Conditional QTL mapping of three yield components in common wheat (Triticum aestivum L.)

Zhang, Han; Chen, Jiansheng; Li, Ruyu; Deng, Zhiying; Zhang, Kunpu; Liu, Bin; Tian, Jichun

doi:10.1016/j.cj.2016.01.007

Cited by 51 publications

(46 citation statements)

References 26 publications

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“…The QTLs for KN on 2D were located in the same position in SC population and ITMI population through comparison of genetic maps in previous studies (Song et al., ; Sourdille et al., ; Yu, Mao, Chen, Pu et al., ). This QTL location was consistent with that of QKNPS ‐ DH ‐ 2D ‐ 1.1 (Zhang et al., ). The QTL for KN in ITMI on chromosome 1A is independent of the environment, and its expression is stable in both investigated environments.…”

Section: Discussionsupporting

confidence: 87%

“…This QTL located in marker interval Xbcd98 and Xbcd1407 and corresponded to the QTL identified by Zhang et al. (). Similar QTL locations on 1A ( Xbcd98 ‐ Xbcd1407 ), 2D and 5A ( Xbarc360 ‐ Xmwg624 ) were reported in ITMI population in previous study (García‐Suárez et al., ; Kumar et al., ).…”

Section: Discussionsupporting

confidence: 74%

“…These were QTLs for y (GY|TN) on chromosomes 1D, 2D and 5B, QTLs for y (GY|FSN) on chromosomes 1B, 4A, 5A, and 7B, QTLs for y (GY|KN) on chromosomes 1B and 5A and a QTL for y (GY|STKW) on chromosome 6D. Several previous studies have reported conditional QTL maps for wheat yield and yield‐related traits in multiple genetic backgrounds (Cui et al., ; Ding et al., ; Zhang et al., , ). However, their objective traits and related components were not evaluated consistently, and so few comparable QTLs have been identified in conditional analyses between SC (or ITMI) populations and populations in other genetic backgrounds.…”

Section: Discussionmentioning

confidence: 98%

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Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level

Mao

Chen

et al. 2018

Plant Breeding

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In wheat, strong genetic correlations have been found between grain yield (GY) and tiller number per plant (TN), fertile spikelet number per spike (FSN), kernel number per spike (KN) and thousand‐kernel weight (TKW). To investigate their genetic relationships at the individual quantitative trait locus (QTL) level, we performed both normal and multivariate conditional QTL analysis based on two recombinant inbred lines (RILs) populations. A total of 79 and 48 normal QTLs were identified in the International Triticeae Mapping Initiative (ITMI)/SHW‐L1 × Chuanmai 32 (SC) populations, respectively, as well as 55 and 35 conditional QTLs. Thirty‐two QTL clusters in the ITMI population and 18 QTL clusters in the SC population explained 0.9%–46.2% of phenotypic variance for two to eight traits. A comparison between the normal and conditional QTL mapping analyses indicated that FSN made the smallest contribution to GY among the four GY components that were considered at the QTL level. The effects of TN, KN and TKW on GY were stronger at the QTL level.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 98%

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Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level

Mao

Chen

et al. 2018

Plant Breeding

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“…iari-7A ) were identified in our study for TKW. Earlier reports for TKW on chromosome 1A [40, 44], 2A [41], 2B [40, 44], 5B [40, 44] and 7A [41, 43, 45] in different mapping populations confirms the involvement of these chromosomes.…”

Section: Discussionmentioning

confidence: 62%

“…However, in most studies on identification of QTLs for Fe [28–35], Zn [28–30, 32–37], PC [28, 32–33, 38–39], and TKW [40–45] the size of mapping population was limited. The present study was aimed to identify the genomic region(s) associated with grain Fe, Zn, PC, and TKW in a set of the 286 RILs.…”

Section: Introductionmentioning

confidence: 99%

Molecular mapping of the grain iron and zinc concentration, protein content and thousand kernel weight in wheat (Triticum aestivum L.)

