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• W2898023404 abstract "•GWAS provides understanding of genetic factors shaping adaptive immune system•Common and less common variants explain 10% of variance in cellular variables•Associations pinpoint key regulators of B and T cell differentiation•Associations offer therapeutic targets for controlling pro-inflammatory traits The immune system is highly diverse, but characterization of its genetic architecture has lagged behind the vast progress made by genome-wide association studies (GWASs) of emergent diseases. Our GWAS for 54 functionally relevant phenotypes of the adaptive immune system in 489 healthy individuals identifies eight genome-wide significant associations explaining 6%–20% of variance. Coding and splicing variants in PTPRC and COMMD10 are involved in memory T cell differentiation. Genetic variation controlling disease-relevant T helper cell subsets includes RICTOR and STON2 associated with Th2 and Th17, respectively, and the interferon-lambda locus controlling regulatory T cell proliferation. Early and memory B cell differentiation stages are associated with variation in LARP1B and SP4. Finally, the latrophilin family member ADGRL2 correlates with baseline pro-inflammatory interleukin-6 levels. Suggestive associations reveal mechanisms of autoimmune disease associations, in particular related to pro-inflammatory cytokine production. Pinpointing these key human immune regulators offers attractive therapeutic perspectives. The immune system is highly diverse, but characterization of its genetic architecture has lagged behind the vast progress made by genome-wide association studies (GWASs) of emergent diseases. Our GWAS for 54 functionally relevant phenotypes of the adaptive immune system in 489 healthy individuals identifies eight genome-wide significant associations explaining 6%–20% of variance. Coding and splicing variants in PTPRC and COMMD10 are involved in memory T cell differentiation. Genetic variation controlling disease-relevant T helper cell subsets includes RICTOR and STON2 associated with Th2 and Th17, respectively, and the interferon-lambda locus controlling regulatory T cell proliferation. Early and memory B cell differentiation stages are associated with variation in LARP1B and SP4. Finally, the latrophilin family member ADGRL2 correlates with baseline pro-inflammatory interleukin-6 levels. Suggestive associations reveal mechanisms of autoimmune disease associations, in particular related to pro-inflammatory cytokine production. Pinpointing these key human immune regulators offers attractive therapeutic perspectives. The immune system is characterized by enriched polymorphism in genetic control factors, coupled to a high degree of cellular plasticity and sensitivity to environmental drivers. The resulting functional diversity serves as an important control mechanism for limiting the impact of transmissible pathogens on the population. Conversely, this same diversity contributes to the susceptibility or resistance of individuals to a broad set of sterile diseases, from those with an obvious immunological component, such as autoimmunity, allergy, inflammation, and cancer, to the increasingly recognized immune-influenced diseases, such as cardiovascular, metabolic, and neurological diseases. Despite this, the characterization of the genotype-phenotype relationship of the immune system components has lagged behind the vast progress made by genome-wide association studies (GWASs) of emergent diseases. The recent advent of in-depth immune phenotyping across large sample sizes has enabled characterization of the extent and identification of the factors shaping variation in the human immune profile (Liston et al., 2016Liston A. Carr E.J. Linterman M.A. Shaping variation in the human immune system.Trends Immunol. 