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• W2080215939 abstract "•Resource of heritabilities and genetic associations of 80,000 immune traits in 669 twins•Genetic associations with immune cell frequencies and surface protein expression levels•Of the top 150 traits, 11 genetic loci explained up to 36% of variation of 19 traits•Loci include autoimmune susceptibility genes, providing etiological hypotheses Despite recent discoveries of genetic variants associated with autoimmunity and infection, genetic control of the human immune system during homeostasis is poorly understood. We undertook a comprehensive immunophenotyping approach, analyzing 78,000 immune traits in 669 female twins. From the top 151 heritable traits (up to 96% heritable), we used replicated GWAS to obtain 297 SNP associations at 11 genetic loci, explaining up to 36% of the variation of 19 traits. We found multiple associations with canonical traits of all major immune cell subsets and uncovered insights into genetic control for regulatory T cells. This data set also revealed traits associated with loci known to confer autoimmune susceptibility, providing mechanistic hypotheses linking immune traits with the etiology of disease. Our data establish a bioresource that links genetic control elements associated with normal immune traits to common autoimmune and infectious diseases, providing a shortcut to identifying potential mechanisms of immune-related diseases. Despite recent discoveries of genetic variants associated with autoimmunity and infection, genetic control of the human immune system during homeostasis is poorly understood. We undertook a comprehensive immunophenotyping approach, analyzing 78,000 immune traits in 669 female twins. From the top 151 heritable traits (up to 96% heritable), we used replicated GWAS to obtain 297 SNP associations at 11 genetic loci, explaining up to 36% of the variation of 19 traits. We found multiple associations with canonical traits of all major immune cell subsets and uncovered insights into genetic control for regulatory T cells. This data set also revealed traits associated with loci known to confer autoimmune susceptibility, providing mechanistic hypotheses linking immune traits with the etiology of disease. Our data establish a bioresource that links genetic control elements associated with normal immune traits to common autoimmune and infectious diseases, providing a shortcut to identifying potential mechanisms of immune-related diseases. The immune system has evolved over millions of years into a remarkable defense mechanism with rapid and specific protection of the host from major environmental threats and pathogens. Such pathogen encounters have contributed to a selection of immune genes at the population level that determine not only host-specific pathogen responses but also susceptibility to autoimmune disease and immunopathogenesis. Understanding how such genes interplay with the environment to determine immune protection and pathology is critical for unravelling the mechanisms of common autoimmune and infectious diseases and future development of vaccines and immunomodulatory therapies. Studies of rare disease established major genes, and their associated pathways, that regulate pathogen-specific immune responses (Casanova and Abel, 2004Casanova J.L. Abel L. The human model: a genetic dissection of immunity to infection in natural conditions.Nat. Rev. Immunol. 2004; 4: 55-66Crossref PubMed Scopus (230) Google Scholar) and genome-wide association studies (GWAS) of autoimmune disease have also been productive for finding common variants (Cotsapas and Hafler, 2013Cotsapas C. Hafler D.A. Immune-mediated disease genetics: the shared basis of pathogenesis.Trends Immunol. 2013; 34: 22-26Abstract Full Text Full Text PDF PubMed Scopus (77) Google Scholar, Parkes et al., 2013Parkes M. Cortes A. van Heel D.A. Brown M.A. Genetic insights into common pathways and complex relationships among immune-mediated diseases.Nat. Rev. Genet. 2013; 14: 661-673Crossref PubMed Scopus (387) Google Scholar, Raj et al., 2014Raj T. Rothamel K. Mostafavi S. Ye C. Lee M.N. Replogle J.M. Feng T. Lee M. Asinovski N. Frohlich I. et al.Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes.Science. 2014; 344: 519-523Crossref PubMed Scopus (332) Google Scholar). Despite this progress, there are still major limitations in our understanding of the genetics of complex autoimmune or infectious diseases. A key missing piece is the elucidation of the genes controlling critical components of a normal human immune system under homeostatic conditions. These include the relative frequencies of circulating immune cell subsets and the regulation of cell-surface expression of key proteins that we expect have strong regulatory mechanisms. Previous studies in humans and rodents have shown that variation in the levels of circulating blood T cells is in part heritable (Amadori et al., 1995Amadori A. Zamarchi R. De Silvestro G. Forza G. Cavatton G. Danieli G.A. Clementi M. Chieco-Bianchi L. Genetic control of the CD4/CD8 T-cell ratio in humans.Nat. Med. 1995; 1: 1279-1283Crossref PubMed Scopus (342) Google Scholar, Kraal et al., 1983Kraal G. Weissman I.L. Butcher E.C. Genetic control of T-cell subset representation in inbred mice.Immunogenetics. 1983; 18: 585-592Crossref PubMed Scopus (55) Google Scholar). Identifying the underlying genetic elements would help us understand the mechanisms of homeostasis—and its dysregulation. Twin studies are ideal to quantify the heritability of immune traits in healthy humans by allowing adjustment for the influence of genes, early environment, age, and cohort, plus a number of known and unknown confounders (van Dongen et al., 2012van Dongen J. Slagboom P.E. Draisma H.H. Martin N.G. Boomsma D.I. The continuing value of twin studies in the omics era.Nat. Rev. Genet. 2012; 13: 640-653Crossref PubMed Scopus (254) Google Scholar). Early studies from our group demonstrated genetic control of CD8 and CD4 T cell levels in twins (Ahmadi et al., 2001Ahmadi K.R. Hall M.A. Norman P. Vaughan R.W. Snieder H. Spector T.D. Lanchbury J.S. Genetic determinism in the relationship between human CD4+ and CD8+ T lymphocyte populations?.Genes Immun. 2001; 2: 381-387Crossref PubMed Scopus (22) Google Scholar), and others have shown similar heritable effects in non-twins and rodents and with broad white cell phenotypes (Amadori et al., 1995Amadori A. Zamarchi R. De Silvestro G. Forza G. Cavatton G. Danieli G.A. Clementi M. Chieco-Bianchi L. Genetic control of the CD4/CD8 T-cell ratio in humans.Nat. Med. 1995; 1: 1279-1283Crossref PubMed Scopus (342) Google Scholar, Clementi et al., 1999Clementi M. Forabosco P. Amadori A. Zamarchi R. De Silvestro G. Di Gianantonio E. Chieco-Bianchi L. Tenconi R. CD4 and CD8 T lymphocyte inheritance. Evidence for major autosomal recessive genes.Hum. Genet. 1999; 105: 337-342Crossref PubMed Scopus (26) Google Scholar, Damoiseaux et al., 1999Damoiseaux J.G. Cautain B. Bernard I. Mas M. van Breda Vriesman P.J. Druet P. Fournié G. Saoudi A. A dominant role for the thymus and MHC genes in determining the peripheral CD4/CD8 T cell ratio in the rat.J. Immunol. 1999; 163: 2983-2989PubMed Google Scholar, Evans et al., 1999Evans D.M. Frazer I.H. Martin N.G. Genetic and environmental causes of variation in basal levels of blood cells.Twin Res. 1999; 2: 250-257Crossref PubMed Scopus (151) Google Scholar, Ferreira et al., 2010Ferreira M.A. Mangino M. Brumme C.J. Zhao Z.Z. Medland S.E. Wright M.J. Nyholt D.R. Gordon S. Campbell M. McEvoy B.P. et al.International HIV Controllers StudyQuantitative trait loci for CD4:CD8 lymphocyte ratio are associated with risk of type 1 diabetes and HIV-1 immune control.Am. J. Hum. Genet. 2010; 86: 88-92Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar, Hall et al., 2000Hall M.A. Ahmadi K.R. Norman P. Snieder H. MacGregor A.J. Vaughan R.W. Spector T.D. Lanchbury J.S. Genetic influence on peripheral blood T lymphocyte levels.Genes Immun. 2000; 1: 423-427Crossref PubMed Scopus (51) Google Scholar, Kraal et al., 1983Kraal G. Weissman I.L. Butcher E.C. Genetic control of T-cell subset representation in inbred mice.Immunogenetics. 