Abstract

Our understanding of familial predisposition to lymphoma (collectively defined as non-Hodgkin lymphoma [NHL], Hodgkin lymphoma [HL], and chronic lymphocytic leukemia [CLL]) outside of rare hereditary syndromes has progressed rapidly during the last decade. First-degree relatives of NHL, HL, and CLL patients have an ∼1.7-fold, 3.1-fold, and 8.5-fold elevated risk of developing NHL, HL, and CLL, respectively. These familial risks are elevated for multiple lymphoma subtypes and do not appear to be confounded by nongenetic risk factors, suggesting at least some shared genetic etiology across the lymphoma subtypes. However, a family history of a specific subtype is most strongly associated with risk for that subtype, supporting subtype-specific genetic factors. Although candidate gene studies have had limited success in identifying susceptibility loci, genome-wide association studies (GWAS) have successfully identified 67 single nucleotide polymorphisms from 41 loci, predominately associated with specific subtypes. In general, these GWAS-discovered loci are common (minor allele frequency >5%), have small effect sizes (odds ratios, 0.60-2.0), and are of largely unknown function. The relatively low incidence of lymphoma, modest familial risk, and the lack of a screening test and associated intervention, all argue against active clinical surveillance for lymphoma in affected families at this time.

Introduction

Lymphomas, defined as non-Hodgkin (NHL), Hodgkin (HL), and chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma, are the most common hematologic malignancies in western countries, and combined there are an estimated 95 520 newly diagnosed cases each year in the United States.1  Although there has been a long history of case reports of familial clustering of lymphomas and leukemias, it has only been relatively recently that these malignancies were considered to have an important inherited genetic component outside of very rare hereditary cancer syndromes.2  In 2001, the World Health Organization introduced an updated classification system for lymphomas based on the Revised European American Lymphoma classification,3  which became the international gold standard.4  This classification provided the first biologically based, integrated framework for consistently defining lymphoma subtypes, thereby greatly facilitating research on this heterogeneous group of diseases.

Building from prior reviews,5-11  we focus on the strongest data addressing familial predisposition (including twin, case-control, and registry-based studies) and germline susceptibility loci (including linkage and genetic-association studies) for lymphoma, and put these findings into clinical context. One emerging theme on the etiology of lymphoma is that there is both commonality and heterogeneity for risk factors by subtype,12  and thus we consider this issue as well in the context of familial predisposition and genetic risk factors.

Evidence for familial predisposition

Twin studies

If the concordance rate of a phenotype in monozygotic twins (who share all genes) is higher than the concordance rate for dizygotic twins (who share on average half of their genes), then there is evidence for a genetic component. In a study of 44 788 pairs of twins from Scandinavia,13  there was an excess of concordant monozygotic twins compared with dizygotic twins for leukemia, and the heritability was estimated to be 21% (95% confidence interval [CI], 0-0.54); these results have been attributed to CLL, as acute lymphoblastic and myeloid leukemia have shown minimal evidence of familial clustering.14  There were insufficient cases to estimate heritability for NHL or HL. In a twin study of lymphomas,15  there was a 100-fold higher risk of HL in monozygotic twins of patients with HL compared to background rates (standardized incidence ratio = 99; 95% CI, 48-182), whereas there was no excess risk in dizygotic twins; in contrast, there was a 23-fold higher risk of NHL in monozygotic twins of patients with NHL and a 14-fold higher risk in dizygotic twins, suggesting a stronger role for shared environment for NHL.

Familial aggregation

We summarize the strongest results across different study designs that evaluate the extent that family history of lymphoma is associated with risk of developing lymphoma, including case-control, cohort, and registry-based studies. We note that none of these study designs can definitively establish an inherited genetic contribution to risk of lymphoma, as these approaches are unable to distinguish the role of shared genetics from a shared environment. Family size itself may also be associated with lymphoma risk, which can introduce bias in estimating the association of familial aggregation with lymphoma risk (Table 1).

Table 1

Risk of lymphoma subtypes by family history of selected cancers in first degree relatives

Family history of lymphoma subtype in first-degree relative*
OutcomeStudy DesignReferencesNHLHLCLL/LeukemiaDLBCLFLLPL/WM
NHL Case-control 12 1.8 (1.5-2.1) 1.7 (1.2-2.3) 1.5 (1.3-1.8) — — — 
 Registry 22 1.7 (1.4-2.2) 1.4 (1.0-2.0) 1.3 (0.9-1.9) — — — 
CLL Case-control 12 1.9 (1.4-2.6) 1.3 (0.6-2.6) 2.4 (1.9-3.1) — — — 
 Registry 23 1.9 (1.5-2.3) 1.5 (1.0-2.3) 8.5 (6.1-12) 1.0 (0.4-2.5) 1.6 (0.9-2.8) 4.0 (2.0-8.2) 
DLBCL Case-control 12 1.8 (1.5-2.3) 2.1 (1.4-3.2) 1.2 (0.9-1.5) — — — 
 Registry 24 — 2.4 (P < .05) — 9.8 (3.1-31) No increase — 
FL Case-control 12 2.0 (1.6-2.5) 1.5 (0.9-2.4) 1.0 (0.7-1.3) — — — 
 Registry 24 — 1.4 (P > .05) 1.8 (1.0-3.3) No increase 4.0 (1.6-9.5) — 
LPL/WM Case-control 12 1.2 (0.5-2.8) 2.2 (0.5-9.4) 2.2 (1.2-4.0) — — — 
 Registry 25 3.0 (2.0-4.4) 0.8 (0.3-2.2) 3.4 (1.7-6.6) — — 20 (4.1-98) 
MZL Case-control 12 1.7 (1.1-2.5) 2.7 (1.4-5.5) 1.7 (1.2-2.4) — — — 
MCL Case-control 12 2.0 (1.1-3.3) 1.5 (0.5-5.0) 2.0 (1.2-3.2) — — — 
PTCL Case-control 12 1.7 (0.9-3.1) 0.9 (0.1-4.4) 1.8 (1.1-3.1) — — — 
 Registry 24 — No increase No increase No increase No increase — 
HL Case-control 26 3.3 (1.3-8.0) 3.3 (0.5-22) 6.3 (1.3-30) — — — 
 Registry 24,27 1.3 (0.9-1.8) 3.1 (1.8-5.3) 2.1 (1.2-3.8) 2.0 (1.1-4.0) 1.4 (P > .05) — 
Family history of lymphoma subtype in first-degree relative*
OutcomeStudy DesignReferencesNHLHLCLL/LeukemiaDLBCLFLLPL/WM
NHL Case-control 12 1.8 (1.5-2.1) 1.7 (1.2-2.3) 1.5 (1.3-1.8) — — — 
 Registry 22 1.7 (1.4-2.2) 1.4 (1.0-2.0) 1.3 (0.9-1.9) — — — 
CLL Case-control 12 1.9 (1.4-2.6) 1.3 (0.6-2.6) 2.4 (1.9-3.1) — — — 
 Registry 23 1.9 (1.5-2.3) 1.5 (1.0-2.3) 8.5 (6.1-12) 1.0 (0.4-2.5) 1.6 (0.9-2.8) 4.0 (2.0-8.2) 
DLBCL Case-control 12 1.8 (1.5-2.3) 2.1 (1.4-3.2) 1.2 (0.9-1.5) — — — 
 Registry 24 — 2.4 (P < .05) — 9.8 (3.1-31) No increase — 
FL Case-control 12 2.0 (1.6-2.5) 1.5 (0.9-2.4) 1.0 (0.7-1.3) — — — 
 Registry 24 — 1.4 (P > .05) 1.8 (1.0-3.3) No increase 4.0 (1.6-9.5) — 
LPL/WM Case-control 12 1.2 (0.5-2.8) 2.2 (0.5-9.4) 2.2 (1.2-4.0) — — — 
 Registry 25 3.0 (2.0-4.4) 0.8 (0.3-2.2) 3.4 (1.7-6.6) — — 20 (4.1-98) 
MZL Case-control 12 1.7 (1.1-2.5) 2.7 (1.4-5.5) 1.7 (1.2-2.4) — — — 
MCL Case-control 12 2.0 (1.1-3.3) 1.5 (0.5-5.0) 2.0 (1.2-3.2) — — — 
PTCL Case-control 12 1.7 (0.9-3.1) 0.9 (0.1-4.4) 1.8 (1.1-3.1) — — — 
 Registry 24 — No increase No increase No increase No increase — 
HL Case-control 26 3.3 (1.3-8.0) 3.3 (0.5-22) 6.3 (1.3-30) — — — 
 Registry 24,27 1.3 (0.9-1.8) 3.1 (1.8-5.3) 2.1 (1.2-3.8) 2.0 (1.1-4.0) 1.4 (P > .05) — 

DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; LPL, lymphoplasmacytic lymphoma; MCL, mantle cell lymphoma; MZL, marginal zone lymphoma; PTCL, peripheral T-cell lymphoma; WM, Waldenstrom macroglobulinemia.

*

ORs (for case-control design) or RR (for registry design) and 95% CI.

Estimate not reported.

Case-control studies.