et al. 2017

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Genomic regions responsible for accumulation of grain iron concentration (Fe), grain zinc concentration (Zn), grain protein content (PC) and thousand kernel weight (TKW) were investigated in 286 recombinant inbred lines (RILs) derived from a cross between an old Indian wheat variety WH542 and a synthetic derivative (Triticum dicoccon PI94624/Aegilops squarrosa [409]//BCN). RILs were grown in six environments and evaluated for Fe, Zn, PC, and TKW. The population showed the continuous distribution for all the four traits, that for pooled Fe and PC was near normal, whereas, for pooled Zn, RILs exhibited positively skewed distribution. A genetic map spanning 2155.3cM was constructed using microsatellite markers covering the 21 chromosomes and used for QTL analysis. 16 quantitative trait loci (QTL) were identified in this study. Four QTLs (QGFe.iari-2A, QGFe.iari-5A, QGFe.iari-7A and QGFe.iari-7B) for Fe, five QTLs (QGZn.iari-2A, QGZn.iari-4A, QGZn.iari-5A, QGZn.iari-7A and QGZn.iari-7B) for Zn, two QTLs (QGpc.iari-2A and QGpc.iari-3A) for PC, and five QTLs (QTkw.iari-1A, QTkw.iari-2A, QTkw.iari-2B, QTkw.iari-5B and QTkw.iari-7A) for TKW were identified. The QTLs together explained 20.0%, 32.0%, 24.1% and 32.3% phenotypic variation, respectively, for Fe, Zn, PC and TKW. QGpc.iari-2A was consistently expressed in all the six environments, whereas, QGFe.iari-7B and QGZn.iari-2A were identified in two environments each apart from pooled mean. QTkw.iari-2A and QTkw.iari-7A, respectively, were identified in four and three environments apart from pooled mean. A common region in the interval of Xgwm359-Xwmc407 on chromosome 2A was associated with Fe, Zn, and PC. One more QTL for TKW was identified on chromosome 2A but in a different chromosomal region (Xgwm382-Xgwm359). Two more regions on 5A (Xgwm126-Xgwm595) and 7A (Xbarc49-Xwmc525) were found to be associated with both Fe and Zn. A QTL for TKW was identified (Xwmc525-Xbarc222) in a different chromosomal region on the same chromosome (7A). This reflects at least a partly common genetic basis for the four traits. It is concluded that fine mapping of the regions of the three chromosomes of A genome involved in determining the accumulation of Fe, Zn, PC, and TKW in this mapping population may be rewarding.

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Phenotypic and genetic analysis of spike and kernel characteristics in wheat reveals long-term genetic trends of grain yield components

et al. 2018

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Phenotypic and genetic analysis of six spike and kernel characteristics in wheat revealed geographic patterns as well as long-term trends arising from breeding progress, particularly in regard to spikelet fertility, i.e. the number of kernels per spikelet, a grain yield component that appears to underlie the increase in the number of kernels per spike. Wheat is a staple crop of global relevance that faces continuous demands for improved grain yield. In this study, we evaluated a panel of 407 winter wheat cultivars for six characteristics of spike and kernel development. All traits showed a large genotypic variation and had high heritabilities. We observed geographic patterns for some traits in addition to long-term trends showing a continuous increase in the number of kernels per spike. This breeding progress is likely due to the increase in spikelet fertility, i.e. the number of kernels per spikelet. While the number of kernels per spike and spikelet fertility were significantly positively correlated, both traits showed a significant negative correlation with thousand-kernel weight. Genome-wide association mapping identified only small- and moderate-effect QTL and an effect of the phenology loci Rht-D1 and Ppd-D1 on some of the traits. The allele frequencies of some QTL matched the observed geographic patterns. The quantitative inheritance of all traits with contributions of additional small-effect QTL was substantiated by genomic prediction. Taken together, our results suggest that some of the examined traits were already the basis of grain yield progress in wheat in the past decades. A more targeted exploitation of the available variation, potentially coupled with genomic approaches, may assist wheat breeding in continuing to increase yield levels globally.

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Conditional QTL mapping of three yield components in common wheat (Triticum aestivum L.)

Cited by 51 publications

References 26 publications

Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level

Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level

Molecular mapping of the grain iron and zinc concentration, protein content and thousand kernel weight in wheat (Triticum aestivum L.)

Phenotypic and genetic analysis of spike and kernel characteristics in wheat reveals long-term genetic trends of grain yield components

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