Genetic architecture underpinning yield component traits in wheat

Cao, Shuanghe; Xu, Dengan; Hanif, Mamoona; Xia, Xianchun; He, Zhonghu

doi:10.1007/s00122-020-03562-8

Cited by 142 publications

(116 citation statements)

References 127 publications

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“…As expected, most QTLs associated with TKW coincided with GL and/or GW with an exception of the locus on chromosome 5AL, which was consistent with the common agreement that TKW was the core of other grain traits. According to the location of the latest marker AX-110680682, the physical location was at 413 Mb on chromosome 6A, which was located in the same position as the previously reported QTL region (QTKW-6A-AN and QTKW.caas-6AL) of TKW [23,37,44,57,58]. Likewise, TaTPP-6AL1 was 13-48 Mb away from these loci and might be the causal gene for this QTL region [44].…”

Section: Comparison Of Stable Qtl For Grain-related Traits With Previous Studiessupporting

confidence: 59%

“…According to the location of the latest marker AX-110680682, the physical location was at 413 Mb on chromosome 6A, which was located in the same position as the previously reported QTL region (QTKW-6A-AN and QTKW.caas-6AL) of TKW [23,37,44,57,58]. Likewise, TaTPP-6AL1 was 13-48 Mb away from these loci and might be the causal gene for this QTL region [44]. However, the effect of this locus on GW and GL has not been reported yet.…”

Section: Comparison Of Stable Qtl For Grain-related Traits With Previous Studiesmentioning

confidence: 54%

“…As it is known, grain weight and size are essential components determining wheat yield. Many QTL or homology-based cloned genes for grain-related traits (TKW, GL and GW) have been discovered on all 21 chromosomes [44,55,56]. Based on the genetic analysis of the 194 RIL population of HN5290/06Dn23, 17 stable QTLs were found in chromosome arms 1BS, 2AS, 2AL, 2BL, 2DS, 2DL, 4BS, 5AL, 6AL and 6DL (Supplementary Table S2, Table 4).…”

Section: Comparison Of Stable Qtl For Grain-related Traits With Previous Studiesmentioning

confidence: 99%

“…[4,35,[40][41][42][43]. Nevertheless, there is not yet a grain weight-related gene isolated via map-based cloning in wheat, due to its large and complex genome and as well as minor effect [5,44]. With the advancement of more high-quality reference genomes of common wheat and its relatives [45][46][47][48][49], the efficiency of map-based cloning will become faster.…”

Section: Introductionmentioning

confidence: 99%

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Dissection of Genetic Basis Underpinning Kernel Weight-Related Traits in Common Wheat

Wang

Yue

et al. 2021

Plants

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Genetic dissection kernel weight-related traits is of great significance for improving wheat yield potential. As one of the three major yield components of wheat, thousand kernel weight (TKW) was mainly affected by grain length (GL) and grain width (GW). To uncover the key loci for these traits, we carried out a quantitative trait loci (QTL) analysis of an F6 recombinant inbred lines (RILs) population derived from a cross of Henong 5290 (small grain) and 06Dn23 (big grain) with a 50 K single nucleotide polymorphism (SNP) array. A total of 17 stable and big effect QTL, including 5 for TKW, 8 for GL and 4 for GW, were detected on the chromosomes 1B, 2A, 2B, 2D, 4B, 5A, 6A and 6D, respectively. Among these, there were two co-located loci for three traits that were mapped on the chromosome 4BS and 6AL. The QTL on 6AL was the most stable locus and explained 15.4–24.8%, 4.1–8.8% and 15.7–24.4% of TKW, GW and GL variance, respectively. In addition, two more major QTL of GL were located on chromosome arm 2BL and 2DL, accounting for 9.7–17.8% and 13.6–19.8% of phenotypic variance, respectively. In this study, we found one novel co-located QTL associated with GL and TKW in 2DL, QGl.haaf-2DL.2/QTkw.haaf-2DL.2, which could explain 13.6–19.8% and 9.8–10.7% phenotypic variance, respectively. Genetic regions and linked markers of these stable QTL will help to further refine mapping of the corresponding loci and marker-assisted selection (MAS) breeding for wheat grain yield potential improvement.