In case-control studies, the prevalence of a family history is compared in case patients to that of controls using an odds ratio (OR) to quantify the magnitude of risk. The largest study to date is a pooled analysis of 17 471 NHL cases and 23 096 controls from 20 case-control studies in the International Lymphoma Epidemiology Consortium,12  which found an 1.8-fold increased risk of NHL (OR = 1.8, 95% CI, 1.5-2.1) for those with a first-degree (blood) relative with NHL; there was also elevated NHL risk for individuals who reported a first-degree relative with HL (OR = 1.7, 95% CI, 1.2-2.3) or leukemia (OR = 1.5, 95% CI, 1.3-1.8), suggesting susceptibility across these lymphomas.

Further evaluation of NHL subtypes in Table 1 reveals that risk of CLL was only slightly stronger for a family history of leukemia (OR = 2.4) than for NHL (OR = 1.9). In contrast, risk of DLBCL and FL were more strongly associated with a family history of NHL (ORs, 1.8-2.0) than for leukemia (ORs = 1.0-1.2), whereas MZL, MCL, and PTCL showed similar risks for either type of family history (ORs = 1.7-2.0). A family history of HL was associated with increased risk of DLBCL (OR = 2.1) and MZL (OR = 2.7), but was not significantly associated with the risk of CLL, FL, LPL/WM, MCL, or PTCL, although ORs were above 1.0 for all subtypes except PTCL. In a comprehensive analysis of all subtypes simultaneously, there was no statistically significant heterogeneity across risk of most common NHL subtypes for either a family history of NHL (PHomogeneity = 0.52) or HL (PHomogeneity = 0.47). In contrast, there was strong evidence for heterogeneity for a family history of leukemia (PHomogeneity = 3.9 × 10−5), with family history of leukemia most strongly associated with risk of CLL, LPL/WM, MCL, and PTCL. Of note, the associations for family history of NHL with risk of NHL12  or specific NHL subtypes (eg, DLBCL,16  FL,17  CLL,18  MZL,19  LPL/WM,20  and PTCL21 ) remained unchanged after adjusting for extensive subtype-specific risk factors, suggesting that the association of family history may be predominately driven by shared genetics over a shared environment (Table 1).12,22-27 

Although the International Lymphoma Epidemiology Consortium did not report pooled results for risk of HL, a large case-control study conducted in Scandinavia26  reported an elevated risk of HL with a family history of HL (OR = 3.3), NHL (OR = 3.3), and CLL (OR = 6.3). Some smaller studies have reported larger ORs for risk of HL with a family history of HL.28,29 

These data provide strong evidence for familial predisposition to lymphoma. However, the case-control study design is susceptible to several types of bias, particularly selection and reporting bias. The former bias can occur when there are systematic differences in how cases and controls are enrolled, most commonly due to exclusion of more aggressive cases (who die before they can be enrolled into a study) and how controls are selected (ie, controls who are not representative of the underlying population that generated the cases due to selection factors or participation rates). The main concern with reporting bias is that cases and controls can differentially report a family history. In a study from Scandinavia that compared self-report to cancer registry data,30  specificity of reporting a hematologic malignancy was very high for both cases (98%) and controls (99%), whereas sensitivity was much lower at 60% for cases and only 38% for controls. This led to inflated ORs (up to 30%) based on self-reported family history data.

Cohort studies.

Prospective cohort studies overcome many of the limitations of case-control studies, but there are few cohorts that have had detailed data on lymphoma in family members or a sufficient number of lymphoma outcomes to assess risk of specific NHL subtypes. In a national cohort study of 3.5 million people in Sweden born between 1973 and 2008, family history of HL in a parent or sibling was associated with a 7.2 and 8.8-fold higher risk of childhood/young adult HL, respectively,31  whereas another study reported a sixfold higher risk for siblings.32  In a cohort study of over 120 000 female teachers in California,33  a history of lymphoma in a first-degree relative was associated with a 1.7-fold higher risk of B-cell NHL (relative risk [RR] =1.74; 95% CI, 1.16-2.60) based on 478 cases; data on risk for NHL subtypes were not available. The latter finding was highly consistent with pooled case-control data (Table 1) and suggests a lack of major biases at least for the overall NHL association.

Registry-based studies.

Another major approach to evaluate familial aggregation is to link population-based family registry data with cancer registry data to determine the excess risk of cancer in people with a family history of cancer. Advantages of this approach include population-based assessment, which minimizes selection bias and enhances generalizability, and validation of cancer diagnoses through the use of cancer registries, which eliminates reporting bias. Based on data from the Utah Population Database and the Utah Cancer Registry,34  the risk of NHL was increased 1.7-fold in first-degree relatives of a proband with NHL (familial RR = 1.68; 95% CI, 1.04-2.48) and the risk of lymphocytic leukemia was greater than fivefold in first-degree relatives of a proband with lymphocytic leukemia (familial RR = 5.69; 95% CI, 2.58-10.0). In contrast, the risk of HL was only elevated 1.3-fold in first-degree relatives of a HL proband (familial RR = 1.27; 95% CI, 0.12-3.65), although power for this estimate was low (only 2 exposed cases). Using updated data and a different analytic approach that estimates the Genealogical Index of Familiality,14  excess relatedness was observed for NHL, HL, and CLL. For CLL and NHL, but not HL, the excess relatedness was observed for both distant and overall relatedness. Distant relatedness is due to distant relatives and may be interpreted as providing evidence that familial clustering is more likely due to shared genetic vs shared environmental contribution, as the latter would be lower in distant relationships.

The most comprehensive data available on familial aggregation by lymphoma subtypes has been published using registry data from Sweden and Denmark (summarized in Table 1). This approach compares the cancer experience in first-degree relatives of lymphoma patients with the cancer experience in relatives of matched population controls. First-degree relatives of HL patients had a 3.1-fold increase in risk of HL (95% CI, 1.8-5.3), whereas risk of HL was not associated with a family history of NHL (RR = 1.3; 95% CI, 0.9-1.8) but was associated with a family history of CLL (RR = 2.1; 95% CI, 1.2-3.8).27  In other registry-based studies, the risk of HL in first-degree relatives of HL patients has ranged from 1.2 to 5.8.35-37  Across studies, the risk of HL is stronger for HL in siblings than in parents.27,31,35,36 

First-degree relatives of cases with NHL had a 1.7-fold higher risk of developing NHL (95% CI, 1.4-2.2), whereas the risk of NHL was weaker and not statistically significant for first-degree relatives with HL (RR = 1.4; 95% CI, 1.0-2.0) or CLL (RR = 1.3; 95% CI, 0.9-1.9).22  First-degree relatives of CLL patients had an 8.5-fold increased risk of CLL (RR = 8.5; 95% CI, 6.1-12), whereas the risk of CLL was also increased with a first-degree relative with NHL (RR = 1.9; 95% CI, 1.5-2.3) or HL (RR = 1.5; 95% CI, 1.0-2.3).23  It is notable that most of the risk estimates from the population-based registry studies in Table 1 were very similar or only modestly weaker than the estimates from the pooled case-control studies, again suggesting that there was only modest bias in estimates from case-control studies. The most prominent exception is for a family history of CLL, which showed a much stronger association in the registry studies compared with that of case-control studies. This may be in part due to the confusion of patients reporting a CLL as a leukemia or lymphoma.

The registry studies have also been able to evaluate risk for more detailed lymphoma subtypes. One striking finding is the clustering of risk by NHL subtype. For example, first-degree relatives of DLBCL cases had a 9.8-fold increased risk of DLBCL,24  first-degree relatives of FL had a fourfold increased risk of FL,24  and first-degree relatives of LPL/WM had a 20-fold increased risk of LPL/WM.25  In contrast, the risk of a different subtype was much weaker, and notably, relatives of DLBCL patients were not at increased risk of FL and relatives of FL patients were not at increased risk of DLBCL.24  There is very limited data on PTCL, and registry data suggests no increased risk among first-degree relatives with HL, CLL, DLBCL, or FL.24 

Summary.

Multiple lines of data suggest that a family history of lymphoma is associated with an increased risk of lymphoma, familial risk is elevated for multiple lymphoma subtypes, and familial risk does not seem to be confounded by nongenetic risk factors, although there are likely unidentified risk factors and clustering of known (and unknown) risk factors within families that are difficult to exclude. This suggests at least some shared genetic etiology across the lymphoma subtypes. However, because a family history of a specific lymphoma subtype is also most strongly associated with a risk for that specific lymphoma, genetic factors are also likely to be unique to a subtype.

Genetic risk factors

We now review studies that show not only clear evidence of a genetic contribution to lymphoma risk, but also provide chromosomal locations that are associated with risk.

