Evidence for a pleiotropic QTL on chromosome 5q13 influencing both time to asthma onset and asthma score in French EGEA families |
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Authors: | Emmanuelle Bouzigon Ayse Ulgen Marie-Hélène Dizier Valérie Siroux Mark Lathrop Francine Kauffmann Isabelle Pin Florence Demenais |
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Institution: | (1) INSERM, U794, Tour Evry 2, 523 Place des Terrasses de l’Agora, 91034 Evry, France;(2) Université d’Evry, Evry, France;(3) INSERM, U535, Villejuif, France;(4) IFR69, Univ. Paris-Sud, Villejuif, France;(5) INSERM, U823, Grenoble, France;(6) Centre National de Génotypage, Evry, France;(7) INSERM, U780, Villejuif, France |
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Abstract: | 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. |
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