An interval quantitative trait locus (QTL) mapping method for complex polygenic diseases (as binary traits) showing QTL by environment interactions (QEI) was developed for outbred populations on a ...within-family basis. The main objectives, within the above context, were to investigate selection of genetic models and to compare liability or generalized interval mapping (GIM) and linear regression interval mapping (RIM) methods. Two different genetic models were used: one with main QTL and QEI effects (QEI model) and the other with only a main QTL effect (QTL model). Over 30 types of binary disease data as well as six types of continuous data were simulated and analysed by RIM and GIM. Using table values for significance testing, results show that RIM had an increased false detection rate (FDR) for testing interactions which was attributable to scale effects on the binary scale. GIM did not suffer from a high FDR for testing interactions. The use of empirical thresholds, which effectively means higher thresholds for RIM for testing interactions, could repair this increased FDR for RIM, but such empirical thresholds would have to be derived for each case because the amount of FDR depends on the incidence on the binary scale. RIM still suffered from higher biases (15–100% over- or under-estimation of true values) and high standard errors in QTL variance and location estimates than GIM for QEI models. Hence GIM is recommended for disease QTL mapping with QEI. In the presence of QEI, the model including QEI has more power (20–80% increase) to detect the QTL when the average QTL effect is small (in a situation where the model with a main QTL only is not too powerful). Top-down model selection is proposed in which a full test for QEI is conducted first and then the model is subsequently simplified. Methods and results will be applicable to human, plant and animal QTL mapping experiments.
Recemment, la regression des phenotypes sur les genotypes pour les marqueurs a ete decrite pour la detection de loci de caracteres quantitatifs (QTL) dans des populations F2. Elle a ete montree ...equivalente a la detection sur intervalles par regression (RIM). Dans cette etude, la regression sur les marqueurs a ete etendue aux schemas demi-freres avec transmission incertaine des alleles aux marqueurs et les proprietes des parametres concernant les QTLs ont ete examinees analytiquement. Dans cette methode, les phenotypes de la descendance ont ete d'abord regresses sur la probabilite de transmission d'un allele donne issu du parent commun a des loci de marqueurs flanquants. Les coefficients de regression resultant peuvent alors etre interpretes a partir d'un modele genetique suppose. En presence d'un seul QTL par intervalle de marqueurs, on a montre que les valeurs esperees des coefficients de regression pour les marqueurs flanquants contenaient toute l'information a propos de la position et de l'effet du QTL, et etaient independantes de la probabilite de transmission des alleles aux marqueurs. Par simulation, on a montre que la regression du phenotype sur la probabilite de transmission des alleles aux marqueurs est equivalente au RIM avec le meme modele genetique suppose. La regression sur les genotypes aux marqueurs demande moins de temps de calcul que la detection de QTLs par intervalle, parce qu'eliminant la necessite de chercher la meilleure position pour le QTL dans les intervalles entre marqueurs. Ceci peut former la base de methodes plus efficaces avec des modeles plus complexes, incluant les modeles a seuils ou logistiques pour l'analyse des variables discretes
A generalized interval mapping (GIM) method to map quantitative trait loci (QTL) for binary
polygenic traits in a multi-family half-sib design is developed based on threshold theory and
implemented ...using a Newton–Raphson algorithm. Statistical power and bias of QTL mapping for
binary traits by GIM is compared with linear regression interval mapping (RIM) using simulation.
Data on 20 paternal half-sib families were simulated with two genetic markers that bracketed an
additive QTL. Data simulated and analysed were: (1) data on the underlying normally distributed
liability (NDL) scale, (2) binary data created by truncating NDL data based on three thresholds
yielding data sets with three different incidences, and (3) NDL data with polygenic and QTL
effects reduced by a proportion equal to the ratio of the heritabilities on the binary versus NDL
scale (reduced-NDL). Binary data were simulated with and without systematic environmental
(herd) effects in an unbalanced design. GIM and RIM gave similar power to detect the QTL and
similar estimates of QTL location, effects and variances. Presence of fixed effects caused differences
in bias between RIM and GIM, where GIM showed smaller bias which was affected less by
incidence. The original NDL data had higher power and lower bias in QTL parameter estimates
than binary and reduced-NDL data. RIM for reduced-NDL and binary data gave similar power
and estimates of QTL parameters, indicating that the impact of the binary nature of data on QTL
analysis is equivalent to its impact on heritability.
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