Linkage studies

Linkage studies use multicase families or sib pairs to screen the genome in an unbiased manner to identify chromosomal regions that show excessive sharing of inherited alleles among affected individuals. These regions can then be interrogated for causal variants using a variety of approaches, most commonly fine-mapping using dense genotyping, or sequencing. The expectation is to identify highly penetrant variants of modest to large effect size, although these variants are generally rare or very rare in the general population. Linkage studies in HL have identified both HLA class I (for Epstein-Barr virus [EBV]+) and class II (for EBV−) risk, and protective alleles and haplotypes.10,11  Beyond HLA, linkage studies in CLL,38  HL,39  and WM40  have not definitively identified genes with large effects, and there are no published studies in FL, DLBCL, or other NHL subtypes. For CLL, significant linkage was identified at 2q21.2, which contains the chemokine receptor (CXCR4) gene and for which rare coding mutations have been identified.41  The lack of strong findings for these linkage studies may be due to small sample sizes, but also raises the hypothesis that multiple, low-to-moderate risk variants that are common in the population, defined as minor allele frequency (MAF) >5%, may be more relevant in lymphoma etiology than single, highly penetrant variants that are very rare, which is referred to as the common-disease, common-variant hypothesis.42 

Genetic association studies

With the advent of high-throughput and relatively inexpensive genotyping technologies, case-control studies (also commonly called association studies in the genetics literature) of sequence variation in germline DNA have become a predominant study design in genetic epidemiology.43  This design is a very efficient strategy to identify low penetrance alleles relative to linkage studies, which are underpowered for this task.44  The most common type of genetic variation in the human genome is the single nucleotide polymorphism (SNP), which is a single base-pair change in the DNA sequence. In this setting, the SNP allele or genotype frequencies in cases (patients) are compared with that of unrelated controls (who do not have the phenotype of interest) using an OR. When the genetic model (eg, dominant vs recessive) is not known a priori, the OR is typically modeled as “per risk allele” (ie, ordinal test of 0, 1, or 2 risk alleles). Although other genetic variations are of interest, including rare variants (<5% frequency), insertion/deletions, block substitutions, inversions, translocations and copy number alterations, these have not been studied as extensively.45  Two major types of association studies are candidate gene and genome-wide association studies (GWAS).

Candidate gene studies

The choice of a candidate gene has been mainly driven by a priori biologic knowledge of lymphoma and diseases associated with lymphoma (eg, infectious or autoimmune), or results identified in other cancers. Candidate gene studies have included pathways related to immune function, cell cycle/proliferation, apoptosis, DNA repair, and carcinogen metabolism. Early studies tended to evaluate a small number of genes (ie, <5) and were generally restricted to 1 or 2 SNPs within a gene. These SNPs often had some evidence for their functionality based on laboratory data or anticipated changes in protein coding or gene activity (eg, changes in promotor function). As genotyping technologies increased in throughput and decreased in cost, more SNPs within genes and more genes (often grouped into pathways) were assessed. Also, the International Haplotype Map46  and later the 1000 Genomes47  projects, which catalog human genetic variation, became available as a reference and allowed “tagging” of genes and gene regions to take advantage of linkage disequilibrium (LD) to efficiently cover all of the common genetic variations for more comprehensive genotyping studies.43 

Although many studies of candidate genes have been published7-9,11,48 , most findings have failed to replicate likely due to study design, bias from population stratification (ie, confounding by race or ethnicity), small sample size (low power), uncontrolled multiple testing (leading to false-positive associations), and unrealistic expectations in our ability to choose variants and genes.49  The most robust findings have been for an LTA-TNF haplotype with DLBCL (P = 2.93 × 10−8)50,51 ; an SNP (rs3789068) in the proapoptotic BCL2L11 gene and risk for B-cell NHL (OR = 1.21; P = 2.21 × 10−11)52 ; SNPs in CASP8/CASP10 and risk of CLL53 ; an SNP (rs3132453) in PRRC2A in HLA class III and risk of B-cell NHL (OR = 0.68; P = 1.07 × 10−9)52 ; and certain HLA alleles in class I (including HLA-A*01 and *02) with EBV+ HL and class II (including HLA-DRB1) with EBV− HL.11 

GWAS

In contrast to candidate gene/pathway studies, GWAS uses dense microarrays with a large number of SNPs (commonly 250 000 to 750 000 or more) spread across all chromosomes to identify genetic markers associated with case-control status.54  Although SNPs on these platforms have generally focused on common variants (MAF ≥5%), more recent arrays are enriching for rarer variants (MAF <5%). GWAS is considered agnostic (“hypothesis-free”) as all loci are considered equally. Given the large number of statistical tests involved, a stringent level of evidence (currently P < 5 × 10−8) and replication across multiple independent studies are required to declare an association as “genome-wide significant.” An advantage of having a large number of typed SNPs is that any underlying difference in population structure between cases and controls can be identified and controlled to ensure that confounding by race/ethnicity does not bias the results (Table 2).55-75 

