Conceived and designed the experiments: JX BC ML. Analyzed the data: BC ML. Wrote the paper: BC ML. Other: Contributed to compilation of data: JX WI LL CM ML BC.
The authors have declared that no competing interests exist.
Recent identifications of associations between novel variants in inflammation-related genes and several common diseases emphasize the need for systematic evaluations of these genes in disease susceptibility. Considering that many genes are involved in the complex inflammation responses and many genetic variants in these genes have the potential to alter the functions and expression of these genes, we assembled a list of key inflammation-related genes to facilitate the identification of genetic associations of diseases with an inflammation-related etiology. We first reviewed various phases of inflammation responses, including the development of immune cells, sensing of danger, influx of cells to sites of insult, activation and functional responses of immune and non-immune cells, and resolution of the immune response. Assisted by the Ingenuity Pathway Analysis, we then identified 17 functional sub-pathways that are involved in one or multiple phases. This organization would greatly increase the chance of detecting gene-gene interactions by hierarchical clustering of genes with their functional closeness in a pathway. Finally, as an example application, we have developed tagging single nucleotide polymorphism (tSNP) arrays for populations of European and African descent to capture all the common variants of these key inflammation-related genes. Assays of these tSNPs have been designed and assembled into two Affymetrix ParAllele customized chips, one each for European (12,011 SNPs) and African (21,542 SNPs) populations. These tSNPs have greater coverage for these inflammation-related genes compared to the existing genome-wide arrays, particularly in the African population. These tSNP arrays can facilitate systematic evaluation of inflammation pathways in disease susceptibility. For additional applications, other genotyping platforms could also be employed. For existing genome-wide association data, this list of key inflammation-related genes and associated subpathways can facilitate comprehensive inflammation pathway- focused association analyses.
Inflammation is an essential component of immune-mediated protection against pathogens and tissue damage. Immune responses are also responsible for the unfavorable rejection of tissue/organ transplants, hypersensitivity reactions (e.g., atopy, anaphylaxis, contact hypersensitivity, delayed-type hypersensitivity), and septic shock. Aberrant or unchecked immune responses may lead to a state of chronic inflammation
Inflammation may also be a contributing factor for some diseases. The role of chronic inflammation in a wide variety of diseases is well-appreciated, including rheumatoid arthritis and other autoimmune disorders
Disease | Gene | Encoded protein | Variant | Odds ratio | p-value | Confirmation method |
Age-related macular degeneration | Complement factor H | rs1061170 | 3.40 | <1×10−5 | Case-control/meta-analysis | |
Atopic asthma | IL-4 receptor alpha | rs1801275 | 1.79 | 3×10−9 | Meta-analysis of 7 study populations | |
Atopic asthma | TNF-alpha | -308 G/A | 1.46 | 1×10−4 | Meta-analysis of 15 study populations | |
Crohn's disease | Nod2 | 1007fsinsC | 4.3 | 7×10−28 | Meta-analysis of 16 study populations | |
Breast cancer | Caspase-8 | rs1045485 | 0.90 | 0.016 | Analysis of 3 study populations (6351 cases/5708 controls) | |
Breast cancer | TGF-beta 1 | rs1982073 | 1.08 | 0.0088 | Analysis of 3 study populations (6863 cases/5587 controls) | |
Breast cancer | TNF-alpha | rs361525 | 1.18 | 0.008 | Analysis of two independent study populations | |
Graves' disease | Lymphoid-specific phosphatase | C1858T | 1.61 | <1×10−5 | Meta-analysis of 3 study populations | |
Inflammatory bowel disease | IL-23 receptor beta | rs11209026 | 0.26 | 5×10−9 | Genome-wide screen (raw p-value) | |
0.45 | 8×10−4 | Case-control replication | ||||
∼0.5 | 1.3×10−10 | Family-based TDT replication | ||||
Psoriatic arthritis | TNF-alpha | -238 G/A | 2.29 | 2×10−4 | Meta-analysis of 8 study populations | |
Rheumatoid arthritis | Lymphoid-specific phosphatase | C1858T (R620W) | 1.68 | <1×10−5 | Meta-analysis of 12 study populations | |
Systemic lupus erythamatosus | Interferon response factor 5 | rs2004640 | 1.47 | 4.2×10−21 | Case-control/meta-analysis+replication in family-based | |
Systemic lupus erythamatosus | Lymphoid-specific phosphatase | C1858T (R620W) | 1.49 | <1×10−5 | Meta-analysis of 5 study populations | |
Systemic lupus erythamatosus | TNF-alpha | -308 G/A | 2.1 | <0.001 | Meta-analysis of 10 study populations of European descent | |
Type 1 diabetes | CTLA-4 | rs3087243 | 1.18 | 5.6×10−6 | Family-based TDT | |
Type 1 diabetes | CTLA-4 | rs3087243 | 1.17 | 6×10−4 | Family-based TDT | |
1.21 | 1.3×10−7 | Case-control | ||||
Type 1 diabetes | CTLA-4 | rs3087243 | 1.20 | 3.7×10−10 | Case-control | |
Type 1 diabetes | Mda-5, Helicard | rs1990760 | 0.86 | 1.42×10−10 | Genome-wide, validated in case-control+family-based | |
Type 1 diabetes | Lymphoid-specific phosphatase | C1858T (R620W) | 1.85 | <1×10−5 | Meta-analysis of 6 study populations |
Odds ratio for allele test (multiplicative model), unless otherwise indicated.
