FAMILY BASED ASSOCIATION ANALYSIS OF MODIFIERS OF CYSTIC FIBROSIS LUNG DISEASE

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Patients with cystic fibrosis (CF) display a range of disease severity. Lung disease in individual CF patients varies widely and what account for this heterogeneity is unclear. Some of this variability can be attributed to mutations within the cystic fibrosis transmembrane conductance regulator gene (CFTR) and by other non-genetic factors such as patient's age, pancreatic insufficiency, treatment regimen, nutritional status and colonization with strains of Pseudomonas or Burkholderia cepacia. This study will identify genes that are modifiers of CF lung disease. We propose an extensive phenotype and genotyping analysis in our CF population enrolling over 500 families with at least one individual with CF who is followed at Children's Hospital, Boston and Massachusetts General Hospital. We propose that modifier genes that impact the course of CF can be identified by performing an association based analysis of candidate loci using single nucleotide polymorphisms (SNPs) in a family based association study. Single nucleotide polymorphisms are a catalog of variations, common genetic differences that function as genetic markers and are observed in at least 1% of the general population. They may represent a functional change if observed at a greater frequency than the general population. We hypothesize that the phenotypic differences within the CF population can be accounted for by modifier genes and will correlate to varying degrees of lung function in patients with the same CF genotype. We believe the genetic factors that contribute to the degree of pulmonary disease in CF will improve diagnosis and define new therapeutic targets.