germany, 4 additional genome-wide significant risk loci were identified at 3q26.2 (rs10936599), 6p21.33 (rs2285803), 17p11.2 (rs4273077), and 22q13.1 (rs877529; ref. 6). For these common risk variants, the per allele ORs and risk allele frequencies (RAF) ranged from 1.19 to 1.39 and 0.11 to 0.76, respectively. In a European study involving a large multiple myeloma consortium, 3 of these regions (2p23.3, 3p22.1, and 7p15.3) replicated at P < 0.05 (7). In the most recent published GWAS, the 2q12.3 region was implicated in multiple myeloma risk in a discovery set of 972 cases and 1,064 controls of European origin and was replicated in a similar set of 297 cases (8). This study also replicated 6 of the 7 known regions for multiple myeloma risk (8).

For common susceptibility alleles shared across populations, underlying genetic differences in linkage disequilibrium (LD) across racial/ethnic groups can be leveraged to more precisely localize markers of disease risk (9). In the present study, we examined multiple myeloma susceptibility regions for individuals from North America of African and European ancestry and conducted GWAS plus imputation-based fine mapping in an attempt to identify putative functional variants that better capture risk in these populations.

All studies had approval from their respective Institutional Review Boards according to the Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects in 1964. Signed informed consent was obtained from all participants at the time of blood/saliva collection. The participants in this study were recruited at multiple sites described below.

Study participants included 1,150 African ancestry patients with multiple myeloma enrolled in the phase 1 collection (through November 11, 2014) of the African American Multiple Myeloma Study (AAMMS) from 11 clinical centers [Winship Cancer Institute and Grady Memorial Hospital at Emory University (Atlanta, GA), MD Anderson Cancer Center at University of Texas (Houston, TX), Robert H. Lurie Comprehensive Cancer Center at Northwestern University (Chicago, IL), Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University (Baltimore, MD), Karmanos Cancer Institute at Wayne State University (Detroit, MI), University of Chicago Comprehensive Cancer Center (Chicago, IL), Siteman Cancer Center at Washington University (St. Louis, MO), St. John Providence Health System (Providence, RI), Norris Comprehensive Cancer Center at the University of Southern California (USC; Los Angeles, CA), and the Henry Ford Health System (Detroit, MI)] and 4 National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) cancer registries [CA, Detroit (excluding patients from Karmanos Cancer Center and Henry Ford Hospital), NJ, and LA]. USC is the data coordinating center that receives, processes, and maintains all de-identified clinical and questionnaire data and biospecimens. Patients of African ancestry diagnosed with active or smoldering multiple myeloma at age 20 years or older were eligible for enrollment. Forty-three African ancestry patients with multiple myeloma were included from the Multiethnic Cohort (MEC), a cohort of 215,251 men and women aged 45 to 75 years at recruitment from HI and CA (10). Incident cancer cases were identified through linkage with the Hawaii Tumor Registry and/or the Los Angeles County Cancer Surveillance Program; both NCI-funded SEER registries. An additional 28 African ancestry patients with multiple myeloma from the University of California at San Francisco (UCSF) study were also included. That study enrolled 370 patients with multiple myeloma of all races treated for multiple myeloma at UCSF between 1989 and 2010 (11). Additional details of the study, which also contributed patients to the European ancestry GWAS meta-analysis, can be found in Supplementary Methods. Finally, samples from 84 African ancestry patients with multiple myeloma, collected from the Multiple Myeloma Research Consortium (MMRC) institutions and shipped to the MMRC Tissue Bank at the Mayo Clinic Scottsdale, were provided (2).

A comparison set of 7,078 multiple myeloma–free participants (4,447 males and 2,631 females) from the African Ancestry Prostate Cancer GWAS Consortium (AAPC, consisting of 13 independent studies) and from a breast cancer GWAS of African ancestry women (AABC, consisting of 9 independent studies) were used as controls (12, 13). Further details on the contributing studies are provided in the Supplementary Methods.

DNA was extracted at the USC Genomics Core Laboratory from buffy coat or saliva samples from the 1,150 AAMMS and 43 MEC patients. For the 28 UCSF patients, DNA was extracted from white blood cells harvested after mobilization of stem cells with granulocyte colony-stimulating factor in preparation for autologous bone marrow transplant and shipped to USC for genotyping. For the 84 MMRF patients, DNA was extracted from ACK-lysed peripheral blood samples using a Puregene kit (Qiagen). All 1,305 samples were then genotyped using the Illumina HumanCore GWAS array at the USC Genomics Core Laboratory.

