Genetic architecture of maize kernel row number and whole genome prediction.
Genetic architecture of maize kernel row number and whole genome prediction.
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DOI：
10.1007/s00122-015-2581-2
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发表时间：
2015-11
期刊：
影响因子：
--
通讯作者：
Zhang Z
中科院分区：
文献类型：
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作者：
Liu L;Du Y;Huo D;Wang M;Shen X;Yue B;Qiu F;Zheng Y;Yan J;Zhang Z
玉米粒行数可能由一组大的加性或部分显性基因座和几个小的显性基因座主导，并且可以通过少于 300 个顶级 KRN 相关 SNP 来准确预测。籽粒行数（KRN）是玉米产量的重要组成部分，直接影响籽粒产量。在本研究中，我们结合连锁和关联作图来揭示玉米 KRN 的遗传结构，并使用这些检测到的基因座评估表型的可预测性。一项全基因组关联研究揭示了 31 个相关的单核苷酸多态性 (SNP)，代表 17 个基因组位点，至少在五个单独环境之一中产生影响，并且在所有环境中具有最佳线性无偏预测 (BLUP)。三个 F2:3 群体的连锁作图鉴定出 33 个 KRN 数量性状基因座 (QTL)，代表多个群体/环境共有的 21 个 QTL。这些表现出较大效应的常见 QTL 中的大多数是加性的或部分显性的。我们发现 70% 的 KRN 相关基因组位点被映射到本研究中确定的 KRN QTL、NAM 群体中检测到的 KRN 相关 SNP 热点和/或之前确定的 KRN QTL 热点中。此外，自交系和杂种的KRN可以通过SNP的加性效应来预测，这是使用自交系作为训练集来估计的。使用顶级KRN相关标签SNP的预测精度明显高于随机选择的SNP，大约300个顶级KRN相关标签SNP足以预测自交系和杂种的KRN。结果表明，本研究中检测到的 KRN 相关位点和 QTL 在玉米育种中通过基因组选择改进 KRN 具有巨大潜力。本文的在线版本 (doi:10.1007/s00122-015-2581-2) 包含补充材料，可供授权用户使用。
Maize kernel row number might be dominated by a set of large additive or partially dominant loci and several small dominant loci and can be accurately predicted by fewer than 300 top KRN-associated SNPs. Kernel row number (KRN) is an important yield component in maize and directly affects grain yield. In this study, we combined linkage and association mapping to uncover the genetic architecture of maize KRN and to evaluate the phenotypic predictability using these detected loci. A genome-wide association study revealed 31 associated single nucleotide polymorphisms (SNPs) representing 17 genomic loci with an effect in at least one of five individual environments and the best linear unbiased prediction (BLUP) over all environments. Linkage mapping in three F2:3 populations identified 33 KRN quantitative trait loci (QTLs) representing 21 QTLs common to several population/environments. The majority of these common QTLs that displayed a large effect were additive or partially dominant. We found 70 % KRN-associated genomic loci were mapped in KRN QTLs identified in this study, KRN-associated SNP hotspots detected in NAM population and/or previous identified KRN QTL hotspots. Furthermore, the KRN of inbred lines and hybrids could be predicted by the additive effect of the SNPs, which was estimated using inbred lines as a training set. The prediction accuracy using the top KRN-associated tag SNPs was obviously higher than that of the randomly selected SNPs, and approximately 300 top KRN-associated tag SNPs were sufficient for predicting the KRN of the inbred lines and hybrids. The results suggest that the KRN-associated loci and QTLs that were detected in this study show great potential for improving the KRN with genomic selection in maize breeding. The online version of this article (doi:10.1007/s00122-015-2581-2) contains supplementary material, which is available to authorized users.