While considerable evidence exists for familial aggregation of respiratory diseases, little work has been done to measure the degree of familial aggregation of quantitative measures of pulmonary function. Our goal in this proposal is to quantitate the degree of genetic and non-genetic control over several different measures of pulmonary function using variance components analysis. We have a unique opportunity to use existing data on pulmonary function measured in families ascertained through a case with defined airways obstruction and in families ascertained through a matched control patient. Preliminary analysis of the control families attributed 34% of the variation in forced expiratory volume in 1 second to genetic factors after adjusting for covariates such as age, height, sex and smoking. Here we propose to apply this variance components analysis to other measures of pulmonary function from spirometry, from the single breath nitrogen test, and from tests of diffusing capacity. Analysis will be done on the families of controls and families of cases separately (after adjusting for ascertainment) and tests for heterogeneity between these two groups will be done. In addition, we plan to extend the current variance components models to allow testing for interactions between observable risk factors and the unobserved controlling factors whose variance is being estimated. Such interactions are quite likely for environmental factors such as smoking where there may be genetic differences in the susceptibility to the harmful effects of smoking. Lastly, we will extend the analysis to consider 2 or more measures of pulmonary function simultaneously employing a recently proposed extension. By carefully testing for interaction between generation status and the estimated components as described above, we will be able to overcome a major weakness of the current method. This multivariate variance components analysis will contribute greatly to the understanding of complex physiological traits such as pulmonary function.