2016; 37: 637-646Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar). Longitudinal studies have reported a high level of interindividual variation, with low longitudinal variation and a highly elastic structure, where transient antigen-induced changes are followed by a return to the individual’s unique baseline (Carr et al., 2016Carr E.J. Dooley J. Garcia-Perez J.E. Lagou V. Lee J.C. Wouters C. Meyts I. Goris A. Boeckxstaens G. Linterman M.A. Liston A. The cellular composition of the human immune system is shaped by age and cohabitation.Nat. Immunol. 2016; 17: 461-468Crossref PubMed Scopus (182) Google Scholar, Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar, Tsang et al., 2014Tsang J.S. Schwartzberg P.L. Kotliarov Y. Biancotto A. Xie Z. Germain R.N. Wang E. Olnes M.J. Narayanan M. Golding H. et al.Baylor HIPC CenterCHI ConsortiumGlobal analyses of human immune variation reveal baseline predictors of postvaccination responses.Cell. 2014; 157: 499-513Abstract Full Text Full Text PDF PubMed Scopus (287) Google Scholar). Twin and family-based studies provide heritability estimates of 20%–40% on average but cover a wide range across individual cellular or cytokine traits (Brodin et al., 2015Brodin P. Jojic V. Gao T. Bhattacharya S. Angel C.J. Furman D. Shen-Orr S. Dekker C.L. Swan G.E. Butte A.J. et al.Variation in the human immune system is largely driven by non-heritable influences.Cell. 2015; 160: 37-47Abstract Full Text Full Text PDF PubMed Scopus (617) Google Scholar, Carr et al., 2016Carr E.J. Dooley J. Garcia-Perez J.E. Lagou V. Lee J.C. Wouters C. Meyts I. Goris A. Boeckxstaens G. Linterman M.A. Liston A. The cellular composition of the human immune system is shaped by age and cohabitation.Nat. Immunol. 2016; 17: 461-468Crossref PubMed Scopus (182) Google Scholar, Mangino et al., 2017Mangino M. Roederer M. Beddall M.H. Nestle F.O. Spector T.D. Innate and adaptive immune traits are differentially affected by genetic and environmental factors.Nat. Commun. 2017; 8: 13850Crossref PubMed Scopus (71) Google Scholar, Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar, Roederer et al., 2015Roederer M. Quaye L. Mangino M. Beddall M.H. Mahnke Y. Chattopadhyay P. Tosi I. Napolitano L. Terranova Barberio M. Menni C. et al.The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis.Cell. 2015; 161: 387-403Abstract Full Text Full Text PDF PubMed Scopus (186) Google Scholar). Aging contributes up to 5% of total immune variation (Aguirre-Gamboa et al., 2016Aguirre-Gamboa R. Joosten I. Urbano P.C.M. van der Molen R.G. van Rijssen E. van Cranenbroek B. Oosting M. Smeekens S. Jaeger M. Zorro M. et al.Differential effects of environmental and genetic factors on T and B cell immune traits.Cell Rep. 2016; 17: 2474-2487Abstract Full Text Full Text PDF PubMed Scopus (104) Google Scholar, Brodin et al., 2015Brodin P. Jojic V. Gao T. Bhattacharya S. Angel C.J. Furman D. Shen-Orr S. Dekker C.L. Swan G.E. Butte A.J. et al.Variation in the human immune system is largely driven by non-heritable influences.Cell. 2015; 160: 37-47Abstract Full Text Full Text PDF PubMed Scopus (617) Google Scholar, Carr et al., 2016Carr E.J. Dooley J. Garcia-Perez J.E. Lagou V. Lee J.C. Wouters C. Meyts I. Goris A. Boeckxstaens G. Linterman M.A. Liston A. The cellular composition of the human immune system is shaped by age and cohabitation.Nat. Immunol. 2016; 17: 461-468Crossref PubMed Scopus (182) Google Scholar, Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar, Patin et al., 2018Patin E. Hasan M. Bergstedt J. Rouilly V. Libri V. Urrutia A. Alanio C. Scepanovic P. Hammer C. Jönsson F. et al.Milieu Intérieur ConsortiumNatural variation in the parameters of innate immune cells is preferentially driven by genetic factors.Nat. Immunol. 