Table 2

GWAS-discovered loci for lymphoma

SubtypeLocusSNPNearest GeneRAF* (controls)ORPReference
CLL 2p22.2 rs3770745 QPCT, PRKD3 0.22 1.24 1.68 × 10−8 55 
CLL 2q13 rs13401811 ACOXL, BCL2L11 0.81 1.41 2.08 × 10−18 55 
CLL 2q13 rs17483466 ACOXL, BCL2L11 0.20 1.39 2.36 × 10−10 56 
CLL 2q33.1 rs3769825 CASP10/CASP8 0.45 1.19 2.50 × 10−9 55 
CLL 2q37.1 rs13397985 SP140, SP110 0.19 1.41 5.40 × 10−10 56 
CLL 2q37.3 rs757978 FARP2 0.11 1.39 2.11 × 10−9 57 
CLL 3q26.2 rs10936599 MYNN 0.75 1.26 1.74 × 10−9 58 
CLL 4q25 rs898518 LEF1 0.59 1.20 4.24 × 10−10 55 
CLL 4q26 rs6858698 CAMK2D 0.16 1.31 3.07 × 10−9 58 
CLL 5p15.33 rs10069690 TERT 0.25 1.20 1.12 × 10−10 58 
CLL 6p21.31 rs210142 BAK1 0.70 1.40 9.47 × 10−16 59 
CLL 6p21.32 rs9273363 HLA-DQB1 0.27 1.24 2.24 × 10−10 55 
CLL 6p21.32 rs674313 HLA-DRB5 0.26 1.69 6.92 × 10−9 60 
CLL 6p25.3 rs872071 IRF4 0.54 1.54 1.91 × 10−20 56 
CLL 6q25.2 rs2236256 IPCEF1 0.44 1.23 1.50 × 10−10 58 
CLL 7q31.33 rs17246404 POT1 0.71 1.22 3.40 × 10−8 58 
CLL 8q22.3 rs2511714 ODF1 0.41 1.16 2.90 × 10−9 58 
CLL 8q24.21 rs2456449 CASC19 0.36 1.26 7.84 × 10−10 57 
CLL 9p21.3 rs1679013 CDKN2B-AS1 0.52 1.19 1.27 × 10−8 55 
CLL 10q23.31 rs4406737 ACTA2, FAS 0.57 1.27 1.22 × 10−14 55 
CLL 11p15.5 rs7944004 C11orf21 0.49 1.20 2.15 × 10−10 55 
CLL 11q24.1 rs735665 GRAMD1B 0.21 1.45 3.78 × 10−12 56 
CLL 12q24.13 rs10735079 OAS3 0.36 1.18 2.34 × 10−8 61 
CLL 15q15.1 rs8024033 BMF 0.51 1.22 2.71 × 10−10 55 
CLL 15q21.3 rs7169431 RFX7 0.08 1.36 4.74 × 10−7 57 
CLL 15q23 rs7176508 RPLP1 0.37 1.37 4.54 × 10−12 56 
CLL 16q24.1 rs305061 IRF8 0.66 1.22 3.60 × 10−7 57 
CLL 16q24.1 rs2292982 IRF8 0.34 0.65 6.48 × 10−9 60 
CLL 18q21.32 rs4368253 PMAIP1 0.69 1.19 2.51 × 10−8 55 
CLL 18q21.33 rs4987855 BCL2 0.91 1.47 2.66 × 10−12 55 
CLL 18q21.33 rs4987852 BCL2 0.06 1.41 7.76 × 10−11 55 
CLL 19q13.32 rs11083846 PRKD2 0.22 1.35 3.96 × 10−9 56 
FL 3q28 rs6444305 LPP 0.27 1.21 1.10 × 10−10 62 
FL 6p21.32 rs10484561 MHC class II 0.13 1.95 1.12 × 10−29 63 
FL 6p21.32 rs2647012 HLA-DQB1 0.44 0.64 2.00 × 10−21 64 
FL 6p21.32  HLA-DRβ1 Glu-28 0.30 1.86 7.89 × 10−69 62 
FL 6p21.32 rs17203612 HLA-DRA 0.49 1.44 4.59 × 10−16 62 
FL 6p21.33 rs3130437 HLA-C 0.62 1.23 8.23 × 10−9 62 
FL 6p21.33 rs6457327 C6orf15 et al (STG) 0.38 0.59 4.70 × 10−11 65 
FL 8q24.21 rs13254990 PVT1 0.32 1.18 1.06 × 10−8 62 
FL 11q23.3 rs4938573 CXCR5 0.20 1.34 5.79 × 10−20 62 
FL 11q24.3 rs4937362 ETS1 0.46 1.19 6.76 × 10−11 62 
FL 18q21.33 rs17749561 BCL2 0.91 1.34 8.28 × 10−10 62 
DLBCL 2p23.3 rs79480871 NCOA1 0.076 1.34 4.23 × 10−8 66 
DLBCL 3q27 rs6773854 BCL6/LPP 0.22 1.47 1.14 × 10−11 67 
DLBCL 6p21.33 rs2523607 HLA-B 0.12 1.32 2.40 × 10−10 66 
DLBCL 6p25.3 rs116446171 EXOC2 0.019 2.20 2.33 × 10−21 66 
DLBCL 8q24.21 rs13255292 PVT1 0.32 1.22 9.98 × 10−13 66 
DLBCL 8q24.21 rs4733601 PVT1 0.48 1.18 3.63 × 10−11 66 
MZL 6p21.32 rs9461741 BTNL2 0.018 2.66 3.95 × 10−15 68 
MZL 6p21.33 rs2922994 HLA-B 0.11 1.64 2.43 × 10−9 68 
HL 2p16.1 rs1432295 REL 0.40 1.22 1.91 × 10−8 69 
HL 3p24.1 rs3806624 EOMES 0.45 1.26 1.14 × 10−12 70 
HL (EBV−) 5q31 rs20541 IL13 0.18 1.53 5.40 × 10−9 71 
HL 6p21 rs2248462 MICB 0.22 0.61 1.30 × 10−13 71 
HL 6p21 rs2395185 HLA-DRA 0.33 0.56 8.30 × 10−25 71 
HL (EBV+) 6p21 rs2734986 HLA-A 0.18 2.45 1.20 × 10−15 71 
HL (EBV+) 6p21 rs6904029 HCG9 0.30 0.46 5.50 × 10−10 71 
HL 6p21.32 rs2281389 HLA-DPB1 0.83 1.73 6.31 × 10−13 72 
HL 6p21.32 rs6903608 HLA-DRA 0.27 1.70 2.84 × 10−50 69 
HL (NS) 6p21.32 rs2858870 HLA-DRB1 0.13 0.40 5.61 × 10−9 73 
HL (NS) 6p21.32 rs204999 PRRT1 0.27 0.60 2.34 × 10−8 73 
HL 6q23.3 rs7745098 HBS1L, MYB 0.48 1.21 3.42 × 10−9 70 
HL 8q24.21 rs2019960 PVT1 0.23 1.33 1.26 × 10−13 69 
HL 10p14 rs501764 GATA3 0.19 1.25 7.05 × 10−8 69 
HL 19p13.3 rs1860661 TCF3 0.41 0.81 3.50 × 10−10 74 
LYM 11q12.1 rs12289961 LPXN 0.28 1.29 3.89 × 10−8 75 
SubtypeLocusSNPNearest GeneRAF* (controls)ORPReference
CLL 2p22.2 rs3770745 QPCT, PRKD3 0.22 1.24 1.68 × 10−8 55 
CLL 2q13 rs13401811 ACOXL, BCL2L11 0.81 1.41 2.08 × 10−18 55 
CLL 2q13 rs17483466 ACOXL, BCL2L11 0.20 1.39 2.36 × 10−10 56 
CLL 2q33.1 rs3769825 CASP10/CASP8 0.45 1.19 2.50 × 10−9 55 
CLL 2q37.1 rs13397985 SP140, SP110 0.19 1.41 5.40 × 10−10 56 
CLL 2q37.3 rs757978 FARP2 0.11 1.39 2.11 × 10−9 57 
CLL 3q26.2 rs10936599 MYNN 0.75 1.26 1.74 × 10−9 58 
CLL 4q25 rs898518 LEF1 0.59 1.20 4.24 × 10−10 55 
CLL 4q26 rs6858698 CAMK2D 0.16 1.31 3.07 × 10−9 58 
CLL 5p15.33 rs10069690 TERT 0.25 1.20 1.12 × 10−10 58 
CLL 6p21.31 rs210142 BAK1 0.70 1.40 9.47 × 10−16 59 
CLL 6p21.32 rs9273363 HLA-DQB1 0.27 1.24 2.24 × 10−10 55 
CLL 6p21.32 rs674313 HLA-DRB5 0.26 1.69 6.92 × 10−9 60 
CLL 6p25.3 rs872071 IRF4 0.54 1.54 1.91 × 10−20 56 
CLL 6q25.2 rs2236256 IPCEF1 0.44 1.23 1.50 × 10−10 58 
CLL 7q31.33 rs17246404 POT1 0.71 1.22 3.40 × 10−8 58 
CLL 8q22.3 rs2511714 ODF1 0.41 1.16 2.90 × 10−9 58 
CLL 8q24.21 rs2456449 CASC19 0.36 1.26 7.84 × 10−10 57 
CLL 9p21.3 rs1679013 CDKN2B-AS1 0.52 1.19 1.27 × 10−8 55 
CLL 10q23.31 rs4406737 ACTA2, FAS 0.57 1.27 1.22 × 10−14 55 
CLL 11p15.5 rs7944004 C11orf21 0.49 1.20 2.15 × 10−10 55 
CLL 11q24.1 rs735665 GRAMD1B 0.21 1.45 3.78 × 10−12 56 
CLL 12q24.13 rs10735079 OAS3 0.36 1.18 2.34 × 10−8 61 
CLL 15q15.1 rs8024033 BMF 0.51 1.22 2.71 × 10−10 55 
CLL 15q21.3 rs7169431 RFX7 0.08 1.36 4.74 × 10−7 57 
CLL 15q23 rs7176508 RPLP1 0.37 1.37 4.54 × 10−12 56 
CLL 16q24.1 rs305061 IRF8 0.66 1.22 3.60 × 10−7 57 
CLL 16q24.1 rs2292982 IRF8 0.34 0.65 6.48 × 10−9 60 
CLL 18q21.32 rs4368253 PMAIP1 0.69 1.19 2.51 × 10−8 55 
CLL 18q21.33 rs4987855 BCL2 0.91 1.47 2.66 × 10−12 55 
CLL 18q21.33 rs4987852 BCL2 0.06 1.41 7.76 × 10−11 55 
CLL 19q13.32 rs11083846 PRKD2 0.22 1.35 3.96 × 10−9 56 
FL 3q28 rs6444305 LPP 0.27 1.21 1.10 × 10−10 62 
FL 6p21.32 rs10484561 MHC class II 0.13 1.95 1.12 × 10−29 63 
FL 6p21.32 rs2647012 HLA-DQB1 0.44 0.64 2.00 × 10−21 64 
FL 6p21.32  HLA-DRβ1 Glu-28 0.30 1.86 7.89 × 10−69 62 
FL 6p21.32 rs17203612 HLA-DRA 0.49 1.44 4.59 × 10−16 62 
FL 6p21.33 rs3130437 HLA-C 0.62 1.23 8.23 × 10−9 62 
FL 6p21.33 rs6457327 C6orf15 et al (STG) 0.38 0.59 4.70 × 10−11 65 
FL 8q24.21 rs13254990 PVT1 0.32 1.18 1.06 × 10−8 62 
FL 11q23.3 rs4938573 CXCR5 0.20 1.34 5.79 × 10−20 62 
FL 11q24.3 rs4937362 ETS1 0.46 1.19 6.76 × 10−11 62 
FL 18q21.33 rs17749561 BCL2 0.91 1.34 8.28 × 10−10 62 
DLBCL 2p23.3 rs79480871 NCOA1 0.076 1.34 4.23 × 10−8 66 
DLBCL 3q27 rs6773854 BCL6/LPP 0.22 1.47 1.14 × 10−11 67 
DLBCL 6p21.33 rs2523607 HLA-B 0.12 1.32 2.40 × 10−10 66 
DLBCL 6p25.3 rs116446171 EXOC2 0.019 2.20 2.33 × 10−21 66 
DLBCL 8q24.21 rs13255292 PVT1 0.32 1.22 9.98 × 10−13 66 
DLBCL 8q24.21 rs4733601 PVT1 0.48 1.18 3.63 × 10−11 66 
MZL 6p21.32 rs9461741 BTNL2 0.018 2.66 3.95 × 10−15 68 
MZL 6p21.33 rs2922994 HLA-B 0.11 1.64 2.43 × 10−9 68 
HL 2p16.1 rs1432295 REL 0.40 1.22 1.91 × 10−8 69 
HL 3p24.1 rs3806624 EOMES 0.45 1.26 1.14 × 10−12 70 
HL (EBV−) 5q31 rs20541 IL13 0.18 1.53 5.40 × 10−9 71 
HL 6p21 rs2248462 MICB 0.22 0.61 1.30 × 10−13 71 
HL 6p21 rs2395185 HLA-DRA 0.33 0.56 8.30 × 10−25 71 
HL (EBV+) 6p21 rs2734986 HLA-A 0.18 2.45 1.20 × 10−15 71 
HL (EBV+) 6p21 rs6904029 HCG9 0.30 0.46 5.50 × 10−10 71 
HL 6p21.32 rs2281389 HLA-DPB1 0.83 1.73 6.31 × 10−13 72 
HL 6p21.32 rs6903608 HLA-DRA 0.27 1.70 2.84 × 10−50 69 
HL (NS) 6p21.32 rs2858870 HLA-DRB1 0.13 0.40 5.61 × 10−9 73 
HL (NS) 6p21.32 rs204999 PRRT1 0.27 0.60 2.34 × 10−8 73 
HL 6q23.3 rs7745098 HBS1L, MYB 0.48 1.21 3.42 × 10−9 70 
HL 8q24.21 rs2019960 PVT1 0.23 1.33 1.26 × 10−13 69 
HL 10p14 rs501764 GATA3 0.19 1.25 7.05 × 10−8 69 
HL 19p13.3 rs1860661 TCF3 0.41 0.81 3.50 × 10−10 74 
LYM 11q12.1 rs12289961 LPXN 0.28 1.29 3.89 × 10−8 75 

LYM, lymphoma; NS, nodular sclerosis.

