Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize
Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize
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DOI：
10.1007/s00122-015-2637-3
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发表时间：
2016-02-01
影响因子：
5.4
通讯作者：
Melchinger, Albrecht E.
中科院分区：
文献类型：
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作者：
Han, Sen;Utz, H. Friedrich;Melchinger, Albrecht E.
关键信息 多个相连家族的镰刀菌抗性性状 QTL 分析比单家族分析检测到更多的 QTL。预测准确性与验证集和训练集的亲缘关系密切相关。QTL 作图最近已从单个家族的分析转向多个相关家族的分析，并且已经提出了几种生物识别模型。使用具有 2472 个标记位点的高密度共识图谱，我们对玉米中 5 个相连的双亲家系的 639 个双单倍体 (DH) 品系进行了 QTL 作图，以了解玉米穗腐病抗性，并分析了 DON、赤霉穗腐烂严重程度 (GER) 和吐丝天数 (DS) 等性状。对 QTL 等位基因数量和影响的假设不同的五种生物统计模型进行了比较。模型 2 至 5 对所有科进行联合分析，并使用连锁和/或连锁不平衡 (LD) 信息，比模型 1（单科分析）识别出所有甚至更多的 QTL，并且通常解释了所有三个性状的基因型方差的较高比例 p (G)。 DON 和 GER 的 QTL 大多是家系特异性的，但 DS 的几个 QTL 出现在多个家系中。许多QTL表现出很大的加性效应，大多数增加抗性的等位基因源自抗性亲本。检测到的QTL 与遗传背景（家系）之间的相互作用很少发生且相对较小。对三个完全连接的族的详细分析显示，模型 3 或 4 的 p (G) 值高于模型 2 和 5，无论训练集 (TS) 的大小 N (TS) 是多少。总之，模型 3 和 4 可以推荐用于较大家族的基于 QTL 的预测。在 TS 中包含足够多的全同胞有助于提高针对 TS 组成不同的各种场景的基于 QTL 的预测准确性 (r (VS))。
Key message QTL analysis for Fusarium resistance traits with multiple connected families detected more QTL than single-family analysis. Prediction accuracy was tightly associated with the kinship of the validation and training set.QTL mapping has recently shifted from analysis of single families to multiple, connected families and several biometric models have been suggested. Using a high-density consensus map with 2472 marker loci, we performed QTL mapping with five connected bi-parental families with 639 doubled-haploid (DH) lines in maize for ear rot resistance and analyzed traits DON, Gibberella ear rot severity (GER), and days to silking (DS). Five biometric models differing in the assumption about the number and effects of alleles at QTL were compared. Model 2 to 5 performing joint analyses across all families and using linkage and/or linkage disequilibrium (LD) information identified all and even further QTL than Model 1 (single-family analyses) and generally explained a higher proportion p (G) of the genotypic variance for all three traits. QTL for DON and GER were mostly family specific, but several QTL for DS occurred in multiple families. Many QTL displayed large additive effects and most alleles increasing resistance originated from a resistant parent. Interactions between detected QTL and genetic background (family) occurred rarely and were comparatively small. Detailed analysis of three fully connected families yielded higher p (G) values for Model 3 or 4 than for Model 2 and 5, irrespective of the size N (TS) of the training set (TS). In conclusion, Model 3 and 4 can be recommended for QTL-based prediction with larger families. Including a sufficiently large number of full sibs in the TS helped to increase QTL-based prediction accuracy (r (VS)) for various scenarios differing in the composition of the TS.