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Section: Comparison Of Stable Qtl For Grain-related Traits With Previous Studiessupporting

confidence: 59%

Section: Comparison Of Stable Qtl For Grain-related Traits With Previous Studiesmentioning

confidence: 54%

Section: Comparison Of Stable Qtl For Grain-related Traits With Previous Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dissection of Genetic Basis Underpinning Kernel Weight-Related Traits in Common Wheat

Wang

Yue

et al. 2021

Plants

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“…Based on gene-sequence polymorphisms, candidate gene association analysis has been shown to be an efficient approach to reveal relationships between gene/polymorphic loci and traits, and it has been widely used in many crop species, including maize ( Wang et al , 2016 b ) and wheat ( Zhang et al , 2017 ). A series of elite alleles and molecular markers associated with important traits have been discovered using this method ( Cao et al , 2020 ), and pyramiding these elite alleles through marker-assisted selection (MAS) will greatly accelerate the process of wheat breeding. Hence, finding elite alleles associated with important agronomic traits is considered as being fundamental to wheat genetic improvement.…”

Section: Introductionmentioning

confidence: 99%

RING finger ubiquitin E3 ligase gene TaSDIR1-4A contributes to determination of grain size in common wheat

Wang

Mao

et al. 2020

Journal of Experimental Botany

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Salt and Drought Induced RING finger1 (SDIR1) is a RING-type E3 ubiquitin ligase that plays a pivotal role in ABA-mediated response to salinity and drought stress by the ubiquitination pathway in some plant species. However, its function in wheat (Triticum aestivum) is unknown. Here, we isolated a SDIR1 member, TaSDIR1-4A in wheat and characterized its E3 ubiquitin ligase activity. DNA polymorphism assays showed the presence of two nucleotide variation sites in the promoter region of TaSDIR1-4A; these formed haplotypes Hap-4A-1 and Hap-4A-2 that were detected in wheat populations. Association analysis showed that TaSDIR1-4A was associated with 1,000-grain weight (TGW) in all test environments, including well watered and heat stress situations. Genotypes with Hap-4A-2 had higher TGW than those with Hap-4A-1. The phenotypes in both gene-silenced wheat and transgenic Arabidopsis showed that TaSDIR1-4A was a negative regulator of grain size. Gene expression assays revealed that TaSDIR1-4A was most highly expressed in flag leaves, and expression levels of TaSDIR1-4A were higher in Hap-4A-1 accessions than in Hap-4A-2 accessions. The difference might be attributable to TaERF3 (Ethylene Response Factor) can act as a transcriptional repressor of TaSDIR1-4A in Hap-4A-2, but not of TaSDIR1-4A in Hap-4A-1. The favorable haplotype has been positively selected in breeding programs in China. The functional marker for TaSDIR1-4A should be helpful for wheat breeding.

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UAV‐based RGB imagery and ground measurements for high‐throughput phenotyping of senescence and QTL mapping in bread wheat

Li,

Hassan,

Song

et al. 2023

Crop Science

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Sustainable wheat production is challenged by climate change events such as drought, salinity, and heat stress. Senescence is a gradual programmed cell death trait occurring post anthesis in wheat (Triticum aestivum), with significant affecting stability of yield and quality‐related traits under climate severities. Phenotyping of complex traits is increasingly perceived as a bottleneck due to elevated labour costs and time in large field conditions. Unmanned aerial vehicle (UAV) based platforms using RGB imagery and ground phenotyping based novel traits can facilitate the repeated non‐destructive measurements of crop canopy traits cost‐effectively. Here, we described combined application of UAV‐based RGB imaging and ground measurements based novel trait to quantify canopy senescence in wheat. We reported senescence related traits with high heritability in a recombinant inbred line (RIL) population derived from the cross Zhongmai 175/Lunxuan 987. Our results showed that the selection of slow senescence genotypes using UAV‐based VIs was equally effective as ground‐based traits and illustrated significant variations among the genotypes. We also identified five quantitative trait loci (QTL) for canopy senescence in both UAV and ground‐based data sets using a 50K single‐nucleotide polymorphism array. QTL for UAV‐based VIs from RGB imaging and ground measurements based traits were mapped on chromosomes 1B, 2B, 3A, and 4B. The integration of both data sets with genetic analysis identified an important slow senescence/stay‐green related locus on chromosome 4B that explained phenotypic variation up to 23.07% for both UAV and ground traits at late grain filling stage. Three QTL (QTL‐caas.1B, QTL‐caas.3A, QTL‐caas.4B2) on chromosomes 1B, 3A, and 4B were also validated for slow senescence with high chlorophyll level and cool canopy temperature in a natural population of 160 lines by developing kompetitive allele‐specific PCR markers. QTL‐caas.4B1 was validated not only for senescence but also for plant height. Our findings suggest that UAV‐based RGB imagery is advantageous for precise and rapid assessment of senescence related traits and genetic studies of senescence in wheat.This article is protected by copyright. All rights reserved

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Genetic architecture underpinning yield component traits in wheat

Cited by 142 publications

References 127 publications

Dissection of Genetic Basis Underpinning Kernel Weight-Related Traits in Common Wheat

Dissection of Genetic Basis Underpinning Kernel Weight-Related Traits in Common Wheat

RING finger ubiquitin E3 ligase gene TaSDIR1-4A contributes to determination of grain size in common wheat

UAV‐based RGB imagery and ground measurements for high‐throughput phenotyping of senescence and QTL mapping in bread wheat

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