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.
Improvement of effectiveness and durability of disease resistance in crops most often relies on the use of quantitative resistance, with the hypothesis that a wide range of quantitative resistance factors (QTL) makes the overcoming of the resistance by the pathogen more difficult. For an optimum use of these QTL in effective and durable strategies of resistance deployment, there is a need to precisely know their localization but also their stability/specificity and their allelic effects in various genetic backgrounds. Stem canker caused by the fungus Leptosphaeria maculans is one of the most important diseases in oilseed rape. In this Brassica napus-L. maculans pathosystem, QTL were previously identified by linkage analysis using populations derived from biparental crosses that were analyzed separately. In this study, we explored new quantitative resistance factors using a multi-cross connected design derived from four resistant lines crossed with a single susceptible line. Independent and connected mapping analyses revealed to be complementary to get an overview of QTL organization. We validated different QTL across different years and genetic backgrounds and identified novel QTL which had not yet been mapped. Population-common and population-specific QTL were identified. Knowledge of QTL organization and effects should help in the rational choice of relevant factors in breeding resistant genotypes to be integrated with other control means such as cultural practices and rotations for durable management of the disease.
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.
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