scholarly journals Identification of main-effect quantitative trait loci (QTLs) for low-temperature stress tolerance germination- and early seedling vigor-related traits in rice (Oryza sativa L.)

2020 ◽  
Vol 40 (1) ◽  
Author(s):  
S. Najeeb ◽  
J. Ali ◽  
A. Mahender ◽  
Y.L. Pang ◽  
J. Zilhas ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Low Temperature ◽  
Quantitative Trait ◽  
Root Length ◽  
Stress Index ◽  
Multiple Traits ◽  
Main Effect ◽  
Trait Loci ◽  
Early Seedling ◽  
Genomic Regions

AbstractAn attempt was made in the current study to identify the main-effect and co-localized quantitative trait loci (QTLs) for germination and early seedling growth traits under low-temperature stress (LTS) conditions in rice. The plant material used in this study was an early backcross population of 230 introgression lines (ILs) in BCIF7 generation derived from the Weed Tolerant Rice-1 (WTR-1) (as the recipient) and Haoannong (HNG) (as the donor). Genetic analyses of LTS tolerance revealed a total of 27 main-effect quantitative trait loci (M-QTLs) mapped on 12 chromosomes. These QTLs explained more than 10% of phenotypic variance (PV), and average PV of 12.71% while employing 704 high-quality SNP markers. Of these 27 QTLs distributed on 12 chromosomes, 11 were associated with low-temperature germination (LTG), nine with low-temperature germination stress index (LTGS), five with root length stress index (RLSI), and two with biomass stress index (BMSI) QTLs, shoot length stress index (SLSI) and root length stress index (RLSI), seven with seed vigor index (SVI), and single QTL with root length (RL). Among them, five significant major QTLs (qLTG(I)1, qLTGS(I)1–2, qLTG(I)5, qLTGS(I)5, and qLTG(I)7) mapped on chromosomes 1, 5, and 7 were associated with LTG and LTGS traits and the PV explained ranged from 16 to 23.3%. The genomic regions of these QTLs were co-localized with two to six QTLs. Most of the QTLs were growth stage-specific and found to harbor QTLs governing multiple traits. Eight chromosomes had more than four QTLs and were clustered together and designated as promising LTS tolerance QTLs (qLTTs), as qLTT1, qLTT2, qLTT3, qLTT5, qLTT6, qLTT8, qLTT9, and qLTT11. A total of 16 putative candidate genes were identified in the major M-QTLs and co-localized QTL regions distributed on different chromosomes. Overall, these significant genomic regions of M-QTLs are responsible for multiple traits and this suggested that these could serve as the best predictors of LTS tolerance at germination and early seedling growth stages. Furthermore, it is necessary to fine-map these regions and to find functional markers for marker-assisted selection in rice breeding programs for cold tolerance.

scholarly journals Identification of quantitative trait loci governing early germination and seedling vigor traits related to weed competitive ability in rice

Euphytica ◽  
2020 ◽  
Vol 216 (10) ◽  
Author(s):  
Niña Gracel B. Dimaano ◽  
Jauhar Ali ◽  
Anumalla Mahender ◽  
Pompe C. Sta. Cruz ◽  
Aurora M. Baltazar ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Competitive Ability ◽  
Snp Markers ◽  
Limiting Factor ◽  
Seedling Vigor ◽  
Backcross Population ◽  
Trait Loci ◽  
Early Seedling ◽  
Genomic Regions

