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Detection of quantitative trait loci associated with alcohol‐dependence: Use of model‐free sib‐pair method and combined segregation‐linkage analysis based on regressive models
Author(s) -
Durrieu Gilles,
Meunier Flavie,
O'Connell Jeff,
Martinez Maria,
Demenais Florence
Publication year - 1999
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.1370170725
Subject(s) - homoscedasticity , linkage (software) , statistics , estimator , mathematics , regression analysis , regression , trait , normality , genetic linkage , linear regression , genetics , heteroscedasticity , biology , computer science , gene , programming language
Two linkage methods were used to detect loci underlying neurophysiological measures associated with alcohol dependence 1) the Haseman‐Elston (H‐E) sib pair method for genome‐wide search, and 2) the combined segregation‐linkage (CSL), based on regressive models, to confirm positive linkages found by the genome screening. Among 14 linkage results that were significant at the 0.5% level using H‐E, the CSL method leads to similar p‐values in only three cases but to higher p‐values in all others. Investigation of these discrepancies shows that assumptions (normality and homoscedasticity of the error term) of H‐E least‐squares regression method are not verified. A robust estimator of slope parameters without assuming any distribution function for the linear model error terms increases the p‐values and reduces the difference between H‐E and CSL results. Alternatively, the CSL approach may lack power when multiple genes with small effects are involved.