ARTICLE Received 9 Jan 2015 | Accepted 17 Aug 2015 | Published 29 Sep 2015 DOI: 10.1038/ncomms9382 OPEN Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis Harriet Corvol1,2, Scott M. Blackman3, Pierre-Yves Boe¨lle2,4, Paul J. Gallins5, Rhonda G. Pace6, Jaclyn R. Stonebraker6, Frank J. Accurso7,8,9, Annick Clement1,2, Joseph M. Collaco10, Hong Dang6, Anthony T. Dang6, Arianna Franca11, Jiafen Gong12, Loic Guillot1, Katherine Keenan13, Weili Li12, Fan Lin12, Michael V. Patrone6, Karen S. Raraigh11, Lei Sun14,15, Yi-Hui Zhou16, Wanda K. O’Neal6, Marci K. Sontag7,8,9, Hara Levy17, Peter R. Durie13,18, Johanna M. Rommens12,19, Mitchell L. Drumm20, Fred A. Wright21,22, Lisa J. Strug12,15, Garry R. Cutting11,23 & Michael R. Knowles6 The identification of small molecules that target specific CFTR variants has ushered in a new era of treatment for cystic fibrosis (CF), yet optimal, individualized treatment of CF will require identification and targeting of disease modifiers. Here we use genome-wide association analysis to identify genetic modifiers of CF lung disease, the primary cause of mortality. Meta-analysis of 6,365 CF patients identifies five loci that display significant association with variation in lung disease. Regions on chr3q29 (MUC4/MUC20; P ¼ 3.3 Â 10 À 11), chr5p15.3 (SLC9A3; P ¼ 6.8 Â 10 À 12), chr6p21.3 (HLA Class II;P¼ 1.2 Â 10 À 8) and chrXq22-q23 (AGTR2/SLC6A14; P ¼ 1.8 Â 10 À 9) contain genes of high biological relevance to CF patho- physiology. The fifth locus, on chr11p12-p13 (EHF/APIP; P ¼ 1.9 Â 10 À 10), was previously shown to be associated with lung disease. These results provide new insights into potential targets for modulating lung disease severity in CF. 1 Assistance Publique-Hoˆpitaux de Paris (AP-HP), Hoˆpital Trousseau, Pediatric Pulmonary Department; Institut National de la Sante´ et la Recherche Me´dicale (INSERM) U938, Paris 75012, France. 2 Sorbonne Universite´s, Universite´ Pierre et Marie Curie (UPMC) Paris 06, Paris 75005, France. 3 Division of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA. 4 AP-HP, Hoˆpital St Antoine, Biostatistics Department; Inserm U1136, Paris 75012, France. 5 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 6 Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. 7 Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Center, Aurora, Colorado 80045, USA. 8 Children’s Hospital Colorado, Anschutz Medical Center, Aurora, Colorado 80045, USA. 9 Department of Pediatrics, School of Medicine, Anschutz Medical Center, Aurora, Colorado 80045, USA. 10 Division of Pediatric Pulmonology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA. 11 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA. 12 Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4. 13 Program in Physiology and Experimental Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4. 14 Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada M5S 3G3. 15 Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada M5T 3M7. 16 Bioinformatics Research Center and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA. 17 Division of Pulmonary Medicine, Department of Pediatrics, Stanley Manne Research Institute, Northwestern University Feinberg School of Medicine, Ann and Robert Lurie Children’s Hospital of Chicago, Chicago, Illinois 60611, USA. 18 Department of Pediatrics, University of Toronto, Toronto, Ontario M5G 1X8, Canada. 19 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8. 20 Department of Pediatrics, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA. 21 Bioinformatics Research Center and Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, USA. 22 Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA. 23 Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA. Correspondence and requests for materials should be addressed to L.J.S. (email: [email protected]) or to G.R.C. (email: [email protected]) or to M.R.K. (email: [email protected]). NATURE COMMUNICATIONS | 6:8382 | DOI: 10.1038/ncomms9382 | www.nature.com/naturecommunications 1 & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms9382 ystic fibrosis (CF) affects B70,000 individuals worldwide lung phenotype as in the previous GWAS (Consortium lung and is caused by loss-of-function variants in CFTR. phenotype (KNoRMA))5, which allows for direct comparison CAlthough CF is regarded as a single-gene disorder, patients of the lung function of CF patients irrespective of age and who have the same variants in CFTR exhibit substantial