Prototype for developing SNP markers from GWAS and biparental QTL for rice panicle and grain traits

Eizenga, G. C.; Jackson, Aaron; Edwards, Jeremy D.

doi:10.1002/ael2.20047

Cited by 6 publications

(6 citation statements)

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“…The concentrations of Zn, Mo, As, and Cu in 300 brown rice varieties were determined by using GWAS mapping [35]. These elements show variations in grain composition that are linked to the number of candidate genes and SNPs, and the main reason of variation is environmental circumstances [36]. Mappings of GWAS and QTL are performed in combination to investigate traits such as Al accumulates, although combined research on grain nutrient content is rare [37].…”

Section: Gwas Analysis Improves Rice Nutritional Quality Traitsmentioning

confidence: 99%

Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice (<em>Oryza sativa</em> L.)

Zaghum¹,

Ali²,

Teng³

2022

Preprint

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The primary considerations while producing rice (Oryza sativa L.) include improving its nutritional quality and production. To tackle widespread hunger globally, better nutritional, high-yielding rice cultivars need to be developed. The conventional ways are to increase the production of rice and add balanced nutrients in the daily diet to fulfill the need of yield and nutrient quality. This article focuses on nutritional strategies for rice and illustrates the availability of omics technologies. Current advancements providing many methodologies and approaches for exploring genetic resources and for understanding the molecular mechanisms involved in trait formation have been highlighted. Studying the genetic influences of various characteristics has been proven to expedite crop breeding processes. In this perspective, genome-wide association research, genome selection (GS), and QTL mapping are all genetic analysis that helps in increasing the nutritional content of rice. Implementation of several omic techniques are effective approaches to enhance and regulate the nutritional quality of rice cultivars. Advancements in different types of omics including genomics and pangenomics, transcriptomics, metabolomics, nutrigenomics, and proteomics are also relevant to rice development initiatives. This review article compiles genes, locus, mutants and all omic approaches for rice enhancement. This knowledge will be very useful for now and for the future regarding rice studies.

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Section: Gwas Analysis Improves Rice Nutritional Quality Traitsmentioning

confidence: 99%

Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice (<em>Oryza sativa</em> L.)

Zaghum¹,

Ali²,

Teng³

2022

Preprint

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“…The parents, Estrela and NSFTV199, and the six RIL selections, SC14:060, SC14:072, SC14:122, SC14:166, SC14:236, and SC14:131-2016-R40131, were genotyped with a subset of 28 markers used to genotype the population (Eizenga, Chen, et al, 2019; and the 18 newly developed SNP markers designated as DB (Eizenga et al, 2021), which targeted panicle architecture and grain traits (Supplemental Table S1). For genotyping, DNA was isolated from leaf tissue collected from the 2018 breeding nursery panicle rows of these six selections and the parents, Estrela and NSFTV199.…”

Section: Genotyping Of the Selected Linesmentioning

confidence: 99%

“…DNA was extracted via the acetyl trimethyl ammonium bromide method as described by Hulbert and Bennetzen (1991) and modified by Fjellstrom et al (2004). Procedures for visualizing the markers initially used for QTL mapping were according to and for the DB SNP markers according to Eizenga et al (2021). The markers spanned all 12 chromosomes, targeting genes for panicle architecture, grain dimensions, as well as the segregating traits of apparent amylose content, the Pi-z gene for resistance to rice blast disease, and glabrous or pubescent leaves.…”

Section: Genotyping Of the Selected Linesmentioning

confidence: 99%

“…7 (DB80) markers were associated with panicle weight (Eizenga et al, 2021; Supplemental Table S2). For grain dimensions, the highly significant associations with kernel length and width (Eizenga et al, 2021) were validated by the chr. 3 markers (DB71-DB75) and chr.…”

Section: Notementioning

confidence: 99%

“…The quantitative trait loci (QTL) mapping of six panicle architecture traits (panicle length; number of primary branches, florets, seeds and sterile florets per panicle; and fertility) and four grain traits (seed length, width, length/width ratio, and 100-seed weight) revealed that QTL regions with "percentage of total phenotypic variation explained (PVE)" values > 11.0 (Table 2 in colocalized with previously reported genes (Supplemental Table S1). Subsequently, 15 SNP markers targeting the aforementioned genes and/or QTL regions were developed (Eizenga, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Registration of twoOryza sativatropicaljaponicagermplasm lines selected for panicle architecture and grain size traits

