Identification of Seedling Vigor-Associated Quantitative Trait Loci in <i>Temperate Japonica</i> Rice

Cordero-Lara, Karla I.; Kim, Hyun-Jung; Tai, Thomas H.

doi:10.9787/pbb.2016.4.4.426

Cited by 13 publications

(11 citation statements)

References 31 publications

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“…Seedling vigor is controlled by many QTLs and is also one of several important agronomic traits for direct-seeding rice systems (Yano et al, 2012). During the last few decades, a large number of QTLs associated with seedling vigor have been identified (Cui et al, 2002;Zhang et al, 2005;Lu et al, 2007;Zhou et al, 2007;Cairns et al, 2009;Abe et al, 2012;Yano et al, 2012;Cheng et al, 2013;Diwan et al, 2013;Dang et al, 2014;Cordero-Lara et al, 2016;Singh et al, 2017;Zhang et al, 2017). Among these QTLs, only one major QTL for seedling height (SH) on the long arm of chromosome 3 has been finely mapped, and OsGA20ox1 is the most likely candidate gene at this locus (Abe et al, 2012;Yano et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Trait Locus Analysis of Seed Germination and Early Seedling Growth in Rice

Yang

et al. 2019

Front. Plant Sci.

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Seed germination and early seedling growth are important agricultural traits for developing populations of both irrigated and directly seeded rice (DSR). To investigate the genetic mechanisms underlying seed germination and early seedling growth in rice, 275 recombinant inbred lines (RILs) were genotyped in this study via the genotyping-bysequencing (GBS) approach to construct a high-density linkage bin map based on the parent-independent genotyping method. Quantitative trait loci (QTLs) for 12 traits related to seed germination and early seedling growth were analyzed. Totally, 22 additive loci were detected, after analysis of the interaction between additive QTLs and environments, five stable additive loci were obtained. Among them, loci 4, 5, 12 and 14 exhibited clear pleiotropic effects that were associated with multiple traits. Analysis of the effects of the five additive stable loci showed that a single locus increased the corresponding phenotypic value. Ten of the 275 RILs pyramided the excellent alleles of the five stable genetic loci. Most phenotypic values of the ten RILs were greater than the average values. Four RILs (G260, G342, G371, and G401) with more excellent phenotypic values were subsequently selected; these RILs could serve as donor parents of favorable alleles in the breeding process. Due to the existence of pleiotropy, the use of these genetic loci for pyramid breeding can further increase the efficiency to reach breeding goals. In addition, these five stable loci have an average physical interval of only 170 kb, we also further identified five promising candidate genes by qRT-PCR, which provides us with a basis for future cloning of these genes. Overall, this work will help broaden our understanding of the genetic control of seed germination and early seedling growth, and this study provides both a good theoretical basis and a new genetic resource for the breeding of direct-seeded rice.

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Section: Introductionmentioning

confidence: 99%

Quantitative Trait Locus Analysis of Seed Germination and Early Seedling Growth in Rice

Yang

et al. 2019

Front. Plant Sci.

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“…In the present study, two single-locus mixed models, MLM and CMLM, and one multilocus mixed model, MLMM, were used to map QTL and identify SNP markers associated with seedling vigor and regrowth vigor traits. Morphological traits, such as shoot length, number of shoots and shoot dry weight, have been used previously to evaluate plant or seedling vigor in different crops, including wheat [10] and rice [15,26,27]. In the present study, winter wheat lines in the wheat diversity panel showed significant phenotypic variation in shoot length, number of shoots and shoot dry weight from both the initial growth and the regrowth of the plants.…”

Section: Discussionmentioning

confidence: 60%

“…Genome-wide association mapping can link genotype to phenotype in natural diversity populations for identifying natural allelic variations associated with traits of interest [19]. Over the years, GWAS has been successfully used to identify genomic regions associated with quantitative traits in different crop species, such as wheat [20][21][22][23], sorghum [24,25], rice [26][27][28], barley (Hordeum vulgare L.) [29,30] and maize (Zea mays L.) [31,32].…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Mapping of Seedling Vigor and Regrowth Vigor in Winter Wheat