2018; 19: 302-314Crossref PubMed Scopus (121) Google Scholar, Shen-Orr et al., 2016Shen-Orr S.S. Furman D. Kidd B.A. Hadad F. Lovelace P. Huang Y.W. Rosenberg-Hasson Y. Mackey S. Grisar F.A. Pickman Y. et al.Defective signaling in the JAK-STAT pathway tracks with chronic inflammation and cardiovascular risk in aging humans.Cell Syst. 2016; 3: 374-384.e4Abstract Full Text Full Text PDF PubMed Scopus (75) Google Scholar), and environmental factors shaping the immune system include obesity, cohabitation, and chronic viral infections (Aguirre-Gamboa et al., 2016Aguirre-Gamboa R. Joosten I. Urbano P.C.M. van der Molen R.G. van Rijssen E. van Cranenbroek B. Oosting M. Smeekens S. Jaeger M. Zorro M. et al.Differential effects of environmental and genetic factors on T and B cell immune traits.Cell Rep. 2016; 17: 2474-2487Abstract Full Text Full Text PDF PubMed Scopus (104) Google Scholar, Brodin et al., 2015Brodin P. Jojic V. Gao T. Bhattacharya S. Angel C.J. Furman D. Shen-Orr S. Dekker C.L. Swan G.E. Butte A.J. et al.Variation in the human immune system is largely driven by non-heritable influences.Cell. 2015; 160: 37-47Abstract Full Text Full Text PDF PubMed Scopus (617) Google Scholar, Carr et al., 2016Carr E.J. Dooley J. Garcia-Perez J.E. Lagou V. Lee J.C. Wouters C. Meyts I. Goris A. Boeckxstaens G. Linterman M.A. Liston A. The cellular composition of the human immune system is shaped by age and cohabitation.Nat. Immunol. 2016; 17: 461-468Crossref PubMed Scopus (182) Google Scholar, Patin et al., 2018Patin E. Hasan M. Bergstedt J. Rouilly V. Libri V. Urrutia A. Alanio C. Scepanovic P. Hammer C. Jönsson F. et al.Milieu Intérieur ConsortiumNatural variation in the parameters of innate immune cells is preferentially driven by genetic factors.Nat. Immunol. 2018; 19: 302-314Crossref PubMed Scopus (121) Google Scholar). Identification of the genetic factors controlling variation in the immune system is still in the initial discovery phase, reminiscent of the early days of disease-susceptibility GWASs, with novel and strong associations emerging from the pioneer studies (Aguirre-Gamboa et al., 2016Aguirre-Gamboa R. Joosten I. Urbano P.C.M. van der Molen R.G. van Rijssen E. van Cranenbroek B. Oosting M. Smeekens S. Jaeger M. Zorro M. et al.Differential effects of environmental and genetic factors on T and B cell immune traits.Cell Rep. 2016; 17: 2474-2487Abstract Full Text Full Text PDF PubMed Scopus (104) Google Scholar, Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar, Patin et al., 2018Patin E. Hasan M. Bergstedt J. Rouilly V. Libri V. Urrutia A. Alanio C. Scepanovic P. Hammer C. Jönsson F. et al.Milieu Intérieur ConsortiumNatural variation in the parameters of innate immune cells is preferentially driven by genetic factors.Nat. Immunol. 2018; 19: 302-314Crossref PubMed Scopus (121) Google Scholar, Roederer et al., 2015Roederer M. Quaye L. Mangino M. Beddall M.H. Mahnke Y. Chattopadhyay P. Tosi I. Napolitano L. Terranova Barberio M. Menni C. et al.The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis.Cell. 2015; 161: 387-403Abstract Full Text Full Text PDF PubMed Scopus (186) Google Scholar), but the overlap of loci reported in more than one study is still limited (Liston and Goris, 2018Liston A. Goris A. The origins of diversity in human immunity.Nat. Immunol. 2018; 19: 209-210Crossref PubMed Scopus (5) Google Scholar). Hence, we undertook a GWAS for 54 immune traits enriched for functionally relevant adaptive immune system phenotypes, including 30 T cell and 8 B cell subsets based on proliferation, differentiation, activation, or cytokine production, as well as baseline ex vivo plasma levels of ten pro- or anti-inflammatory cytokines. Our GWAS covers the genetic contributions from both common (>5%) and less common (1%–5%) variants. Genome-wide significant associations explain a median of 10% of variance in adaptive immune system variation and identify variant genes and pathways as key regulators of the adaptive immune system in humans. Coding and splicing variants in PTPRC and COMMD10 are involved in memory T cell differentiation. Genetic variation controlling T helper cell subsets with crucial roles in protection against infection and susceptibility to autoimmune disease include the second mTOR signaling complex (RICTOR) and endocytosis-related stonin 2 (STON2) associated with Th2 and Th17, respectively, and the interferon-lambda locus controlling regulatory T (Treg) cell proliferation. Early and memory B cell