Priyadarshini Kachroo1, Julian Hecker2, Bo L Chawes3, Tarunveer S Ahluwalia3, Michael H Cho1, Dandi Qiao1, Rachel S Kelly1, Su H Chu1, Yamini V Virkud4, Mengna Huang1, Kathleen C Barnes5, Esteban G Burchard6, Celeste Eng7, Donglei Hu7, Juan C Celedón8, Michelle Daya5, Albert M Levin9, Hongsheng Gui10, L Keoki Williams10, Erick Forno8, Angel C Y Mak7, Lydiana Avila11, Manuel E Soto-Quiros11, Michelle M Cloutier12, Edna Acosta-Pérez13, Glorisa Canino13, Klaus Bønnelykke3, Hans Bisgaard3, Benjamin A Raby14, Christoph Lange15, Scott T Weiss1, Jessica A Lasky-Su16. 1. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. 2. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA. 3. Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark. 4. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Department of Pediatrics, Massachusetts General Hospital for Children and Harvard Medical School, Boston, MA. 5. Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Colorado, CO. 6. Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA. 7. Department of Medicine, University of California San Francisco, San Francisco, CA. 8. Division of Pediatric Pulmonary Medicine, Allergy and Immunology, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA. 9. Department of Public Health Sciences, Henry Ford Health System, Detroit, MI; Center for Bioinformatics, Henry Ford Health System, Detroit, MI. 10. Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI; Department of Internal Medicine, Henry Ford Health System, Detroit, MI. 11. Department of Pediatrics, Hospital Nacional de Niños, San José, Costa Rica. 12. Department of Pediatrics, University of Connecticut, Farmington, CT. 13. Behavioral Sciences Research Institute, University of Puerto Rico, San Juan, Puerto Rico. 14. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Boston Children's Hospital and Harvard Medical School, Boston, MA. 15. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA. 16. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. Electronic address: rejas@channing.harvard.edu.
Abstract
BACKGROUND: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician's diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV1/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10-8 in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10-6). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV1 (P = 3.3 × 10-3), postbronchodilator (PB) FEV1 (7.3 × 10-3), and PB FEV1/FVC ratio (P = 2.7 × 10-3). The identified baseline FEV1/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.
BACKGROUND:Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician's diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV1/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10-8 in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10-6). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV1 (P = 3.3 × 10-3), postbronchodilator (PB) FEV1 (7.3 × 10-3), and PB FEV1/FVC ratio (P = 2.7 × 10-3). The identified baseline FEV1/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.
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