InDel Marker Based Estimation of Multi-Gene Allele Contribution and Genetic Variations for Grain Size and Weight in Rice (Oryza sativa L.)
InDel Marker Based Estimation of Multi-Gene Allele Contribution and Genetic Variations for Grain Size and Weight in Rice (Oryza sativa L.)
复制标题
基于 InDel 标记的水稻 (Oryza sativa L.) 多基因等位基因贡献和籽粒大小和重量遗传变异的估计
DOI：
10.3390/ijms20194824
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发表时间：
2019-10-01
影响因子：
5.6
通讯作者：
Liang, Guohua
中科院分区：
文献类型：
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作者：
Gull, Sadia;Haider, Zulqarnain;Liang, Guohua
任何水稻品种的市场成功在很大程度上取决于它的籽粒外观和粮食产量，这决定了消费者和种植者对它的需求。利用插入/缺失(Indel)标记，研究了9个主效基因qPE9~1、GW2、SLG7、GW5、GS3、GS7、GW8、GS5和GS2对204份不同水稻种质粒长、粒宽、粒厚和千粒重4个大小和重量相关性状的影响。所研究的种质在四个研究性状上表现出广泛的变异。除了3个基因外，所有6个基因都显示出与这些性状有相当大的关联，但强度各不相同。204个品种的全部种质可分为三大类，它们具有不同的粒型和粒重，可用于稻米外观和粒重的育种。研究表明，GL对TGW的影响为24.9%，GW的影响为37.4%，GT的影响为49.1%。因此，假设性状选择的趋势，即GT>；GW>；GL，用于在水稻增产计划中改善TGW。Indel标记共成功鉴定出38个等位基因，其中27个是主效等位基因，分布在20多个基因型中。GL与4个基因(GS3、GS7、GW8和GS2)相关。在所研究的9个基因中，GT还被发现由4个不同的基因(GS3、GS7、GW8和GS2)调控。GW受所研究的3个基因(GW5、GW8和GS2)控制，而TGW受4个基因(SLG7、GW5、GW8和GS5)控制。基于所研究的Indel标记座位的未加权算术平均配对方法(UPGMA)树将整个种质分为三个不同的类群，它们的籽粒大小和重量不同。利用基于Indel标记的主坐标分析(PCoA)构建的二维散点图进一步将204份水稻种质资源划分为4个亚群，其中超长、长、中、短粒型种质具有显著的分界，可相应地用于育种。本研究可帮助水稻育种工作者选择合适的INDELL标记，制定改良稻米外观和粒重的育种策略，培育出能以较高的产量回报竞争国际市场需求的水稻品种。本研究还证实了Indel标记在研究水稻种质资源类型、等位基因频率、多基因等位基因贡献、标记-性状关联以及可进一步探索的遗传变异等方面的有效应用。
The market success of any rice cultivar is exceedingly dependent on its grain appearance, as well as its grain yield, which define its demand by consumers as well as growers. The present study was undertaken to explore the contribution of nine major genes, qPE9~1, GW2, SLG7, GW5, GS3, GS7, GW8, GS5, and GS2, in regulating four size and weight related traits, i.e., grain length (GL), grain width (GW), grain thickness (GT), and thousand grain weight (TGW) in 204 diverse rice germplasms using Insertion/Deletion (InDel) markers. The studied germplasm displayed wide-ranging variability in the four studied traits. Except for three genes, all six genes showed considerable association with these traits with varying strengths. Whole germplasm of 204 genotypes could be categorized into three major clusters with different grain sizes and weights that could be utilized in rice breeding programs where grain appearance and weight are under consideration. The study revealed that TGW was 24.9% influenced by GL, 37.4% influenced by GW, and 49.1% influenced by GT. Hence, assuming the trend of trait selection, i.e., GT > GW > GL, for improving TGW in the rice yield enhancement programs. The InDel markers successfully identified a total of 38 alleles, out of which 27 alleles were major and were found in more than 20 genotypes. GL was associated with four genes (GS3, GS7, GW8, and GS2). GT was also found to be regulated by four different genes (GS3, GS7, GW8, and GS2) out of the nine studied genes. GW was found to be under the control of three studied genes (GW5, GW8, and GS2), whereas TGW was found to be under the influence of four genes (SLG7, GW5, GW8, and GS5) in the germplasm under study. The Unweighted Pair Group Method with Arithmetic means (UPGMA) tree based on the studied InDel marker loci segregated the whole germplasm into three distinct clusters with dissimilar grain sizes and weights. A two-dimensional scatter plot constructed using Principal Coordinate Analysis (PCoA) based on InDel markers further separated the 204 rice germplasms into four sub-populations with prominent demarcations of extra-long, long, medium, and short grain type germplasms that can be utilized in breeding programs accordingly. The present study could help rice breeders to select a suitable InDel marker and in formulation of breeding strategies for improving grain appearance, as well as weight, to develop rice varieties to compete international market demands with higher yield returns. This study also confirms the efficient application of InDel markers in studying diverse types of rice germplasm, allelic frequencies, multiple-gene allele contributions, marker-trait associations, and genetic variations that can be explored further.