Dataset Information

Variations in DENSE AND ERECT PANICLE 1 (DEP1) contribute to the diversity of the panicle trait in high-yielding japonica rice varieties in northern China.

ABSTRACT: Rice is one of mankind's major food staples, and the erect panicle architecture in rice is an important morphological improvement. The dense and erect panicle 1 (DEP1) locus corresponds with the formation of erect panicles and has been widely used in rice breeding. However, the genetic diversity of DEP1 remains narrow. In order to improve the genetic diversity of DEP1, we used a rice germplasm collection of 72 high yielding japonica rice varieties to analyze the contribution of DEP1 to the panicle traits. We found 45 SNPs and 26 insertions and deletions (indels) within the DNA fragment of DEP1. We further detected 7 haplotypes and found that the replacement of 637 bp by a 12 bp fragment could explain the erect panicle architecture in all 72 germplasms. An SNP (G/C) at the -1253 bp of the promoter region caused a core sequence shift (TGGGCC) of a site II transcriptional regulatory element. The association analysis showed that the SNP(G/C) largely affects the number of primary and secondary branches, and grain number per panicle. Our results provide novel insights into the function and genetic diversity of DEP1. The SNP (G/C) at the promoter region will contribute to the flexible application of DEP1 in rice breeding.

SUBMITTER: Zhao M

PROVIDER: S-EPMC5010314 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Variations in DENSE AND ERECT PANICLE 1 (DEP1) contribute to the diversity of the panicle trait in high-yielding japonica rice varieties in northern China.

Zhao Mingzhu M Sun Jian J Xiao Zhiqiu Z Cheng Fei F Xu Hai H Tang Liang L Chen Wenfu W Xu Zhengjin Z Xu Quan Q

Breeding science 20160727 4

Rice is one of mankind's major food staples, and the erect panicle architecture in rice is an important morphological improvement. The dense and erect panicle 1 (DEP1) locus corresponds with the formation of erect panicles and has been widely used in rice breeding. However, the genetic diversity of DEP1 remains narrow. In order to improve the genetic diversity of DEP1, we used a rice germplasm collection of 72 high yielding japonica rice varieties to analyze th ...[more]

PMID: 27795685

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Project description:Long grain geng/japonica rice has a higher market preference due its excellent appearance quality. The dense and erect-panicle 1 (dep1) gene has been widely used in the breeding of high-yielding geng/japonica rice cultivars in China. However, this gene causes short and round grain shape thus making it less attractive in global rice markets. Therefore, breeding of high-yielding long-grain geng/japonica rice cultivars by incorporating dep1 with major-effect grain shape gene is of high priority in rice industry. Up to now, multiple grain shape genes' effect mechanism has been clearly elaborated, however, under the background of erect-panicle geng/japonica rice, the effect of major grain shape genes on the appearance quality need to be further clarified, as detailed reports are limited. Utilizing CRISPR/Cas9 technology, a series of near-isogenic lines (NILs) (YF47dep1-gw8, YF47dep1-gs3, YF47dep1-gl7, YF47dep1-qgl3 and YF47dep1-tgw6) in Yanfeng 47(YF47dep1) background were created. Grain appearance and yield components analysis showed that: (1) All NILs' grain length to width ratio was significantly increased compared to that of YF47dep1, excepted YF47dep1-gs3, (2) The chalkiness degree was significantly reduced in all of the NILs, (3) In all of the NILs, YF47dep1-gw8 grains exhibited the greatest length to width ratio and the lowest chalkiness degree, (4) The composition of glume cells and filling characteristics of the endosperm were two key factors contributing grain shape and grain chalk variations, respectively, and v) Owning to a substantial increase in the thousand grain weight, the yields of YF47dep1-gs3 and YF47dep1-tgw6 were significantly higher than that of YF47dep1, whereas YF47dep1-qgl3 exhibited the lowest yield because of a dramatic decrease in the effective panicle number and thousand grain weight. All the results revealed that pyramiding dep1 with major-effect grain shape alleles was an effective approach to improving the appearance quality of erect-panicle geng/japonica rice, owning to both of the appearance quality and yield improvement, GS3 and TGW6 alleles can be applied directly for breeding long-grain shape geng/japonica rice, and editing GW8 resulted in excellent appearance quality but low yield, therefore, this gene would be difficult to use directly but can be considered as the core germplasm resource.

Project description:BackgroundSeed-set density is an important agronomic trait in rice. However, its genetic mechanism is complex. Chromosome segment substitution lines (CSSLs) are ideal materials for studying complex traits.ResultsA rice CSSL, Z749, with a dense and erect panicle phenotype, was identified among progeny of the recipient parent Nipponbare and the donor parent Xihui 18. Z749 carried seven substitution segments (average length 2.12 Mb). Compared with Nipponbare, Z749 showed significant increases in the numbers of primary (NPB) and secondary branches (NSB), number of spikelets (SPP) and grains per panicle (GPP), seed-set density (SSD), and decrease in panicle length (PL). A secondary F2 population derived from a cross between Nipponbare and Z749 was used to map quantitative trait loci (QTLs) for associated traits. Fifteen QTLs distributed on chromosomes 5, 7, 8, and 10 were detected. The QTL qPL7 might be an allele of OsFAD8 and the remaining 14 QTLs (e.g., qSSD5 and qSSD10 etc.) might be novel. Fourteen QTLs were verified using five single-segment substitution lines (SSSLs). The seed-set density of Z749 was controlled predominantly by one major QTL (qSSD10) and two minor QTLs (qSSD5 and qSSD8). The QTLs qSSD10, qSSD5, and qSSD8 were fine-mapped to intervals of 1.05, 1.46, and 1.53 Mb on chromosomes 10, 5, and 8, respectively. Analysis of QTL additive effects indicated that qSSD5, qSSD8, and qSSD10 from Xihui18 increased seed-set density of Z749 by 14.10, 11.38, and 5.11 spikelets per 10 cm panicle, respectively. Analysis of QTL epistatic effects revealed that pyramiding of qSSD5 and qSSD8, qSSD5 and qSSD10, qSSD8 and qSSD10, and qSSD5, qSSD8 and qSSD10 produced novel genotypes with increased seed-set density.ConclusionsInheritance of seed-set density in Z749 was controlled predominantly by one major QTL (qSSD10) and two minor QTLs (qSSD5 and qSSD8). Then, they were fine-mapped to intervals of 1.05, 1.46, and 1.53 Mb on chromosomes 10, 5, 8, respectively. Two MAPK genes (OsMPK9 and OsMPK17) and one gene (candidate gene 6) involved in auxin metabolism might be candidate genes for qSSD5, and OsSAUR32 might be the candidate gene for qSSD8. Pyramiding of qSSD5, qSSD8, and qSSD10 enhanced seed-set density.