variation gender. A meta-analysis of both imputed and genotyped in severity of lung disease, of which 450% is explained by non- variants is reported that combines data from the new subjects CFTR genetic variation1. The identification of small molecules with the previously reported GWAS, allowing for an that target specific CFTR variants has ushered in a new era of unprecedented sample size of 6,365 CF patients and analysis of treatment for cystic fibrosis (CF)2, but optimal individualized over 8 million variants. To maximize power, linear mixed models treatment will require identification and targeting of disease are used to allow for inclusion of CF-affected siblings. The modifiers. combined analysis confirms a previous genome-wide association The advent of large-scale genome-wide association studies and identifies four new loci that contain genes with high (GWAS) and capability for imputation has made it possible to biological relevance for pathophysiology of CF lung disease. explore millions of polymorphisms in search of genetic determinants of phenotypic variation. Our previously reported GWAS in 3,444 CF patients led to identification of genome-wide Results significant single-nucleotide polymorphism (SNP) associations Characteristics of patients in GWAS2 and GWAS1. The cohort with lung disease severity in an intergenic region between EHF study design and the demographic and clinical characteristics of and APIP (chr11p13), as well as several additional suggestive loci GWAS2 subjects are detailed in Table 1 and Methods. In the (chr6p21.3 and chrXq22-q23)3. In other published candidate gene combined GWAS1 þ 2 data set, 99.8% of subjects were pancreatic studies4, additional loci/genes have been reported to reach exocrine insufficient (primarily defined by CFTR genotypes); significance thresholds for the individual study, but many of 65.0% were p.Phe508del homozygotes; 95.5% were of European these studies were based on relatively small sample sizes and/or ancestry; and only 5.4% were diagnosed by newborn screening. limited phenotyping, and most have not been replicated, GWAS1 subjects from three cohorts were genotyped on the same generating uncertainty as to their pathophysiological relevance. Illumina platform3, while GWAS2 included 10 subgroups defined In this manuscript, we have extended our study of CF gene by different combinations of site and Illumina genotyping modifiers by testing 2,921 additional patients from North platforms. These 13 subgroups had similar distributions of the America (n ¼ 1,699) and France (n ¼ 1,222). We use the same lung disease phenotype (Supplementary Fig. 1). Table 1 | Characteristics of patients enrolled in GWAS2 and GWAS1 by the International Cystic Fibrosis Gene Modifier Consortium. Age (years) Lead Design Subjects Mean Range Male n (%) European* p.Phe508del/ Pancreatic Subjects Institution(s) n (±s.d.) n (%) p.Phe508del exocrine identified n (%) insufficient by NBS n (%) n (%) GWAS2 French CF University of Pierre Population 1,222 21.0 (9.2) 6.0–57.6 627 (51.3) 1,211 (99.1) 716 (58.6) 1,222 (100.0) 63 (5.2) Gene and Marie Curie, based Modifier Inserm U938 Consortium (FrGMC) Genetic University of North Extremes 469 25.8 (10.9) 7.9–62.2 256 (54.6) 407 (86.8) 191 (40.7) 467 (99.6) 3 (0.01) Modifier Carolina/ Case of phenotype Study (GMS) Western Reserve University Population- 357 20.3 (10.0) 6.6–60.2 191 (53.5) 336 (94.1) 214 (59.9) 357 (100.0) 137 (38.4) basedw Canadian Hospital for Sick Population 285 13.0 (7.6) 6.4–40.0 150 (52.6) 268 (94.0) 189 (66.3) 282 (98.9) 0 (0.0) Consortium Children basedz for Genetic Studies (CGS) Twins and Johns Hopkins Family based 588 15.8 (10.3) 6.0–56.0 305 (51.9) 533 (90.6) 315 (53.6) 583 (99.1) 54 (9.2) Sibs Study University and (TSS) population basedy Summary GWAS2 2,921 19.9 (10.4) 6.0–62.2 1,529 (52.3) 2,755 (94.3) 1,625 (55.6) 2,911 (99.7) 257 (8.8) GWAS1 Summary GWAS1|| 3,444 19.2 (8.5) 6.0–56.0 1,839 (53.4) 3,324 (96.5) 2,514 (73.0) 3,444 (100.0) 84 (2.4) GWAS1 þ 2 Summary 6,365 19.5 (9.4) 6.0–62.2 3,368 (52.9) 6,079 (95.5) 4,139 (65.0) 6,355 (99.8) 341 (5.4) GWAS1 þ 2 GWAS, genome-wide association study; NBS, newborn screening. *On the basis of Eigenstrat principal components analysis and closeness to CEU. wIncludes patients enrolled into studies at Children’s Hospitals in Boston, Colorado and Wisconsin, and through UNC/CWRU; includes 3 two-sibling families. zIncludes 13 two-sibling families and 1 three-sibling family, plus 256 singletons.