*

Risk AF among controls.

OR (per allele).

Considered genome-wide significant at the time of initial publication.

CLL.

The estimated contribution of all common variations to the heritability of CLL is 46% to 59%.55,76  The first GWAS in a lymphoid malignancy was conducted for CLL56  and to date, GWAS analyses55,57-61  have identified 32 SNPs from 28 loci for CLL, which accounts for ∼19% of familial risk of CLL.58  Many of the established SNPs are near or in genes plausibly linked to CLL, including genes involved in apoptosis (including FAS, PMAIP1, BAK1, BCL2, BCL2L11, BMF, and CASP8/CASP10), telomere function (POT1, TERT, and TERC), transcription factors important in B-cell differentiation (IRF8, LEF1, PRKD3, and SP140), and B-cell receptor activation (IRF3 and HLA-DQA1). Notably, there has been little evidence of interaction among these SNPs, suggesting independent effects. None of the SNPs have individually shown a strong relationship with age at diagnosis, although cases diagnosed at a younger age tended to carry a greater number of risk alleles,58  supporting the hypothesis that early onset CLL is enriched for genetic susceptibility.

In an East Asian population, GWAS-discovered SNPs for CLL near IRF4 (rs872071), SP140 (rs13397985), and ACOXL (rs17483466) were associated with CLL risk (nominal P < .05), with a suggestive association with GRAMD1B (rs735665).77  The MAFs of these SNPs were much lower than in populations of European descent, supporting the hypothesis that the lower prevalence of CLL genetic risk factors might explain part of the lower incidence of CLL in East Asian populations.

FL.

Three early GWA studies based on small discovery sets (<400 cases) identified loci at 6p21.3365  and 6p21.3263,64  in the major histocompatibility complex (MHC) associated with FL. In a meta-analysis of those studies plus a new GWAS of over 2100 cases, the HLA region showed overwhelming association with FL, with 8104 SNPs achieving genome-wide significance. A top SNP from this region, rs12195582, reached P = 5.35 × 10−100 after additional validation.62  HLA alleles and amino acids (AA) were imputed and the top signal mapped to four-linked DRβ1 multi-allelic AA at positions at 11, 13, 28, and 30, suggesting an important role for DRβ1 peptide presentation in FL.62  Additional independent signals were also identified in HLA class II (rs17203612) and class I (rs3130437, near HLA-C); after accounting for all of these signals, no other previously identified SNPs from the MHC achieved genome-wide significance. Outside of the HLA region, 5 novel loci have been identified including 11q23.3 (near CXCR5), 11q24.3 (near ETS1), 3q28 (in LPP), 18q21.33 (near BCL2), and 8q24 (near PVT1).62  These genes are linked to B-cell biology making them plausible in the etiology of FL.

HL.

Classical HL (cHL) makes up ∼95% of HL, and cHL compromises several subtypes: in young children and older adults, mixed cellularity HL (typically EBV+) predominates, whereas in adolescents and young adults, nodular sclerosing HL (typically EBV−) predominates.78  Five GWAS analyses have been published in HL,69-71,73,74  and the strongest findings have been for SNPs mapping to HLA class II69,71,73  in close proximity to HLA-DRA and HLA-DRB1, regions previously linked to HL by HLA-typing studies.79,80  The 6p21.32 locus marked by rs6903608 (near HLA-DRA) was associated with cHL overall, and more specifically to EBV− cHL,69,71  early onset,69,72  and young adult nodular sclerosing HL73  (largely EBV−). Additional GWAS signals at 6p21 have been identified in HLA class I,71  with statistically independent associations for rs2248462 (near MICB) for all cHL (irrespective of EBV status); and rs2734986 (3′ untranslated region of HLA-G and near HLA-A) and rs6904029 (near HCG9) for EBV+ cHL. These results confirm earlier studies linking HLA-A*01 and *02 to EBV+ cHL,81-83  and support a role for class I but not class II genes in EBV+ HL. Using SNPs to impute classic HLA alleles, two independent signals in the HLA class II region (rs6903608 and rs2281389) were linked to early onset HL, but no specific classical HLA alleles from this region were significant after conditioning on these two SNPs.72 The class II SNP rs6903608 was estimated to account for ∼6% of the familial risk in HL.72 

Outside of the MHC region, GWAS-discovered loci for HL include 2p16.169  (near REL), 10p1469  (near GATA3), 8q24.2169  (telomeric to PVT1 and near MYC), 5q3171  (a nonsynonymous SNP in IL13), 3p24.170  (5′ to EOMES), 6q23.370  (intergenic to HBS1L and MYB), and 19p13.374  (in intron 2 of TCF3), with only the 2p16.1 and 5q31 loci showing stronger associations with EBV (negative) status. Genes from these non-HLA regions are involved in hematopoiesis and immunoregulation, making them plausible susceptibility loci for cHL. HLA and non–HLA-linked loci appear to be independent, and non-HLA loci were estimated to account for ∼7% of the familial risk in HL.70 

DLBCL.

In a GWAS conducted in an East Asian population, a locus at 3q27 (near BCL6 and LPP) was identified,67  although this could not be replicated in independent studies of East Asian84  or European ancestry.66  In a large GWAS of European ancestry,66  novel loci identified included 6p25.3 (EXOC2), 6p21.33 (HLA-B), 2p23.3 (NCOA1), and 8q24.21 (near PVT1 and MYC); the strongest finding after imputing HLA alleles and AA was with HLA-B*08:01, although this could not be statistically distinguished from the HLA-B SNP due to high LD. The latter study also estimated that common SNPs, including but not limited to the GWAS-discovered loci, explained ∼16% of the variance in DLBCL risk overall.

Three of the five GWAS-discovered SNPs for DLBCL in Europeans were significantly associated with DLBCL in an East Asian population,84  including EXOC2 (OR = 2.04; P = 3.9 × 10−10), PVT1 (OR = 1.34; P = 2.1 × 10−6), and HLA-B (OR = 3.05; P = .009). Overall, MAFs were similar or only modestly lower in the East Asian population for all SNPs except for one of the 8q24 SNPs, which was much rarer.

MZL.

The only GWAS of this subtype68  identified two distinct loci at 6p21.32 (intragenic to BTNL2, in HLA class II) and 6p21.33 (HLA-B, in HLA class I); these two loci were in low LD and were statistically independent of each other. There was no strong heterogeneity in these results when stratified on mucosa-associated lymphoid tissue vs non–mucosa-associated lymphoid tissue (splenic MZL and nodal MZL) subtypes, although this was based on a modest sample size. These loci are also associated with autoimmune diseases and immune response, suggesting shared biologic underpinnings with MZL.

Lymphoma.

Only one GWAS has been conducted based on all lymphomas (including HL, multiple myeloma, and T-cell cases) as the outcome in both the discovery and validation stages.75  An SNP at 11q12.1 (near LPXN) was identified, and the associations were consistent across the common subtypes. However, this locus has not been replicated in larger GWAS studies based on specific subtypes.

Summary

To date, GWAS have successfully identified 67 SNPs from 41 genetic loci, mainly associated with specific subtypes (Figure 1), with only two regions (ie, the HLA region and 8q24) associated with multiple lymphoma subtypes; few candidate gene loci have been replicated by GWAS. As shown in Figure 2, the established loci are common (MAF >5%) and have small effect sizes, supporting a polygenic model for susceptibility. In contrast to GWAS, candidate gene studies in lymphoma have had only minimal success, similar to other cancers.85  Linkage studies have also not been successful in identifying rare alleles causing Mendelian disease, and the evaluation of low-frequency variants with intermediate effects is still in early research phases for lymphoma, but will be challenged by sample size issues.86  The GWAS-identified SNPs that have been identified are largely of unknown function. However, a leading hypothesis related for the mechanistic role of these common SNPs is their effect on gene expression (eg, through effects on promotors or enhancers), but this effect is difficult to identify given an expected modest impact of these SNPs on gene expression and the fact that this impact could occur at any time before diagnosis.58 

Figure 1

GWAS-discovered loci for lymphoma subtypes mapped to chromosomal location. Except for 6p21 and 8q24, there is minimal or little overlap of loci for lymphoma subtype-specific susceptibility loci. Lym, lymphoma.

Figure 1

GWAS-discovered loci for lymphoma subtypes mapped to chromosomal location. Except for 6p21 and 8q24, there is minimal or little overlap of loci for lymphoma subtype-specific susceptibility loci. Lym, lymphoma.

Figure 2

Lymphoma susceptibility loci by effect size and AF. The blue diamonds represent established lymphoma susceptibility loci plotted by AF (x-axis) vs effect size (y-axis). For lymphoma, most of the loci are common variants of low to modest effect size (mainly discovered by GWAS), although a few low-frequency variants have been identified. No rare alleles of low frequency (generally identified through linkage studies and sequencing) have been definitively linked to lymphoma. Very rare variants of low effect size are difficult to identify using current genetic approaches, whereas there are very few examples of common variants of high effect size for common diseases (and none in lymphoma).