differentiation stages are associated with variation in the as yet poorly characterized genes LARP1B and SP4. Finally, our results implicate the latrophilin family member ADGRL2 as genetic variant for baseline pro-inflammatory cytokine production. Our data furthermore unravel the mechanism of action of established genotype-disease associations, involving key cytokines such as tumor necrosis factor alpha (TNF-α) and interleukin-2 (IL-2) in autoimmune diseases and granulocyte-macrophage colony-stimulating factor (GM-CSF) in immune-proliferative diseases. Finally, clinical implications resulting from associations in this study offer attractive therapeutic intervention points. We performed a GWAS in a study population of 502 healthy white individuals for 54 immune phenotypes. Immune phenotypes were enriched for functionally relevant adaptive immune system parameters and included 42 cellular phenotypes determined by flow cytometry and ten cytokines measured in plasma as described previously (Carr et al., 2016Carr E.J. Dooley J. Garcia-Perez J.E. Lagou V. Lee J.C. Wouters C. Meyts I. Goris A. Boeckxstaens G. Linterman M.A. Liston A. The cellular composition of the human immune system is shaped by age and cohabitation.Nat. Immunol. 2016; 17: 461-468Crossref PubMed Scopus (182) Google Scholar), as well as two DNA markers reflecting newly formed B and T cells (excision circles sjKREC [kappa-deleting recombination excision circle] and sjTREC [T cell receptor excision circle]) (van Zelm et al., 2011van Zelm M.C. van der Burg M. Langerak A.W. van Dongen J.J. PID comes full circle: applications of V(D)J recombination excision circles in research, diagnostics and newborn screening of primary immunodeficiency disorders.Front. Immunol. 2011; 2: 12Crossref PubMed Scopus (52) Google Scholar) (Table S1). We previously demonstrated stability over time for cellular immune variables in a subset of 177 individuals from this dataset who were sampled at multiple time points with an average of 6 months between samplings (Carr et al., 2016Carr E.J. Dooley J. Garcia-Perez J.E. Lagou V. Lee J.C. Wouters C. Meyts I. Goris A. Boeckxstaens G. Linterman M.A. Liston A. The cellular composition of the human immune system is shaped by age and cohabitation.Nat. Immunol. 2016; 17: 461-468Crossref PubMed Scopus (182) Google Scholar). The latest-generation imputation-based genotyping array allowed investigation of up to 10,246,977 autosomal variants with imputation accuracy (INFO) ≥ 0.4, including 6,994,434 common (minor allele frequency [MAF] >5%) and 3,252,543 less common (1 ≤ MAF ≤ 5%) variants in 489 individuals after quality control (QC) (Figures S1 and S2). We observed nominal significance for five genome-wide significant associations previously reported in the Sardinian population (Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar) (Table 1). Replication of previously known loci demonstrates the reproducibility of our dataset, despite different ethnic composition, different definitions for immunological variables, and independent generation of immune phenotyping platforms.Table 1Replication of Previously Known Genotype-Immune Phenotype AssociationsChrPosrsIDEANEAEAFBETA (SE)p ValueTraitCandidate GenesTrait (Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar)238897074rs13011383GA0.87−0.34 (0.12).0070CD4+ EMRAGALM, HNRPLLTD CD4+ %GP238921934rs7583259GC0.49−0.31 (0.09).00051CD8+ EMGALM, DHX57, HNRPLLCD45RA− CD28− CD8br %P287014377rs2944254CT0.720.25 (0.09).0073CD4+ proliferatingCD8A, RMND5A, CD8B, VPS24CD4+ CD8dim AC126899181rs2855537GT0.760.18 (0.08).023TRECCD4naive (CD4+ CD8+) AC1733797371rs9916257TG0.47−0.23 (0.06).00028NKSLFN13, SLFN12L, CCL1NK %GPWe observed nominal significance for five genome-wide significant associations previously reported in the Sardinian population (Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar). Trait names in Orrù et al. study: AC, absolute count; %GP, percentage of grandparental cells; NK, natural killer (cells); %P, percentage of parental cells; TD, terminally