Controls were previously genotyped using the Illumina 1M-Duo (Illumina Inc.). Quality control (QC) steps for the controls are described in detail elsewhere (12, 13). Among cases, 37,046 SNPs and 11 samples with a call rate < 98% were removed. Cases were further excluded on the basis of the following criteria: (i) unexpected replicates (n = 14); (ii) first- or second-degree relatives (n = 2); and (iii) self-reported sex conflicting with sex estimated by X chromosome heterozygosity or XXY sex chromosome aneuploidy (n = 6). A subset of controls (n = 100) were genotyped on both arrays for QC purposes; any SNP that was discordant between the 2 platforms was removed (n = 3,134). To minimize error due to platform differences, only SNPs genotyped in both cases and controls were included for imputation (n = 188,835). Before merging the case and control genotype data, variant alleles were translated to the 1000 Genomes Project (1KGP) forward strand and base pair positions were mapped to GRCh37/hg19. Imputation to 1KGP (March 2012 release) was conducted for 500-kb regions around the 8 previously identified risk variants and SNPs with info > 0.80 and minor allele frequency (MAF) > 0.01 were included in the analysis. The number of genotyped and imputed SNPs by info score (<0.8 and >0.8) for each region is provided in Supplementary Table S1.

Principal components (PC) were calculated with EIGENSTRAT v5.0 (14) using 19,070 common SNPs (MAF > 0.05) with low pairwise linkage disequilibrium (LD; r² < 0.20) selected from the 188,835 overlapping genotyped SNPs in cases and controls. Unconditional logistic regression was performed adjusting for age (at diagnosis for cases and at blood draw for controls), sex, and PC1–5, as these PCs captured the variability of the study sample (results were similar when adjusted for 10 PCs). The dosage effects of the risk allele assuming an additive genetic model were analyzed in a one degree-of-freedom likelihood ratio test implemented in SNPTEST v2.4.0 (15).

Study participants included 1,318 multiple myeloma patients of European ancestry and 1,480 controls of similar ancestry from 4 genotyping centers: USC, UCSF (San Francisco, CA; ref. 11), Mayo Clinic (Rochester, MN; Mayo), and University of Utah (UU; Salt Lake City, UT; Supplementary Methods). The USC GWAS consisted of 4 case–control studies [Los Angeles SEER (16), Seattle/Detroit SEER (17), University of British Columbia, University of Alabama at Birmingham] and 2 cohort studies [MEC (10) and the Melbourne Collaborative Cohort Study (18)]. The Mayo Clinic study included cases and controls from Mayo Clinic and Washington University (19).

Patients and controls were genotyped at each center and imputation was performed using IMPUTE2 (20) or Beagle (21) with 1KGP as the reference panel. A description of each of the European ancestry studies, genotyping platforms and methods, as well as imputation and quality control procedures is provided in the Supplementary Methods.

Each study analyzed their data separately using unconditional logistic regression, adjusting for age, sex, and PCs (Supplementary Methods; ref. 14). Data for 500 kb around each of the 8 loci were extracted from each center. Summary statistics were meta-analyzed using a fixed-effects model weighted by the inverse standard error in METAL (22).

The goal of our statistical analysis was 2-fold: (i) to enhance the localization of the regions found to be genome-wide significant in the previous studies in Europeans using combined African ancestry–European ancestry meta-analyses and (ii) to search for new associations in regions within −/+ 250 kb of these index SNPs. Accordingly, within each of the 8 regions of interest, SNPs (both typed and imputed) were classified into 2 groups: Group A SNPs (r² ≥ 0.50 with index estimated in 1KGP EUR populations) and Group B (r² < 0.50). For Group A SNPs, we used region-wide significance as our type I error rate (α-level), but for Group B SNPs, we required a more stringent experiment-wide significance across all regions. We were less stringent in our choice of criteria for statistical significance for the Group A SNPs because of the prior knowledge of association of risk with the more strongly correlated Group A SNPs.

Alpha levels for each region were separately derived for the 2 groups of SNPs using permutation testing. To achieve numerically stable results, 1,000 replicates randomly shuffling the case/control status of all samples while preserving the original case–control ratio were generated for Groups A and B SNPs within each region. For each replicate, we recorded the minimum P value of all tested SNPs and regarded the fifth percentile of the 1,000 minimum P values as the permutation-based significance level for the Group A SNPs in that particular region. The minimum α-level for all Group A SNPs across the 8 regions was 1.48 × 10⁻³. In contrast, the significance levels for Group B SNPs were found at the 0.625^th percentile (0.05/8 × 100% = 0.625), a Bonferroni correction accounting for a total of 8 regions. The significance levels for both groups across the 8 regions are presented in Supplementary Table S2.