Figure 2

Lymphoma susceptibility loci by effect size and AF. The blue diamonds represent established lymphoma susceptibility loci plotted by AF (x-axis) vs effect size (y-axis). For lymphoma, most of the loci are common variants of low to modest effect size (mainly discovered by GWAS), although a few low-frequency variants have been identified. No rare alleles of low frequency (generally identified through linkage studies and sequencing) have been definitively linked to lymphoma. Very rare variants of low effect size are difficult to identify using current genetic approaches, whereas there are very few examples of common variants of high effect size for common diseases (and none in lymphoma).

Practice implications

Given the estimated lifetime risk of NHL is 1 in 48 (2.1%) in the United States1  and an RR of 1.7 for the risk of NHL in a first-degree relative, then the absolute lifetime risk of NHL is 3.6% in first-degree relatives of an NHL patient. The absolute risk is even lower for specific lymphoma subtypes, which are less common. Although the absolute lifetime risk of NHL is not trivial, the relatively low incidence of lymphoma, the modest familial risk, and the lack of a screening test and associated intervention all argue against active clinical surveillance of family members of lymphoma patients at this time. One hope is that genetic risk scores, alone or in combination with other risk factors, might improve prediction ability.87  Although there are currently no validated risk scores for lymphoma, this advance is anticipated as more loci are characterized.

Future directions

Characterization of genetic susceptibility in lymphoma is rapidly evolving. It is expected that additional common variants will be discovered for the different lymphoma subtypes,88  and perhaps pan-lymphoma loci will also be identified. As new lymphoma entities and precursor lesions are defined, the evaluation of heritability and genetic susceptibility should be addressed. Additional work needs to occur in other racial and ethnic groups, particularly with contrasting lymphoma incidence rates. It is not yet clear if rare and low-frequency variants will play a major role in lymphoma susceptibility. This will be challenging to address due to phenotype heterogeneity and the need for large sample sizes for these relatively rare entities, and both family and association study designs along with bioinformatics and laboratory-based studies will all need to be integrated to achieve progress.86  Other genetic mechanisms (eg, copy number variation), epigenetics, and gene-environment interactions are additional frontiers.85  Finally, integrating somatic and germline genomics should provide additional insights into lymphoma etiology and pathogenesis,89  and hopefully provide novel insights into how to prevent and treat this malignancy.

Acknowledgments

The authors thank Dr Thomas Habermann for his critical review of the manuscript, Curtis Olswald for technical assistance, and Sondra Buehler for editorial assistance.

This work was supported by grants from the National Institutes of Health National Cancer Institute (R01 CA92153, U01 CA118444, and P50 CA97274).

Authorship

Contribution: J.R.C. and S.L.S. did the background research and wrote the manuscript.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: James R. Cerhan, Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905; e-mail: cerhan.james@mayo.edu.

References

1
Siegel
 
RL
Miller
 
KD
Jemal
 
A
Cancer statistics, 2015.
CA Cancer J Clin
2015
, vol. 
65
 
1
(pg. 
5
-
29
)
2
Segel
 
GB
Lichtman
 
MA
Familial (inherited) leukemia, lymphoma, and myeloma: an overview.
Blood Cells Mol Dis
2004
, vol. 
32
 
1
(pg. 
246
-
261
)
3
Jaffe
 
ES
Harris
 
NL
Stein
 
H
Vardiman
 
JW
World Health Organization Classification of Tumours: Pathology and Genetics, Tumours of Hematopoietic and Lymphoid Tissues
2001
Lyon, France
IARC Press
4
Swerdlow
 
S
Campo
 
E
Harris
 
N
World Health Organization Classification of Tumours of Haematopoietic and Lymphoid Tissues
2008
Lyon, France
IARC Press
5
Houlston
 
RS
Catovsky
 
D
Yuille
 
MR
Genetic susceptibility to chronic lymphocytic leukemia.
Leukemia
2002
, vol. 
16
 
6
(pg. 
1008
-
1014
)
6
Goldin
 
LR
Slager
 
SL
Caporaso
 
NE
Familial chronic lymphocytic leukemia.
Curr Opin Hematol
2010
, vol. 
17
 
4
(pg. 
350
-
355
)
7
Skibola
 
CF
Curry
 
JD
Nieters
 
A
Genetic susceptibility to lymphoma.
Haematologica
2007
, vol. 
92
 
7
(pg. 
960
-
969
)
8
Cerhan
 
JR
Host genetics in follicular lymphoma.
Best Pract Res Clin Haematol
2011
, vol. 
24
 
2
(pg. 
121
-
134
)
9
Slager
 
SL
Caporaso
 
NE
de Sanjose
 
S
Goldin
 
LR
Genetic susceptibility to chronic lymphocytic leukemia.
Semin Hematol
2013
, vol. 
50
 
4
(pg. 
296
-
302
)
10
Diepstra
 
A
Niens
 
M
te Meerman
 
GJ
Poppema
 
S
van den Berg
 
A
Genetic susceptibility to Hodgkin’s lymphoma associated with the human leukocyte antigen region.
Eur J Haematol Suppl
2005
, vol. 
75
 
66
(pg. 
34
-
41
)
11
Kushekhar
 
K
van den Berg
 
A
Nolte
 
I
Hepkema
 
B
Visser
 
L
Diepstra
 
A
Genetic associations in classical hodgkin lymphoma: a systematic review and insights into susceptibility mechanisms.
Cancer Epidemiol Biomarkers Prev
2014
, vol. 
23
 
12
(pg. 
2737
-
2747
)
12
Morton
 
LM
Slager
 
SL
Cerhan
 
JR
, et al. 
Etiologic heterogeneity among non-Hodgkin lymphoma subtypes: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J Natl Cancer Inst Monogr
2014
, vol. 
2014
 
48
(pg. 
130
-
144
)
13
Lichtenstein
 
P
Holm
 
NV
Verkasalo
 
PK
, et al. 
Environmental and heritable factors in the causation of cancer--analyses of cohorts of twins from Sweden, Denmark, and Finland.
N Engl J Med
2000
, vol. 
343
 
2
(pg. 
78
-
85
)
14
Albright
 
F
Teerlink
 
C
Werner
 
TL
Cannon-Albright
 
LA
Significant evidence for a heritable contribution to cancer predisposition: a review of cancer familiality by site.
BMC Cancer
2012
, vol. 
12
 pg. 
138
 
15
Mack
 
TM
Cozen
 
W
Shibata
 
DK
, et al. 
Concordance for Hodgkin’s disease in identical twins suggesting genetic susceptibility to the young-adult form of the disease.
N Engl J Med
1995
, vol. 
332
 
7
(pg. 
413
-
418
)
16
Cerhan
 
JR
Kricker
 
A
Paltiel
 
O
, et al. 
Medical history, lifestyle, family history, and occupational risk factors for diffuse large B-cell lymphoma: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J Natl Cancer Inst Monogr
2014
, vol. 
2014
 
48
(pg. 
15
-
25
)
17
Linet
 
MS
Vajdic
 
CM
Morton
 
LM
, et al. 
Medical history, lifestyle, family history, and occupational risk factors for follicular lymphoma: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J Natl Cancer Inst Monogr
2014
, vol. 
2014
 
48
(pg. 
26
-
40
)
18
Slager
 
SL
Benavente
 
Y
Blair
 
A
, et al. 
Medical history, lifestyle, family history, and occupational risk factors for chronic lymphocytic leukemia/small lymphocytic lymphoma: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J Natl Cancer Inst Monogr
2014
, vol. 
2014
 
48
(pg. 
41
-
51
)
19
Bracci
 
PM
Benavente
 
Y
Turner
 
JJ
, et al. 
Medical history, lifestyle, family history, and occupational risk factors for marginal zone lymphoma: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J Natl Cancer Inst Monogr
2014
, vol. 
2014
 
48
(pg. 
52
-
65
)
20
Vajdic
 
CM
Landgren
 
O
McMaster
 
ML
, et al. 
Medical history, lifestyle, family history, and occupational risk factors for lymphoplasmacytic lymphoma/Waldenström’s macroglobulinemia: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J Natl Cancer Inst Monogr
2014
, vol. 
2014
 
48
(pg. 
87
-
97
)
21
Wang
 
SS
Flowers
 
CR
Kadin
 
ME
, et al. 
Medical history, lifestyle, family history, and occupational risk factors for peripheral T-cell lymphomas: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J Natl Cancer Inst Monogr
2014
, vol. 
2014
 
48
(pg. 
66
-
75
)
22
Goldin
 
LR
Landgren
 
O
McMaster
 
ML
, et al. 
Familial aggregation and heterogeneity of non-Hodgkin lymphoma in population-based samples.
Cancer Epidemiol Biomarkers Prev
2005
, vol. 
14
 
10
(pg. 
2402
-
2406
)
23
Goldin
 
LR
Björkholm
 
M
Kristinsson
 
SY
Turesson
 
I
Landgren
 
O
Elevated risk of chronic lymphocytic leukemia and other indolent non-Hodgkin’s lymphomas among relatives of patients with chronic lymphocytic leukemia.
Haematologica
2009
, vol. 
94
 
