posted on 2016-11-21, 12:48authored byVictoria Emily Jackson
Lung Function is a physiological measurement used for monitoring respiratory health and in the diagnosis of chronic obstructive pulmonary disease (COPD), a leading cause of morbidity and mortality worldwide. Lung function and COPD are influenced by a combination of environmental and genetic factors. This thesis aims to investigate the genetic basis of these traits, with a particular focus on the effect of low frequency and rare genetic variants, so far largely overlooked in genome-wide association studies (GWAS).
An analysis of exome array data and COPD identifies novel associations between COPD risk and low frequency single nucleotide polymorphisms (SNPs) in MOCS3 and IFIT3 and between a rare SNP in SERPINA12 and percent predicted forced expiratory volume in one second (FEV1) in COPD cases. Recently developed methods for the meta-analysis of gene-based tests are empirically evaluated and shown to be approximately equivalent to a mega-analysis using individual level data for a quantitative trait. These methods are then applied in a meta-analysis of exome array data and quantitative lung function measures. This meta-analysis identifies no gene-based associations; however genome-wide significant (P<5x10⁻⁸) single variant associations are identified in two novel regions: a SNP near LY86 associated with the ratio of FEV1 to forced vital capacity (FVC) and a SNP near FGF10 associated with FVC in ever smokers. Finally the largest GWAS to date of two lung function flow measures (peak expiratory flow [PEF] and forced expiratory flow between 25% and 75% of FVC [FEF25-75]) is described. The overlap in variants associated with PEF and FEF25-75 and volumetric measures of lung function (FEV1, FVC and FEV1/FVC) is examined, and 10 SNPs are identified as showing association with PEF (P<5x10⁻⁸), but no other lung function trait with P<5x10⁻⁵. These findings have the potential to provide insight into the biological mechanisms underlying lung health and disease.