Odds ratio for dominant model
Risk ratio from family-based transmission disequilibrium test (TDT).
Numerous genetic linkage and case-control association studies have implicated genetic variations in genes important in immunity and inflammation and inflammatory diseases. Single missense heritable mutations can be the sole or major determinant for inflammatory diseases, such as Familial Cold Autoinflammatory Syndrome (missense mutations in exon 3 of
In addition to inflammatory/autoimmune diseases, polymorphisms in inflammation-associated genes may also contribute to risk for diseases in which inflammatory/immune-disorders are not the primary characteristic. There is evidence from the Breast Cancer Association Consortium that
Because genetic associations of disease and genetic variations in inflammatory genes are often relatively modest, it is likely that polymorphisms in multiple inflammatory genes cooperate in an additive or synergistic manner to impact disease risk. Pathway analyses may help to reveal gene-gene interactions or risks imparted independently from other genes in the pathway. The advantages of performing analyses at pathway levels are illustrated by Dinu et al.
Various aspects of immunity contribute to the development of an overall inflammatory immune response. These phases include the development of immune cells, sensing of danger, influx of cells to sites of insult, activation and functional responses of immune and non-immune cells, and resolution of the immune response. To broadly cover most aspects of inflammatory responses, the various
Depicted is a schematic representation of an immune response to a generic pathogenic insult. The phases of immune responses (described in
Phase of immune response | Description |
Hematopoiesis/homeosta-sis/tolerance | The generation and differentiation of immune cells and maintenance of their number in circulation and tissues; prevention of self-reactivity. |
Danger signal | Innate recognition of and response to pathogenic foreign substances or stress. |
Mobilization of immune cells | Systemic soluble mediators informing immune cells in circulation and lymphoid tissues of danger. |
Extravasation | The process of circulating immune cells crossing from blood into peripheral tissues and secondary lymphoid tissues. |
Migration to site of inflammation | The process of immune cells, after extravasation, reaching the site of inflammatory insult, including chemoattraction, adhesion to substrates, and degradation of extracellular matrix. |
Interactions between resident cells, immune cells, and pathogens at site of inflammation | Interactions between resident cells, immune cells, and pathogens at site of inflammation–how infiltrating cells interact with the resident inflammatory cells, non-immune cells (e.g., epithelia), pathogens, and other infiltrating cells, that leads to activation of effector functions. |
Activation of inflammatory cells | The signaling pathways and transcription factors stimulated by activating, co-stimulatory, and inhibitory receptors that leads to activation, proliferation, differentiation, and survival of responding immune cells. |
Effector functions of inflammatory cells | The factors produced/released by immune cells in attempt to resolve the pathogenic insults, including release of cytotoxic/cytostatic mediators and mediators to enhance or fine-tune the immune response. |
Response of target cells | The pathways in non-immune cells (e.g., epithelia) activated in response to the effector functions of immune cells. |
Resolution of immune response vs. chronic inflammation | The pathways that lead to the downregulation of immune responses and inflammation after the pathogenic insult is cleared; the factors maintaining late-phase immune responses when the insult is not totally resolved. |
Priority was given first to genes of known function in inflammatory responses (in both immune and non-immune cells), and then to genes expressed in immune cells with function implied by homology to other genes but exact function not clear. Ubiquitously expressed genes required for the normal function of most cell types of diverse origin were given lower priority. However, special emphasis was placed on genes at nodes for signaling to and from multiple pathways, most notably genes in NF-κB, MAPK, and PI3K signaling pathways.
Pathways were built using Ingenuity Pathways Analysis, as described in
Multiple functional pathways are involved each of the immune response phases, and each functional pathway may contribute to several of the immune response phases. For example (
The concerted action of multiple functional subpathways in the initial response of a macrophage to bacteria or virus is depicted. Solid arrows indicate signaling events and dashed arrows stimulated production of proteins and other inflammatory mediators (including autocrine/paracrine responses of the macrophage to the released molecules).
Because immune response phases utilize multiple functional pathways and these pathways are overlapping among phases, the genes chosen for the SNP array panel were assigned to one of the following
Examples of the functional subpathways and types of genes chosen for the different phases of immune responses are presented in
Phase of immune response | Examples of pathways, proteins, and inflammatory mediators involved in immune response phases |
Hematopoiesis/homeostasis/ tolerance | hematopoietic cytokines (M-,G-,GM-CSF;IL-4,-5,-7,-13), stromal factors (c-kit, SCF, Flt3L), regulatory T cell function (Foxp3) |
Danger signal | innate pathogen recognition receptors (TLRs, CARDs/NODs, peptidoglycan recognition proteins), scavenger receptors (MSR1), endothelins, adenosine receptors, complement, stress-induced responses (MIC-A,-B), eicosanoid synthesis genes, cytokines, antigen presentation genes |
Mobilization of immune cells | systemic inflammatory mediators (IL-1β, IL-6, TNF-a), chemokines, eicosanoids, GPCR signaling (eicosanoids, histamines) |
Extravasation | adhesion molecules (integrins, -CAMs), chemokines, vasodilators (eicosanoids/GPCR), cytoskeletal rearrangement singaling molecules (Vav, VASP, MENA), non-muscle myosins |
Migration to site of inflammation | adhesion molecules (integrins, -CAMs, maxtrix receptors), chemokines, matrix proteases (MMPs), cytoskeletal rearrangement singaling molecules (Vav,VASP,MENA), focal adhesion proteins (Vav,ROCK), non-muscle myosins |
Interactions between resident cells, immune cells, and pathogens at site of inflammation | adhesion molecules, innate detectors of pathogens (TLRs, CARDs/NODs), Fc receptors (FcgRI,II,III; FceRI,II), stress-induced ligands (MIC-A,-B), NK cell-activating receptors, cytokines and receptors, other activating receptors (TCR, BCR complexes; growth factor receptors); co-stimulatory receptors (B7 family, CD2 family), inhibitory receptors (KIRs, LIRs/ILTs), phagocytosis/antigen presentation (XBOX genes, CIITA, TAP, immunoproteasome, HLA molecules) |
Activation of inflammatory cells | MAPK pathways (Erk, p38, Jnk), PI3K/Akt signaling, NF-kB signaling, cytokine signaling (JAK/STAT/Tyk, NFIL3, NFIL6, IRFs), GPCR signaling (PKA, PLCb, phosphodiesterases, CREB, Pyk2, Rap1, Src), adaptor signaling proteins (TRAFs, IRAKs, MyD88, DAP10, DAP12, ZAP70, Syk, LAT, SLP76, MyD88, CD3ζ, FcεRγ) |
Effector functions of inflammatory cells | cytokines (IFN-γ, IFN-α, TNF-α superfamily, CSFs, interleukins), death receptor ligands (FasL, TRAIL, TNF-a), eicosanoids (prostaglandins, thromboxane, prostacyclin, leukotrienes), cytotoxic mediators (glutathiones/PHOX/reactive oxygen species, RNS, perforin/granzymes), antibody production, acute phase/fever response (C-reactive protein, factor P) |
Response of target cells | cytokine receptors, GPCRs, death receptors, apoptosis signaling, adhesion molecules, growth factor receptors |
Resolution of immune response vs. chronic inflammation | apoptosis (death receptor and mitochondrial pathways), TGF-β, IL-10, Foxp3, prostaglandins, phosphatases, inhibitors of cytokine signaling (SOCS, A20/TNFAIP3) |
Subpathway | Number of genes in subpathway | Number of SNPs in subpathway |
Adhesion-Extravasation-Migration | 142 | 1385 |
Apoptosis Signaling | 68 | 682 |
Calcium Signaling | 14 | 409 |
Complement Cascase | 40 | 419 |
Cytokine signaling | 172 | 1598 |
Eicosanoid Signaling | 39 | 374 |
Glucocorticoid/PPAR signaling | 21 | 230 |
G-Protein Coupled Receptor Signaling | 42 | 1125 |
Innate pathogen detection | 50 | 457 |
Leukocyte signaling | 121 | 1743 |