1983; 18: 585-592Crossref PubMed Scopus (55) Google Scholar, Nalls et al., 2011Nalls M.A. Couper D.J. Tanaka T. van Rooij F.J. Chen M.H. Smith A.V. Toniolo D. Zakai N.A. Yang Q. Greinacher A. et al.Multiple loci are associated with white blood cell phenotypes.PLoS Genet. 2011; 7: e1002113Crossref PubMed Scopus (88) Google Scholar, Okada et al., 2011Okada Y. Hirota T. Kamatani Y. Takahashi A. Ohmiya H. Kumasaka N. Higasa K. Yamaguchi-Kabata Y. Hosono N. Nalls M.A. et al.Identification of nine novel loci associated with white blood cell subtypes in a Japanese population.PLoS Genet. 2011; 7: e1002067Crossref PubMed Scopus (62) Google Scholar). A recent study, with a family design, was the first to perform GWAS on a larger range of immune subtypes. The authors analyzed 272 correlated immune traits derived from 95 cell types and described 23 independent genetic variants within 13 independent loci (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 (222) Google Scholar). Here, we report a comprehensive and high-resolution deep immunophenotyping flow cytometry analysis in 669 female twins using 7 distinct 14-color immunophenotyping panels that captured nearly 80,000 cell types (comprising ∼1,800 independent phenotypes) to analyze both immune cell subset frequency (CSF) and immune cell-surface protein expression levels (SPELs). This gave us a roughly 30-fold richer view of the healthy immune system than was previously achievable. Taking advantage of the twin model, we used a pre-specified analysis plan that prioritized 151 independent immune traits for genome-wide association analysis and replication. We find 241 genome-wide significant SNPs within 11 genetic loci, 9 of which are previously unreported. Importantly, they explain up to 36% of the variation of 19 immune traits (18 previously unexplored). We identify pleiotropic “master” genetic loci controlling multiple immune traits and key immune traits under tight genetic control by multiple genetic loci. In addition, we show the importance of quantifying cell-surface antigen expression rather than just cell-type frequency. Critically, we show overlap between these genetic associations of normal immune homeostasis with previously established autoimmune and infectious disease associations. This rich database provides a vital, publicly accessible bioresource as a bridge between genetic and immune discoveries that will expedite the identification of disease mechanisms in autoimmunity and infection. The discovery stage comprised 497 female participants from the UK Adult Twin Register (TwinsUK). There were 75 complete monozygotic (MZ) twin pairs, 170 dizygotic (DZ) pairs, and 7 singletons (arising from quality control [QC] failures in one co-twin). The mean age was 61.4 years (range: 40–77). The replication stage comprised a further 172 participants, mean age 58.2 years (range: 32–83), with 46 MZ, 118 DZ, and 8 singletons. We stained cryopreserved peripheral blood mononuclear cells (PBMC) from each, using a set of 7 14-color immunophenotyping panels that delineate a large range of immune subsets (Figures 1A, S1, S2, and S3 and Table S1). Immune traits analyzed included the CSF (i.e., the proportionate representation of a given phenotype) and the SPEL (i.e., a quantitative measure of gene expression on a per-cell basis). The variability of all traits was assessed using longitudinal sampling on a small cohort of individuals as described in the Experimental Procedures; of the 50,000 traits meeting the first filter criterion (Figure S4), the mean covariance across samples drawn 6 months apart is 0.86. All trait values and summary analyses, including variability, are available for download. Data and statistical analysis of the discovery stage was completed per a pre-defined statistical analysis plan before samples from the replication stage were thawed.Figure S1Fluorescence Distribution of All Immunophenotyping Panels for All Subjects, Related to Experimental ProceduresShow full captionFor each of the seven panels, 1000 events from each data file for each of the 543 samples analyzed for the discovery cohort were merged into a single file. Each of the fluorescent markers used in the panel (abscissa) are shown in separate graphics, plotted against all subjects (ordinate). Clusters of rows constitute a single run encompassing 10 to 30 separate subjects. After three runs, the panels were modified slightly to incorporate a different CD4 reagent after 3 runs, and a different “dump” reagents after 7 runs, but otherwise were identical across the entire cohort.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure S2Gating Hierarchy to Identify Subsets within PBMC for Immunophenotyping Panels 1 through 4, Related to Experimental ProceduresShow full captionAs shown in the very top row, all data files were first preprocessed to include only singlet events as well as live cells based on aqua blue staining. The remaining gating hierarchy for each of the panels is shown. The top line(s) of graphs show the gates used to identify “lineages” or “differentiation stages.” Within each lineage/stage, all combinations of the gates shown on the bottom line were applied in a Boolean fashion (e.g., in panel one, there are eight separate gates to identify the expression pattern of eight different markers; the complete combination results in 256 gates applied to each of the four “lineages”). During post-processing analysis, combinatorial analysis on these 256 Boolean combinations was done to create all 38 possible combinations of gates for each marker (i.e., positive, negative, or ignored: “tri-boolean” combination gates). Canonical names of “lineages” and “differentiation stages” are provided as a rough guide for the cell populations and are not meant to be definitive.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure S3Gating Hierarchy to Identify Subsets within PBMC for Immunophenotyping Panels 5 through 7, Related to Experimental ProceduresShow full captionSimilar to Figure S2, cells were divided into subsets first by “lineage” gating as shown by the arrows, and then, as indicated, the combinatorial boolean gating was applied to each of these. In panel six, mature memory B cells that are negative for IgM, IgA, and IgG are likely to be reasonably pure populations of IgE expressing cells and are denoted as “(IgE+).” Canonical names of “lineages” and “differentiation stages” are provided as a rough guide for the cell populations and are not meant to be definitive.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Figure S4Summary of the Immunophenotyping Traits Analyzed, Related to Figure 1Show full caption(A) Of the nearly 80,000 traits (frequencies and fluorescence intensities), more than one third were eliminated from analyses as they represented subset frequencies that were too low for robust analysis (less than 0.1% of the parental “lineage” gate). For our initial GWAS analysis reported in this manuscript, we chose about 200 traits for analysis. 97 represent “canonical” populations defined in the literature (such as differentiation or activation stages); 100 were chosen from the most heritable.(B) Of the nearly 50,000 traits, many are highly inter-correlated because they represent strongly overlapping populations of cells. To estimate degrees of freedom in the dataset, we computed how many traits remain after eliminating those that covary with another trait with a variance greater than a certain threshold. We cross-correlated all traits against all traits, and progressively eliminated those with the highest covariance. For example, eliminating traits that correlate with a variance greater than 0.9 reduces the number of independent traits from 50,000 to just over 5,000. There were nearly 1,800 traits with an intra-correlation less than 0.7.