Cargando…

Mapping of QTLs for Yield Traits Using F(2:3:4) Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara

Wild introgressions play a crucial role in crop improvement by transferring important novel alleles and broadening allelic diversity of cultivated germplasm. In this study, two stable backcross alien introgression lines 166s and 14s derived from Swarn/Oryza nivara IRGC81848 were used as parents to g...

Descripción completa

Detalles Bibliográficos
Autores principales: Beerelli, Kavitha, Balakrishnan, Divya, Addanki, Krishnam Raju, Surapaneni, Malathi, Rao Yadavalli, Venkateswara, Neelamraju, Sarla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959756/
https://www.ncbi.nlm.nih.gov/pubmed/35356124
http://dx.doi.org/10.3389/fpls.2022.790221
_version_ 1784677229333053440
author Beerelli, Kavitha
Balakrishnan, Divya
Addanki, Krishnam Raju
Surapaneni, Malathi
Rao Yadavalli, Venkateswara
Neelamraju, Sarla
author_facet Beerelli, Kavitha
Balakrishnan, Divya
Addanki, Krishnam Raju
Surapaneni, Malathi
Rao Yadavalli, Venkateswara
Neelamraju, Sarla
author_sort Beerelli, Kavitha
collection PubMed
description Wild introgressions play a crucial role in crop improvement by transferring important novel alleles and broadening allelic diversity of cultivated germplasm. In this study, two stable backcross alien introgression lines 166s and 14s derived from Swarn/Oryza nivara IRGC81848 were used as parents to generate populations to map quantitative trait loci (QTLs) for yield-related traits. Field evaluation of yield-related traits in F(2), F(3), and F(4) population was carried out in normal irrigated conditions during the wet season of 2015 and dry seasons of 2016 and 2018, respectively. Plant height, tiller number, productive tiller number, total dry matter, and harvest index showed a highly significant association to single plant yield in F(2), F(3), and F(4). In all, 21, 30, and 17 QTLs were identified in F(2), F(2:3), and F(2:4), respectively, for yield-related traits. QTLs qPH6.1 with 12.54% phenotypic variance (PV) in F(2), qPH1.1 with 13.01% PV, qTN6.1 with 10.08% PV in F(2:3), and qTGW6.1 with 15.19% PV in F(2:4) were identified as major effect QTLs. QTLs qSPY4.1 and qSPY6.1 were detected for grain yield in F(2) and F(2:3) with PV 8.5 and 6.7%, respectively. The trait enhancing alleles of QTLs qSPY4.1, qSPY6.1, qPH1.1, qTGW6.1, qTGW8.1, qGN4.1, and qTDM5.1 were from O. nivara. QTLs of the yield contributing traits were found clustered in the same chromosomal region. qTGW8.1 was identified in a 2.6 Mb region between RM3480 and RM3452 in all three generations with PV 6.1 to 9.8%. This stable and consistent qTGW8.1 allele from O. nivara can be fine mapped for identification of causal genes. From this population, lines C(2)12, C(2)124, C(2)128, and C(2)143 were identified with significantly higher SPY and C(2)103, C(2)116, and C(2)117 had consistently higher thousand-grain weight values than both the parents and Swarna across the generations and are useful in gene discovery for target traits and further crop improvement.
format Online
Article
Text
id pubmed-8959756
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-89597562022-03-29 Mapping of QTLs for Yield Traits Using F(2:3:4) Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara Beerelli, Kavitha Balakrishnan, Divya Addanki, Krishnam Raju Surapaneni, Malathi Rao Yadavalli, Venkateswara Neelamraju, Sarla Front Plant Sci Plant Science Wild introgressions play a crucial role in crop improvement by transferring important novel alleles and broadening allelic diversity of cultivated germplasm. In this study, two stable backcross alien introgression lines 166s and 14s derived from Swarn/Oryza nivara IRGC81848 were used as parents to generate populations to map quantitative trait loci (QTLs) for yield-related traits. Field evaluation of yield-related traits in F(2), F(3), and F(4) population was carried out in normal irrigated