Dataset Information

Integration of mouse and human genome-wide association data identifies KCNIP4 as an asthma gene.

ABSTRACT: Asthma is a common chronic respiratory disease characterized by airway hyperresponsiveness (AHR). The genetics of asthma have been widely studied in mouse and human, and homologous genomic regions have been associated with mouse AHR and human asthma-related phenotypes. Our goal was to identify asthma-related genes by integrating AHR associations in mouse with human genome-wide association study (GWAS) data. We used Efficient Mixed Model Association (EMMA) analysis to conduct a GWAS of baseline AHR measures from males and females of 31 mouse strains. Genes near or containing SNPs with EMMA p-values <0.001 were selected for further study in human GWAS. The results of the previously reported EVE consortium asthma GWAS meta-analysis consisting of 12,958 diverse North American subjects from 9 study centers were used to select a subset of homologous genes with evidence of association with asthma in humans. Following validation attempts in three human asthma GWAS (i.e., Sepracor/LOCCS/LODO/Illumina, GABRIEL, DAG) and two human AHR GWAS (i.e., SHARP, DAG), the Kv channel interacting protein 4 (KCNIP4) gene was identified as nominally associated with both asthma and AHR at a gene- and SNP-level. In EVE, the smallest KCNIP4 association was at rs6833065 (P-value 2.9e-04), while the strongest associations for Sepracor/LOCCS/LODO/Illumina, GABRIEL, DAG were 1.5e-03, 1.0e-03, 3.1e-03 at rs7664617, rs4697177, rs4696975, respectively. At a SNP level, the strongest association across all asthma GWAS was at rs4697177 (P-value 1.1e-04). The smallest P-values for association with AHR were 2.3e-03 at rs11947661 in SHARP and 2.1e-03 at rs402802 in DAG. Functional studies are required to validate the potential involvement of KCNIP4 in modulating asthma susceptibility and/or AHR. Our results suggest that a useful approach to identify genes associated with human asthma is to leverage mouse AHR association data.

SUBMITTER: Himes BE

PROVIDER: S-EPMC3572953 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Integration of mouse and human genome-wide association data identifies KCNIP4 as an asthma gene.

Himes Blanca E BE Sheppard Keith K Berndt Annerose A Leme Adriana S AS Myers Rachel A RA Gignoux Christopher R CR Levin Albert M AM Gauderman W James WJ Yang James J JJ Mathias Rasika A RA Romieu Isabelle I Torgerson Dara G DG Roth Lindsey A LA Huntsman Scott S Eng Celeste C Klanderman Barbara B Ziniti John J Senter-Sylvia Jody J Szefler Stanley J SJ Lemanske Robert F RF Zeiger Robert S RS Strunk Robert C RC Martinez Fernando D FD Boushey Homer H Chinchilli Vernon M VM Israel Elliot E Mauger David D Koppelman Gerard H GH Postma Dirkje S DS Nieuwenhuis Maartje A E MA Vonk Judith M JM Lima John J JJ Irvin Charles G CG Peters Stephen P SP Kubo Michiaki M Tamari Mayumi M Nakamura Yusuke Y Litonjua Augusto A AA Tantisira Kelan G KG Raby Benjamin A BA Bleecker Eugene R ER Meyers Deborah A DA London Stephanie J SJ Barnes Kathleen C KC Gilliland Frank D FD Williams L Keoki LK Burchard Esteban G EG Nicolae Dan L DL Ober Carole C DeMeo Dawn L DL Silverman Edwin K EK Paigen Beverly B Churchill Gary G Shapiro Steve D SD Weiss Scott T ST

PloS one 20130214 2

Asthma is a common chronic respiratory disease characterized by airway hyperresponsiveness (AHR). The genetics of asthma have been widely studied in mouse and human, and homologous genomic regions have been associated with mouse AHR and human asthma-related phenotypes. Our goal was to identify asthma-related genes by integrating AHR associations in mouse with human genome-wide association study (GWAS) data. We used Efficient Mixed Model Association (EMMA) analysis to conduct a GWAS of baseline A ...[more]

PMID: 23457522

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Project description:BackgroundRecent genome-wide association studies (GWAS) for asthma have been successful in identifying novel associations which have been well replicated. The aim of this study is to identify the genetic variants that influence predisposition towards asthma in an ethnic Chinese population in Singapore using a GWAS approach.MethodsA two-stage GWAS was performed in case samples with allergic asthma, and in control samples without asthma and atopy. In the discovery stage, 490 case and 490 control samples were analysed by pooled genotyping. Significant associations from the first stage were evaluated in a replication cohort of 521 case and 524 control samples in the second stage. The same 980 samples used in the discovery phase were also individually genotyped for purposes of a combined analysis. An additional 1445 non-asthmatic atopic control samples were also genotyped.Results19 promising SNPs which passed our genome-wide P value threshold of 5.52 × 10-8 were individually genotyped. In the combined analysis of 1011 case and 1014 control samples, SNP rs2941504 in PERLD1 on chromosome 17q12 was found to be significantly associated with asthma at the genotypic level (P = 1.48 × 10-6, ORAG = 0.526 (0.369-0.700), ORAA = 0.480 (0.361-0.639)) and at the allelic level (P = 9.56 × 10-6, OR = 0.745 (0.654-0.848)). These findings were found to be replicated in 3 other asthma GWAS studies, thus validating our own results. Analysis against the atopy control samples suggested that the SNP was associated with allergic asthma and not to either the asthma or allergy components. Genotyping of additional SNPs in 100 kb flanking rs2941504 further confirmed that the association was indeed to PERLD1. PERLD1 is involved in the modification of the glycosylphosphatidylinositol anchors for cell surface markers such as CD48 and CD59 which are known to play multiple roles in T-cell activation and proliferation.ConclusionsThese findings reveal the association of a PERLD1 as a novel asthma candidate gene and reinforce the involvement of genes on the 17q12-21 chromosomal region in the etiology of asthma.

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Integration of mouse and human genome-wide association data identifies KCNIP4 as an asthma gene.

Publications

Integration of mouse and human genome-wide association data identifies KCNIP4 as an asthma gene.

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