(C) Falconer’s formula was applied to the intraclass correlations on MZ and DZ twin pairs to estimate the covariation due to common environmental factors (upper) as well as genetic heritability (lower) for the 1,775 traits with a variance less than 0.7. Of these, 9% showed estimated heritability of greater than 80%.View Large Image Figure ViewerDownload Hi-res image Download (PPT) For each of the seven panels, 1000 events from each data file for each of the 543 samples analyzed for the discovery cohort were merged into a single file. Each of the fluorescent markers used in the panel (abscissa) are shown in separate graphics, plotted against all subjects (ordinate). Clusters of rows constitute a single run encompassing 10 to 30 separate subjects. After three runs, the panels were modified slightly to incorporate a different CD4 reagent after 3 runs, and a different “dump” reagents after 7 runs, but otherwise were identical across the entire cohort. As shown in the very top row, all data files were first preprocessed to include only singlet events as well as live cells based on aqua blue staining. The remaining gating hierarchy for each of the panels is shown. The top line(s) of graphs show the gates used to identify “lineages” or “differentiation stages.” Within each lineage/stage, all combinations of the gates shown on the bottom line were applied in a Boolean fashion (e.g., in panel one, there are eight separate gates to identify the expression pattern of eight different markers; the complete combination results in 256 gates applied to each of the four “lineages”). During post-processing analysis, combinatorial analysis on these 256 Boolean combinations was done to create all 38 possible combinations of gates for each marker (i.e., positive, negative, or ignored: “tri-boolean” combination gates). Canonical names of “lineages” and “differentiation stages” are provided as a rough guide for the cell populations and are not meant to be definitive. Similar to Figure S2, cells were divided into subsets first by “lineage” gating as shown by the arrows, and then, as indicated, the combinatorial boolean gating was applied to each of these. In panel six, mature memory B cells that are negative for IgM, IgA, and IgG are likely to be reasonably pure populations of IgE expressing cells and are denoted as “(IgE+).” Canonical names of “lineages” and “differentiation stages” are provided as a rough guide for the cell populations and are not meant to be definitive. (A) Of the nearly 80,000 traits (frequencies and fluorescence intensities), more than one third were eliminated from analyses as they represented subset frequencies that were too low for robust analysis (less than 0.1% of the parental “lineage” gate). For our initial GWAS analysis reported in this manuscript, we chose about 200 traits for analysis. 97 represent “canonical” populations defined in the literature (such as differentiation or activation stages); 100 were chosen from the most heritable. (B) Of the nearly 50,000 traits, many are highly inter-correlated because they represent strongly overlapping populations of cells. To estimate degrees of freedom in the dataset, we computed how many traits remain after eliminating those that covary with another trait with a variance greater than a certain threshold. We cross-correlated all traits against all traits, and progressively eliminated those with the highest covariance. For example, eliminating traits that correlate with a variance greater than 0.9 reduces the number of independent traits from 50,000 to just over 5,000. There were nearly 1,800 traits with an intra-correlation less than 0.7. (C) Falconer’s formula was applied to the intraclass correlations on MZ and DZ twin pairs to estimate the covariation due to common environmental factors (upper) as well as genetic heritability (lower) for the 1,775 traits with a variance less than 0.7. Of these, 9% showed estimated heritability of greater than 80%. GWAS analysis of all 78,000 immune traits is computationally prohibitive and would require a multiple comparisons correction that dramatically reduces sensitivity. The ability to infer heritability (proportion of variance explained solely by genetic factors) by the use of twins dramatically enhanced our ability to focus on those that are most likely to be informative. Co-variation of all traits was computed; about 1,800 were independent at r < 0.7 (Figure S4). We found no significant association of the analyzed traits with self-reported tobacco use or alcohol consumption and so did not include those behaviors as covariates. We identified many traits