Summary statistics from the African ancestry analysis and European ancestry meta-analysis were meta-analyzed using a fixed-effects model weighted by the inverse standard error using METAL (22). Region-specific α-levels defined in the African ancestry analysis were applied to the African ancestry–European ancestry combined meta-analysis, as they are the most conservative. All r² values presented in the results are calculated using European (EUR) and African (AFR) populations from 1KGP.

To choose an efficient group of SNPs to move forward for functional annotation, we used the regions that replicated in African ancestry population with the Group A criteria. We included SNPs that were correlated (r² ≥ 0.50) with the most significant SNP in a 500-kb region and within 2 orders of magnitude of the smallest P value observed. To integrate chromatin biofeature annotations with our genotyping data in these regions, we used the R package FunciSNP (Bioconductor.org; ref. 23). We selected publicly available datasets relevant to the development of the B-cell lineage, most closely representing multiple myeloma pathogenesis. The following ENCODE datasets were employed to filter correlated SNPs that lie within putative enhancer regions with Gene Expression Omnibus (GEO) accession IDs: B cells CD20+ RO01778 DGF Peaks (GSM1014525), B cells CD20+ RO01778 DNase I HS Peaks (GSM1024765, GSM1024766), B cells CD20+ RO01794 HS Peaks (GSM1008588), CD20+ (RO 01778) H3K4me3 Histone Mod ChIP-seq Peaks (GSM945229), CD20+ RO01794 H3K27ac Histone Mod ChIP-seq Peaks (GSM1003459), CD20+ (RO 01794) H3K4me3 Histone Mod ChIP-seq Peaks (GSM945198), CD20+ CTCF ChIP-seq Peaks (GSM1003474), CD20+ H2A.Z Histone Mod ChIP-seq Peaks (GSM1003476), CD20+ H3K4me2 Histone Mod ChIP-seq Peaks (GSM1003471). The combinations of these histone modifications were used to segment the genome in these ENCODE cell lines into active and poised promoter regions with or without DNase I hypersensitivity, active and poised enhancer regions with or without DNase I hypersensitivity, putative regulatory sites with open chromatin, and CTCF-bound sites outside promoters and enhancers. SNPs that could be mapped to core regions (DNase hypersensitive sites) of putative noncoding regulatory regions (enhancers and promoters) were further subjected to analysis of transcription factor binding site disruptiveness using the R/Bioconductor package motifbreakR (24). To define other physical map features [transcription start sites, 5′ untranslated region (UTR), 3′UTR], we downloaded annotations from the February 2009 release of the human genome (GRCh37/hg19) available from the UCSC genome browser (25). Finally, we used the highly conserved set of predicted targets of microRNA targeting at mircode.org (miRcode 11, June 2012 release; ref. 26) and conserved high-quality microRNA target species from microRNA.org (June 2010 release; ref. 27).

Among subjects of African ancestry, we replicated 3 of the previously published risk variants at P < 0.05 (7p15.3, P = 8.30 × 10⁻⁵; 17p11.2, P = 1.60 × 10⁻²; 22q13.1, P = 1.47 × 10⁻², Table 1); 4 regions in total including 3p22.1. All previously reported risk variants were common among African ancestry subjects (Table 1; Supplementary Fig. S1). We had ≥90% power to detect the published effect size observed in African ancestry for 6 SNPs (rs4487645, rs4273077, and rs877529 were significant) and 73% to 80% power for the other 2 SNPs (Table 1). There were no statistically significant associations using Group B α-levels, although a marginally significant association was observed in the 6p21.33 region [rs190055148, P = 1.37 × 10⁻⁶, r² = 0.002 (1KGP EUR) and r² = 0.06 (1KGP AFR) with the index marker rs2285803; Supplementary Fig. S2].

In subjects of European ancestry, we replicated 4 variants at P < 0.05 (3p22.1, P = 4.42 × 10⁻³; 7p15.3, P = 7.47 × 10⁻⁴; 17p11.2, P = 2.46 × 10⁻⁴; 22q13.1, P = 4.31 × 10⁻⁴; Table 1). We had ≥90% power to detect the reported effect size for 6 SNPs (3 of the 6 were significant at P < 0.05) and 83% to 84% power for the other 2 SNPs (rs4273077 was significant) in Table 1. No statistically significant Group B SNPs were observed. The previously reported locus 2q12.3 (8) was not associated with multiple myeloma risk in either African ancestry or European ancestry subjects.