5
(pg. 
647
-
653
)
24
Goldin
 
LR
Björkholm
 
M
Kristinsson
 
SY
Turesson
 
I
Landgren
 
O
Highly increased familial risks for specific lymphoma subtypes.
Br J Haematol
2009
, vol. 
146
 
1
(pg. 
91
-
94
)
25
Kristinsson
 
SY
Björkholm
 
M
Goldin
 
LR
McMaster
 
ML
Turesson
 
I
Landgren
 
O
Risk of lymphoproliferative disorders among first-degree relatives of lymphoplasmacytic lymphoma/Waldenstrom macroglobulinemia patients: a population-based study in Sweden.
Blood
2008
, vol. 
112
 
8
(pg. 
3052
-
3056
)
26
Chang
 
ET
Smedby
 
KE
Hjalgrim
 
H
, et al. 
Family history of hematopoietic malignancy and risk of lymphoma.
J Natl Cancer Inst
2005
, vol. 
97
 
19
(pg. 
1466
-
1474
)
27
Goldin
 
LR
Pfeiffer
 
RM
Gridley
 
G
, et al. 
Familial aggregation of Hodgkin lymphoma and related tumors.
Cancer
2004
, vol. 
100
 
9
(pg. 
1902
-
1908
)
28
Rudant
 
J
Menegaux
 
F
Leverger
 
G
, et al. 
Family history of cancer in children with acute leukemia, Hodgkin’s lymphoma or non-Hodgkin’s lymphoma: the ESCALE study (SFCE).
Int J Cancer
2007
, vol. 
121
 
1
(pg. 
119
-
126
)
29
Villeneuve
 
S
Orsi
 
L
Monnereau
 
A
, et al. 
Increased frequency of hematopoietic malignancies in relatives of patients with lymphoid neoplasms: a French case-control study.
Int J Cancer
2009
, vol. 
124
 
5
(pg. 
1188
-
1195
)
30
Chang
 
ET
Smedby
 
KE
Hjalgrim
 
H
Glimelius
 
B
Adami
 
HO
Reliability of self-reported family history of cancer in a large case-control study of lymphoma.
J Natl Cancer Inst
2006
, vol. 
98
 
1
(pg. 
61
-
68
)
31
Crump
 
C
Sundquist
 
K
Sieh
 
W
Winkleby
 
MA
Sundquist
 
J
Perinatal and family risk factors for Hodgkin lymphoma in childhood through young adulthood.
Am J Epidemiol
2012
, vol. 
176
 
12
(pg. 
1147
-
1158
)
32
Friedman
 
DL
Kadan-Lottick
 
NS
Whitton
 
J
, et al. 
Increased risk of cancer among siblings of long-term childhood cancer survivors: a report from the childhood cancer survivor study.
Cancer Epidemiol Biomarkers Prev
2005
, vol. 
14
 
8
(pg. 
1922
-
1927
)
33
Lu
 
Y
Sullivan-Halley
 
J
Cozen
 
W
, et al. 
Family history of haematopoietic malignancies and non-Hodgkin’s lymphoma risk in the California Teachers Study.
Br J Cancer
2009
, vol. 
100
 
3
(pg. 
524
-
526
)
34
Goldgar
 
DE
Easton
 
DF
Cannon-Albright
 
LA
Skolnick
 
MH
Systematic population-based assessment of cancer risk in first-degree relatives of cancer probands.
J Natl Cancer Inst
1994
, vol. 
86
 
21
(pg. 
1600
-
1608
)
35
Paltiel
 
O
Schmit
 
T
Adler
 
B
, et al. 
The incidence of lymphoma in first-degree relatives of patients with Hodgkin disease and non-Hodgkin lymphoma: results and limitations of a registry-linked study.
Cancer
2000
, vol. 
88
 
10
(pg. 
2357
-
2366
)
36
Altieri
 
A
Hemminki
 
K
The familial risk of Hodgkin’s lymphoma ranks among the highest in the Swedish Family-Cancer Database.
Leukemia
2006
, vol. 
20
 
11
(pg. 
2062
-
2063
)
37
Pang
 
D
Alston
 
RD
Eden
 
TO
Birch
 
JM
Cancer risks among relatives of children with Hodgkin and non-Hodgkin lymphoma.
Int J Cancer
2008
, vol. 
123
 
6
(pg. 
1407
-
1410
)
38
Sellick
 
GS
Goldin
 
LR
Wild
 
RW
, et al. 
A high-density SNP genome-wide linkage search of 206 families identifies susceptibility loci for chronic lymphocytic leukemia.
Blood
2007
, vol. 
110
 
9
(pg. 
3326
-
3333
)
39
Goldin
 
LR
McMaster
 
ML
Ter-Minassian
 
M
, et al. 
A genome screen of families at high risk for Hodgkin lymphoma: evidence for a susceptibility gene on chromosome 4.
J Med Genet
2005
, vol. 
42
 
7
(pg. 
595
-
601
)
40
McMaster
 
ML
Goldin
 
LR
Bai
 
Y
, et al. 
Genomewide linkage screen for Waldenstrom macroglobulinemia susceptibility loci in high-risk families.
Am J Hum Genet
2006
, vol. 
79
 
4
(pg. 
695
-
701
)
41
Crowther-Swanepoel
 
D
Qureshi
 
M
Dyer
 
MJ
, et al. 
Genetic variation in CXCR4 and risk of chronic lymphocytic leukemia.
Blood
2009
, vol. 
114
 
23
(pg. 
4843
-
4846
)
42
Collins
 
FS
Guyer
 
MS
Charkravarti
 
A
Variations on a theme: cataloging human DNA sequence variation.
Science
1997
, vol. 
278
 
5343
(pg. 
1580
-
1581
)
43
Balding
 
DJ
A tutorial on statistical methods for population association studies.
Nat Rev Genet
2006
, vol. 
7
 
10
(pg. 
781
-
791
)
44
Risch
 
N
Merikangas
 
K
The future of genetic studies of complex human diseases.
Science
1996
, vol. 
273
 
5281
(pg. 
1516
-
1517
)
45
Frazer
 
KA
Murray
 
SS
Schork
 
NJ
Topol
 
EJ
Human genetic variation and its contribution to complex traits.
Nat Rev Genet
2009
, vol. 
10
 
4
(pg. 
241
-
251
)
46
International HapMap Consortium
The International HapMap Project.
Nature
2003
, vol. 
426
 
6968
(pg. 
789
-
796
)
47
Abecasis
 
GR
Auton
 
A
Brooks
 
LD
, et al. 
1000 Genomes Project Consortium
An integrated map of genetic variation from 1,092 human genomes.
Nature
2012
, vol. 
491
 
7422
(pg. 
56
-
65
)
48
Sud
 
A
Hemminki
 
K
Houlston
 
RS
Candidate gene association studies and risk of Hodgkin lymphoma: a systematic review and meta-analysis [published online ahead of print June 5, 2015].
Hematol Oncol
49
Pharoah
 
PD
Dunning
 
AM
Ponder
 
BA
Easton
 
DF
Association studies for finding cancer-susceptibility genetic variants.
Nat Rev Cancer
2004
, vol. 
4
 
11
(pg. 
850
-
860
)
50
Skibola
 
CF
Bracci
 
PM
Nieters
 
A
, et al. 
Tumor necrosis factor (TNF) and lymphotoxin-alpha (LTA) polymorphisms and risk of non-Hodgkin lymphoma in the InterLymph Consortium.
Am J Epidemiol
2010
, vol. 
171
 
3
(pg. 
267
-
276
)
51
Rothman
 
N
Skibola
 
CF
Wang
 
SS
, et al. 
Genetic variation in TNF and IL10 and risk of non-Hodgkin lymphoma: a report from the InterLymph Consortium.
Lancet Oncol
2006
, vol. 
7
 
1
(pg. 
27
-
38
)
52
Nieters
 
A
Conde
 
L
Slager
 
SL
, et al. 
PRRC2A and BCL2L11 gene variants influence risk of non-Hodgkin lymphoma: results from the InterLymph consortium.
Blood
2012
, vol. 
120
 
23
(pg. 
4645
-
4648
)
53
Enjuanes
 
A
Benavente
 
Y
Bosch
 
F
, et al. 
Genetic variants in apoptosis and immunoregulation-related genes are associated with risk of chronic lymphocytic leukemia.
Cancer Res
2008
, vol. 
68
 
24
(pg. 
10178
-
10186
)
54
Ioannidis
 
JP
Thomas
 
G
Daly
 
MJ
Validating, augmenting and refining genome-wide association signals.
Nat Rev Genet
2009
, vol. 
10
 
5
(pg. 
318
-
329
)
55
Berndt
 
SI
Skibola
 
CF
Joseph
 
V
, et al. 
Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia.
Nat Genet
2013
, vol. 
45
 
8
(pg. 
868
-
876
)
56
Di Bernardo
 
MC
Crowther-Swanepoel
 
D
Broderick
 
P
, et al. 
A genome-wide association study identifies six susceptibility loci for chronic lymphocytic leukemia.
Nat Genet
2008
, vol. 
40
 