associated with age and included age as a covariate in all analyses. Notably, an advantage of using a twin-based cohort is to render age and other cohort effects minimally impactful. The age range of our cohort was optimal for our goal of identifying immune traits associated with genetic elements that show a risk for autoimmune diseases. Because incidence for such diseases often increases with age, the greatest power for such correlations will be obtained using samples measurements most proximal to the common onset of disease. Falconer’s traditional formula (twice the difference in intraclass correlations) was used to roughly estimate the heritabilities of all 78,000 immune traits; after ranking, traits were selected for further pre-specified analyses (Figure S4). Variance components analysis (additive genetics, common environment, and unique environment, or ACE model) was used to more precisely estimate heritabilities of chosen traits. The heritabilities ranged widely from 0%—suggesting purely environmental or stochastic influences—to 96% (e.g., CD32 expression on dendritic cells), indicating a strong genetic effect. Figure S5 shows the range of heritabilities for selected traits, and the components of the model are tabulated in Table S2 with full trait descriptions. Single-variant associations were performed on 151 immune traits selected for high heritability or biological interest, comprising cell frequency (129 CSFs) and cell-surface protein expression (22 SPELs). Many significant associations were found despite the stringent Bonferroni multiple testing threshold of p < 3.3 × 10−10. We also performed a conditional analysis, including the top SNP of each locus as a covariate, to identify potential independent secondary signals. This analysis did not reveal any significant evidence for additional independent signals. Six SPELs were significant (Table 1), with the strongest between MFI:516 (CD39 SPEL on CD4 T cells) and the ENTPD1 (CD39 gene) SNP rs7096317 (p = 9.4 × 10−40). Many other variants of ENTPD1 were also associated with this trait (Table S3). Expression of five others (MFI:189, MFI:212, MFI:231, MFI:504, and MFI:552, which include CD27 expression on B and T cell subsets, and CD161 expression on CD4 T cells) were associated with variants on chromosome 1q23 in a genetic region containing the important immune-regulating genes FCGR2A, FCGR2B, and FCRLA (Table 1). These associations were independently verified in the replication cohort, and the combined discovery and replication set p values of the 6 SPELs ranged from 2.8 × 10−11 to 1.6 × 10−54 (Table 1 and Figure 1B). Table S3 illustrates other examples of genetic control of cell-surface expression, including the expression of CD11c, CD123, and CD274 on myeloid subsets.Table 1Discovery and Replication Results for the Top Significant SNPs at Each Locus for Each Immune TraitLocus:GenesTrait IDTrait PhenotypeMarkerChrEA/NEAEAFBeta (SE)p ValueBeta (SE)p ValueBeta (SE)p Value1: FCGR2A, FCGR2B, FCRLAMFI:189CD27 on IgA+ Brs18012741A/G0.490.128 (0.02)6.48E−110.07 (0.03)3.70E−020.11 (0.02)2.8E−111: FCGR2A, FCGR2B, FCRLAMFI:212CD27 on IgG+ Brs18012741A/G0.490.136 (0.02)5.38E−120.12 (0.03)1.11E−040.13 (0.02)2.9E−151: FCGR2A, FCGR2B, FCRLAMFI:231CD161 on CD4 Trs18012741A/G0.490.131 (0.02)2.64E−110.12 (0.03)2.17E−040.13 (0.02)2.7E−141: FCGR2A, FCGR2B, FCRLAMFI:504CD27 on CD4 Trs18012741A/G0.490.145 (0.02)5.42E−140.14 (0.03)4.20E−050.14 (0.02)1.1E−171: FCGR2A, FCGR2B, FCRLAMFI:552CD27 on CD8 Trs18012741A/G0.490.186 (0.02)1.26E−210.12 (0.03)1.72E−040.17 (0.02)4.2E−241: FCGR2A, FCGR2B, FCRLAP7:110iMDC: %CD32+rs104943591C/G0.120.343 (0.03)2.52E−290.43 (0.05)1.05E−150.36 (0.03)5.9E−431: FCGR2A, FCGR2B, FCRLAP7:224CD1c+ mDC: %CD32rs46570901A/G0.27−0.174 (0.02)1.30E−14−0.19 (0.04)3.86E−06−0.18 (0.02)2.7E−192: NFIAP4:3551NK: %CD314−CD158a+rs120723791G/C0.16−0.131 (0.02)1.73E−10−0.10 (0.05)4.87E−02−0.13 (0.02)2.7E−113: NRXN1P4:3551NK: %CD314−CD158a+rs170409072T/C0.07−0.208 (0.03)2.68E−10−0.16 (0.08)4.22E−02−0.02 (0.03)3.9E−114: PRKCIP4:3551NK: %CD314−CD158a+rs26502203G/A0.16−0.15 (0.02)3.18E−10−0.10 (0.05)4.55E−02−0.14 (0.02)6.0E−115: