Towards a deeper haplotype mining of complex traits in rice with <scp>RFGB</scp> v2.0

Wang, Chunchao; Yu, Hong; Huang, Ji; Wang, Wensheng; Faruquee, Muhiuddin; Zhang, Fan; Zhao, Xiuqin; Fu, Binying; Chen, Kai; Zhang, Hong‐Liang; Tai, Shuaishuai; Wei, Chaochun; McNally, Kenneth L.; Alexandrov, Nickolai; Gao, Xiuying; Li, Jiayang; Li, Zhikang; Xu, Jianlong; Zheng, Tianqing

doi:10.1111/pbi.13215

Cited by 104 publications

(72 citation statements)

References 7 publications

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“…To analyze the retrocopy variation caused by retrogene indels in rice populations, all 74 rice retrocopies were used, and the genomic variation data were obtained from the 3,000 Rice Genomes Project [27] , [28] , [29] . To avoid the complexity of variation, we focused on only the indels of biallelic variation.…”

Section: Resultsmentioning

confidence: 99%

“…The presently constructed set of rice retrocopies was employed to identify RDVs, and we downloaded and searched the sequence variants detected in 3,010 accessions in the 3,000 Rice Genomes Project [27] , [28] , [29] , aiming to detect indels that overlapped with retrogene loci using BEDtools [73] . We incorporated only indels causing the insertion or deletion of at least 8 bp of retrogene sequence for further analysis.…”

Section: Methodsmentioning

confidence: 99%

“…RNA-Seq data from the Rice Expression Database [26] were utilized to evaluate the expression of retrogenes and their parental genes. The 3,000 Rice Genomes Project [27] , [28] , [29] was adopted for assessment of the RDVs among rice populations. The mutated, ancestral and expressed retrogenes were detected in rice genomes, and their RDV influence on rice phenotypes was analyzed with statistical methods.…”

Section: Introductionmentioning

confidence: 99%

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Interpopulation differences of retroduplication variations (RDVs) in rice retrogenes and their phenotypic correlations

Zeng

Chen

et al. 2021

Computational and Structural Biotechnology Journal

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interpopulation differences of retroduplication variations (RDVs) in rice retrogenes and their phenotypic correlations

Zeng

Chen

et al. 2021

Computational and Structural Biotechnology Journal

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“…A suite of tools designed to explore the rice 3K collection in such a fashion has been recently developed and described by Wang et al (2019). The Rice Functional and Genomic Breeding RFGB v2.0 tool integrates a vast amount of both genetic and phenotypic data on the collection of 3K sequenced rice accessions.…”

Section: New Opportunity To More Rationally Explore Pgrsmentioning

confidence: 99%

Time for a paradigm shift in the use of plant genetic resources

Belzile

Abed

Torkamaneh

2020

Genome

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For all major crops, sizeable genebanks are maintained across the world and serve as repositories of genetic diversity and key sources of novel traits used in breeding. Although molecular markers have been used to characterize diversity in a broad sense, the most common approach to exploring these resources has been through phenotypic characterization of subsets of these large collections. With the advent of affordable large-scale genotyping technologies and the increasing body of candidate genes for traits of interest, we argue here that it is time for a paradigm shift in the way that we explore and exploit these considerable and highly useful resources. By combining dense genotypic information in and around candidate genes, it is possible to classify accessions based on their haplotype, something approximating the actual alleles at these genes of interest.

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“…et al 2018). These available molecular genome data enabled the discovery of valuable genetic resources and highthroughput data for scienti c research and practical molecular rice breeding (Wang et al 2020;Zheng et al 2015).…”

mentioning

confidence: 99%

Identification and Allele Mining of New Candidate Genes Underlying Rice Grain Weight and Grain Shape by Genome-Wide Association Study

Niu

Chen

et al. 2020

Preprint

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Background: Grain weight and grain shape are crucial agronomic traitsthat affect grain yield potential and grain quality of rice. Both Grain weight and grain shape are controlled by multiple genes known as quantitative trait locus/loci (QTL).The 3,000 Rice Genomes Project enables the discovery of valuable genetic variants and resources for grain weight and grain shape.Results:2445 rice accessions were classified into 12 sub-populations which showeddiversevariations in grain weight and grain shape.Genome-wide association study (GWAS) revealedthreenovel QTL (qTGW3.1,qTGW9and qTGW11) forgrain weight and four novel QTL(qGL4/qRLW4,qGL10, qGL11andqRLW1) forgrain shape. We finally predicted eightcandidate genes (Os03g0186600, Os09g0544400,Os11g0163600,Os04g0580700, Os10g0399700, Os10g0400100, Os11g0143700 and Os01g0171000) for the seven novel QTL by gene-based association, haplotype and bioinformatics analyses.The accessions with excellent haplotypes at the eight candidate genes and five previously cloned genes were also identified.Conclusion: The identified candidate genes and accessions provided valuable sources for future functional characterization and genetic improvement of rice grain yield and grain quality.

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Towards a deeper haplotype mining of complex traits in rice with RFGB v2.0

Cited by 104 publications

References 7 publications

Interpopulation differences of retroduplication variations (RDVs) in rice retrogenes and their phenotypic correlations

Interpopulation differences of retroduplication variations (RDVs) in rice retrogenes and their phenotypic correlations

Time for a paradigm shift in the use of plant genetic resources

Identification and Allele Mining of New Candidate Genes Underlying Rice Grain Weight and Grain Shape by Genome-Wide Association Study

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