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Meta-Analysis
. 2016 Jun;45(3):728-40.
doi: 10.1093/ije/dyv364. Epub 2016 Mar 12.

Meta-analysis of Genome-Wide Association Studies Reveals Genetic Overlap Between Hodgkin Lymphoma and Multiple Sclerosis

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Free PMC article
Meta-Analysis

Meta-analysis of Genome-Wide Association Studies Reveals Genetic Overlap Between Hodgkin Lymphoma and Multiple Sclerosis

Pouya Khankhanian et al. Int J Epidemiol. .
Free PMC article

Abstract

Background: Based on epidemiological commonalities, multiple sclerosis (MS) and Hodgkin lymphoma (HL), two clinically distinct conditions, have long been suspected to be aetiologically related. MS and HL occur in roughly the same age groups, both are associated with Epstein-Barr virus infection and ultraviolet (UV) light exposure, and they cluster mutually in families (though not in individuals). We speculated if in addition to sharing environmental risk factors, MS and HL were also genetically related. Using data from genome-wide association studies (GWAS) of 1816 HL patients, 9772 MS patients and 25 255 controls, we therefore investigated the genetic overlap between the two diseases.

Methods: From among a common denominator of 404 K single nucleotide polymorphisms (SNPs) studied, we identified SNPs and human leukocyte antigen (HLA) alleles independently associated with both diseases. Next, we assessed the cumulative genome-wide effect of MS-associated SNPs on HL and of HL-associated SNPs on MS. To provide an interpretational frame of reference, we used data from published GWAS to create a genetic network of diseases within which we analysed proximity of HL and MS to autoimmune diseases and haematological and non-haematological malignancies.

Results: SNP analyses revealed genome-wide overlap between HL and MS, most prominently in the HLA region. Polygenic HL risk scores explained 4.44% of HL risk (Nagelkerke R(2)), but also 2.36% of MS risk. Conversely, polygenic MS risk scores explained 8.08% of MS risk and 1.94% of HL risk. In the genetic disease network, HL was closer to autoimmune diseases than to solid cancers.

Conclusions: HL displays considerable genetic overlap with MS and other autoimmune diseases.

Figures

Figure 1.
Figure 1.
Study design and data analysis procedures. Results from previously reported genome-wide associations studies (GWAS) of Hodgkin lymphoma (HL) and multiple sclerosis (MS) were used to assess genetic overlap between the two diseases. Single nucleotide polymorphisms (SNPs) independently associated with both HL and MS were identified, and disease-specific polygenic risk scores were compared in HL cases, MS cases and healthy controls. Protein-interaction network-based pathway analysis (PINBPA) was performed on the intersection of nominally associated ( P < 0.05) SNPs in HL and MS and gene ontology (GO) analysis was performed to identify common genetic pathways. Genetic similarity between HL and MS was further evaluated in the context of other immune diseases, haematological malignancies and solid cancers by constructing a diseasome using data from previously reported GWAS.
Figure 2.
Figure 2.
Legend. Classical HLA alleles were imputed in each disease using SNP data. Each point in each plot represents a classical HLA allele. Axes represent the odds ratio of association for each allele in the designated disease. Protective alleles have odds ratios less than 1 (lower values on each axis) and risk alleles have odds ratios greater than 1 (high values on each axis). (A) HLA risk alleles for EBV-positive HL tend to be neutral for EBV-negative HL, while HLA risk alleles for EBV-negative HL are neutral to protective for EBV-positive HL. Some HLA alleles are protective for both diseases. (B) HLA risk alleles for EBV-positive HL are neutral or protective for MS, and HLA risk alleles for MS are neutral or protective for EBV-positive HL. There are a large number of HLA alleles which are protective for both MS and EBV-positive HL. (C) There is an overlap between HLA risk alleles for MS and EBV-negative HL, and overlap between protective alleles for MS and EBV-negative HL.
Figure 3.
Figure 3.
Polygenic risk scores demonstrate overlap between diseases. Hodgkin lymphoma (HL) and multiple sclerosis (MS) polygenic risk scores in HL cases, MS cases and healthy controls. A. MS genetic burden (MSGB) on y-axis, an aggregate measure of MS genetic risk across the genome of a given individual (includes human leukocyte antigen region of chromosome 6). MSGB is higher in HL cases than controls, indicating genetic overlap between HL and MS. B. HL genetic burden (HLGB) on y-axis, an aggregate measure of HL genetic risk across the genome of a given individual (includes human leukocyte antigen region of chromosome 6). HLGB is higher in MS cases than controls, indicating genetic overlap between HL and MS.
Figure 4.
Figure 4.
Protein-interaction network-based pathway analysis (PINBPA) and gene ontology (GO). Four top pathways identified using GO analysis on PINBPA networks discovered in both Hodgkin lymphoma (HL) and multiple sclerosis (MS). A. Positive regulation of JUN kinase activity. B. Antigen processing and presentation of peptide antigen. C. Peptidyl-tyrosine phosphorylation. D. Lymphocyte-mediated immunity. Individual gene P -values for MS and HL are indicated when P < 0.05 (*) or when P < 0.1 (‡).
Figure 5.
Figure 5.
Diseasome analysis reveals that haematological malignancies lie somewhere between autoimmune diseases and solid cancer. A. Proximity of autoimmune diseases to other diseases. Density plots represent all possible pair-wise proximities between autoimmune diseases and solid cancers (orange), and all pair-wise proximities between autoimmune diseases and other autoimmune disease (purple). Higher degree of proximity (higher values on the x-axis) indicates more genetic similarity to autoimmune diseases. The P -value indicates that autoimmune diseases are closer to other autoimmune diseases than to solid cancers. B. Proximity of haematological malignancies to solid cancers (orange) and to autoimmune diseases (purple). Haematological malignancies show genetic overlap with both solid cancers and autoimmune diseases.C. Proximity of solid cancers to other solid cancers (orange) and to autoimmune diseases (purple).Solid cancers are closer to other solid cancers than to autoimmune diseases. D. Proximity of MS to all diseases. Each circle represents a disease in the diseasome. Higher degrees of proximity (higher values on x-axis) represent more genetic similarity with MS. Solid cancers are orange, autoimmune diseases are purple, HL is white. The P -value indicates MS is closer to autoimmune diseases than to solid cancers. E. Proximity of HL to all diseases. HL is closer to autoimmune diseases than to solid cancers.
Figure 6.
Figure 6.
Human disease network shows distinct autoimmune and solid cancer clusters and places hematologic cancers in context. In a network of disease proximity, constructed using systematic GWAS data, autoimmune diseases (purple) tightly cluster. Solid cancers (orange) also form a distinct cluster, but exhibit less relatedness in terms of genetic etiology than autoimmune diseases. Hematologic cancers (white) do not form a cohesive cluster and instead ranged from autoimmune related to solid cancer related. Hodgkin lymphoma (HL), in particular, appeared strongly autoimmune. See table 3 for a list of abbreviations.

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