Background : Allergic rhinitis (AR) is strongly associated with a type 2 response, characterized by the cytokines IL-5, IL-4 and IL-13. Several studies have implicated ILC2 and TH2A (CD161+ TH2) but it is not yet entirely clear which subsets are driving the common allergic reactions underlying AR. The objective of this study aims to identify critical pathogenic cell populations associated with AR and to determine their phenotype and functional contribution to disease progression. Methods : We identified integral allergic-specific cell types by transcriptomic sequencing. Whole blood, PBMCs and plasma from a cross-sectional cohort of 216 individuals were analysed by 9-colour flow cytometry and ultra-sensitive cytokine bead arrays using unsupervised clustering algorithms. Clinically active AR cases were further analysed by functional mass cytometry to define phenotype and cytokine secretion (IL-2, IL-3, IL-4, IL-5, IL-9, IL13, IL-17A and IL-22) as well as the expression of the hematopoietic prostaglandin D synthetase (HPGDS). Results : The unbiased analysis revealed that atopy and AR manifestation corelated only with eosinophils, plasma IL-5 and CD161+ TH2 cells. In-depth characterization further revealed that the CD45RB CD161+ TH2 subset were most closely associated with severity. This subset is able to concomitantly secrete multiple cytokines including IL-5, IL-13 and IL-4 and has been previously reported to be associated with other eosinophilic allergies. Conclusion : CD45RB CD161+ TH2 have key roles in driving the allergic response in AR. Neutralizing the CD45RB CD161+ TH2 subset should disrupt the allergic response pathway, thus providing a target for lasting therapeutic interventions.
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Table S1. Figure S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Summary While many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear. To identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium. We observed that cis -eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis -eQTLs to pinpoint causal genes within susceptibility loci. In contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology. We observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.
The Toll-like receptor proteins are important in host defense and initiation of the innate and adaptive immune responses. A number of studies have identified associations between genetic variation in the Toll-like receptor genes and allergic disorders such as asthma and allergic rhinitis. The present study aim to search for genetic variation associated with allergic rhinitis in the Toll-like receptor genes.A first association analysis genotyped 73 SNPs in 182 cases and 378 controls from a Swedish population. Based on these results an additional 24 SNPs were analyzed in one Swedish population with 352 cases and 709 controls and one Chinese population with 948 cases and 580 controls.The first association analysis identified 4 allergic rhinitis-associated SNPs in the TLR7-TLR8 gene region. Subsequent analysis of 24 SNPs from this region identified 7 and 5 significant SNPs from the Swedish and Chinese populations, respectively. The corresponding risk-associated haplotypes are significant after Bonferroni correction and are the most common haplotypes in both populations. The associations are primarily detected in females in the Swedish population, whereas it is seen in males in the Chinese population. Further independent support for the involvement of this region in allergic rhinitis was obtained from quantitative skin prick test data generated in both populations.Haplotypes in the TLR7-TLR8 gene region were associated with allergic rhinitis in one Swedish and one Chinese population. Since this region has earlier been associated with asthma and allergic rhinitis in a Danish linkage study this speaks strongly in favour of this region being truly involved in the development of this disease.