Race-specific results for all regions are provided in Supplementary Tables S3 and S4 and Supplementary Fig. S2.

In an attempt to better localize the region harboring a functional variant, summary statistics from the African ancestry and European ancestry studies were meta-analyzed for 7 of the 8 published risk regions. The HLA region on chromosome 6p21.33 was excluded from the meta-analysis because of extreme sensitivity to population stratification due to race-specific extended haplotypes and underlying LD patterns requiring greater SNP density than available here for interpretable results (28).

We found statistically significant associations for Group A SNPs that were in LD with the index SNP (r² ≥ 0.50) in all regions except 2q12.3 and 3q26.2 (Table 1; Supplementary Fig. S3); however, there were no significant associations for Group B SNPs in any region. Five of the 8 index SNPs and 3 of the most significant SNPs from the combined analysis were more common among individuals of African compared with those of European ancestry, with rs1052501 showing the largest difference (RAF in African ancestry 0.63, in European ancestry 0.22; Table 1; Supplementary Fig. S1). Below we describe the most significant associations and functional annotation in the 4 regions that replicated in the African ancestry population with the Group A criteria.

Variant rs143531651 was the most significantly associated SNP (OR, 1.26; P = 2.02 × 10⁻⁵) and was correlated with the index SNP only in European ancestry populations (African ancestry: RAF = 0.11, r² = 0.02; European ancestry: RAF = 0.17, r² = 0.79; Table 1). In this region, all the significant correlated variants cluster within the gene body of ULK4, which encodes the serine–threonine protein kinase. Among these are 2 missense variants of unknown significance, rs17215589 (OR, 1.20; 1.04 × 10⁻³) and rs35263917 (OR, 0.84; P = 1.39 × 10⁻³). In addition, there are 3 SNPs, rs73830585 (OR, 1.19; P = 1.60 × 10⁻³), rs73071261 (OR, 1.19; P = 1.61 × 10⁻³), and rs55916855 (OR, 0.83; P = 7.35 × 10⁻⁴) located within DNase I hypersensitive sites in the active promoter of ULK4. Variants rs73830585 and rs55916855 disrupt EGR1 and INSM1 transcription factor–binding sites, respectively (Fig. 1, Supplementary Tables S5 and S6).

Variant rs12540021 (OR, 1.31; P = 1.27 × 10⁻⁷), located in intron 79 of DNAH11 and downstream of CDCA7L, was the most significantly associated SNP in this region and was correlated with the index SNP in African ancestry and European ancestry (r² = 0.71 and r² = 0.67, respectively). The 8 top correlated SNPs in this region are clustered around a solitary enhancer toward the 3′ end of the DNAH11 gene region and 3′ of the CDCA7L transcription termination site. DNAH11 encodes for a ciliary outer dynein arm protein and CDCA7L encodes a cell-cycle gene that is expressed in malignant plasma cells (29). The index SNP in this region, rs4487645 (OR, 1.28; P = 4.00 × 10⁻⁷), is situated in the DNase I hypersensitive site in the center of the active enhancer, where transcription factors are most likely to be bound. The risk allele of rs4487645 (C) disrupts GATA1, GATA2, and GATA5 motifs. Thus, the correlated variants in 7p15.3 overlap putative regulatory features consistent with an active enhancer region (Fig. 1; Supplementary Tables S5 and S6).

rs34562254 (OR, 1.32; P = 2.93 × 10⁻⁷) was the most significantly associated SNP in this region in the combined analysis and in the race-specific analyses (Table 1; Supplementary Tables S3 and S4). This variant occurs roughly equally in both populations (MAF_AA = 0.13; MAF_EA = 0.11) but is more highly correlated with the reported index SNP in European ancestry (r² = 0.90) compared with African ancestry (r² = 0.33, Table 1) individuals. This missense variant (Pro251Leu) is located in exon 5 of TNFRSF13B, a lymphocyte-specific TNF receptor that interacts with the NF-κB pathway and regulates B-cell development (30, 31). This variant is predicted to be possibly damaging in PolyPhen2 (32) with a score of 0.72 (sensitivity = 0.86, specificity = 0.92), while it is labeled as a tolerated mutation in SIFT (33). Variant rs34562254 is conserved across some species (Rhesus, dog, and elephant) but is not present in others (mouse or zebrafish).