Eizenga

McClung

Huggins

2021

J of Plant Registrations

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Two rice (Oryza sativa L.) germplasm lines, SC14_166 (Reg. no. PI 698103) and SC14_072 (Reg. no. GP-147, PI 698102), containing alleles for increased panicle branching, seeds per panicle, and grain length were developed by the USDA-ARS.They are recombinant inbred lines selected from a cross of 'Estrela' (GSOR 301227) and NSFTV199 (GSOR 301190), both of tropical japonica origin. SC14_166 is a medium grain cultivar and contains alleles for two major genes, SPIKELET NUM-BER on chromosome (chr.) 4 and FRIZZY PANICLE on chr. 7, which result in a greater number of primary branches, seeds per panicle, and panicle weight compared with the parents. SC14_072 is an extra-long grain cultivar and contains alleles for two major genes on chr. 3, GRAIN SIZE 3 and GRAIN LENGTH 3.2, resulting in greater grain length, width, thickness, and kernel weight. The germplasm lines possess Pi-ks, whereas SC_072 also has Pi-z, both of which are major genes for resistance to rice blast disease. These germplasm lines are well adapted to growing conditions in the southern United States and will benefit long-grain and medium-grain breeding programs that desire to increase grain dimensions and panicle architecture traits. Because they are derived from the tropical japonica subpopulation like most U.S. cultivars, they will be highly compatible for making breeding crosses and will contribute novel alleles to the gene pool.

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Yield component QTLs identified by genome‐wide association mapping validated in a diverse tropical japonica × tropical japonica rice biparental mapping population

Eizenga

Rice²,

Huggins

et al. 2023

Crop Science

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The Rice Diversity Panel 1 (RDP1) is a resource for genome‐wide association (GWA) studies to explore the five major rice (Oryza sativa L.) subpopulations indica, aus, aromatic, temperate japonica, and tropical japonica (TRJ). Previously collected phenotype data for yield‐related traits in RDP1 were reanalyzed using a recently released 4.8 million single nucleotide polymorphism (SNP) dataset to enhance GWA mapping precision. GWA mapping revealed numerous GWA quantitative trait loci (QTLs) that were specific to one or more subpopulations. Most southern US cultivars are classified tropical japonica (TRJ) or TRJ admixtures, thus TRJ was the focus for validating Rice Diversity Panel 1 GWA QTLs. Two diverse TRJ accessions, L‐202 originating from California, USA, and Trembese from Indonesia, were selected for developing a recombinant inbred line (RIL) population. This RIL population was phenotyped for 20 yield component traits, 16 of which were measured in RDP1, including six agronomic, six panicle architecture, and four seed traits. Genotyping with 1439 polymorphic markers and QTL mapping revealed 43 RIL QTLs. Examination of the GWA QTLs identified 163 GWA QTLs for the same or related traits co‐located with 37 RIL QTLs. A significant overlap was on chromosome 7 (21.8–25.4; 26.8–29.6 Mb) where the well‐known yield related genes, GRAIN WEIGHT7 (GW7) and FRIZZY PANICLE (FZP), respectively, are located and specific to TRJ. In this region, 10 RIL QTLs were co‐located with 36 GWA QTLs for panicle architecture or seed traits, especially secondary branching to increase the number of seeds. RIL QTLs co‐located with GWA QTLs are targets for developing SNP markers that can be utilized in cultivar development.

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Prototype for developing SNP markers from GWAS and biparental QTL for rice panicle and grain traits

Cited by 6 publications

References 45 publications

Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice (<em>Oryza sativa</em> L.)

Integrated Genetic and Omics Approaches for the Regulation of Nutritional Activities in Rice (<em>Oryza sativa</em> L.)

Registration of twoOryza sativatropicaljaponicagermplasm lines selected for panicle architecture and grain size traits

Yield component QTLs identified by genome‐wide association mapping validated in a diverse tropical japonica × tropical japonica rice biparental mapping population

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