NT5EP2:4195CD4 T: %CD39-CD73+rs94443466G/A0.19−0.2 (0.03)1.18E−14−0.12 (0.04)4.98E−03−0.18 (0.02)8.8E−16RP11-30P6P2:4204CD4 T: %CD73+rs94443466G/A0.19−0.195 (0.03)5.85E−14−0.12 (0.04)2.82E−03−0.18 (0.02)1.8E−156: SLC18A1P4:3551NK: %CD314−CD158a+rs13909428T/C0.15−0.163 (0.02)1.39E−15−0.20 (0.05)1.70E−04−0.17 (0.02)1.4E−187: SLC25A16P4:3551NK: %CD314−CD158a+rs301707210T/C0.15−0.153 (0.02)2.75E−13−0.15 (0.05)2.08E−03−0.15 (0.02)2.2E−158: FAS, ACTA2P1:6601CD8 T: %TSCMrs709757210C/T0.48−0.168 (0.02)8.51E−16−0.18 (0.03)2.72E−06−0.17 (0.02)1.3E−209: ALDH18A1, ENTPD1, ENTPD1-AS1, RP11-7D5, SORBS1, TCTN3MFI:516CD39 on CD4 Trs709631710G/A0.42−0.255 (0.02)9.40E−40−0.30 (0.04)9.92E−17−0.27 (0.02)1.6E−549: ALDH18A1, ENTPD1, ENTPD1-AS1, RP11-7D5, SORBS1, TCTN3P2:10491CD8 T: %CD39+rs407442410G/C0.42−0.219 (0.02)4.11E−27−0.19 (0.04)2.40E−07−0.21 (0.02)8.2E−339: ALDH18A1, ENTPD1, ENTPD1-AS1, RP11-7D5, SORBS1, TCTN3P2:3460CD4 T:%CD39+CD38+PD1−rs458290210C/T0.47−0.164 (0.02)4.55E−16−0.19 (0.03)2.58E−08−0.17 (0.02)9.2E−239: ALDH18A1, ENTPD1, ENTPD1-AS1, RP11-7D5, SORBS1, TCTN3P2:4159CD4 T:%CD39+CD73−rs658402710G/A0.47−0.212 (0.02)1.54E−25−0.20 (0.04)1.09E−08−0.21 (0.02)1.1E−329: ALDH18A1, ENTPD1, ENTPD1-AS1, RP11-7D5, SORBS1, TCTN3P2:4186CD4 T:%CD39+rs658402710G/A0.47−0.215 (0.02)2.20E−26−0.21 (0.04)5.27E−09−0.21 (0.02)7.8E−349: ALDH18A1, ENTPD1, ENTPD1-AS1, RP11-7D5, SORBS1, TCTN3P2:4213CD4 T:%CD39+CD73+rs1088267610A/C0.47−0.195 (0.02)6.76E−22−0.19 (0.04)2.19E−07−0.19 (0.02)9.0E−2810: KLRC1, KLRC2, KLRC4, KLRK1, RP11-277P12P4:3551NK: %CD314−CD158a+rs273456512C/T0.3−0.144 (0.02)1.34E−10−0.18 (0.04)1.67E−05−0.15 (0.02)1.4E−1410: KLRC1, KLRC2, KLRC4, KLRK1, RP11-277P12P4:4832NK: %CD314−CCR7−rs273456512C/T0.3−0.233 (0.02)1.27E−24−0.33 (0.04)3.95E−15−0.26 (0.02)2.7E−3710: KLRC1, KLRC2, KLRC4, KLRK1, RP11-277P12P4:5538NK: %CD314−CD335+rs273456512C/T0.3−0.275 (0.02)3.40E−34−0.38 (0.04)2.27E−19−0.30 (0.02)6.4E−5111: FTOP4:3551NK: %CD314−CD158a+rs142031816A/G0.1−0.146 (0.02)9.34E−11−0.19 (0.06)4.05E−02−0.14 (0.02)1.2E−11For the Discovery stage, we used a significance threshold of p < 3.3 × 10−10. This threshold corresponds to the standard genome-wide threshold of 5 × 10−8 after further adjustment for 151 independent tests. 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 (222) Google Scholar also identified Locus 1 (associated with a single trait, CD62L– dendritic cells, not measured in our panel), and Locus 9 (associated with a single trait: CD39+ CD4 T cell frequency, P2:4186 in our list). The trait ID is fully described in Table S2. Open table in a new tab For the Discovery stage, we used a significance threshold of p < 3.3 × 10−10. This threshold corresponds to the standard genome-wide threshold of 5 × 10−8 after further adjustment for 151 independent tests. 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 (222) Google Scholar also identified Locus 1 (associated with a single trait, CD62L– dendritic cells, not measured in our panel), and Locus 9 (associated with a single trait: CD39+ CD4 T cell frequency, P2:4186 in our list). The trait ID is fully described in Table S2. Overall, 241 SNP variants with a minor allele frequency above 5% were significantly associated with various SPELs (Table S3); of these, 35 SNPs were pleiotropically associated with multiple SPELs. Genetic control of SPEL may simply be due to promoter/enhancer element variants or more complex regulation of transcription, translation, or protein localization. In contrast, genetic control of CSF may reveal homeostatic mechanisms regulating cell subset representation in the blood. Genome-wide significant associations were identified with 13 different CSFs (Figure 1C and Table 1). Nearly all were verified in the replication cohort (Table 1), and some reached a p value of 10−43. Suggestive associations, wh" @default.
• W2080215939 created "2016-06-24" @default.
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• W2080215939 title "The Genetic Architecture of the Human Immune System: A Bioresource for Autoimmunity and Disease Pathogenesis" @default.
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