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Genome Wide Single Locus Single Trait Multi-Locus and Multi-Trait Association Mapping for Some Important Agronomic Traits in Common Wheat (T. aestivum L.)

机译:基因组范围的单基因座单性状多基因座和多性状的关联图谱为普通小麦(T. aestivum L.)的一些重要农艺性状

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摘要

Genome wide association study (GWAS) was conducted for 14 agronomic traits in wheat following widely used single locus single trait (SLST) approach, and two recent approaches viz. multi locus mixed model (MLMM), and multi-trait mixed model (MTMM). Association panel consisted of 230 diverse Indian bread wheat cultivars (released during 1910–2006 for commercial cultivation in different agro-climatic regions in India). Three years phenotypic data for 14 traits and genotyping data for 250 SSR markers (distributed across all the 21 wheat chromosomes) was utilized for GWAS. Using SLST, as many as 213 MTAs (p ≤ 0.05, 129 SSRs) were identified for 14 traits, however, only 10 MTAs (~9%; 10 out of 123 MTAs) qualified FDR criteria; these MTAs did not show any linkage drag. Interestingly, these genomic regions were coincident with the genomic regions that were already known to harbor QTLs for same or related agronomic traits. Using MLMM and MTMM, many more QTLs and markers were identified; 22 MTAs (19 QTLs, 21 markers) using MLMM, and 58 MTAs (29 QTLs, 40 markers) using MTMM were identified. In addition, 63 epistatic QTLs were also identified for 13 of the 14 traits, flag leaf length (FLL) being the only exception. Clearly, the power of association mapping improved due to MLMM and MTMM analyses. The epistatic interactions detected during the present study also provided better insight into genetic architecture of the 14 traits that were examined during the present study. Following eight wheat genotypes carried desirable alleles of QTLs for one or more traits, WH542, NI345, NI170, Sharbati Sonora, A90, HW1085, HYB11, and DWR39 (Pragati). These genotypes and the markers associated with important QTLs for major traits can be used in wheat improvement programs either using marker-assisted recurrent selection (MARS) or pseudo-backcrossing method.
机译:根据广泛使用的单基因座单性状(SLST)方法和最近的两种方法,对小麦的14个农艺性状进行了全基因组关联研究(GWAS)。多基因座混合模型(MLMM)和多特征混合模型(MTMM)。协会小组由230个不同的印度面包小麦品种组成(1910–2006年发布,用于印度不同农业气候地区的商业化种植)。 GWAS利用了14个性状的三年表型数据和250个SSR标记的基因型数据(分布在所有21个小麦染色体上)。使用SLST,鉴定出14个性状的多达213个MTA(p≤0.05,129个SSR),但是,只有10个MTA(〜9%; 123个MTA中的10个)合格的FDR标准;这些MTA没有显示任何关联拖累。有趣的是,这些基因组区域与已知具有相同或相关农艺性状的QTL的基因组区域重合。使用MLMM和MTMM,可以识别更多的QTL和标记。使用MLMM识别了22个MTA(19个QTL,21个标记),使用MTMM识别了58个MTA(29个QTL,40个标记)。此外,还鉴定了14个性状中的13个的63个上位QTL,唯一的例外是旗叶长度(FLL)。显然,由于MLMM和MTMM分析,关联映射的功能得到了改善。在本研究中检测到的上位性相互作用还提供了对本研究中检查的14个性状的遗传结构的更好了解。在随后的八种小麦基因型中,WH542,NI345,NI170,Sharbati Sonora,A90,HW1085,HYB11和DWR39(Pragati)具有一个或多个性状的理想QTL等位基因。这些基因型和与重要性状的重要QTL相关的标记可通过标记辅助轮回选择(MARS)或伪回交法用于小麦改良计划。

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