differentiated. BETA, effect; Chr, chromosome; EA, effect allele; EAF, effect allele frequency; NEA, non-effect allele; Pos, position in GRCh37; rsID, reference SNP identification; SE, standard error. Open table in a new tab We observed nominal significance for five genome-wide significant associations previously reported in the Sardinian population (Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar). Trait names in Orrù et al. study: AC, absolute count; %GP, percentage of grandparental cells; NK, natural killer (cells); %P, percentage of parental cells; TD, terminally differentiated. BETA, effect; Chr, chromosome; EA, effect allele; EAF, effect allele frequency; NEA, non-effect allele; Pos, position in GRCh37; rsID, reference SNP identification; SE, standard error. Subsequently, we identified eight regions reaching genome-wide significance (p < 5 × 10−8) to at least one immunological parameter (Figure 1); all of them were not previously reported. Lead variants in these regions had MAFs between 2% and 37% and explained 6.22% to 20.08% of the variance in the corresponding trait (Table 2). For all three regions where both trait and variant have appropriate equivalents in three previous GWASs (Aguirre-Gamboa et al., 2016Aguirre-Gamboa R. Joosten I. Urbano P.C.M. van der Molen R.G. van Rijssen E. van Cranenbroek B. Oosting M. Smeekens S. Jaeger M. Zorro M. et al.Differential effects of environmental and genetic factors on T and B cell immune traits.Cell Rep. 2016; 17: 2474-2487Abstract Full Text Full Text PDF PubMed Scopus (104) Google Scholar, Orrù et al., 2013Orrù V. Steri M. Sole G. Sidore C. Virdis F. Dei M. Lai S. Zoledziewska M. Busonero F. Mulas A. et al.Genetic variants regulating immune cell levels in health and disease.Cell. 2013; 155: 242-256Abstract Full Text Full Text PDF PubMed Scopus (221) Google Scholar, Roederer et al., 2015Roederer M. Quaye L. Mangino M. Beddall M.H. Mahnke Y. Chattopadhyay P. Tosi I. Napolitano L. Terranova Barberio M. Menni C. et al.The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis.Cell. 2015; 161: 387-403Abstract Full Text Full Text PDF PubMed Scopus (186) Google Scholar), our findings replicated with nominal significance (p < 0.05) or showed a trend in the same direction in publicly available data (Table S2). Of note, the PTPRC variant with an MAF 2% fell beyond the scope of common variants in previous GWASs but is identified in our study covering the entire range of common and less common variants. For the other five genome-wide significant regions, the same trait and immunological definition was not investigated in previous GWASs.Table 2Novel Genome-wide Significant Genotype-Immune Phenotype AssociationsChrPosrsIDEANEAEAFBETA (SE)% varINFOnp valueTraitGeneAnnotation182196322rs9324185CT0.370.36 (0.06)6.220.984743.51 × 10−8IL-6ADGRL2G1198665917rs17612648GC0.02−2.04 (0.26)15.700.8833626.22 × 10−15CD4+ EMPTPRCCo, Q, G, L1198665917rs17612648GC0.02−1.76 (0.27)11.840.8593628.94 × 10−11CD4+ CMPTPRCCo, Q, G, L1198830942rs113116201CT0.021.72 (0.29)12.260.9962557.58 × 10−9CD4+ EMRAPTPRCCo, Q, L1198830942rs113116201CT0.02−2.17 (0.27)20.080.9962487.52 × 10−14CD8+ EMPTPRCCo, Q, L4128924522rs373482106CCA0.230.49 (0.08)8.210.9194494.90 × 10−10KRECLARP1BQ, L, E538974929rs16867919GA0.210.43 (0.08)6.1314753.56 × 10−8Th2RICTORR, G, L5115413042rs6886944TC0.67−0.54 (0.10)11.570.9662484.83 × 10−8CD8+ CMCOMMD10Co, Q, Cs, N, L721115110rs917812GC0.22−0.46 (0.08)6.870.9674685.86 × 10−9Memory BSP4Cs, L, E1482778603rs1457990AG0.51−0.36 (0.06)6.560.9984661.91 × 10−8Th17STON2Cs, L, E1939745146rs10853728GC0.67−0.48 (0.08)9.9512942.89 × 10−8proliferating TregIFNλ clusterN, LVariants associated with p < 5 × 10−8. SNPs rs17612648 and rs113116201 at the PTPRC locus are in LD (r2 = 0.62 in EURs, r2 = 1 in CEU) and conditional analyses were not able to distinguish between them (see also Table S3). Annotation indicates whether the variant is or is in LD with (r2 > 0.8) a coding variant (Co) or known splicing or expression quantitative trait locus (Q) or