10
(pg. 
1204
-
1210
)
57
Crowther-Swanepoel
 
D
Broderick
 
P
Di Bernardo
 
MC
, et al. 
Common variants at 2q37.3, 8q24.21, 15q21.3 and 16q24.1 influence chronic lymphocytic leukemia risk.
Nat Genet
2010
, vol. 
42
 
2
(pg. 
132
-
136
)
58
Speedy
 
HE
Di Bernardo
 
MC
Sava
 
GP
, et al. 
A genome-wide association study identifies multiple susceptibility loci for chronic lymphocytic leukemia.
Nat Genet
2014
, vol. 
46
 
1
(pg. 
56
-
60
)
59
Slager
 
SL
Skibola
 
CF
Di Bernardo
 
MC
, et al. 
Common variation at 6p21.31 (BAK1) influences the risk of chronic lymphocytic leukemia.
Blood
2012
, vol. 
120
 
4
(pg. 
843
-
846
)
60
Slager
 
SL
Rabe
 
KG
Achenbach
 
SJ
, et al. 
Genome-wide association study identifies a novel susceptibility locus at 6p21.3 among familial CLL.
Blood
2011
, vol. 
117
 
6
(pg. 
1911
-
1916
)
61
Sava
 
GP
Speedy
 
HE
Di Bernardo
 
MC
, et al. 
Common variation at 12q24.13 (OAS3) influences chronic lymphocytic leukemia risk.
Leukemia
2015
, vol. 
29
 
3
(pg. 
748
-
751
)
62
Skibola
 
CF
Berndt
 
SI
Vijai
 
J
, et al. 
Genome-wide association study identifies five susceptibility loci for follicular lymphoma outside the HLA region.
Am J Hum Genet
2014
, vol. 
95
 
4
(pg. 
462
-
471
)
63
Conde
 
L
Halperin
 
E
Akers
 
NK
, et al. 
Genome-wide association study of follicular lymphoma identifies a risk locus at 6p21.32.
Nat Genet
2010
, vol. 
42
 
8
(pg. 
661
-
664
)
64
Smedby
 
KE
Foo
 
JN
Skibola
 
CF
, et al. 
GWAS of follicular lymphoma reveals allelic heterogeneity at 6p21.32 and suggests shared genetic susceptibility with diffuse large B-cell lymphoma.
PLoS Genet
2011
, vol. 
7
 
4
pg. 
e1001378
 
65
Skibola
 
CF
Bracci
 
PM
Halperin
 
E
, et al. 
Genetic variants at 6p21.33 are associated with susceptibility to follicular lymphoma.
Nat Genet
2009
, vol. 
41
 
8
(pg. 
873
-
875
)
66
Cerhan
 
JR
Berndt
 
SI
Vijai
 
J
, et al. 
Genome-wide association study identifies multiple susceptibility loci for diffuse large B cell lymphoma.
Nat Genet
2014
, vol. 
46
 
11
(pg. 
1233
-
1238
)
67
Tan
 
DE
Foo
 
JN
Bei
 
JX
, et al. 
Genome-wide association study of B cell non-Hodgkin lymphoma identifies 3q27 as a susceptibility locus in the Chinese population.
Nat Genet
2013
, vol. 
45
 
7
(pg. 
804
-
807
)
68
Vijai
 
J
Wang
 
Z
Berndt
 
SI
, et al. 
A genome-wide association study of marginal zone lymphoma shows association to the HLA region.
Nat Commun
2015
, vol. 
6
 pg. 
5751
 
69
Enciso-Mora
 
V
Broderick
 
P
Ma
 
Y
, et al. 
A genome-wide association study of Hodgkin’s lymphoma identifies new susceptibility loci at 2p16.1 (REL), 8q24.21 and 10p14 (GATA3).
Nat Genet
2010
, vol. 
42
 
12
(pg. 
1126
-
1130
)
70
Frampton
 
M
da Silva Filho
 
MI
Broderick
 
P
, et al. 
Variation at 3p24.1 and 6q23.3 influences the risk of Hodgkin’s lymphoma.
Nat Commun
2013
, vol. 
4
 pg. 
2549
 
71
Urayama
 
KY
Jarrett
 
RF
Hjalgrim
 
H
, et al. 
Genome-wide association study of classical Hodgkin lymphoma and Epstein-Barr virus status-defined subgroups.
J Natl Cancer Inst
2012
, vol. 
104
 
3
(pg. 
240
-
253
)
72
Moutsianas
 
L
Enciso-Mora
 
V
Ma
 
YP
, et al. 
Multiple Hodgkin lymphoma-associated loci within the HLA region at chromosome 6p21.3.
Blood
2011
, vol. 
118
 
3
(pg. 
670
-
674
)
73
Cozen
 
W
Li
 
D
Best
 
T
, et al. 
A genome-wide meta-analysis of nodular sclerosing Hodgkin lymphoma identifies risk loci at 6p21.32.
Blood
2012
, vol. 
119
 
2
(pg. 
469
-
475
)
74
Cozen
 
W
Timofeeva
 
MN
Li
 
D
, et al. 
A meta-analysis of Hodgkin lymphoma reveals 19p13.3 TCF3 as a novel susceptibility locus.
Nat Commun
2014
, vol. 
5
 pg. 
3856
 
75
Vijai
 
J
Kirchhoff
 
T
Schrader
 
KA
, et al. 
Susceptibility loci associated with specific and shared subtypes of lymphoid malignancies.
PLoS Genet
2013
, vol. 
9
 
1
pg. 
e1003220
 
76
Di Bernardo
 
MC
Broderick
 
P
Catovsky
 
D
Houlston
 
RS
Common genetic variation contributes significantly to the risk of developing chronic lymphocytic leukemia.
Haematologica
2013
, vol. 
98
 
3
(pg. 
e23
-
e24
)
77
Lan
 
Q
Au
 
WY
Chanock
 
S
, et al. 
Genetic susceptibility for chronic lymphocytic leukemia among Chinese in Hong Kong.
Eur J Haematol
2010
, vol. 
85
 
6
(pg. 
492
-
495
)
78
Glaser
 
SL
Gulley
 
ML
Clarke
 
CA
, et al. 
Racial/ethnic variation in EBV-positive classical Hodgkin lymphoma in California populations.
Int J Cancer
2008
, vol. 
123
 
7
(pg. 
1499
-
1507
)
79
Klitz
 
W
Aldrich
 
CL
Fildes
 
N
Horning
 
SJ
Begovich
 
AB
Localization of predisposition to Hodgkin disease in the HLA class II region.
Am J Hum Genet
1994
, vol. 
54
 
3
(pg. 
497
-
505
)
80
Oza
 
AM
Tonks
 
S
Lim
 
J
Fleetwood
 
MA
Lister
 
TA
Bodmer
 
JG
A clinical and epidemiological study of human leukocyte antigen-DPB alleles in Hodgkin’s disease.
Cancer Res
1994
, vol. 
54
 
19
(pg. 
5101
-
5105
)
81
Niens
 
M
Jarrett
 
RF
Hepkema
 
B
, et al. 
HLA-A*02 is associated with a reduced risk and HLA-A*01 with an increased risk of developing EBV+ Hodgkin lymphoma.
Blood
2007
, vol. 
110
 
9
(pg. 
3310
-
3315
)
82
Hjalgrim
 
H
Rostgaard
 
K
Johnson
 
PC
, et al. 
HLA-A alleles and infectious mononucleosis suggest a critical role for cytotoxic T-cell response in EBV-related Hodgkin lymphoma.
Proc Natl Acad Sci USA
2010
, vol. 
107
 
14
(pg. 
6400
-
6405
)
83
Diepstra
 
A
Niens
 
M
Vellenga
 
E
, et al. 
Association with HLA class I in Epstein-Barr-virus-positive and with HLA class III in Epstein-Barr-virus-negative Hodgkin’s lymphoma.
Lancet
2005
, vol. 
365
 
9478
(pg. 
2216
-
2224
)
84
Bassig
 
BA
Cerhan
 
JR
Au
 
W-Y
, et al. 
Genetic susceptibility to diffuse large B-cell lymphoma in a pooled study of three Eastern Asian populations [published online ahead of print January 22, 2015].
Eur J Haematol
85
Fletcher
 
O
Houlston
 
RS
Architecture of inherited susceptibility to common cancer.
Nat Rev Cancer
2010
, vol. 
10
 
5
(pg. 
353
-
361
)
86
Cirulli
 
ET
Goldstein
 
DB
Uncovering the roles of rare variants in common disease through whole-genome sequencing.
Nat Rev Genet
2010
, vol. 
11
 
6
(pg. 
415
-
425
)
87
Kooperberg
 
C
LeBlanc
 
M
Obenchain
 
V
Risk prediction using genome-wide association studies.
Genet Epidemiol
2010
, vol. 
34
 
7
(pg. 
643
-
652
)
88
Park
 
JH
Wacholder
 
S
Gail
 
MH
, et al. 
Estimation of effect size distribution from genome-wide association studies and implications for future discoveries.
Nat Genet
2010
, vol. 
42
 
7
(pg. 
570
-
575
)
89
Feigelson
 
HS
Goddard
 
KA
Hollombe
 
C
, et al. 
Approaches to integrating germline and tumor genomic data in cancer research.
Carcinogenesis
2014
, vol. 
35
 
10
(pg. 
2157
-
2163
)