Exome Sequencing of Phenotypic Extremes Identifies CAV2 and TMC6 as Interacting Modifiers of Chronic Pseudomonas aeruginosa Infection in Cystic Fibrosis

Discovery of rare or low frequency variants in exome or genome data that are associated with complex traits often will require use of very large sample sizes to achieve adequate statistical power. For a fixed sample size, sequencing of individuals sampled from the tails of a phenotype distribution (i.e., extreme phenotypes design) maximizes power and this approach was recently validated empirically with the discovery of variants in DCTN4 that influence the natural history of P. aeruginosa airway infection in persons with cystic fibrosis (CF; MIM219700). The increasing availability of large exome/genome sequence datasets that serve as proxies for population-based controls affords the opportunity to test an alternative, potentially more powerful and generalizable strategy, in which the frequency of rare variants in a single extreme phenotypic group is compared to a control group (i.e., extreme phenotype vs. control population design). As proof-of-principle, we applied this approach to search for variants associated with risk for age-of-onset of chronic P. aeruginosa airway infection among individuals with CF and identified variants in CAV2 and TMC6 that were significantly associated with group status. These results were validated using a large, prospective, longitudinal CF cohort and confirmed a significant association of a variant in CAV2 with increased age-of-onset of P. aeruginosa airway infection (hazard ratio = 0.48, 95% CI=[0.32, 0.88]) and variants in TMC6 with diminished age-of-onset of P. aeruginosa airway infection (HR = 5.4, 95% CI=[2.2, 13.5]) A strong interaction between CAV2 and TMC6 variants was observed (HR=12.1, 95% CI=[3.8, 39]) for children with the deleterious TMC6 variant and without the CAV2 protective variant. Neither gene showed a significant association using an extreme phenotypes design, and conditions for which the power of an extreme phenotype vs. control population design was greater than that for the extreme phenotypes design were explored.