is conserved (Cs), whether the variant disrupts a regulatory motif (R), whether the variant is located in the candidate gene (G) or the candidate gene is the nearest gene to the variant (N), and whether the candidate gene is supported by biological evidence in the literature (L) or by expression data obtained in this study (E). For all three regions where both trait and variant have appropriate equivalents in previous GWASs, our findings replicated with nominal significance (p < 0.05) or showed a trend in the same direction (see also Table S2). BETA, effect; Chr, chromosome; EA, effect allele; EAF, effect allele frequency; INFO, imputation quality, with 1 for directly genotyped variants; n, number of individuals with genotype and immune phenotype; NEA, non-effect allele; Pos, position in GRCh37; rsID, reference SNP identification; SE, standard error; % var, percentage of variance explained. Open table in a new tab Variants associated with p < 5 × 10−8. SNPs rs17612648 and rs113116201 at the PTPRC locus are in LD (r2 = 0.62 in EURs, r2 = 1 in CEU) and conditional analyses were not able to distinguish between them (see also Table S3). Annotation indicates whether the variant is or is in LD with (r2 > 0.8) a coding variant (Co) or known splicing or expression quantitative trait locus (Q) or is conserved (Cs), whether the variant disrupts a regulatory motif (R), whether the variant is located in the candidate gene (G) or the candidate gene is the nearest gene to the variant (N), and whether the candidate gene is supported by biological evidence in the literature (L) or by expression data obtained in this study (E). For all three regions where both trait and variant have appropriate equivalents in previous GWASs, our findings replicated with nominal significance (p < 0.05) or showed a trend in the same direction (see also Table S2). BETA, effect; Chr, chromosome; EA, effect allele; EAF, effect allele frequency; INFO, imputation quality, with 1 for directly genotyped variants; n, number of individuals with genotype and immune phenotype; NEA, non-effect allele; Pos, position in GRCh37; rsID, reference SNP identification; SE, standard error; % var, percentage of variance explained. Through a combination of bioinformatics and experimental functional analyses, the biologically most likely candidate gene stood out for all eight genome-wide significant associations. These associations highlight genes with critical roles in the adaptive immune system that have previously been demonstrated in mice but for which human data were lacking so far. Additionally, they shed light on the role of recently described but still poorly characterized protein families. Finally, they have important clinical implications. Two of the genome-wide hits correspond to or are in high linkage disequilibrium (LD) with splicing or coding variants (Figure 2). The single-nucleotide polymorphisms (SNPs) rs17612648 and rs113116201 are in LD (r2 = 0.62 in Europeans [EURs], r2 = 1 in Utah Residents [CEPH] with Northern and Western ancestry [CEU]), and conditional analyses were not able to distinguish between them (Table S3). However, rs17612648 is a synonymous variant (P59P) located in exon 4 of PTPRC, the gene encoding protein-tyrosine phosphatase receptor-type C or CD45. Its minor allele (frequency = 2%) disrupts an exonic splicing silencer and increases levels of the splice form including exon 4 (CD45RA) in T cell lines (Jacobsen et al., 2000Jacobsen M. Schweer D. Ziegler A. Gaber R. Schock S. Schwinzer R. Wonigeit K. Lindert R.B. Kantarci O. Schaefer-Klein J. et al.A point mutation in PTPRC is associated with the development of multiple sclerosis.Nat. Genet. 