MAPK signaling | 118 | 1949 |
Natural Killer Cell Signaling | 31 | 259 |
NF-kB signaling | 33 | 297 |
Phagocytosis-Ag presentation | 39 | 286 |
PI3K/AKT Signaling | 37 | 307 |
ROS/Glutathione/Cytotoxic granules | 22 | 162 |
TNF Superfamily Signaling | 38 | 328 |
Because the components comprising inflammation are very numerous and interacting across many pathways, without strong
There is a commercially available product, Affymetrix GeneChip® Human Immune and Inflammation 9K SNP panel, that attempts to serve the purpose. This application -specific panel contains ∼9,000 SNPs to cover ∼1,000 immunity- and inflammation- related genes (
tSNPs for the 1027 inflammation-associated candidate genes were chosen based on a pair-wise r2 threshold of 0.8 and MAF ≥5% using data in the HapMap Phase II database (HapMap Data Release 21a/phaseII;
The resulting inflammation tSNP panels, WFINFLAM-CEU for Caucasians and WFINFLAM-YRI for African descent, include 12,011 SNPs and 21,542 SNPs respectively in 1027 inflammation-associated candidate genes. There is an average of 11.7 and 21 SNPs in each candidate gene in WFINFLAM-CEU and WFINFLAM-YRI panels, respectively.
Various components and complex interactions comprise immune and inflammation responses, and numerous genes are involved in this complex network. With a thorough review of various aspects of inflammatory immune responses, and a systematic search for gene-gene interactions using Ingenuity Pathway Analysis, we have provided a comprehensive list of inflammation-associated genes and subpathways for genetic association studies.
Genome-wide association studies have been a very popular approach to test the association between disease phenotypes and genetic variations. However, we believe there are still several advantages for a pathway-focused study. First of all, compared to whole-genome analyses, restricting analyses to SNPs in a specific pathway reduces the number of multiple tests performed in the analysis of a study population, thereby reducing the probability of false positive associations and increasing the effective power of the study. This kind of study design is particularly effective when inflammation plays an important role in disease etiology and the goal of the studies is to delineate genetic variations in inflammation pathway to disease risk and/or progression. A related second advantage of restricted pathway analysis is in study design. A large proportion of investigators may not have access to the very large number of subjects and multiple confirmation populations needed to overcome false positive associations due to multiple testing in genome-wide association studies. Studies restricted to a pathway analysis permit the use of study populations that are not large enough for use in whole-genome association studies. When target diseases are not prevalent and inflammation is obviously involved in disease etiology, researchers will gain the most out of an inflammation pathway-specific study design. Although some genes not related to inflammation found in whole-genome panels may impart some risk to inflammation-associated diseases, associated genetic variants would be anticipated to be concentrated in a panel of SNPs in inflammation-associated genes. Therefore, the drawback of potentially missing associated non-inflammation genes is offset by the increased probability of detecting true associations in an inflammation-restricted panel. Thirdly, pathway analysis is far less expensive to perform than whole-genome analysis, especially considering the cost for second, and/or third stage confirmation studies needed to follow-up the significant results from an initial screening in order to rule out false positive associations. Lastly, the functional subpathways are also pre-defined with available biological information. This refined information provides investigators with the opportunity to test gene-gene interactions within subpathways in which synergistic interactions are more likely to be concentrated. Additionally, the interplays between subpathways are also clearly defined to enable investigators to test biologically feasible interactions between subpathways.