Maulana

Huang

Anderson

et al. 2021

Crops

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Seedling vigor and regrowth ability are important traits for the forage production of winter wheat. The objectives of this study were to map quantitative trait loci (QTL) associated with seedling vigor and regrowth vigor traits using a genome-wide association mapping study (GWAS). Seedling vigor and regrowth vigor were evaluated with shoot length, the number of shoots per plant and shoot dry weight per plant 45 days after planting and 15 days after cutting. A large phenotypic variation was observed for all the traits studied. In total, 12 significant QTL for seedling vigor and 16 for regrowth vigor traits were detected on various chromosomes. Four QTL on chromosomes 2B, 4B, 5A and 7A for seedling vigor co-localized with QTL for regrowth vigor due to significant correlations between corresponding traits of the initial growth and regrowth. A BLAST search using DNA sequences of the significant loci revealed candidate genes playing roles in vegetative and reproductive development in different crop species. The QTL and single-nucleotide polymorphism (SNP) markers identified in this study will be further validated and used for marker-assisted selection of the traits during forage wheat breeding.

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“…with WCA traits present on 12 chromosomes [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] . Each study used different determinants to assess and identify genomic regions associated with WCA viz., shoot length, shoot weight, coleoptile length, shoot dry weight, leaf area and specific leaf area.…”

Section: Dynamic Expression Of Weed Competitive Qtls In Rice Severamentioning

confidence: 99%

“…To dissect the weed competitive traits in rice, QTL mapping has been employed and large number of QTLs for rice WCA traits 2 , 24 were identified using biparental segregating populations (F 2 , Backcross inbred line, double haploids, recombinant inbred lines) derived mainly from intrasub-specific 25 – 28 and inter-subspecific crosses 29 – 33 . Recently, association mapping panels 34 – 37 and SNP based genotyping by sequencing 38 – 40 have been employed. However, no efforts were done to exploit weed competitive QTLs from inherent weed competitive O. glaberrima species.…”

Section: Introductionmentioning

confidence: 99%

Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud.

Bharamappanavara

Siddaiah

Ponnuvel

et al. 2020

Sci Rep

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To improve grain yield under direct seeded and aerobic conditions, weed competitive ability of a rice genotype is a key desirable trait. Hence, understanding and dissecting weed competitive associated traits at both morphological and molecular level is important in developing weed competitive varieties. In the present investigation, the QTLs associated with weed competitive traits were identified in BC1F2:3 population derived from weed competitive accession of O. glaberrima (IRGC105187) and O. sativa cultivar IR64. The mapping population consisting of 144 segregating lines were phenotyped for 33 weed competitive associated traits under direct seeded condition. Genetic analysis of weed competitive traits carried out in BC1F2:3 population showed significant variation for the weed competitive traits and predominance of additive gene action. The population was genotyped with 81 genome wide SSR markers and a linkage map covering 1423 cM was constructed. Composite interval mapping analysis identified 72 QTLs linked to 33 weed competitive traits which were spread on the 11 chromosomes. Among 72 QTLs, 59 were found to be major QTLs (> 10% PVE). Of the 59 major QTLs, 38 had favourable allele contributed from the O. glaberrima parent. We also observed nine QTL hotspots for weed competitive traits (qWCA2a, qWCA2b, qWCA2c, qWCA3, qWCA5, qWCA7, qWCA8, qWCA9, and qWCA10) wherein several QTLs co-localised. Our study demonstrates O. glaberrima species as potential source for improvement for weed competitive traits in rice and identified QTLs hotspots associated with weed competitive traits.

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Identification of Seedling Vigor-Associated Quantitative Trait Loci in Temperate Japonica Rice

Cited by 13 publications

References 31 publications

Quantitative Trait Locus Analysis of Seed Germination and Early Seedling Growth in Rice

Quantitative Trait Locus Analysis of Seed Germination and Early Seedling Growth in Rice

Genome-Wide Association Mapping of Seedling Vigor and Regrowth Vigor in Winter Wheat

Mapping QTL hotspots associated with weed competitive traits in backcross population derived from Oryza sativa L. and O. glaberrima Steud.

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