Identification of QTLs for rice grain size and weight by high-throughput SNP markers in the IR64 x Sadri population

Aslam, Kashif; Naveed, Shahzad Amir; Sabar, Muhammad Farooq; Shabir, Ghulam; Shah, Shahid Masood; Khan, Abdul Rehman; Shah, Muhammad Musaddiq; Fiaz, Sajid; Xu, Jianlong; Arif, Muhammad

doi:10.3389/fgene.2022.955347

Cited by 5 publications

(6 citation statements)

References 40 publications

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“…This marker accounts for 3.55% of the overall variance observed in the trait under consideration. The existence of QTL related to grain width on chromosome 8 has also been documented by others(Aslam et al 2022;Yang et al 2021). Fahliani et al (2011) documented a correlation between RM515 and the characteristics of grain length and grain shape in rice.Kibria et al…”

supporting

confidence: 61%

Genetic diversity and marker trait association analysis for grain quality, yield and yield attributes in hilly rice of North-Eastern Himalayan region

Verma

Theunuo

Das

et al. 2023

Preprint

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Background: The demand for quality rice is increasing due to the influence of population growth and rising living standards. North eastern region of India is considered as centre of origin of rice and has diversified rice landraces with unique cooking quality and yield attributing traits. The studies on genetic diversity and marker-trait association analysis of north-eastern hilly rice for grain quality and yield attributes are of enormous importance for the identification of promising rice genotypes with better grain and cooking quality vis a vis markers associated with grain quality and yield attributes. This will assist in development of high-yielding cultivars. Results: Genetic diversity among 130 rice genotypes of North East India and marker-trait association was assessed using genotypic data of 90 SSR markers and phenotypic data. The phenotypic diversity analysis showed considerable variation across genotypes for grain quality, yield and yield-attributing traits. The principal component analysis revealed that plant height, panicle length, spikelet fertility, decorticated grain length and width, days to maturity, days to 50% flowering, and grain yield govern the diversity. Population structure analysis, distance-based neighbour joining cluster and principal coordinate analysis using genotypic data grouped the genotypes into two sub-populations. Analysis of molecular variance and pairwise FST values showed significant differentiation among all the pairs of sub-population. Marker trait association analysis using the mixed linear model revealed a total of six associations at P < 0.0001 for grain quality and yield attributing traits with R2 ranging from 3.55 to 11.91% under the upland ecosystem. The present study validated the association of RM240 marker with Gel consistency for use in MAS and existence of plant height gene on chromosome 3. The study indicated the presence of novel QTLs for days to maturity with RM210 and RM105 and days to flowering with RM101 under consideration. Conclusions: The present study suggests that the diverse germplasm of NE India and identified markers could be used in rice breeding programs for the improvement of grain quality and yield attributes.

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supporting

confidence: 61%

Genetic diversity and marker trait association analysis for grain quality, yield and yield attributes in hilly rice of North-Eastern Himalayan region

Verma

Theunuo

Das

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Grain length-related QTLs have been reported in Chr1 (qGL1), Chr2 (qGL2.1, qGL2.2), Chr3 (GS3), Chr4 (qGL4), Chr6 (qGL6), Chr7 (qGS7, qGL7), Chr8 (qGL8.1), Chr10 (qGL10), and Chr11 [70,71]. Here, we noted that the major QTL qGL1-1 BFSG associated with the control of grain length in rice harbors genes proposed to be involved in reproduction, post-embryonic development, and embryo-development or protein-modification events (Os01g07880 (HY5: elongated hypocotyl 5) and Os01g07940 (AGC-PVPK)).…”

Section: The Qgl1-1 Bfsg Qtl Harbors Genes Involved In Post-embryonic...mentioning

confidence: 99%

“…3.4. The Grain Width, Thickness, and LWR-Associated QTLs qGW1-1 BSFG , qGT1 Blink , qGT2-1 Farm and qLWR2 BSFG Carry Genes Involved in Flower Development, Post-Embryonic Development and Reproduction Several loci controlling GT, GW, and LWR have been reported under various growth conditions, and mapped to almost all chromosomes of rice (Chr1, 2, 3, 6-9, 11, 12) [2,70,71]. In the qGW1 BF region, we noticed the presence of a gene encoding the flowering time-like 8 locus (OsFTL8, Os01g10590), associated with flower development and reproduction.…”

Section: The Qgl1-1 Bfsg Qtl Harbors Genes Involved In Post-embryonic...mentioning

confidence: 99%

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

Kabange,

Dzorkpe,

Park

et al. 2023

Plants

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This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint-strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]–High-Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and QTLs for grain length, co-detected by all GAPIT models on chromosomes (Chr) 1–3, 5, 7, and 11, were qGL1-1BFSG (AX-95918134, Chr1: 3,820,526 bp) explains 65.2–72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5,623,288 bp) for grain width explains 15.5–18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18,023,142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: 2,118,477 bp) of the observed PVE. For the grain length-to-width ratio (LWR), the qLWR2BFSG (AX-274833045, Chr2: 10,000,097 bp) explains nearly 15.2–32% of the observed PVE. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28,484,619 bp) and explains 32.8–54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8–54% of PVE. Putative candidate genes pooled from major QTLs for each grain trait have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

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“…Grain length-related QTLs have been reported on Chr1 (qGL1), Chr2 (qGL2.1, qGL2.2), Chr3 (GS3), Chr4 (qGL4), Chr6 (qGL6), Chr7 (qGS7, qGL7), Chr8 (qGL8.1), Chr10 (qGL10), Chr11 [70,71]. Here, we noted that the major QTL qGL1-1 BFSG associated with the control of grain length in rice harbors genes proposed to be involved in reproduction, post-embryonic development, and embryodevelopment or protein modification events (Os01g07880 (HY5: elongated hypocotyl 5) and Os01g07940 (AGC-PVPK)).…”

Section: The Qgl1-1 Bfsg Qtl Harbors Genes Involved In Post-embryonic...mentioning

confidence: 99%

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

Kabange,

Dzorkpe,

Park

et al. 2023

Preprint

View full text Add to dashboard Cite

This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]–High Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and quantitative trait loci (QTLs) for grain length, co-detected by all GAPIT models on (Chr) 1–3, 5, 7, and 11), were qGL1-1BFSG (AX-95918134, Chr1: 3820526 bp) explains 65.2%–72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5623288 bp) for grain width explains 15.5%–18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18023142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: AX-154787777 bp) of the observed PVE. For t length-to-width ratio, the qLWR2BFSG (AX-274833045, Chr2: 10000097 bp) explains nearly 15.2%–32% of PVE for LWR. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28484619 bp) and explains 32.8%–54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8%–54% of PVE. Putative Candidate genes pooled from major QTLs for each grain traits have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

show abstract

Identification of QTLs for rice grain size and weight by high-throughput SNP markers in the IR64 x Sadri population

Cited by 5 publications

References 40 publications

Genetic diversity and marker trait association analysis for grain quality, yield and yield attributes in hilly rice of North-Eastern Himalayan region

Genetic diversity and marker trait association analysis for grain quality, yield and yield attributes in hilly rice of North-Eastern Himalayan region

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified Using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

Rice (Oryza sativa L.) Grain Size, Shape, and Weight-Related QTLs Identified using GWAS with Multiple GAPIT Models and High-Density SNP Chip DNA Markers

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