Abstract Weed competitive ability (WCA) is vital for the improvement of grain yield under direct-seeded and aerobic rice ecosystems where weeds are a major limiting factor. Early seed germination (ESG) and early seedling vigor (ESV) are the crucial traits for WCA. This study attempted to map the quantitative trait loci (QTLs) and hotspot regions governing ESG and ESV traits. A total of 167 BC1F5 selective introgression lines developed from an early backcross population involving Weed Tolerant Rice 1 (WTR-1) as the recipient parent and Y-134 as the donor parent were phenotyped for ESG and ESV traits. Analysis of variance revealed significant differences in ESG-related traits except for root length and in ESV-related traits except for plant height at 7 days after sowing. A total of 677-high quality single nucleotide polymorphism (SNP) markers were used to analyze the marker-trait association from a 6 K SNP genotyping array. Forty-three QTLs were identified on all chromosomes, except on chromosomes 4 and 8. Thirty QTLs were contributed by a desirable allele from Y-134, whereas 13 QTLs were from WTR-1. Twenty-eight of the identified genetic loci associated with ESG and ESV traits were novel. Two QTL hotspot regions were mapped on chromosomes 11 and 12. The genomic regions of QTL hotspots were fine-tuned and a total of 13 putative candidate genes were discovered on chromosomes 11 and 12 collectively. The mapped QTLs will be useful in advancing the marker aided-selection schemes and breeding programs for the development of rice cultivars with WCA traits.

Application of the False Discovery Rate to Quantitative Trait Loci Interval Mapping With Multiple Traits

Genetics ◽  
2002 ◽  
Vol 161 (2) ◽  
pp. 905-914 ◽  
Author(s):  
Hakkyo Lee ◽  
Jack C M Dekkers ◽  
M Soller ◽  
Massoud Malek ◽  
Rohan L Fernando ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
False Discovery Rate ◽  
Error Rate ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Error Rates ◽  
Multiple Traits ◽  
Test Statistic ◽  
False Discovery ◽  
Trait Loci

Abstract Controlling the false discovery rate (FDR) has been proposed as an alternative to controlling the genomewise error rate (GWER) for detecting quantitative trait loci (QTL) in genome scans. The objective here was to implement FDR in the context of regression interval mapping for multiple traits. Data on five traits from an F2 swine breed cross were used. FDR was implemented using tests at every 1 cM (FDR1) and using tests with the highest test statistic for each marker interval (FDRm). For the latter, a method was developed to predict comparison-wise error rates. At low error rates, FDR1 behaved erratically; FDRm was more stable but gave similar significance thresholds and number of QTL detected. At the same error rate, methods to control FDR gave less stringent significance thresholds and more QTL detected than methods to control GWER. Although testing across traits had limited impact on FDR, single-trait testing was recommended because there is no theoretical reason to pool tests across traits for FDR. FDR based on FDRm was recommended for QTL detection in interval mapping because it provides significance tests that are meaningful, yet not overly stringent, such that a more complete picture of QTL is revealed.

Mapping of quantitative trait loci for cold tolerance at the early seedling stage in landrace rice Xiang 743

Euphytica ◽  
2014 ◽  
Vol 201 (3) ◽  
pp. 401-409 ◽  
Author(s):  
Wenqiang Liu ◽  
Tingting Lu ◽  
Yongchao Li ◽  
Xiaowu Pan ◽  
Yonghong Duan ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Cold Tolerance ◽  
Quantitative Trait ◽  
Seedling Stage ◽  
Trait Loci ◽  
Early Seedling

scholarly journals Identification and Verification of Quantitative Trait Loci Affecting Milling Yield of Rice

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 75 ◽  
Author(s):  
Hui Zhang ◽  
Yu-Jun Zhu ◽  
An-Dong Zhu ◽  
Ye-Yang Fan ◽  
Ting-Xu Huang ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Brown Rice ◽  
Yield Traits ◽  
Multiple Traits ◽  
Milled Rice ◽  
Milling Quality ◽  
Head Rice ◽  
Trait Loci ◽  
Milling Yield

