Genetic studies of lung disease in Cystic Fibrosis are faced with the challenge of identifying a severity measure that accounts for chronic disease progression and mortality attrition. Further, combining analyses across studies requires common phenotypes that are robust to study design and patient ascertainment. This thesis uses data from the North American Cystic Fibrosis Modifier Consortium (Canadian Consortium for CF Genetic Studies (CGS), Johns Hopkins University Twins and Siblings Study (TSS), and University of North Carolina/Case Western Reserve University Gene Modifier Study (GMS)), to calculate two novel phenotypes using age-specific CF percentile values of FEV1 (Forced Expiratory Volume in 1 second), with adjustment for CF age-specific mortality. The normalized residual, mortality adjusted (NoRMA) was designed for population based samples, while KNoRMA, using Kulich percentiles, is robust to sample ascertainment; both account for the effects of age-related disease progression and mortality attrition. NoRMA was computed for 2122 patients representing the Canadian CF population. KNoRMA was computed for these 2122 patients and also 1137 extreme phenotype patients in the GMS study and 1323 patients from multiple CF sib families in the TSS study. Phenotype was distributed in all three samples in a manner consistent with ascertainment differences, reflecting the lung disease severity of each individual in the underlying population. The new phenotype was highly correlated with the previously recommended mixed model phenotype1; 2, but computationally much easier and suited to studies with limited follow up time. As an example of its use, KNoRMA was used to test the association between locus variants in a previously published candidate gene, Transforming Growth Factor β1(TGFβ1), and lung function in CF, in an attempt to provide insight into discrepant results in the literature. A disease progression and mortality adjusted phenotype reduces the need for stratification or additional covariates, increasing statistical power and avoiding possible interpolation distortions.

Genetic studies of lung disease in Cystic Fibrosis are faced with the challenge of identifying a severity measure that accounts for chronic disease progression and mortality attrition. Further, combining analyses across studies requires common phenotypes that are robust to study design and patient ascertainment. This thesis uses data from the North American Cystic Fibrosis Modifier Consortium (Canadian Consortium for CF Genetic Studies (CGS), Johns Hopkins University Twins and Siblings Study (TSS), and University of North Carolina/Case Western Reserve University Gene Modifier Study (GMS)), to calculate two novel phenotypes using age-specific CF percentile values of FEV1 (Forced Expiratory Volume in 1 second), with adjustment for CF age-specific mortality. The normalized residual, mortality adjusted (NoRMA) was designed for population based samples, while KNoRMA, using Kulich percentiles, is robust to sample ascertainment; both account for the effects of age-related disease progression and mortality attrition. NoRMA was computed for 2122 patients representing the Canadian CF population. KNoRMA was computed for these 2122 patients and also 1137 extreme phenotype patients in the GMS study and 1323 patients from multiple CF sib families in the TSS study. Phenotype was distributed in all three samples in a manner consistent with ascertainment differences, reflecting the lung disease severity of each individual in the underlying population. The new phenotype was highly correlated with the previously recommended mixed model phenotype1; 2, but computationally much easier and suited to studies with limited follow up time. As an example of its use, KNoRMA was used to test the association between locus variants in a previously published candidate gene, Transforming Growth Factor β1(TGFβ1), and lung function in CF, in an attempt to provide insight into discrepant results in the literature. A disease progression and mortality adjusted phenotype reduces the need for stratification or additional covariates, increasing statistical power and avoiding possible interpolation distortions.