The novel coronavirus SARS-CoV-2 is the causative agent of Coronavirus Disease 2019 (COVID-19), a global healthcare and economic catastrophe. Understanding of the host immune response to SARS-CoV-2 is still in its infancy. A 382-nt deletion strain lacking ORF8 (Δ382 herein) was isolated in Singapore in March 2020. Infection with Δ382 was associated with less severe disease in patients, compared to infection with wild-type SARS-CoV-2. Here, we established Nasal Epithelial cells (NECs) differentiated from healthy nasal-tissue derived stem cells as a suitable model for the ex-vivo study of SARS-CoV-2 mediated pathogenesis. Infection of NECs with either SARS-CoV-2 or Δ382 resulted in virus particles released exclusively from the apical side, with similar replication kinetics. Screening of a panel of 49 cytokines for basolateral secretion from infected NECs identified CXCL10 as the only cytokine significantly induced upon infection, at comparable levels in both wild-type and Δ382 infected cells. Transcriptome analysis revealed the temporal up-regulation of distinct gene subsets during infection, with anti-viral signaling pathways only detected at late time-points (72 hours post-infection, hpi). This immune response to SARS-CoV-2 was significantly attenuated when compared to infection with an influenza strain, H3N2, which elicited an inflammatory response within 8 hpi, and a greater magnitude of anti-viral gene up-regulation at late time-points. Remarkably, Δ382 induced a host transcriptional response nearly identical to that of wild-type SARS-CoV-2 at every post-infection time-point examined. In accordance with previous results, Δ382 infected cells showed an absence of transcripts mapping to ORF8, and conserved expression of other SARS-CoV-2 genes. Our findings shed light on the airway epithelial response to SARS-CoV-2 infection, and demonstrate a non-essential role for ORF8 in modulating host gene expression and cytokine production from infected cells.
Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer – but not other cancers – explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter). https://doi.org/10.7554/eLife.39856.001 eLife digest Ageing happens to us all, and as the cabaret singer Maurice Chevalier pointed out, "old age is not that bad when you consider the alternative". Yet, the growing ageing population of most developed countries presents challenges to healthcare systems and government finances. For many older people, long periods of ill health are part of the end of life, and so a better understanding of ageing could offer the opportunity to prolong healthy living into old age. Ageing is complex and takes a long time to study – a lifetime in fact. This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment. While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle. Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average. The DNA of over 500,000 people was read to reveal the specific 'genetic fingerprints' of each participant. Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan. Five of these regions were new and had not been linked to lifespan before. Across the twelve as a whole several were known to be involved in Alzheimer's disease, smoking-related cancer or heart disease. Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average. Many factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone. Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease. https://doi.org/10.7554/eLife.39856.002 Introduction Human lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance. The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998; Herskind et al., 1996; McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018). One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin. Despite this modest heritability, extensive research has gone into genome-wide association studies (GWAS) finding genetic variants influencing human survival, using a variety of trait definitions and study designs (Deelen et al., 2011; Sebastiani et al., 2012; Beekman et al., 2013; Broer et al., 2015; Joshi et al., 2016; Pilling et al., 2016; Zeng et al., 2016; Pilling et al., 2017). GWAS have primarily focused on extreme cases of long-livedness (longevity) – individuals surviving past a certain age threshold – and scanning for differences in genetic variation from controls. While this case-control design has the advantage of focusing on highly statistically-informative individuals, who also often exhibit extreme healthspan and have potentially unique genetic attributes (Sebastiani et al., 2013; Sebastiani et al., 2016), the exceptional nature of the phenotype precludes collection of large samples, and differences in definitions of longevity complicate meta-analysis. As a result, only two robustly replicated, genome-wide significant associations (near APOE and FOXO3) have been made to date (Broer et al., 2015; Deelen et al., 2014). An alternative approach is to study lifespan as a quantitative trait in the general population and use survival models (such as Cox proportional hazards [Cox, 1972]) to allow long-lived survivors to inform analysis. However, given the incidence of mortality in middle-aged subjects is low, studies have shifted to the use of parental lifespans with subject genotypes (an instance