Project description:Plant breeders' knowledge of precise traits preferred by variety users would accelerate varietal turnover and widen adoption of newly developed maize varieties in Ghana. The objective of this research was to provide empirical evidence of trait preferences of farmers and other actors in the maize value chain in northern Ghana, based on which research strategies for maize improvement could be formulated. Participatory rural appraisal was conducted in 2016 to determine key traits preferred by maize value chain actors across the three regions in northern Ghana. A total of 279 maize value chain actors were interviewed. Different scoring and ranking techniques were used to assess the maize traits preferred by the different actors. Participatory variety selection trials were also conducted in the Tolon, West Gonja, Binduri, and Sissala East districts in northern Ghana from 2014 to 2016. The mother-baby trial approach was used to evaluate eight hybrids with 3000 farmers. Data on yield and agronomic performance of the hybrids and farmer's selection criteria were collected. Data analyses were performed using GenStat Edition 16 and SPSS Edition 20 statistical packages. The participatory rural appraisal method identified farmers, input dealers, traders, and processors as the primary maize value chain actors in the study areas. Trait preferences of the different actors overlapped and revolved around grain quality including nutritional value, and stress tolerance and grain yield. Results of the participatory variety selection study revealed that across districts, farmers preferred high-yielding varieties with multiple cobs per plant, white grain endosperm color, and bigger and fully filled cobs. For the first time, our holistic assessment of the trait preferences of key actors of the maize value chain in northern Ghana revealed a comprehensive list of traits, which could be used by breeders to develop varieties that may be preferred by all value chain actors in northern Ghana.Supplementary informationThe online version contains supplementary material available at 10.1007/s13593-021-00708-w.

Project description:BackgroundIn crops, inflorescence complexity and the shape and size of the seed are among the most important characters that influence yield. For example, rice panicles vary considerably in the number and order of branches, elongation of the axis, and the shape and size of the seed. Manual low-throughput phenotyping methods are time consuming, and the results are unreliable. However, high-throughput image analysis of the qualitative and quantitative traits of rice panicles is essential for understanding the diversity of the panicle as well as for breeding programs.ResultsThis paper presents P-TRAP software (Panicle TRAit Phenotyping), a free open source application for high-throughput measurements of panicle architecture and seed-related traits. The software is written in Java and can be used with different platforms (the user-friendly Graphical User Interface (GUI) uses Netbeans Platform 7.3). The application offers three main tools: a tool for the analysis of panicle structure, a spikelet/grain counting tool, and a tool for the analysis of seed shape. The three tools can be used independently or simultaneously for analysis of the same image. Results are then reported in the Extensible Markup Language (XML) and Comma Separated Values (CSV) file formats. Images of rice panicles were used to evaluate the efficiency and robustness of the software. Compared to data obtained by manual processing, P-TRAP produced reliable results in a much shorter time. In addition, manual processing is not repeatable because dry panicles are vulnerable to damage. The software is very useful, practical and collects much more data than human operators.ConclusionsP-TRAP is a new open source software that automatically recognizes the structure of a panicle and the seeds on the panicle in numeric images. The software processes and quantifies several traits related to panicle structure, detects and counts the grains, and measures their shape parameters. In short, P-TRAP offers both efficient results and a user-friendly environment for experiments. The experimental results showed very good accuracy compared to field operator, expert verification and well-known academic methods.

Project description:BackgroundIdentification and characterization of new traits with sound physiological foundation is essential for crop breeding and production management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of identification of physiological traits. Taking the advantage of deep learning, this study aims to develop a novel trait of canopy structure that integrate source and sink in japonica rice.ResultsWe applied a deep learning approach to accurately segment leaf and panicle, and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice canopy during grain filling stage. Images of training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation and the azimuth angles of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy was then selected to check the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPR displayed large spatial and temporal variations as well as genotypic differences. In addition, it was responsive to agronomical practices such as nitrogen fertilization and spraying of plant growth regulators.ConclusionDeep learning technique can achieve high accuracy in simultaneous detection of panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable to detect and quantify crop performance under field conditions. The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.

Dataset Information

Variations in DENSE AND ERECT PANICLE 1 (DEP1) contribute to the diversity of the panicle trait in high-yielding japonica rice varieties in northern China.

Publications

Variations in <i>DENSE AND ERECT PANICLE 1</i> (<i>DEP1</i>) contribute to the diversity of the panicle trait in high-yielding <i>japonica</i> rice varieties in northern China.

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