Variant rs139425 (OR, 1.21; P = 8.41 × 10⁻⁷) was the most significantly associated SNP in this region and is strongly correlated with the reported index SNP in European ancestry but not African ancestry (r² = 0.95 and r² = 0.18, respectively). This SNP did not overlap any biofeatures of interest. The top 35 SNPs in this region cluster within 10 kb in and around the promoter and proximal intronic enhancers of the polycomb group gene CBX7, which are epigenetically marked active regions. CBX7 is a tumor suppressor gene which is downregulated in multiple cancers (34, 35). Seven correlated SNPs overlap with DNase I hypersensitive sites within the aforementioned promoter and enhancer regions (Supplementary Table S6): rs877529 and rs139398 are located within the downstream enhancers; rs877529 disrupts several high-confidence binding sites including ETS1, ETV4, and PAX6; rs1005300, rs6001455, rs5995688, rs12158877, and rs139405 are situated in the promoter region; and the reference allele of rs1005300 disrupts KLF1/KLF4-binding sites (Fig. 1).

This is the first study to examine the 8 published GWAS risk regions for multiple myeloma in African ancestry individuals. We statistically significantly replicated 4 of the European ancestry reported regions in the African ancestry–only analysis, suggesting that these risk regions are shared across populations. In an African ancestry–European ancestry meta-analysis, we identified SNPs in 7 of the 8 reported regions that were more significant than the index SNP; 5 were statistically significant using Group A criteria. The differential LD between African ancestry and European ancestry populations in these combined analyses allows for a finer resolution of the signal and suggests that these alternate SNPs may be better proxies of the functional alleles. The genomic annotation of these variants highlights potential functional impact within enhancer regions, promoter regions, and protein-coding sequence for some of the variants.

We were able to utilize information from the differential LD in the 2 populations as well as the genomic annotation to identify the regions we believe to be the most promising for functional follow-up. Three regions have SNPs that are significantly associated with disease risk and functional annotation that is highly suggestive of regulatory function (3p22.1, 7p15.3, 22q13.1). Both the race-specific and combined analyses identified the missense variant rs34562254 (Pro251Leu) as the most significant SNP in the fourth region (17p11.2). This SNP is located in TNFRSF13B and falls centromeric to a common 17p deletion observed in multiple myeloma cases (36). TNFRSF13B encodes a protein that is a lymphocyte-specific member of the TNF receptor superfamily that interacts with the NF-κB pathway, critical for B-cell activation and survival and proliferation of multiple myeloma neoplastic cells (37, 38), and the target of proteasome inhibitors used in standard multiple myeloma therapy regimens (38).

In 7p15.3, we identified 8 variants that were moved forward for functional annotation. A single SNP, rs4487645, was mapped to DNase I hypersensitive region in the core of a putative enhancer with active histone modifications. This SNP is predicted to disrupt 3 of a highly related family of transcription factor–binding motifs with strong effects, including GATA1, GATA2, and GATA5 transcription factors (match threshold: P < 10⁻⁴) involved in T-cell and hematopoietic stem cell differentiation (Supplementary Table S5 and Supplementary Methods). Weinhold and colleagues recently generated expression quantitative trait loci (eQTL) data on malignant plasma cells in 848 multiple myeloma patients and found that the strongest association was for rs4487645, which showed cis-regulation of CDCA7L (29). This same variant and its enhancer were annotated in our data as a potentially functional candidate in B cells. Thus, our approach utilizing differential LD patterns to identify SNPs for functional annotation may identify truly functional disease correlates even when expression data are unavailable or lack sufficient statistical power.

This study includes the largest existing collection of African ancestry multiple myeloma cases and controls and is the first to examine previously reported risk regions in this disproportionately impacted group. One limitation is that African ancestry cases and controls were genotyped on different arrays with only a small number of overlapping SNPs (n = 188,835 SNPs genome-wide) which limited our ability to identify novel variants (Group B SNPs) and to examine the overlap in the HLA region. However, we performed rigorous QC on genotyped SNPs, which allowed us to impute cases and controls together, thereby providing more accurate imputed data. Nevertheless, there were not a large number of genotyped SNPs in each region which made imputation challenging. For example, in the 17p11.2 and 22q13.1 regions, more than half of the imputed SNPs for the African ancestry with a MAF > 1% were excluded because of poor quality scores (INFO < 0.8 in IMPUTE2, Supplementary Fig. S2 and Supplementary Table S1).