2000; 26: 495-499Crossref PubMed Scopus (173) Google Scholar, Lynch and Weiss, 2001Lynch K.W. Weiss A. A CD45 polymorphism associated with multiple sclerosis disrupts an exonic splicing silencer.J. Biol. Chem. 2001; 276: 24341-24347Crossref PubMed Scopus (100) Google Scholar, Schwinzer and Wonigeit, 1990Schwinzer R. Wonigeit K. Genetically determined lack of CD45R- T cells in healthy individuals. Evidence for a regulatory polymorphism of CD45R antigen expression.J. Exp. Med. 1990; 171: 1803-1808Crossref PubMed Scopus (58) Google Scholar, Zhang et al., 2015bZhang X. Joehanes R. Chen B.H. Huan T. Ying S. Munson P.J. Johnson A.D. Levy D. O’Donnell C.J. Identification of common genetic variants controlling transcript isoform variation in human whole blood.Nat. Genet. 2015; 47: 345-352Crossref PubMed Scopus (73) Google Scholar). The negative association of this allele with relative percentages of CD45RA− effector memory (EM) and central memory (CM) T cells, and positive association with CD45RA+ terminally differentiated memory (EMRA) T cells, reflects persistent isoform expression on the cell surface as a marker for these cells (Figures 2A–2H). Variant rs6886944, associated with CM CD8+ T cells, is in near-perfect LD (r2 = 0.96) with synonymous variant rs1129494 (T68T) in the fourth exon of the nearest gene, COMMD10, a member of the copper metabolism gene MURR1-domain-containing family (Figures 2I and 2J). These variants overlap with an expression quantitative trait locus (eQTL) for COMMD10 in lymphoblastoid cell lines (LCL) and monocytes (Liang et al., 2013Liang L. Morar N. Dixon A.L. Lathrop G.M. Abecasis G.R. Moffatt M.F. Cookson W.O. A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines.Genome Res. 2013; 23: 716-726Crossref PubMed Scopus (108) Google Scholar, Zeller et al., 2010Zeller T. Wild P. Szymczak S. Rotival M. Schillert A. Castagne R. Maouche S. Germain M. Lackner K. Rossmann H. et al.Genetics and beyond—the transcriptome of human monocytes and disease susceptibility.PLoS ONE. 2010; 5: e10693Crossref PubMed Scopus (483) Google Scholar) and correspond to the peak of association with exon-level expression of COMMD10 exon 4 in LCL (r2 with top-associated exon-level eQTL = 0.99) (Lappalainen et al., 2013Lappalainen T. Sammeth M. Friedländer M.R. ’t Hoen P.A. Monlong J. Rivas M.A. Gonzàlez-Porta M. Kurbatova N. Griebel T. Ferreira P.G. et al.Geuvadis ConsortiumTranscriptome and genome sequencing uncovers functional variation in humans.Nature. 2013; 501: 506-511Crossref PubMed Scopus (1212) Google Scholar). Naive CD4+ T cells can differentiate into functionally distinct subsets characterized by a unique cytokine expression pattern and a lineage-associated transcription factor network. The crucial role of these T helper subsets in infection, autoimmunity, and cancer has been demonstrated extensively. Our previous work indicates that in healthy individuals, baseline T helper differentiation is variant between individuals but stable over time (Carr et al., 2016Carr E.J. Dooley J. Garcia-Perez J.E. Lagou V. Lee J.C. Wouters C. Meyts I. Goris A. Boeckxstaens G. Linterman M.A. Liston A. The cellular composition of the human immune system is shaped by age and cohabitation.Nat. Immunol. 2016; 17: 461-468Crossref PubMed Scopus (182) Google Scholar). Our current study reports three genetic variants associated with T helper cell differentiation and activation (Figure 3). Variant rs16867919 is located in an intron of the gene encoding RICTOR, part of the second mTOR signaling complex (mTORC2). This variant is predicted to disrupt the binding site for the T cell transcription factor MEF2 (Blaeser et al., 2000Blaeser F. Ho N. Prywes R. Chatila T.A. Ca(2+)-dependent gene expression mediated by MEF2 transcription factors.J. Biol. Chem. 2000; 275: 197-209Crossref PubMed Scopus (173) Google Scholar) and was associated with Th2 frequency in humans (Figures 3A, 3D, and 3G). SNP rs1457990, associated with Th17 frequency, is located in an intergenic region on chromosome 14 but is hi" @default.
• W2898023404 created "2018-10-26" @default.
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• W2898023404 date "2018-10-01" @default.
• W2898023404 modified "2023-10-14" @default.
• W2898023404 title "Genetic Architecture of Adaptive Immune System Identifies Key Immune Regulators" @default.
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