Rice is generally consumed in the form of milled rice. The yield of total milled rice and head mill rice is affected by both the paddy rice yield and milling efficiency. In this study, three recombinant inbred line (RIL) populations and one F4:5 population derived from a residual heterozygous (RH) plant were used to determine quantitative trait loci (QTLs) affecting milling yield of rice. Seven traits were analyzed, including recovery of brown rice (BR), milled rice (MR) and head rice (HR); grain yield (GY); and the yield of brown rice (BRY), milled rice (MRY) and head rice (HRY). A total of 77 QTLs distributed on 35 regions was detected in the three RIL populations. Four regions, where qBR5, qBR7, qBR10, and qBR12 were located, were validated in the RH-derived F4:5 population. In the three RIL populations, all the 11 QTLs for GY detected were accompanied with QTLs for two or all the three milling yield traits. Not only the allele direction for milling yield traits was unchanged, but also the effects were consistent with GY. In the RH-derived F4:5 population, regions controlling GY also affected all three milling yield traits. Results indicated that variations of BRY and MRY were mainly ascribed to GY, but HRY was determined by both GY and HR. Results also showed that the regions covering GW5–Chalk5 and Wx loci had major effects on milling quality and milling yield of rice. These two regions, which have been known to affect multiple traits determining grain quality and yield of rice, provide good candidates for milled yield improvement.

scholarly journals Identification of quantitative trait loci associated with canopy temperature in soybean

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sumandeep K. Bazzer ◽  
Larry C. Purcell
Keyword(s):  
Quantitative Trait Loci ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Recombinant Inbred Lines ◽  
Canopy Temperature ◽  
Growth And Yield ◽  
Indirect Measure ◽  
Trait Loci ◽  
Genomic Regions ◽  
The Impact

Abstract A consistent risk for soybean (Glycine max L.) production is the impact of drought on growth and yield. Canopy temperature (CT) is an indirect measure of transpiration rate and stomatal conductance and may be valuable in distinguishing differences among genotypes in response to drought. The objective of this study was to map quantitative trait loci (QTLs) associated with CT using thermal infrared imaging in a population of recombinant inbred lines developed from a cross between KS4895 and Jackson. Heritability of CT was 35% when estimated across environments. QTL analysis identified 11 loci for CT distributed on eight chromosomes that individually explained between 4.6 and 12.3% of the phenotypic variation. The locus on Gm11 was identified in two individual environments and across environments and explained the highest proportion of phenotypic variation (9.3% to 11.5%) in CT. Several of these CT loci coincided with the genomic regions from previous studies associated with canopy wilting, canopy temperature, water use efficiency, and other morpho-physiological traits related with drought tolerance. Candidate genes with biological function related to transpiration, root development, and signal transduction underlie these putative CT loci. These genomic regions may be important resources in soybean breeding programs to improve tolerance to drought.

scholarly journals Fine Mapping ofQUICK ROOTING 1and2, Quantitative Trait Loci Increasing Root Length in Rice

2017 ◽  
Vol 8 (2) ◽  
pp. 727-735 ◽  
Author(s):  
Yuka Kitomi ◽  
Emari Nakao ◽  
Sawako Kawai ◽  
Noriko Kanno ◽  
Tsuyu Ando ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Fine Mapping ◽  
Quantitative Trait ◽  
Root Length ◽  
Trait Loci

Identification of Quantitative Trait Loci for Alkaline Tolerance at Early Seedling Stage of Japonica Rice Under Alkaline Stress

2009 ◽  
Vol 35 (2) ◽  
pp. 301-308
Author(s):  
Dong-Ling QI ◽  
Gui-Zhen GUO ◽  
Myung-Chul LEE ◽  
Chun-Gang YANG ◽  
Jun-Guo ZHANG ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Seedling Stage ◽  
Alkaline Stress ◽  
Japonica Rice ◽  
Alkaline Tolerance ◽  
Trait Loci ◽  
Early Seedling

Quantitative trait loci mapping and stability for low temperature germination ability of rice

2009 ◽  
Vol 128 (4) ◽  
pp. 387-392 ◽  
Author(s):  
S. L. Ji ◽  
L. Jiang ◽  
Y. H. Wang ◽  
W. W. Zhang ◽  
X. Liu ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Low Temperature ◽  
Quantitative Trait ◽  
Quantitative Trait Loci Mapping ◽  
Germination Ability ◽  
Trait Loci
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