Another limitation of this study was the relatively small sample size of the race-specific analyses; however, power was greatly enhanced by combining the data across ancestry groups which leveraged the differential LD in these 2 populations in an attempt to more accurately approximate the true signal. For example, in the European ancestry analysis, we had 28% power to detect an OR of 1.25 for an allele frequency of 10%, whereas in the combined analysis, which more than doubled the number of cases and added more than 7,000 additional controls, we had 89% power to detect this same effect size using the minimum α-level for Group A SNPs (1.48 × 10⁻³, Supplementary Table S2). Because multiple myeloma is a rare disease (∼6/100,000 average annual age-adjusted incidence rate) with a relatively poor 5-year survival rate (∼46%), it is challenging to accrue large numbers of patients necessary for detecting associations with small to moderate magnitude of risk. Therefore, unlike similar studies of common solid tumor malignancies, it is often difficult to achieve adequate statistical power. However, we were able to improve power by including a large number of controls from preexisting GWAS in African ancestry men and women.

Although we did not conduct a combined analysis of the HLA region due to its extreme sensitivity to population stratification and long-range LD, we did observe signals in this region for both African ancestry and European ancestry that differed by race, as expected. A possible independent signal (rs190055148, P = 1.37 × 10⁻⁶, r² = 0.06 with index in 1KGP AFR and r² = 0.002 in EUR) was observed in African ancestry that will require confirmation in a larger sample.

In this study, we replicated associations in 4 of 8 published risk regions in African ancestry and 5 in the African ancestry–European ancestry combined analysis, which suggests common shared functional variants across racial groups. We identified 4 regions that are promising for functional follow-up, including 17p11.2, where the most significant SNP in the combined analysis is a missense variant. Traditional large-scale discovery efforts in African ancestry populations will be required to better understand the degree to which there is a genetic basis underlying the excess risk of multiple myeloma in this group.

S. Ailwadhi is a Consultant/Advisory Board member for Amgen Pharmaceutical and Millennium Takeda Oncology. S. Singhal has received speakers bureau honoraria from Celgene and Takeda/Millennium. T.M. Zimmerman has received speakers' bureau honoraria from Onyx and Takeda and is a Consultant/Advisory Board for Celgene. A. Nooka is a Consultant/Advisory Board for Amgen/Onyx, Novartis, and Spectrum. J. Mehta has received speakers' bureau honoraria from Celgene and Takeda/Millennium. H.J. Terebelo has received speakers' bureau honoraria from and is a Consultant/Advisory Board for Celgene. No potential conflicts of interest were disclosed by the other authors.

The content of this article does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the BCFR, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government or the BCFR.

Conception and design: K.A. Rand, C.A. Huff, S. Ailwadhi, E.S. Peters, J.D. Carpten, B. Nemesure, V. Rajkumar, S.L. Slager, R.K. Severson, G.A. Colditz, G.G. Giles, N.C. Munshi, S. Lonial, N.J. Camp, C.M. Vachon, D.O. Stram, D.J. Hazelett, C.A. Haiman, W. Cozen

Development of methodology: K.S. Pawlish, E.S. Peters, J.J. Hu, B. Jones, D.O. Stram, D.J. Hazelett, W. Cozen

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): E. Dean, C.A. Huff, L. Bernal-Mizrachi, M.H. Tomasson, S. Singhal, K.S. Pawlish, E.S. Peters, C.H. Bock, A. Stram, D.J. Van Den Berg, T.M. Zimmerman, A.E. Hwang, J.J. Graff, S.L. Pregja, S.I. Berndt, W.J. Blot, G. Casey, L.W. Chu, W.R. Diver, M.R. Lieber, A.J.M. Hennis, A.W. Hsing, J. Mehta, R.A. Kittles, S. Kolb, E.A. Klein, C.M. Leske, A.B. Murphy, B. Nemesure, C. Neslund-Dudas, S.S. Strom, R. Vij, B.A. Rybicki, J.L. Stanford, J.S. Witte, C.B. Ambrosone, E.M. John, L. Bernstein, J.J. Hu, S.J. Nyante, E.V. Bandera, S.A. Ingles, M.F. Press, M. Glenn, L. Cannon-Albright, B. Jones, G. Tricot, T.G. Martin, J.L. Wolf, S.L. Deming Halverson, N. Rothman, A. Brooks-Wilson, L.N. Kolonel, S.J. Chanock, R.K. Severson, N. Janakirman, H.J. Terebelo, E.E. Brown, A.J. De Roos, G.A. Colditz, G.G. Giles, J.J. Spinelli, B. C. Chiu, J. Levy, J.A. Zonder, R.Z. Orlowski, S. Lonial, N.J. Camp, C.M. Vachon, E. Ziv, C.A. Haiman, W. Cozen