of Wacholder's kin-cohort method [Wacholder et al., 1998]), circumventing the long wait associated with studying age at death in a prospective study (Joshi et al., 2016; Pilling et al., 2016). In addition, the recent increase in genotyped population cohorts around the world, and in particular the creation of UK Biobank (Bycroft et al., 2017), has raised GWAS sample sizes to hundreds of thousands of individuals, providing the statistical power necessary to detect genetic effects on mortality. A third approach is to gather previously published GWAS on risk factors thought to possibly affect lifespan, such as smoking behaviour and cardiovascular disease (CVD), and estimate their actual independent, causal effects on mortality using Mendelian Randomisation. These causal estimates can then be used in a Bayesian framework to inform previously observed SNP associations with lifespan (McDaid et al., 2017). Here, we blend these three approaches to studying lifespan and perform the largest GWAS on human lifespan to date. First, we leverage data from UK Biobank and 26 independent European-heritage population cohorts (Joshi et al., 2017) to carry out a GWAS of parental survival, quantified using Cox models. We then supplement this with data from 58 GWAS on mortality risk factors to conduct a Bayesian prior-informed GWAS (iGWAS). Finally, we use publicly available case-control longevity GWAS statistics to compare the genetics of lifespan and longevity and provide collective replication of our lifespan GWAS results. We also examine the diseases associated with lifespan-altering variants and the effect of known disease variants on lifespan, to provide insight into the interplay between lifespan and disease. Finally, we use our GWAS results to implicate specific genes, biological pathways, and cell types, and use our findings to create and test whole-genome polygenic scores for survival. Results Genome-wide association analysis We carried out GWAS of survival in a sample of 1,012,240 parents (60% deceased) of European ancestry from UK Biobank and a previously published meta-analysis of 26 additional population cohorts (LifeGen [Joshi et al., 2017]; Table 1—source data 1). We performed a sex-stratified analysis and then combined the allelic effects in fathers and mothers into a single parental survival association in two ways. First, we assumed genetic variants with common effect sizes (CES) for both parents, maximising power if the effect is indeed the same. Second, we allowed for sex-specific effect sizes (SSE), maximising power to detect sexually dimorphic variants, including those only affecting one sex. The latter encompasses a conventional sex-stratified analysis, but uses only one statistical test for the much more general alternative hypothesis that there is an effect in at least one sex. Table 1 Twelve genome-wide significant associations with lifespan using UK Biobank and LifeGen. Parental phenotypes from UK Biobank and LifeGen meta-analysis, described in Table 1—source data 1, were tested for association with subject genotype. See Table 1—source data 2 for LD Score regression intercept of each cohort separately and combined. Displayed here are loci associating with lifespan at genome-wide significance (p < 2.5 × 10−8). At or near – Gene, set of genes, or cytogenetic band nearest to the index SNP; rsID – The index SNP with the lowest P value in the standard or sex-specific effect (SSE) analysis. Chr – Chromosome; Position – Base-pair position on chromosome (GRCh37); A1 – the effect allele, increasing lifespan; Freq1 – Frequency of the A1 allele; Years1 – Years of life gained for carrying one copy of the A1 allele; SE – Standard Error; P – the P value for the Wald test of association between imputed dosage and cox model residual; Disease – Category of disease for known associations with SNP or close proxies (r2 > 0.6), see Table 1—source data 3 for details and references. Despite the well-known function of the HTT gene in Huntington's disease, SNPs within the identified locus near this gene have not been associated with the disease at genome-wide significance. https://doi.org/10.7554/eLife.39856.003 At or nearrsIDChrPositionA1Freq1Years1SEPSSE PDiseaseMAGI3rs12306661114173410G0.850.32240.05556.4E-096.1E-08AutoimmuneKCNK3rs1275922226932887G0.740.25790.04436.0E-092.7E-07CardiometabolicHTTrs6134820843089564T0.390.22990.03955.8E-091.2E-07-HLA-DQA1rs34967069632591248T0.070.56130.09564.3E-093.6E-09AutoimmuneLPArs104558726161010118A0.920.76390.07438.5E-253.1E-24CardiometabolicCDKN2B-AS1rs1556516922100176G0.500.25100.03867.5E-116.4E-12CardiometabolicATXN2/BRAPrs1106597912112059557C0.560.27980.03931.0E-126.2E-13Autoimmune/ CardiometabolicCHRNA3/5rs80428491578817929T0.650.43680.04101.6E-261.9E-30Smoking-relatedFURIN/FESrs62241591423543G0.520.25070.03901.3E-101.8E-09CardiometabolicHPrs129248861672075593A0.800.27980.04931.4E-089.1E-08CardiometabolicLDLRrs1421589111911190534A0.120.35500.06168.1E-093.3E-08CardiometabolicAPOErs4293581945411941T0.851.05610.05463.1E-831.8E-85Cardiometabolic/ Neuropsychiatric Table 1—source data 1 Descriptive statistics of the cohorts and lives analysed. Summary statistics for the 1,012,240 parental lifespans passing phenotypic QC (most notably, parent age > 40). In practice, fewer lives than these were analysed for some SNPs, as a SNP may not have passed QC in all cohorts (in particular LifeGen MAF > 1%). Ancestries in UK Biobank are self-declared, except in the case of Gen. British. Gen. British – Participants identified as genomically British by UK Biobank, based on their genomic profile. LifeGen – A consortium of 26 population cohorts of European Ancestry, with UK Biobank lives removed. https://doi.org/10.7554/eLife.39856.004 Download elife-39856-table1-data1-v2.xlsx Table 1—source data 2 LD-score regression intercepts for GWAS results. Regression intercepts (standard error) of the GWAS summary statistics as calculated by LD-score regression, using LD scores from on average 457,407 SNPs from the UK Biobank array. CES – Results under the assumption of common effect sizes across sexes, SSE – Results allowing for sex-specific effects. https://doi.org/10.7554/eLife.39856.005 Download elife-39856-table1-data2-v2.xlsx Table 1—source data 3 Known associations with genome-wide significant lifespan loci. Genome-wide significant associations from the GWAS catalog and PhenoScanner are reported for the lead SNP and proxies (r2 > 0.6). Similar associations have been grouped, keeping the most significant association and the shortest trait name (Trait). At or near – Gene or cluster of genes in close proximity to lead SNP; A1 – the effect allele, increasing lifespan; A0 – the reference allele. Freq1- Frequency of the A1 allele in the original study, or if missing, averaged from all associations; Beta1 – the reported effect on the trait for carrying one copy of the A1 allele; SE – Standard Error; P – P value; Disease – the type of lifespan-shortening diseases linked to the trait, or 'other' if the link is unclear or multiple disease links exist. https://doi.org/10.7554/eLife.39856.006 Download elife-39856-table1-data3-v2.xlsx We find 12 genomic regions with SNPs passing genome-wide significance for one or both analyses (p < 2.5 × 10–8, accounting for the two tests CES/SSE) (Figure 1; Table 1). Among these are five loci discovered here for the first time, at or near MAGI3, KCNK3, HTT, HP, and LDLR. Carrying one copy of a life-extending allele is associated with an increase in lifespan between 0.23 and 1.07 years (around 3 to 13 months). Despite our sample size exceeding 1 million phenotypes, a variant had to have a minor allele frequency exceeding 5% and an effect size of 0.35 years of life or more per allele for our study to detect it with 80% power. Figure 1 Download asset Open asset SNP associations with lifespan across both parents under the assumption of common and sex-specific effect sizes. Miami plot of genetic associations with joint parental survival. In purple are the associations under the assumption of common SNP effect sizes across sexes (CES); in green are the associations under the assumption of sex-specific effect sizes (SSE). P refers to the two-sided P values for association of allelic dosage on survival under the residualised Cox model. The red line represents our multiple testing-adjusted genome-wide significance threshold (p = 2.5 × 10−8). Annotated are the gene, set of genes, or cytogenetic band near the index SNP, marked in red. P values have been capped at –log10(p) = 15 to better visualise associations close to genome-wide significance. SNPs with P values beyond this cap (near APOE, CHRNA3/5 and LPA) are represented by triangles. https://doi.org/10.7554/eLife.39856.007 We also attempted to validate novel lifespan SNPs discovered by Pilling et al. (2017) in UK Biobank at an individual level by using the LifeGen meta-analysis as independent replication sample. Testing 20 candidate SNPs for which we had data available, we find directionally consistent, nominally significant associations for six loci (p < 0.05, one-sided test), of which three have sex-specific effects. We also provide evidence against three putative loci but lack statistical power to assess the remaining 11 (Figure 2, Figure 2—source data 1). Figure 2 with 1 supplement see all Download asset Open asset Validation of SNPs identified in other studies using independent samples of European descent. Discovery – Candidate SNPs or proxies (r2 > 0.95) associated with lifespan (top panels, stratified by sex) and longevity (bottom panel) by previous studies (Zeng et al., 2016; Pilling et al., 2017; Deelen et al., 2014; Flachsbart et al., 2009; Sebastiani et al., 2017; Ben-Avraham et al., 2017). Effect sizes have been rescaled to years of life to make direct comparisons between studies (see Materials and methods and Figure 2—figure supplement 1). Replication – Independent samples, either the LifeGen meta-analysis to replicate Pilling et al. (2017), or the full dataset including UK Biobank. Gene names are as reported by discovery and have been coloured based on overlap between confidence intervals (CIs) of effect estimates. Dark blue – Nominal replication (p < 0.05, one-sided test). Light blue – CIs overlap (Phet > 0.05) and cover zero, but replication estimate is closer to discovery than zero. Yellow – CIs overlap (Phet > 0.05) and cover zero, and replication estimate is closer to zero than discovery. Red – CIs do not overlap (Phet < 0.05) and replication estimate covers zero. Black – no replication data. https://doi.org/10.7554/eLife.39856.008 Figure 2—source data 1 Eight candidate lifespan regions replicate nominally (p < 0.05) in LifeGen or our full sample. Listed are SNPs or close proxies (r2 > 0.95), which have been previously reported to associate with lifespan or extreme longevity. At or near – Gene, cluster of genes, or cytogenetic band in close proximity to lead SNP; Chr – Chromosome, Position – Base-pair position on chromosome (build GRCh37); A1 – the effect allele, increasing lifespan in discovery; Freq1- Frequency of the A1 allele in the replication sample, or if missing, the discovery sample; Sex – sex of the individuals or their parents used in the discovery and replication; Beta1 – the loge(protection ratio) for carrying one copy of A1 under additive dosage model, inferred for discovery (see Materials and methods); SE – Standard Error, calculated from reported P value and inferred effect estimates for discovery, assuming a two-sided test; Years – Years of lifespan gained for carrying one copy of the A1 allele; P – P value reported by original study for discovery, one-sided P value for the Wald test association between imputed dosage and cox model residual for the replication. For discovery, except Pilling et al's SNPs (Pilling et al., 2017), where we re-calculated effects directly from individual UKBB data ourselves, effects sizes have been converted to a common scale to enable comparison. Study – original study that identified the candidate SNP; Sample – independent sample used to replicate the results (a = Full dataset, b = LifeGen excluding UK Biobank). Loci showing nominal replication (p < 0.05) are bolded. https://doi.org/10.7554/eLife.39856.010 Download elife-39856-fig2-data1-v2.xlsx We then used our full sample to test six candidate SNPs previously associated with longevity (Zeng et al., 2016; Deelen et al., 2014; Flachsbart et al., 2009; Sebastiani et al., 2017) for association with lifespan, and find directionally consistent evidence for SNPs near FOXO3 and EBF1. The remaining SNPs did not associate with lifespan despite apparently adequate power to detect any effect similar to that originally reported (Figure 2, Figure 2—source data 1). Finally, we tested a deletion, d3-GHR, reported to affect male lifespan by 10 years when homozygous (Ben-Avraham et al., 2017) by converting its effect size to one we expect to observe when fitting an additive model. We used a SNP tagging the deletion and estimated the expected effect size in a linear regression for the (postulated) recessive effect across the three genotypes, given their frequency (see Materials and methods). While this additive model reduces power relative to the correct model, our large sample size is more than able to offset the loss of power, and we find evidence d3-GHR does not associate with lifespan with any (recessive or additive) effect similar to that originally reported (Figure 2, Figure 2—source data 1). Mortality risk factor-informed GWAS (iGWAS) We integrated 58 publicly available GWAS on mortality risk factors with our CES lifespan GWAS, creating Bayesian priors for each SNP effect based on causal effect estimates of 16 independent risk factors on lifespan. These included body mass index, blood biochemistry, CVD, type 2 diabetes, schizophrenia, multiple sclerosis, education