Gibbs sampling-based segregation analysis of asthma-associated quantitative traits in a population-based sample of nuclear families.

Asthma is a common, complex human disease. Elevated serum immunoglobulin E (IgE) levels, elevated blood eosinophil counts, and increased airway responsiveness are physiological traits that are characteristic of asthma. Few studies have investigated major gene effects for these traits in a population-based sample. Further, it is not known if any putative major genes may be common to two or more of these traits. We investigated the existence and nature of major genes modulating asthma-associated quantitative traits in an Australian population-based sample of 210 Caucasian nuclear families. The sharing of these major genes was also investigated. Segregation analysis was based upon a Markov Chain Monte Carlo (Gibbs sampling) approach as implemented in the program BUGS v0.6. All models included adjustment for age, height, tobacco smoke exposure, and gender. The segregation of total IgE levels, blood eosinophil counts, and dose-response slope (DRS) of methacholine challenge were all consistent with major loci at which a recessive allele acted to increase or decrease the phenotype. The respective estimated frequencies of the recessive alleles were 68% (total IgE), 10% (blood eosinophil count), and 27% (DRS). Extensive modelling suggested that the major loci controlling total serum IgE levels, blood eosinophil counts, and airway responsiveness represent different genes. These data provide evidence, for the first time, of the existence of at least 3 distinct genetic pathways involving major gene effects on physiological traits closely associated with asthma. These results have implications for gene discovery programs.