title: Variance components analyses of multiple asthma traits in a large sample of Australian families ascertained through a twin proband.
abstract: BACKGROUND
  Intermediate phenotypes are often measured as a proxy for asthma. It is largely unclear to what extent the same set of environmental or genetic factors regulate these traits.
  
  
  OBJECTIVE
  Estimate the environmental and genetic correlations between self-reported and clinical asthma traits.
  
  
  METHODS
  A total of 3,073 subjects from 802 families were ascertained through a twin proband. Traits measured included self-reported asthma, airway histamine responsiveness (AHR), skin prick response to common allergens including house dust mite (Dermatophagoides pteronyssinus [D. pter]), baseline lung function, total serum immunoglobulin E (IgE) and eosinophilia. Bivariate and multivariate analyses of eight traits were performed with adjustment for ascertainment and significant covariates.
  
  
  RESULTS
  Overall 2,716 participants completed an asthma questionnaire and 2,087 were clinically tested, including 1,289 self-reported asthmatics (92% previously diagnosed by a doctor). Asthma, AHR, markers of allergic sensitization and eosinophilia had significant environmental correlations with each other (range: 0.23-0.89). Baseline forced expiratory volume in 1 s (FEV(1)) showed low environmental correlations with most traits. Fewer genetic correlations were significantly different from zero. Phenotypes with greatest genetic similarity were asthma and atopy (0.46), IgE and eosinophilia (0.44), AHR and D. pter (0.43) and AHR and airway obstruction (-0.43). Traits with greatest genetic dissimilarity were FEV(1) and atopy (0.05), airway obstruction and IgE (0.07) and FEV(1) and D. pter (0.11).
  
  
  CONCLUSIONS
  These results suggest that the same set of environmental factors regulates the variation of many asthma traits. In addition, although most traits are regulated to great extent by specific genetic factors, there is still some degree of genetic overlap that could be exploited by multivariate linkage approaches.
authors.affiliation: Queensland Institute of Medical Research, Brisbane, Australia.
authors.forename: M A R
authors.initials: MA
authors.lastname: Ferreira
authors.forename: L
authors.initials: L
authors.lastname: O'Gorman
authors.forename: P
authors.initials: P
authors.lastname: Le Souëf
authors.forename: P R
authors.initials: PR
authors.lastname: Burton
authors.forename: B G
authors.initials: BG
authors.lastname: Toelle
authors.forename: C F
authors.initials: CF
authors.lastname: Robertson
authors.forename: N G
authors.initials: NG
authors.lastname: Martin
authors.forename: D L
authors.initials: DL
authors.lastname: Duffy
mesh_terms: D001249:Asthma / Q000235:genetics*; D001315:Australia; D004802:Eosinophilia / Q000235:genetics; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D006967:Hypersensitivity / Q000235:genetics*; D006969:Hypersensitivity, Immediate / Q000235:genetics; D007074:Immunoglobulin G / Q000097:blood; D008297:Male; D010375:Pedigree; D012129:Respiratory Function Tests; D014427:Twins / Q000235:genetics*
chemical_list: D007074:Immunoglobulin G