However, the results from this manuscript also have potential utility for investigators who have more interests in surveying the whole genome. For whole-genome analyses where there is a prior hypothesis for inflammation being associated with the outcome, the inflammation pathway and subpathways defined in this manuscript may provide a framework for testing whether SNPs in the inflammation pathway or subpathways as a whole are overrepresented for significant associations to the outcome. Although pathway networks can be constructed for whole-genome analyses, such networks should be designed
The WFINFLAM and WFINFLAM-YRI SNP array panels for inflammation-associated genes provide a powerful tool for analyzing the contribution of genetic variation in diseases that have inflammatory components. Although whole-genome SNP panels are currently available that include almost all of the genes included in the WFINFLAM panels, the coverage of SNPs in genes included in the WFINFLAM array is superior to the coverage of currently available in whole-genome arrays, especially for populations with African ancestry background. For researchers who would like to use other genotyping platforms, the inflammatory gene list provided here would be a good starting point for designing genotyping assays for other platforms. Additionally, alternate approaches, other than r2 based method, for choosing SNPs based on the inflammatory gene list provided here could also be considered. For example, researchers may specifically focus on “high-prior” polymorphisms that are known to be functional or have been previously linked to the specific diseases under study, alone or in combination with the tSNPs provided in the WFINFLAM panel. This approach may be more efficient and powerful than the r2 based method alone, especially if the targeted “high-prior” polymorphisms are causal and their linkage to the nearby tSNPs is incomplete.
In addition, precaution may be warranted for tSNP panels designed based on the HapMap project. The transferability of the LD patterns between populations studied in HapMap project and other study populations may need to be validated. The transferability of HapMap-based selection of tSNPs using the reference CEU population to several other diverse populations of European ancestry has been demonstrated to be almost as effective for overall SNP coverage in selected genomic regions or randomly selected SNPs in the respective populations
In summary, pathway analysis of inflammation-associated genes is a powerful approach for determining genetic risk factors for both inflammatory diseases and other diseases that may have an under-appreciated modest inflammatory component, such as cancers. The inflammation pathway gene list and functionally-defined subpathways provide useful tools for assessing the impact of genetic variations in inflammation pathways on disease risk, in situations where either pathway-focused studies or genome-wide analyses are employed.
Networks of genes involved in the regulation of the phases of immune responses (described in
These networks were then arranged into inflammation subpathways by:
combining several networks together (e.g., ERK/MAPK, p38 MAPK, and SAPK/JNK canonical pathways into the ‘MAPK signaling’ inflammation subpathway; IL-2-, IL-4-, IL-6-, IL-10-, Interferon-, GM-CSF-, IGF-1-, JAK/STAT6-, and TGF-β- signaling canonical pathways into ‘cytokine signaling’ inflammation subpathway; actin cytoskeleton-, chemokine-, integrin-, and leukocyte extravasation- signaling canonincal pathways into the ‘adhesion-extravasation-migration’ inflammation subpathway; etc.);
adding additional genes to bridge networks within a subpathway and to include appropriate genes not included in the canonical pathways (e.g., additional cytokines and their receptors were added to the ‘cytokine signaling’ inflammation subpathway; additional integrins and chemokines/chemoattractant molecules were added to the ‘adhesion-extravasation-migration’ inflammation subpathway; CD antigens expressed by leukocytes not already included were considered for addition to several subpathways; other missing genes/pathways considered important by the panel of investigators, such as the scavenger receptor network for ‘leukocyte signaling’ inflammation subpathway and Nod1/CARD family networks for ‘innate pathogen detection’ inflammation subpathway; etc.);
trimming the networks of genes with low priority for inclusion in the inflammation panel. Genes with lower priority include: genes not expressed in immune cells or not directly involved in cells responding to inflammation, including non-immune cells (e.g., skeletal muscle-specific myosins in the actin cytoskeleton signaling canonical pathway; calsequestrins expressed mainly in various muscle cells, in the calcium signaling canonical pathway; GH1 and GHR, growth hormone expressed in pituitary gland and its receptor, and NGFB and NGFR, nerve growth factor and its receptor, in the NF-κB canonical pathway; etc.; MAPK8IP1, specific for pancreatic cell function, in the PI3K/AKT canonical pathway; etc.); genes with unknown function, though genes with high homology to known inflammatory mediators were considered (e.g., bcl-2 family homologs, IL-1β family homologs). Special emphasis was placed on genes at nodes for signaling to and from multiple pathways, most notably genes in NF-κB, MAPK, and PI3K signaling pathways.
Tagging SNPs (tSNPs) for candidate genes were chosen using Tagger server (
We used LdCompare (Hao 2006;
List of genes included in WFINFLAM and their associated sub-pathways
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Genes with big intronic regions
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SNPs chosen for the six genes without HapMap genotyping data
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Annotation for tSNPs in WFINFLAM_CEU panel
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Annotation for tSNPs in WFINFLAM_YRI panel
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Summary for gene coverage of Wfinflam panels and other genome wide association panels
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