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): K.A. Rand, C. Song, D.J. Serie, K. Curtin, D. Hu, L. Bernal-Mizrachi, S. Singhal, A. Stram, C.K. Edlund, D.V. Conti, T.M. Zimmerman, S. Huntsman, Y. Kong, R. Vij, B.M. Birmann, M.F. Press, B. Jones, S.K. Kumar, S.J. Chanock, S.L. Slager, E.E. Brown, A. Mohrbacher, G.A. Colditz, K.C. Anderson, R.Z. Orlowski, N.J. Camp, E. Ziv, D.O. Stram, D.J. Hazelett, W. Cozen

Writing, review, and/or revision of the manuscript: K.A. Rand, C. Song, D.J. Serie, C.A. Huff, L. Bernal-Mizrachi, M.H. Tomasson, S. Ailwadhi, S. Singhal, K.S. Pawlish, C.H. Bock, C.K. Edlund, T.M. Zimmerman, A.E. Hwang, J.J. Graff, A. Nooka, S.I. Berndt, J.D. Carpten, W.R. Diver, V.L. Stevens, M.R. Lieber, P.J. Goodman, A.J.M. Hennis, J. Mehta, E.A. Klein, A.B. Murphy, B. Nemesure, C. Neslund-Dudas, S.S. Strom, R. Vij, J.L. Stanford, J.S. Witte, C.B. Ambrosone, P. Bhatti, E.M. John, L. Bernstein, W. Zheng, A.F. Olshan, J.J. Hu, R.G. Ziegler, E.V. Bandera, B.M. Birmann, S.A. Ingles, M.F. Press, D. Atanackovic, L. Cannon-Albright, G. Tricot, T.G. Martin, S.K. Kumar, N. Rothman, V. Rajkumar, L.N. Kolonel, S.L. Slager, R.K. Severson, N. Janakirman, E.E. Brown, A.J. De Roos, A. Mohrbacher, G.A. Colditz, G.G. Giles, J.J. Spinelli, B. C. Chiu, N.C. Munshi, K.C. Anderson, J.A. Zonder, R.Z. Orlowski, S. Lonial, N.J. Camp, C.M. Vachon, E. Ziv, D.O. Stram, D.J. Hazelett, C.A. Haiman, W. Cozen

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): K.A. Rand, E. Dean, D.J. Serie, K. Curtin, X. Sheng, A. Stram, C.K. Edlund, A.E. Hwang, S.L. Pregja, S. Kolb, C.M. Leske, C. Neslund-Dudas, P. Bhatti, L. Bernstein, R.G. Ziegler, S.A. Ingles, B. Jones, S.L. Deming Halverson, D.J. Hazelett, W. Cozen

Study supervision: C.A. Huff, L. Bernal-Mizrachi, C. Neslund-Dudas, A.F. Olshan, N. Janakirman, E.E. Brown, J.A. Zonder, N.J. Camp, D.J. Hazelett, W. Cozen

Other (patient data for inclusion in analysis): P.J. Goodman

Other (submitted data and samples from several studies; PI of those studies and supervised them): L. Bernstein

KAR gratefully acknowledges Gretchen Ponty Smith and is supported in part by the Margaret Kersten Ponty postdoctoral fellowship endowment, Achievement Rewards for College Scientists (ARCS) Foundation, Los Angeles Founder Chapter. The authors would like to thank Drs. Pierre-Antoine Gourraud, University of Nantes (Nates, France) and Loren Gragert (Tulane University, New Orleans, LA) for providing expertise on the difficulties involved with conducting a multiethnic meta-analysis of the HLA region. We would also like to thank Dr. Leah Mechanic, Program Director for the Genomic Epidemiology Branch in the Epidemiology and Genomics Research Program at the National Cancer Institute, for her guidance and advice. Finally, we acknowledge Dr. Brian Henderson in memoriam, whose pioneering work on cancer risk in multiethnic populations laid the foundation for this study. He was the co-director of the Multiethnic Cohort, which provided cases and the majority of the controls.