levels, and smoking traits. The integrated analysis reveals an additional seven genome-wide significant associations with lifespan (Bayes Factor permutation p < 2.5 × 10–8), of which SNPs near TMEM18, GBX2/ASB18, IGF2R, POM12C, ZC3HC1, and ABO are reported at genome-wide significance for the first time (Figure 3; Table 2). A total of 82 independent SNPs associate with lifespan when allowing for a 1% false discovery rate (FDR) (Table 2—source data 2). Figure 3 with 1 supplement see all Download asset Open asset SNP associations with lifespan across both parents when taking into account prior information on mortality risk factors. Bayesian iGWAS was performed using observed associations from the lifespan GWAS and priors based on 16 traits selected by an AIC-based stepwise model. As the P values were assigned empirically using a permutation approach, the minimum P value is limited by the number of permutations; SNPs reaching this limit are represented by triangles. Annotated are the gene, cluster of genes, or cytogenetic band in close proximity to the top SNP. The red line represents the genome-wide significance threshold (p = 2.5 × 10−8). The blue line represents the 1% FDR threshold. Figure 3—figure supplement 1 shows the associations of each genome-wide significant SNP with the 16 risk factors. https://doi.org/10.7554/eLife.39856.011 Table 2 Bayesian GWAS using mortality risk factors reveals seven additional genome-wide significant variants. At or near – Gene or set of genes nearest to the index SNP; rsID – The index SNP with the lowest P value in the risk factor-informed analysis. Chr – Chromosome; Position – Base-pair position on chromosome (GRCh37); A1 – the effect allele, increasing lifespan; Freq1 – Frequency of the A1 allele; Years1 – Years of life gained for carrying one copy of the A1 allele; SE – Standard Error; CES P – the P value for the Wald test of association between imputed dosage and cox model residual, under the assumption of common effects between sexes. Risk – mortality risk factors associated with the variant (p < 3.81 × 10−5, accounting for 82 independent SNPs and 16 independent factors). BF P – Empirical P value derived from permutating Bayes Factors. See Table 2—source data 1 for the causal estimate of each risk factor. See Table 2—source data 2 for all SNPs significant at FDR < 1%. https://doi.org/10.7554/eLife.39856.013 At or nearrsIDChrPositionA1Freq1Years1SECES PRiskBF PCELSR2/PSRC1rs49708361109821797G0.230.22340.04631.4E-06LDL HDL CAD1.6E-09TMEM18rs67446532628524A0.170.27720.05115.8E-08BMI7.0E-10GBX2/ASB18rs102114712237081854C0.800.24010.04931.1E-06Education2.3E-08IGF2Rrs1113330056160487196G0.980.86650.15773.9E-08LDL CAD6.6E-09POM121Crs113160991775094329G0.780.25410.04952.8E-07BMI Insulin7.5E-09ZC3HC1rs561795637129685597A0.390.21070.04062.1E-07CAD5.6E-09ABOrs25190939136141870C0.810.22440.04976.3E-06LDL CAD1.9E-08 Table 2—source data 1 Bayesian GWAS - Multivariate effect estimates for the 16 traits chosen by the AIC based stepwise model selection. The multivariate MR identified 16 traits (58 tested, see McDaid et al., 2017 for an exhaustive list) with significant causal effect on lifespan and used the effect estimates to create the prior assumption of the expected effect size of each variant on lifespan, in the (Bayesian) iGWAS. Effect Estimate – the estimated effect of standardized trait on standardized lifespan, in multivariate model. SE – the standard error of the estimated effect, in multivariate model. P – the P value (two sided) from MR, for testing association between standardized trait and standardized lifespan, in multivariate model. https://doi.org/10.7554/eLife.39856.014 Download elife-39856-table2-data1-v2.xlsx Table 2—source data 2 82 SNPs significantly associated with lifespan at 1% FDR and the SNP's associations with risk factors. Bayesian iGWAS was performed using observed association results from CES GWAS and priors from 16 risk factors selected by AIC based stepwise model selection. Bayes Factors were calculated to compare effect estimates observed in the conventional GWAS to the prior effect computed. Empirical P values were assigned using a permutation approach and further corrected for multiple testing using Benjamini-Hochberg correction. Chr – Chromosome, Position – Base-pair position on chromosome (GRCh37), A1 – Effect Allele, Freq1 – Frequency of the A1 allele (from conventional GWAS), Beta1 (from conventional GWAS), SE – Standard Error of Beta1, Years – Years of lifespan gained for carrying one copy of the A1 allele (from conventional GWAS), P – P value (from conventional GWAS), PriorEffect – Prior effect estimate calculated from the summary statistics data for the 16 risk factors identified, PriorSE – Standard Error of the prior effect estimate, LogBF – Log of the observed Bayes Factor, P_BF – Empirical P value from a permutation approach for the log Bayes Factor. Final columns show the P value of each SNP in the studies used to calculate the prior, if the P value is significant after Bonferroni multiple testing correction (p < 3.81 × 10−5, 82*16 tests) the cell is shaded green. Counts of these significant associations by SNP/trait are shown in the final column/row. https://doi.org/10.7554/eLife.39856.015 Download elife-39856-table2-data2-v2.xlsx As has become increasingly common (Pilling et al., 2017), we attempted to replicate our genome-wide significant findings collectively, rather than individually. This is usually done by constructing polygenic risk scores from genotypic information in an independent cohort and testing for association with the trait of interest subject-by-subject. We used publicly available summary statistics on extreme longevity as an independent replication dataset (Broer et al., 2015; Deelen et al., 2014), but lacking individual data from such studies, we calculated the collective effect of lifespan SNPs on longevity using the same method as inverse-variance meta-analysis two-sample Mendelian randomisation (MR) using summary statistics (Hemani et al., 2018), which gives equivalent results. Prior to doing this, all effects observed in the external longevity studies were converted to hazard ratios using the APOE variant effect size as an empirical conversion factor, to allow the longevity studies to be meta-analysed despite their different study designs (and to be adjusted for sample overlap; see Materials and methods). Although the focus is on collective replication, our method has the advantage of transparency at an individual variant level, which is of particular importance for researchers seeking to follow-up individual loci. Remarkably, all lead lifespan variants show directional consistency with the independent longevity sample, and 4 SNPs or close proxies (r2 > 0.8) reach nominal replication (p < 0.05, one-sided test) (Figure 4—source data 1). Of these, SNPs near ABO, ZC3HC1, and IGF2R are replicated for the first time, and thus appear to affect overall survival and survival to extreme age. The overall ratio of replication effect sizes to discovery effect sizes – excluding APOE – is 0.42 (95% CI 0.23–0.61; p = 1.35 × 10−5). The fact this ratio is significantly greater than zero indicates most lifespan SNPs are indeed longevity SNPs. However, the fact most SNPs have a ratio smaller than one indicates they may affect early mortality more than survival to extreme age, relative to APOE (which itself has a greater effect on late-life mortality than early mortality) (Figure 4). Figure 4 Download asset Open asset Collective replication of individual lifespan SNPs using GWAMAs for extreme long-livedness shows directional consistency in all cases. Forest plot of effect size ratios between genome-wide significant lifespan variants from our study and external longevity studies (Broer et al., 2015; Deelen et al., 2014), having converted longevity effect sizes to our scale using APOE as benchmark (see Materials and methods and Figure 4—source data 1). Alpha – ratio of replication to discovery effect sizes on the common scale and 95% CI (reflecting uncertainty in the numerator and denominator; P values are for one-sided test). A true (rather than estimated) ratio of 1 indicates the relationship between SNP effect on lifetime hazard and extreme longevity is the same as that of APOE, while a ratio of zero suggests no effect on longevity. A true ratio between 0 and 1 suggests a stronger effect on lifetime hazard than longevity relative to APOE. SNPs overlapping both 0 and 1 are individually underpowered. The inverse variance meta-analysis of alpha over all SNPs, excluding APOE, is 0.42 (95% 0.23 to 0.61; p = 1.35 × 10–5) for H0 alpha = 0. https://doi.org/10.7554/eLife.39856.016 Figure 4—source data 1 Replication of lead SNPs associating with lifespan using published longevity GWAS. At or near – gene, cluster of genes, or cytogenetic band near lead SNP; Proxy – the rsID of the nearest (r2) SNP reported by Deelen et al.; Chr – Chromosome; Position – Base-pair position (GRCh37); A1 – the effect allele, A0 – the reference allele, Freq1 – the frequency of A1 allele; Beta1 – the log hazard ratio (in self) for a carrier of 1 copy of A1; SE – standard error; P – P value for tes
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