Rebaï A, Goffinet B.. More about quantitative trait locus mapping with diallel designs. Genet Res 75: 243-247
INRA Centre de Toulouse, Unit of Biometry and Artificial Intelligence, Castanet-Tolosan, France.Genetics Research (Impact Factor: 1.47). 05/2000; 75(2):243-7. DOI: 10.1017/S0016672399004358
We present a general regression-based method for mapping quantitative trait loci (QTL) by combining different populations derived from diallel designs. The model expresses, at any map position, the phenotypic value of each individual as a function of the specific-mean of the population to which the individual belongs, the additive and dominance effects of the alleles carried by the parents of that population and the probabilities of QTL genotypes conditional on those of neighbouring markers. Standard linear model procedures (ordinary or iteratively reweighted least-squares) are used for estimation and test of the parameters.
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- "Therefore, QTL mapping in complex population designs has been proposed as a more efficient and relevant approach for plant breeding and MAS (Beavis 1994; Muranty 1996; Xu 1998). Additional to the possibility of exploring a larger genetic base, simulation studies (Rebai and Goffinet 2000; Jannink and Jansen 2001; Jansen et al. 2003) have shown that these complex populations may increase the statistical power of QTL detection and the accuracy of their location and allelic effect estimates, especially when some inbred lines are used as parents for different segregating populations. In this situation, it creates connections between the segregating populations that may lead to reduction in the number of allelic effects to be estimated, under the hypothesis of additivity. "
ABSTRACT: Advancements in genotyping are rapidly decreasing marker costs and increasing marker density. This opens new possibilities for mapping quantitative trait loci (QTL), in particular by combining linkage disequilibrium information and linkage analysis (LDLA). In this study, we compared different approaches to detect QTL for four traits of agronomical importance in two large multi-parental datasets of maize (Zea mays L.) of 895 and 928 testcross progenies composed of 7 and 21 biparental families, respectively, and genotyped with 491 markers. We compared to traditional linkage-based methods two LDLA models relying on the dense genotyping of parental lines with 17,728 SNP: one based on a clustering approach of parental line segments into ancestral alleles and one based on single marker information. The two LDLA models generally identified more QTL (60 and 52 QTL in total) than classical linkage models (49 and 44 QTL in total). However, they performed inconsistently over datasets and traits suggesting that a compromise must be found between the reduction of allele number for increasing statistical power and the adequacy of the model to potentially complex allelic variation. For some QTL, the model exclusively based on linkage analysis, which assumed that each parental line carried a different QTL allele, was able to capture remaining variation not explained by LDLA models. These complementarities between models clearly suggest that the different QTL mapping approaches must be considered to capture the different levels of allelic variation at QTL involved in complex traits.Theoretical and Applied Genetics 08/2013; 126(11). DOI:10.1007/s00122-013-2167-9 · 3.79 Impact Factor
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- "This was conducted first mainly in a context of biparental populations derived from the cross between two inbred lines. By addressing a broader diversity, multiparental designs 1) increase the power and the accuracy of QTL detection; 2) enable to estimate simultaneously the different parental allele effects and to identify the most favorable ones for selection (Rebaï and Goffinet 2000; Blanc et al. 2006, 2008). Recently, two main types of multiparental designs have received specific interest in the plant breeding community to increase the resolution of QTL mapping by the joint use of dense genotyping of parental lines and linkage analysis in the progenies: the Nested Association Mapping design (NAM; Yu et al. 2008) and the Multiparent Advanced Inter-Cross design (MAGIC; Cavanagh et al. 2008). "
ABSTRACT: Current advances in plant genotyping lead to major progress in the knowledge of genetic architecture of traits of interest. It is increasingly important to develop decision support tools to help breeders and geneticists to conduct marker-assisted selection methods to assemble favorable alleles that are discovered. Algorithms have been implemented, within an interactive graphical interface, to 1) trace parental alleles throughout generations, 2) propose strategies to select the best plants based on estimated molecular scores, and 3) efficiently intermate them depending on the expected value of their progenies. With the possibility to consider a multi-allelic context, OptiMAS opens new prospects to assemble favorable alleles issued from diverse parents and further accelerate genetic gain.The Journal of heredity 04/2013; 104(4). DOI:10.1093/jhered/est020 · 2.09 Impact Factor
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- "Properties of regression interval mapping and multiple interval mapping estimates For single QTL models, theoretical considerations as well as simulation studies (eg Haley and Knott, 1992; Xu 1995, 1998a, b; Dupuis and Siegmund, 1999; Kao, 2000; Rebai and Goffinet, 2000) have shown that regression interval Table 3 Means, empirical SD and MSE of QTL effect estimates based on significant replicates "
ABSTRACT: Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F(2) populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping.Heredity 07/2005; 94(6):599-605. DOI:10.1038/sj.hdy.6800667 · 3.81 Impact Factor