Mixed-model QTL mapping for kernel hardness and dough strength in bread wheat
Mixed-model QTL mapping for kernel hardness and dough strength in bread wheat
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DOI：
10.1007/s00122-005-0190-1
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发表时间：
2006-03-01
影响因子：
5.4
通讯作者：
Bernardo, R
中科院分区：
文献类型：
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作者：
Arbelbide, M;Bernardo, R
植物育种数据包括具有复杂谱系的近交系的不平衡表型数据。由于传统的QTL定位方法不能利用植物育种数据，一种替代方法是通过混合模型程序进行QTL定位。本研究的目的是通过检测小麦籽粒硬度和面团强度的QTL，验证自花授粉作物混合模型QTL定位的有效性。育种计划我们研究了80个亲本和373个实验自交系的65个简单重复序列（SSR）标记和3个候选位点的基因型。该方法包括三个步骤：方差分量估计，单标记分析，和最后的多标记分析与标记效应作为固定效应处理。在1A染色体（靠近候选位点GluA3）和5D染色体（靠近候选位点Ha）上检测到2个影响籽粒硬度的QTL。在1A、1B、1D和5B染色体上检测到4个影响面团强度的QTL。候选基因GluA1，这是与面团强度，是唯一的候选位点发现显着。结果与先前报道的标记和QTL与籽粒硬度和面团强度。与以往的研究假设QTL效应是随机的不同，固定标记效应的假设确定了有利的标记等位基因进行选择。我们的结论是，检测先前映射的QTL验证了混合模型QTL定位在植物育种计划的背景下的有用性。
Plant breeding data comprise unbalanced phenotypic data for inbreds with complex pedigrees. As traditional methods to map quantitative trait loci (QTL) cannot exploit plant breeding data, an alternative approach is QTL mapping via a mixed-model procedure. Our objective was to validate mixed-model QTL mapping for self-pollinated crops by detecting QTL for kernel hardness and dough strength from data in a bread wheat (Triticum aestivum L.) breeding program. We studied 80 parental and 373 experimental inbreds genotyped for 65 simple sequence repeat (SSR) markers and three candidate loci. The methodology involved three steps: variance component estimation, single-marker analyses, and a final multiple-marker analysis with marker effects treated as fixed effects. Two QTLs for kernel hardness were detected on chromosomes 1A (close to candidate locus GluA3) and 5D (close to candidate locus Ha). Four QTLs were detected for dough strength on chromosomes 1A, 1B, 1D, and 5B. Candidate gene GluA1, which was associated with dough strength, was the only candidate locus found significant. Results were consistent with previously reported markers and QTLs associated with kernel hardness and dough strength. Unlike previous studies that have assumed QTL effects as random, the assumption of fixed marker effects identified the favorable marker alleles to select for. We conclude that the detection of previously mapped QTL validates the usefulness of mixed-model QTL mapping in the context of a plant-breeding program.