Genomic dissection of plant development and its impact on thousand grain weight in barley through nested association mapping

AbstractThousand grain weight (TGW) is an important determinant of rice yield, and correlates with grain size, plumpness and grain number per panicle. In rice, there are fewer association mapping studies relating grain weight traits using both SSR and SNP markers. In this study, in order to find robust SSR markers associated with TGW trait and mine elite accessions in rice, we investigated the TGW trait across six environments using a natural population consisted of 462 accessions, and then performed association mapping using both SSR and SNP markers. Using the six datasets from the six environments and their best linear unbiased estimator, we identified eight TGW associated SSR markers, with three environmentally stable and one newly found, on five chromosomes. The associated markers have genetic effect from 3.44% to 20.84%, and two of them carry stable elite allele with positive effect across different environments. Candidate interval association mapping using re-sequencing derived SNP/InDel markers further confirms the TGW-SSR association, and also suggests that 3 TGW-SSR associations were high confident in intervals of size from 176 to 603 kb. These results not only shed more lights on the genetics of TGW trait, but also suggest that the multi-allelic SSR markers should be used as an alternative power tool in gene or QTL mapping.

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Reinvestigation of THOUSAND-GRAIN WEIGHT 6 grain weight genes in wheat and rice indicates a role in pollen development rather than regulation of auxin content in grains

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03804-3 ◽

2021 ◽

Author(s):

Muhammed Rezwan Kabir ◽

Heather M. Nonhebel

Keyword(s):

Pollen Development ◽

Grain Weight ◽

Thousand Grain Weight ◽

Auxin Content

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Genomic Dissection of Peduncle Morphology in Barley through Nested Association Mapping

Plants ◽

10.3390/plants10010010 ◽

2020 ◽

Vol 10 (1) ◽

pp. 10

Author(s):

Sebastian Zahn ◽

Thomas Schmutzer ◽

Klaus Pillen ◽

Andreas Maurer

Keyword(s):

Quantitative Trait Loci ◽

Association Mapping ◽

Cross Section ◽

Quantitative Trait ◽

Spring Barley ◽

Raw Material ◽

Total Thickness ◽

Nested Association Mapping ◽

Elite Cultivar ◽

Major Qtl

Straw biomass and stability are crucial for stable yields. Moreover, straw harbors the potential to serve as a valuable raw material for bio-economic processes. The peduncle is the top part of the last shoot internode and carries the spike. This study investigates the genetic control of barley peduncle morphology. Therefore, 1411 BC1S3 lines of the nested association mapping (NAM) population “Halle Exotic Barley 25” (HEB-25), generated by crossing the spring barley elite cultivar Barke with an assortment of 25 exotic barley accessions, were used. Applying 50k Illumina Infinium iSelect SNP genotyping yielded new insights and a better understanding of the quantitative trait loci (QTL) involved in controlling the peduncle diameter traits, we found the total thickness of peduncle tissues and the area of the peduncle cross-section. We identified three major QTL regions on chromosomes 2H and 3H mainly impacting the traits. Remarkably, the exotic allele at the QTL on chromosome 3H improved all three traits investigated in this work. Introgressing this QTL in elite cultivars might facilitate to adjust peduncle morphology for improved plant stability or enlarged straw biomass production independent of flowering time and without detrimental effects on grain yield.

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Research on a Rice Counting Algorithm Based on an Improved MCNN and a Density Map

Entropy ◽

10.3390/e23060721 ◽

2021 ◽

Vol 23 (6) ◽

pp. 721

Author(s):

Ao Feng ◽

Hongxiang Li ◽

Zixi Liu ◽

Yuanjiang Luo ◽

Haibo Pu ◽

...

Keyword(s):

Grain Weight ◽

Gaussian Kernel ◽

Density Level ◽

Thousand Grain Weight ◽

Original Algorithm ◽

Different Types ◽

Different Shapes ◽

Density Map ◽

Important Basis ◽

Adhesion Condition

The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.

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Genetic Vulnerability and the Relationship of Commercial Germplasms of Maize in Brazil with the Nested Association Mapping Parents

PLoS ONE ◽

10.1371/journal.pone.0163739 ◽

2016 ◽

Vol 11 (10) ◽

pp. e0163739 ◽

Cited By ~ 2

Author(s):

Luciano Rogério Braatz de Andrade ◽

Roberto Fritsche Neto ◽

Ítalo Stefanine Correia Granato ◽

Gustavo César Sant’Ana ◽

Pedro Patric Pinho Morais ◽

...

Keyword(s):

Association Mapping ◽

Nested Association Mapping ◽

Genetic Vulnerability ◽

Relationship Of ◽

The Relationship

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Metabolomic Assessment of Key Maize Resources: GC-MS and NMR Profiling of Grain from B73 Hybrids of the Nested Association Mapping (NAM) Founders and of Geographically Diverse Landraces

Journal of Agricultural and Food Chemistry ◽

10.1021/acs.jafc.5b04901 ◽

2016 ◽

Vol 64 (10) ◽

pp. 2162-2172 ◽

Cited By ~ 17

Author(s):

Tyamagondlu V. Venkatesh ◽

Alexander W. Chassy ◽

Oliver Fiehn ◽

Sherry Flint-Garcia ◽

Qin Zeng ◽

...

Keyword(s):

Association Mapping ◽

Nested Association Mapping

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Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

Plant Phenomics ◽

10.34133/2020/5801869 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Jordan Ubbens ◽

Mikolaj Cieslak ◽

Przemyslaw Prusinkiewicz ◽

Isobel Parkin ◽

Jana Ebersbach ◽

...

Keyword(s):

Image Processing ◽

Association Mapping ◽

Stress Responses ◽

Interspecific Cross ◽

Response To Treatment ◽

Brassica Napus L ◽

Nested Association Mapping ◽

Latent Space ◽

Using Data ◽

Candidate Loci

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

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