This study was supported by the National Cancer Institute at the NIH (1R01CA134786 to W. Cozen and Christopher A. Haiman; 2P50CA100707 to K.C. Anderson; Myeloma SPORE 2P50CA100707 Project 6 to K.C. Anderson, W. Cozen, and D.V. Conti; R01CA152336 and R01CA134674 to N.J. Camp; P50 CA142509 and R01CA184464 to R.Z. Orlowski; R21CA155951, R25CA76023, R01CA186646, U54CA118948 Project 3 and P30CA13148 (seed grant) to E.E. Brown; and R21CA191896 and K24CA169004 to E. Ziv). The study also received support from the Leukemia Lymphoma Society (LLS 6067-090) to N.J. Camp, the American Cancer Society (IRG60-001-47) to E.E. Brown, and the Steve and Nancy Grand Multiple Myeloma Translational Initiative to E. Ziv. Data collection from the cancer registries was supported by the National Cancer Institute Surveillance Epidemiology and End Results Population-based Registry Program, NIH, Department of Health and Human Services, under contracts N01-PC-35139 (to USC for Los Angeles County), HHSN 261201300021I, N01PC-2013-00021 (to the New Jersey State Cancer Registry), and HHSN261201000026C (to the Utah Cancer Registry). Additional support for collection of incident multiple myeloma patient data was obtained from the Utah State Department of Health and the University of Utah, the Utah Population Database (UPDB) and the Utah Cancer Registry (UCR), the National Program of Cancer Registries of the Centers for Disease Control and Prevention (5U58DP003931-02 to the New Jersey State Cancer Registry and 1U58DP000807-01 to the California Cancer Registry), the Huntsman Cancer Institute (HCI) and the HCI Cancer Center Support grant, P30 CA42014 and by the USC Norris Comprehensive Cancer Center Core grant P30CA014089 from the National Cancer Institute. The collection of patients used in this publication was supported in part by the California Department of Health Services as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885.

AAPC studies: The MEC is supported by NIH grants CA63464, CA54281, CA1326792, CA148085, and HG004726. Genotyping of the PLCO samples was funded by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, NCI, NIH. LAAPC was funded by grant 99-00524V-10258 from the Cancer Research Fund, under Interagency Agreement #97-12013 (University of California contract #98-00924V) with the Department of Health Services Cancer Research Program. Cancer incidence data for the MEC and LAAPC studies have been collected by the Los Angeles Cancer Surveillance Program of the University of Southern California with Federal funds from the NCI, NIH, Department of Health and Human Services, under Contract No. N01-PC-35139, and the California Department of Health Services as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885, and grant number 1U58DP000807-3 from the Centers for Disease Control and Prevention. KCPCS was supported by NIH grants CA056678, CA082664, and CA092579, with additional support from the Fred Hutchinson Cancer Research Center and the Intramural Program of the National Human Genome Research Institute. MDA was supported by grants, CA68578, ES007784, DAMD W81XWH-07-1-0645, and CA140388. CaP Genes was supported by CA88164 and CA127298. SELECT was funded in part by Public Health Service grants U10 CA37429 (C.D. Blanke) and UM1 CA182883 (I.M. Thompson/C.M. Tangen) from the National Cancer Institute. GECAP was supported by NIH grant ES011126. SCCS sample preparation was conducted at the Epidemiology Biospecimen Core Lab that is supported in part by the Vanderbilt-Ingram Cancer Center (CA68485).

AABC studies: AABC was supported by a Department of Defense Breast Cancer Research Program Era of Hope Scholar Award to CAH (W81XWH-08-1-0383), the Norris Foundation, P01-CA151135 and U19-CA148065. Each of the participating studies was supported by the following grants: MEC (NIH grants R01-CA63464, R37-CA54281 and UM1-CA164973); CARE (National Institute for Child Health and Development grant NO1-HD-3-3175, K05 CA136967); WCHS [U.S. Army Medical Research and Material Command (USAMRMC) grant DAMD-17-01-0-0334, the NIH grant R01-CA100598, and the Breast Cancer Research Foundation]; SFBCS (NIH grant R01-CA77305 and United States Army Medical Research Program grant DAMD17-96-6071); NC-BCFR (NIH grant U01-CA69417); CBCS (NIH Specialized Program of Research Excellence in Breast Cancer, grant number P50-CA58223, and Center for Environmental Health and Susceptibility National Institute of Environmental Health Sciences, NIH, grant number P30-ES10126); PLCO (Intramural Research Program, National Cancer Institute, NIH); NBHS (National Institutes of Health grant R01-CA100374); WFBC (NIH grant R01-CA73629). The Breast Cancer Family Registry (BCFR) was supported by the National Cancer Institute, NIH under RFA-CA-06-503 and through cooperative agreements with members of the Breast Cancer Family Registry and Principal Investigators.

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