Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Harriet Corvol, Scott M. Blackman, Pierre-Yves Boëlle, Paul J. Gallins, Rhonda G. Pace, Jaclyn R. Stonebraker, Frank J. Accurso, Annick Clement, Joseph M. Collaco, Hong Dang, Anthony T. Dang, Arianna Franca, Jiafen Gong, Loic Guillot, Katherine Keenan, Weili Li, Fan Lin, Michael V. Patrone, Karen S. Raraigh, Lei Sun, Yi-Hui Zhou, Wanda K. O’Neal, Marci K. Sontag, Hara Levy, Peter R. Durie, Johanna M. Rommens, Mitchell L. Drumm, Fred A. Wright, Lisa J. Strug, Garry R. Cutting, Michael R. Knowles

ABSTRACT

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=3.3 × 10(-11)), chr5p15.3 (SLC9A3; P=6.8 × 10(-12)), chr6p21.3 (HLA Class II; P=1.2 × 10(-8)) and chrXq22-q23 (AGTR2/SLC6A14; P=1.8 × 10(-9)) contain genes of high biological relevance to CF pathophysiology. The fifth locus, on chr11p12-p13 (EHF/APIP; P=1.9 × 10(-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. More... »

PAGES

8382

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ncomms9382

DOI

http://dx.doi.org/10.1038/ncomms9382

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023757952

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26417704


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