Although many genome screens have been conducted for asthma as a binary trait, there is limited information regarding the genetic factors underlying variation of asthma expression. Phenotypes related to variable disease expression include time to asthma onset and variation in clinical expression as measured by an asthma score built from EGEA data. A recent genome scan conducted for this score led to detection of a new region (18p11) not revealed by analysis of dichotomous asthma. Our goal was to characterize chromosomal regions harboring genes underlying time to asthma onset and to search for pleiotropic QTL influencing both time to asthma onset and the asthma score. We conducted a genome-wide linkage screen for time to asthma onset, modeled by martingale residuals from Cox survival model, in EGEA families with at least two asthmatic sibs. This was followed by a bivariate linkage scan of these residuals and asthma score. Univariate linkage analysis was performed using the Maximum Likelihood Binomial method that we extended to bivariate analysis. This screen revealed two regions potentially linked to time to asthma onset, 1p31 (LOD = 1.70, P = 0.003) and 5q13 (LOD = 1.87, P = 0.002). Bivariate linkage analysis led to a substantial improvement of the linkage signal on 5q13 (P = 0.00007), providing evidence for a pleiotropic QTL influencing both variation of time to asthma onset and of clinical expression. Use of quantitative phenotypes of variable disease expression and suitable statistical methodology can improve the power to detect new regions harboring genes which may play an important role in onset and course of disease.