Sahu Parmeshwar K, Mondal Suvendu, Sao Richa, Vishwakarma Gautam, Kumar Vikash, Das B K, Sharma Deepak
Department of Genetics and Plant Breeding, Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh 492012 India.
Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Centre, Mumbai, 400085 India.
3 Biotech. 2020 Nov;10(11):487. doi: 10.1007/s13205-020-02467-z. Epub 2020 Oct 24.
A core set of 190 rice landraces were used to decipher the genetic structure and to discover the chromosomal regions containing QTLs, affecting the grain micro-nutrients, fatty acids, and yield-related traits by using 148 molecular markers in this study. Landraces were categorized into three sub-groups based on population stratification study and followed by neighbor-joining tree and principal component analysis. Analysis of variance revealed abundant variations among the landraces for studied traits with less influence of environmental factors. Genome Wide Association Studies (GWAS) revealed 22 significant and consistent QTLs through marker trait association (MTAs) for 12 traits based on 2 years and pooled analysis. Out of 22 QTLs, three have been reported earlier while 19 QTLs are novel. Interestingly, 13 QTLs out of 22 were explained more than 10% phenotypic variance. Association of RM1148 and RM205 with Days to 50% flowering was comparable with flowering control genes and , respectively. Similarly, Zn content was associated with RM44, which is situated within the QTL . Moreover, significant association of RM25 with oleic acid content was closely positioned with QTL Association of RM7434 with grain yield/plant; RM184 with spikelet fertility %; R3M10, R9M42 with hundred seed weight; RM536, RM17467, RM484, RM26063 with Fe content; RM44, RM6839 with Zn content are the major outcomes of this study. In addition, association of R11M23 with days to 50% flowering, panicle length and total spikelets per panicle are explained the possible occurrence of pleiotropism among these traits. Prominent rice landraces viz Anjani (early maturity); Sihar (extra dwarf); Gangabaru (highest grain yield/plant); Karhani (highest iron content); Byalo-2 (highest zinc content) and Kadamphool (highest oleic acid) were identified through this study. The present study will open many avenues towards utilization of these QTLs and superior landraces in rice breeding for developing nutrition-rich high yielding varieties.
在本研究中,利用148个分子标记,对190份水稻地方品种的核心种质进行分析,以解析其遗传结构,并发现包含数量性状基因座(QTL)的染色体区域,这些区域影响谷物微量营养素、脂肪酸及产量相关性状。基于群体分层研究,将地方品种分为三个亚组,随后进行邻接法树状图分析和主成分分析。方差分析表明,在所研究的性状中,地方品种间存在丰富的变异,且环境因素影响较小。全基因组关联研究(GWAS)基于两年的数据及合并分析,通过标记-性状关联(MTA)揭示了与12个性状相关的22个显著且一致的QTL。在这22个QTL中,有3个先前已有报道,19个为新发现的。有趣的是,22个QTL中有13个解释了超过10%的表型变异。RM1148和RM205与50%开花天数的关联分别与开花控制基因 和 相当。同样,锌含量与RM44相关,RM44位于QTL 内。此外,RM25与油酸含量的显著关联与QTL 紧密定位;RM7434与单株产量的关联;RM184与小穗育性百分比的关联;R3M10、R9M42与百粒重的关联;RM536、RM17467、RM484、RM26063与铁含量的关联;RM44、RM6839与锌含量的关联是本研究的主要成果。此外,R11M23与50%开花天数、穗长及每穗总小穗数的关联解释了这些性状间可能存在的多效性。通过本研究鉴定出了一些突出的水稻地方品种,即Anjani(早熟);Sihar(特矮);Gangabaru(单株产量最高);Karhani(铁含量最高);Byalo-2(锌含量最高)和Kadamphool(油酸含量最高)。本研究将为利用这些QTL和优良地方品种进行水稻育种,培育营养丰富的高产水稻品种开辟诸多途径。