Genetics · 2014 · 99 citations · 55 references
Life SciencesGeneticsPlant SciencesHybrid BreedingPlant GeneticsQuantitative GeneticsHybrid Performance EvaluationLinkage AnalysisLinkage DisequilibriumQuantitative Trait LociQuantitative Trait Loci MappingQuantitative Trait Locus MappingFamily-based Qtl MappingLinkage Disequilibrium AnalysisLinkage Disequilibrium PatternsMulti-trait Qtl MappingPleiotropic EffectsQtl DetectionQuantitative Trait Loci (Qtl) MappingSingle Nucleotide Polymorphism Markers
Multiparental designs combined with dense genotyping of parents have been proposed as a way to increase the diversity and resolution of quantitative trait loci (QTL) mapping studies, using methods combining linkage disequilibrium information with linkage analysis (LDLA). Two new nested association mapping designs adapted to European conditions were derived from the complementary dent and flint heterotic groups of maize (Zea mays L.). Ten biparental dent families (N = 841) and 11 biparental flint families (N = 811) were genotyped with 56,110 single nucleotide polymorphism markers and evaluated as test crosses with the central line of the reciprocal design for biomass yield, plant height, and precocity. Alleles at candidate QTL were defined as (i) parental alleles, (ii) haplotypic identity by descent, and (iii) single-marker groupings. Between five and 16 QTL were detected depending on the model, trait, and genetic group considered. In the flint design, a major QTL (R(2) = 27%) with pleiotropic effects was detected on chromosome 10, whereas other QTL displayed milder effects (R(2) < 10%). On average, the LDLA models detected more QTL but generally explained lower percentages of variance, consistent with the fact that most QTL display complex allelic series. Only 15% of the QTL were common to the two designs. A joint analysis of the two designs detected between 15 and 21 QTL for the five traits. Of these, between 27 for silking date and 41% for tasseling date were significant in both groups. Favorable allelic effects detected in both groups open perspectives for improving biomass production.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.
Eric S. Lander, David Botstein · Genetics · 1989 · 5.3K citations · Full text
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Genome-wide association studies of 14 agronomic traits in rice landraces
Xuehui Huang, Xinghua Wei, Tao Sang et al. · Nature Genetics · 2010 · 2.3K citations
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