Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Agrícola (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000601101 |
Resumo: | ABSTRACT: Phenomic characterization through digital imaging (DI) can capture the three dimensional variation in wheat grain size and shape using different image orientations. Digital imaging may help identifying genomic regions controlling grain morphology using association mapping with simple sequence repeats (SSRs) markers. Accordingly, seed shape phenotypic data of a core collection of 55 wheat genotypes, previously characterized for osmotic and drought tolerance, were produced using computer based Smart grain software. Measured dimensions included seed volume, area, perimeters, length, width, length to width ratio, circularity, horizontal deviation from ellipse (HDEV), vertical deviation from ellipse (VDEV), factor form density (FFD) etc. The thousand grain weight (TGW) was positively correlated with grain size direct measurements; however, VDEV, FFD and other derived grain attributes showed no or negative correlation with TGW. Digital imaging divided the genotypes correctly into well-defined clusters. The wheat genotypes studied were further grouped into two sub-clusters by the Bayesian structure analysis using unlinked SSR markers. A number of loci over various chromosomal regions were found associated to grain morphology by the genome wide analysis using mixed linear model (MLM) approach. A considerable number of marker-trait associations (MTAs) on chromosomes 1D and 2D may carry new alleles with potential to enhance grain weight due to the use of untapped wild accessions of Aegilops tauschii. Conclusively, we demonstrated the application of multiple approaches including high throughput phenotyping using DI complemented with genome wide association studies to identify candidate genomic regions underlying these traits, which allows a better understanding on molecular genetics of wheat grain weight. |
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Scientia Agrícola (Online) |
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Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weightsynthetic derived bread wheatsseed digital imagingpath analysismarker-trait associationABSTRACT: Phenomic characterization through digital imaging (DI) can capture the three dimensional variation in wheat grain size and shape using different image orientations. Digital imaging may help identifying genomic regions controlling grain morphology using association mapping with simple sequence repeats (SSRs) markers. Accordingly, seed shape phenotypic data of a core collection of 55 wheat genotypes, previously characterized for osmotic and drought tolerance, were produced using computer based Smart grain software. Measured dimensions included seed volume, area, perimeters, length, width, length to width ratio, circularity, horizontal deviation from ellipse (HDEV), vertical deviation from ellipse (VDEV), factor form density (FFD) etc. The thousand grain weight (TGW) was positively correlated with grain size direct measurements; however, VDEV, FFD and other derived grain attributes showed no or negative correlation with TGW. Digital imaging divided the genotypes correctly into well-defined clusters. The wheat genotypes studied were further grouped into two sub-clusters by the Bayesian structure analysis using unlinked SSR markers. A number of loci over various chromosomal regions were found associated to grain morphology by the genome wide analysis using mixed linear model (MLM) approach. A considerable number of marker-trait associations (MTAs) on chromosomes 1D and 2D may carry new alleles with potential to enhance grain weight due to the use of untapped wild accessions of Aegilops tauschii. Conclusively, we demonstrated the application of multiple approaches including high throughput phenotyping using DI complemented with genome wide association studies to identify candidate genomic regions underlying these traits, which allows a better understanding on molecular genetics of wheat grain weight.Escola Superior de Agricultura "Luiz de Queiroz"2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000601101Scientia Agricola v.77 n.6 2020reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2019-0069info:eu-repo/semantics/openAccessAli,AhmadUllah,ZahidAlam,NaveedNaqvi,S.M. SaqlanJamil,MuhammadBux,HadiSher,Hassaneng2020-01-17T00:00:00Zoai:scielo:S0103-90162020000601101Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2020-01-17T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight |
title |
Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight |
spellingShingle |
Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight Ali,Ahmad synthetic derived bread wheats seed digital imaging path analysis marker-trait association |
title_short |
Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight |
title_full |
Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight |
title_fullStr |
Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight |
title_full_unstemmed |
Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight |
title_sort |
Genetic analysis of wheat grains using digital imaging and their relationship to enhance grain weight |
author |
Ali,Ahmad |
author_facet |
Ali,Ahmad Ullah,Zahid Alam,Naveed Naqvi,S.M. Saqlan Jamil,Muhammad Bux,Hadi Sher,Hassan |
author_role |
author |
author2 |
Ullah,Zahid Alam,Naveed Naqvi,S.M. Saqlan Jamil,Muhammad Bux,Hadi Sher,Hassan |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Ali,Ahmad Ullah,Zahid Alam,Naveed Naqvi,S.M. Saqlan Jamil,Muhammad Bux,Hadi Sher,Hassan |
dc.subject.por.fl_str_mv |
synthetic derived bread wheats seed digital imaging path analysis marker-trait association |
topic |
synthetic derived bread wheats seed digital imaging path analysis marker-trait association |
description |
ABSTRACT: Phenomic characterization through digital imaging (DI) can capture the three dimensional variation in wheat grain size and shape using different image orientations. Digital imaging may help identifying genomic regions controlling grain morphology using association mapping with simple sequence repeats (SSRs) markers. Accordingly, seed shape phenotypic data of a core collection of 55 wheat genotypes, previously characterized for osmotic and drought tolerance, were produced using computer based Smart grain software. Measured dimensions included seed volume, area, perimeters, length, width, length to width ratio, circularity, horizontal deviation from ellipse (HDEV), vertical deviation from ellipse (VDEV), factor form density (FFD) etc. The thousand grain weight (TGW) was positively correlated with grain size direct measurements; however, VDEV, FFD and other derived grain attributes showed no or negative correlation with TGW. Digital imaging divided the genotypes correctly into well-defined clusters. The wheat genotypes studied were further grouped into two sub-clusters by the Bayesian structure analysis using unlinked SSR markers. A number of loci over various chromosomal regions were found associated to grain morphology by the genome wide analysis using mixed linear model (MLM) approach. A considerable number of marker-trait associations (MTAs) on chromosomes 1D and 2D may carry new alleles with potential to enhance grain weight due to the use of untapped wild accessions of Aegilops tauschii. Conclusively, we demonstrated the application of multiple approaches including high throughput phenotyping using DI complemented with genome wide association studies to identify candidate genomic regions underlying these traits, which allows a better understanding on molecular genetics of wheat grain weight. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000601101 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162020000601101 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-992x-2019-0069 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
publisher.none.fl_str_mv |
Escola Superior de Agricultura "Luiz de Queiroz" |
dc.source.none.fl_str_mv |
Scientia Agricola v.77 n.6 2020 reponame:Scientia Agrícola (Online) instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Scientia Agrícola (Online) |
collection |
Scientia Agrícola (Online) |
repository.name.fl_str_mv |
Scientia Agrícola (Online) - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
scientia@usp.br||alleoni@usp.br |
_version_ |
1748936465291673600 |