Dataset Information

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis.

ABSTRACT: Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis.

SUBMITTER: Waage J

PROVIDER: S-EPMC7068780 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis.

Waage Johannes J Standl Marie M Curtin John A JA Jessen Leon E LE Thorsen Jonathan J Tian Chao C Schoettler Nathan N Flores Carlos C Abdellaoui Abdel A Ahluwalia Tarunveer S TS Alves Alexessander C AC Amaral Andre F S AFS Antó Josep M JM Arnold Andreas A Barreto-Luis Amalia A Baurecht Hansjörg H van Beijsterveldt Catharina E M CEM Bleecker Eugene R ER Bonàs-Guarch Sílvia S Boomsma Dorret I DI Brix Susanne S Bunyavanich Supinda S Burchard Esteban G EG Chen Zhanghua Z Curjuric Ivan I Custovic Adnan A den Dekker Herman T HT Dharmage Shyamali C SC Dmitrieva Julia J Duijts Liesbeth L Ege Markus J MJ Gauderman W James WJ Georges Michel M Gieger Christian C Gilliland Frank F Granell Raquel R Gui Hongsheng H Hansen Torben T Heinrich Joachim J Henderson John J Hernandez-Pacheco Natalia N Holt Patrick P Imboden Medea M Jaddoe Vincent W V VWV Jarvelin Marjo-Riitta MR Jarvis Deborah L DL Jensen Kamilla K KK Jónsdóttir Ingileif I Kabesch Michael M Kaprio Jaakko J Kumar Ashish A Lee Young-Ae YA Levin Albert M AM Li Xingnan X Lorenzo-Diaz Fabian F Melén Erik E Mercader Josep M JM Meyers Deborah A DA Myers Rachel R Nicolae Dan L DL Nohr Ellen A EA Palviainen Teemu T Paternoster Lavinia L Pennell Craig E CE Pershagen Göran G Pino-Yanes Maria M Probst-Hensch Nicole M NM Rüschendorf Franz F Simpson Angela A Stefansson Kari K Sunyer Jordi J Sveinbjornsson Gardar G Thiering Elisabeth E Thompson Philip J PJ Torrent Maties M Torrents David D Tung Joyce Y JY Wang Carol A CA Weidinger Stephan S Weiss Scott S Willemsen Gonneke G Williams L Keoki LK Ober Carole C Hinds David A DA Ferreira Manuel A MA Bisgaard Hans H Strachan David P DP Bønnelykke Klaus K

Nature genetics 20180716 8

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries<sup>1,2</sup>. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhini ...[more]

PMID: 30013184

Similar Datasets

Project description:BackgroundGenome-wide association studies can have limited power to identify QTL, partly due to the stringent correction for multiple testing and low linkage-disequilibrium between SNPs and QTL. Regional Heritability Mapping (RHM) has been advanced as an alternative approach to capture underlying genetic effects. In this study, RHM was used to identify loci underlying variation in the 16(th) QTLMAS workshop simulated traits.MethodsThe method was implemented by fitting a mixed model where a genomic region and the overall genetic background were added as random effects. Heritabilities for the genetic regional effects were estimated, and the presence of a QTL in the region was tested using a likelihood ratio test (LRT). Several region sizes were considered (100, 50 and 20 adjacent SNPs). Bonferroni correction was used to calculate the LRT thresholds for genome-wide (p < 0.05) and suggestive (i.e., one false positive per genome scan) significance.ResultsGenomic heritabilities (0.31, 0.32 and 0.48, respectively) and genetic correlations (0.80, -0.42 and 0.19, between trait-pairs 1&2, 1&3 and 2&3) were similar to the simulated ones. RHM identified 7 QTL (4 at genome-wide and 3 at suggestive level) for Trait1; 4 (2 genome-wide and 2 suggestive) for Trait2; and 7 (6 genome-wide and 1 suggestive) for Trait3. Only one of the identified suggestive QTL was a false-positive. The position of these QTL tended to coincide with the position where the largest QTL (or several of them) were simulated. Several signals were detected for the simulated QTL with smaller effect. A combined analysis including all significant regions showed that they explain more than half of the total genetic variance of the traits. However, this might be overestimated, due to Beavis effect. All QTL affecting traits 1&2 and 2&3 had positive correlations, following the trend of the overall correlation of both trait-pairs. All but one QTL affecting traits 1&3 were negatively correlated, in agreement with the simulated situation. Moreover, RHM identified extra loci that were not found by association and linkage analysis, highlighting the improved power of this approach.ConclusionsRHM identified the largest QTL among the simulated ones, with some signals for the ones with small effect. Moreover, RHM performed better than association and linkage analysis, in terms of both power and resolution.

Project description:Aims/hypothesisType 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study.MethodsWe conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci.ResultsFour novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis.Conclusions/interpretationOur findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations.Data availabilityFull summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).

Dataset Information

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis.

Publications

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets