More about quantitative trait locus mapping with diallel designs.

INRA Centre de Toulouse, Unit of Biometry and Artificial Intelligence, Castanet-Tolosan, France.
Genetics Research (Impact Factor: 2.2). 05/2000; 75(2):243-7. DOI: 10.1017/S0016672399004358
Source: PubMed

ABSTRACT 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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    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; · 3.51 Impact Factor
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    ABSTRACT: El objetivo de este trabajo fue evaluar marcadores moleculares y caracteres cuantitativos en un cruzamiento dialélico completo sin recíprocos, entre cinco líneas recombinantes de tomate y sus híbridos. Seobtuvieron perfiles de AFLP ("amplified fragment length polymorphism") y de polipéptidos del pericarpio en cuatro estados de madurez del fruto de 15genotipos. Seevaluaron, entre otros: peso, acidez titulable, pH, vida poscosecha y firmeza. Secalculó el porcentaje de polimorfismo para los marcadores moleculares y el porcentaje de variabilidad genética para los caracteres cuantitativos en el grupo de líneas recombinantes, el de híbridos y el conjunto de genotipos. Serealizaron análisis de agrupamiento con cada nivel de variación genética. Para AFLP, el porcentaje de polimorfismo varió entre 34 y 54% y, para los perfiles polipeptídicos, entre 40 y 78%. Mayor polimorfismo fue observado en el grupo de híbridos. La variabilidad genética fue de 100% para acidez y 34% para firmeza, con los mayores valores en los parentales. La similitud genética varió entre los genotipos según el nivel de variación genética; pero la consistencia en el agrupamiento de algunas líneas recombinantes y sus híbridos fue conservada, lo que evidenció asociaciones entre los datos moleculares y fenotípicos.
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    ABSTRACT: QTL mapping in multiple families identifies trait-specific and pleiotropic QTL for biomass yield and plant height in triticale. Triticale shows a broad genetic variation for biomass yield which is of interest for a range of purposes, including bioenergy. Plant height is a major contributor to biomass yield and in this study, we investigated the genetic architecture underlying biomass yield and plant height by multiple-line cross QTL mapping. We employed 647 doubled haploid lines from four mapping populations that have been evaluated in four environments and genotyped with 1710 DArT markers. Twelve QTL were identified for plant height and nine for biomass yield which cross-validated explained 59.6 and 38.2 % of the genotypic variance, respectively. A major QTL for both traits was identified on chromosome 5R which likely corresponds to the dominant dwarfing gene Ddw1. In addition, we detected epistatic QTL for plant height and biomass yield which, however, contributed only little to the genetic architecture of the traits. In conclusion, our results demonstrate the potential of genomic approaches for a knowledge-based improvement of biomass yield in triticale.
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May 20, 2014