A splice acceptor site mutation in TaGW2-A1 increases thousand grain weight in tetraploid and hexaploid wheat through wider and longer grains

Simmonds, James; Scott, P. R.; Brinton, Jemima; Mestre, Teresa C.; Bush, Maxwell S.; Blanco, Alicia del; Dubcovsky, Jorge; Uauy, Cristóbal

doi:10.1007/s00122-016-2686-2

Cited by 171 publications

(173 citation statements)

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“…These mutations can be combined to study gene function and to reveal previously hidden phenotypic variation. Likewise, the effects of candidate genes from diploid grass species can now be studied directly in wheat, as recently shown for the wheat TaGW2-A1 mutants with increased grain size identified in the tetraploid population (31). The strategy and methods developed herein can be also applied to other young polyploid crops with closely related genomes.…”

Section: Discussionmentioning

confidence: 89%

“…Strategies to account for this high level of background mutation include the use of multiple independent mutants, backcrossing to reduce mutation load, and selecting for isogenic sibling lines that share background mutations. For mutations with subtle phenotypic effects or that require field phenotyping, it is advisable to backcross the mutant lines to the nonmutagenized parent for at least two generations before combining homeologous mutations (30,31). Sibling lines can then be selected for homozygous WT or null-mutant alleles.…”

Section: Discussionmentioning

confidence: 99%

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Uncovering hidden variation in polyploid wheat

Krasileva

Vasquez-Gross

Howell

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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483

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Comprehensive reverse genetic resources, which have been key to understanding gene function in diploid model organisms, are missing in many polyploid crops. Young polyploid species such as wheat, which was domesticated less than 10,000 y ago, have high levels of sequence identity among subgenomes that mask the effects of recessive alleles. Such redundancy reduces the probability of selection of favorable mutations during natural or human selection, but also allows wheat to tolerate high densities of induced mutations. Here we exploited this property to sequence and catalog more than 10 million mutations in the protein-coding regions of 2,735 mutant lines of tetraploid and hexaploid wheat. We detected, on average, 2,705 and 5,351 mutations per tetraploid and hexaploid line, respectively, which resulted in 35-40 mutations per kb in each population. With these mutation densities, we identified an average of 23-24 missense and truncation alleles per gene, with at least one truncation or deleterious missense mutation in more than 90% of the captured wheat genes per population. This public collection of mutant seed stocks and sequence data enables rapid identification of mutations in the different copies of the wheat genes, which can be combined to uncover previously hidden variation. Polyploidy is a central phenomenon in plant evolution, and many crop species have undergone recent genome duplication events. Therefore, the general strategy and methods developed herein can benefit other polyploid crops.wheat | polyploidy | mutations | reverse genetics | exome capture S ince the dawn of agriculture, wheat has been a major dietary source of calories and protein for humans. The cultivated wheat species Triticum turgidum (tetraploid, AABB genome) and Triticum aestivum (hexaploid, AABBDD genome) originated via recent polyploidization events followed by domestication.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Uncovering hidden variation in polyploid wheat

Krasileva

Vasquez-Gross

Howell

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

483

605

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“…Similarly, Liu et al (2015) showed that GS5 contributes to kernel size variation in maize as well as in rice. Notably, the wheat orthologs of rice GW2 and GS5 also were associated significantly with wheat kernel size and weight (Su et al, 2011;Hong et al, 2014;Qin et al, 2014;Jaiswal et al, 2015;Wang et al, 2015Wang et al, , 2016Ma et al, 2016;Simmonds et al, 2016). In addition to GS3, GW2, and GS5, many other genes controlling rice kernel size/weight have been cloned, such as genes involved in G-protein signaling (DEP1 and D1) and genes from phytohormone pathways (DST and Gn1a for cytokinin; D11, SRS5, D61, qGL3, and SMG1 for brassinosteroid; and TGW6 for auxin).…”

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confidence: 99%

The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice

et al. 2017

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Maize (Zea mays) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice (Oryza sativa) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1, a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis (Arabidopsis thaliana) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis.Maize (Zea mays) is one of the most important crops and is cultivated worldwide as a source of staple food, animal feed, and industrial materials. According to the Food and Agriculture Organization, the production of maize was 1,016.7 million tons in 2013, which was far more than rice (Oryza sativa) and wheat (Triticum aestivum; 745.7 and 713.1 million tons, respectively). Yield improvement is a central goal of maize breeding. Kernel size and weight are two significant components of maize yield, and many attempts have been made to elucidate the genetic basis of kernel size and weight.Many studies have mapped quantitative trait loci (QTLs) for natural variations in kernel size and weight. (2015) mapped 28 QTLs in a test cross population. Most of these studies used two diverse inbred lines to develop the segregating population and used a limited number of genetic markers to construct the linkage map, which greatly limited the resolution and power to detect rare and/or small-effect QTLs. Large-scale QTL mapping studies including more diverse genetic backgrounds and dense genetic markers would provide more insight into the number and effect of QTLs controlling the natural variations of kernel size and weight in maize.

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“…Backcrossing to WT Kronos can be started either with the single mutants while carrying out the initial cross and/or with the F2 double mutant at a later stage. Backcrossing to remove background mutations is especially important when studying quantitative traits, such as grain size (Simmonds et al, 2016), and when plants are intended for field phenotyping.…”

Section: Case Studymentioning

confidence: 99%

A roadmap for gene functional characterisation in wheat

Adamski

Borrill

Brinton

et al. 2018

Preprint

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Research in Arabidopsis and other model species has uncovered mechanisms regulating important biological processes in plants. With the advent of high quality functional genomic resources in wheat it is now possible to use this knowledge for crop improvement directly in wheat. In the current review, we describe some of the recent developments in wheat genomics focussing on published and publicly available resources and tools. We lay out a roadmap on how to make use of them and include a case study to exemplify them. We hope this review will be a helpful guide for plant scientists who already work on wheat or who are considering expanding their research into wheat.

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A splice acceptor site mutation in TaGW2-A1 increases thousand grain weight in tetraploid and hexaploid wheat through wider and longer grains

Cited by 171 publications

References 45 publications

Uncovering hidden variation in polyploid wheat

Uncovering hidden variation in polyploid wheat

The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice

A roadmap for gene functional characterisation in wheat

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