conditions during the wet season of 2015 and dry seasons of 2016 and 2018, respectively. Plant height, tiller number, productive tiller number, total dry matter, and harvest index showed a highly significant association to single plant yield in F(2), F(3), and F(4). In all, 21, 30, and 17 QTLs were identified in F(2), F(2:3), and F(2:4), respectively, for yield-related traits. QTLs qPH6.1 with 12.54% phenotypic variance (PV) in F(2), qPH1.1 with 13.01% PV, qTN6.1 with 10.08% PV in F(2:3), and qTGW6.1 with 15.19% PV in F(2:4) were identified as major effect QTLs. QTLs qSPY4.1 and qSPY6.1 were detected for grain yield in F(2) and F(2:3) with PV 8.5 and 6.7%, respectively. The trait enhancing alleles of QTLs qSPY4.1, qSPY6.1, qPH1.1, qTGW6.1, qTGW8.1, qGN4.1, and qTDM5.1 were from O. nivara. QTLs of the yield contributing traits were found clustered in the same chromosomal region. qTGW8.1 was identified in a 2.6 Mb region between RM3480 and RM3452 in all three generations with PV 6.1 to 9.8%. This stable and consistent qTGW8.1 allele from O. nivara can be fine mapped for identification of causal genes. From this population, lines C(2)12, C(2)124, C(2)128, and C(2)143 were identified with significantly higher SPY and C(2)103, C(2)116, and C(2)117 had consistently higher thousand-grain weight values than both the parents and Swarna across the generations and are useful in gene discovery for target traits and further crop improvement. Frontiers Media S.A. 2022-03-09 /pmc/articles/PMC8959756/ /pubmed/35356124 http://dx.doi.org/10.3389/fpls.2022.790221 Text en Copyright © 2022 Beerelli, Balakrishnan, Addanki, Surapaneni, Rao Yadavalli and Neelamraju. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Beerelli, Kavitha
Balakrishnan, Divya
Addanki, Krishnam Raju
Surapaneni, Malathi
Rao Yadavalli, Venkateswara
Neelamraju, Sarla
Mapping of QTLs for Yield Traits Using F(2:3:4) Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara
title Mapping of QTLs for Yield Traits Using F(2:3:4) Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara
title_full Mapping of QTLs for Yield Traits Using F(2:3:4) Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara
title_fullStr Mapping of QTLs for Yield Traits Using F(2:3:4) Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara
title_full_unstemmed Mapping of QTLs for Yield Traits Using F(2:3:4) Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara
title_short Mapping of QTLs for Yield Traits Using F(2:3:4) Populations Derived From Two Alien Introgression Lines Reveals qTGW8.1 as a Consistent QTL for Grain Weight From Oryza nivara
title_sort mapping of qtls for yield traits using f(2:3:4) populations derived from two alien introgression lines reveals qtgw8.1 as a consistent qtl for grain weight from oryza nivara
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959756/
https://www.ncbi.nlm.nih.gov/pubmed/35356124
http://dx.doi.org/10.3389/fpls.2022.790221
work_keys_str_mv AT beerellikavitha mappingofqtlsforyieldtraitsusingf234populationsderivedfromtwoalienintrogressionlinesrevealsqtgw81asaconsistentqtlforgrainweightfromoryzanivara
AT balakrishnandivya mappingofqtlsforyieldtraitsusingf234populationsderivedfromtwoalienintrogressionlinesrevealsqtgw81asaconsistentqtlforgrainweightfromoryzanivara
AT addankikrishnamraju mappingofqtlsforyieldtraitsusingf234populationsderivedfromtwoalienintrogressionlinesrevealsqtgw81asaconsistentqtlforgrainweightfromoryzanivara
AT surapanenimalathi mappingofqtlsforyieldtraitsusingf234populationsderivedfromtwoalienintrogressionlinesrevealsqtgw81asaconsistentqtlforgrainweightfromoryzanivara
AT raoyadavallivenkateswara mappingofqtlsforyieldtraitsusingf234populationsderivedfromtwoalienintrogressionlinesrevealsqtgw81asaconsistentqtlforgrainweightfromoryzanivara
AT neelamrajusarla mappingofqtlsforyieldtraitsusingf234populationsderivedfromtwoalienintrogressionlinesrevealsqtgw81asaconsistentqtlforgrainweightfromoryzanivara