DEVELOPMENT AND APPLICATION OF 1536-PLEX SINGLE NUCLEOTIDE POLYMORPHISM MARKER CHIP FOR GENOME WIDE SCANNING  OF INDONESIAN RICE GERMPLASM

A successful molecular breeding program requires detailed and comprehensive understanding of the diversity of rice germ-plasm and genetic base of target traits. The objective of this research was to develop the high throughput 1536-SNP chip linked to heading date and yield component traits and used it for genotyping the diverse Indonesian rice germplasm. The genotype data obtained could be used for diversity analysis and genome wide association mapping study. A 1536-SNP genome wide assay was developed using the Illumina’s GoldenGate technology. The SNP markers were selected in the rice genome regions containing heading date and yield component genes or regions where the quantitative trait loci (QTLs) of the two traits were mapped. The developed custom SNP chips were then used for genotyping 467 rice accessions showing diversity in heading dates and yield components. The assay can reliably be used for diversity analysis and mapping genes associated with heading date and yield component traits. For 1536-SNP BIO-RiceOPA-1 custom chip designed, a total of 34.832 SNPs distributed in rice genome particularly in the region of heading date and yield component genes or QTLs were identified. A total of 1536-SNP were selected and confirmed to be used for genotyping analysis. Analysis performance and quality of 1536-SNP BIO-RiceOPA1 showed that 60% (918/1536) of total SNP markers had a good differentiating power in scanning the rice accessions tested (MAF > 0.2). The 1536-SNP genome wide assay Illumina’s GoldenGate designed was useful for diversity analysis and could be used as SNP marker for large scale genotyping in rice molecular breeding involving Indica-Indica, Indica-Japonica and Indica-Tropical Japonica crosses. <br />

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DEVELOPMENT AND APPLICATION OF 1536-PLEX SINGLE NUCLEOTIDE POLYMORPHISM MARKER CHIP FOR GENOME WIDE SCANNING OF INDONESIAN RICE GERMPLASM

Indonesian Journal of Agricultural Science ◽

10.21082/ijas.v14n2.2013.71-78 ◽

2013 ◽

Vol 14 (2) ◽

pp. 71

Author(s):

Dwinita W. Utami ◽

I. Rosdianti ◽

P. Lestari ◽

D. Satyawan ◽

H. Rijzaani ◽

...

Keyword(s):

Molecular Breeding ◽

Rice Genome ◽

Heading Date ◽

Snp Markers ◽

Yield Component ◽

Diversity Analysis ◽

Rice Germplasm ◽

Genome Wide ◽

Component Traits ◽

Yield Component Traits

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Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding

G3 Genes|Genome|Genetics ◽

10.1534/g3.119.400073 ◽

2019 ◽

Vol 9 (8) ◽

pp. 2429-2439 ◽

Cited By ~ 3

Author(s):

Prabin Bajgain ◽

Xiaofei Zhang ◽

James A. Anderson

Keyword(s):

Association Study ◽

Genomic Selection ◽

Genome Wide Association Study ◽

Yield Component ◽

Genome Wide Association ◽

Intermediate Wheatgrass ◽

Genome Wide ◽

Component Traits ◽

Yield Component Traits

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QTL mapping for grain yield and three yield components in a population derived from two high-yielding spring wheat cultivars

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03806-1 ◽

2021 ◽

Author(s):

Kyle Isham ◽

Rui Wang ◽

Weidong Zhao ◽

Justin Wheeler ◽

Natalie Klassen ◽

...

Keyword(s):

Grain Yield ◽

Spring Wheat ◽

Yield Components ◽

Heading Date ◽

Kernel Weight ◽

Yield Component ◽

Six Traits ◽

Component Traits ◽

Yield Component Traits ◽

Spikelet Number Per Spike

Abstract Key message Four genomic regions on chromosomes 4A, 6A, 7B, and 7D were discovered, each with multiple tightly linked QTL (QTL clusters) associated with two to three yield components. The 7D QTL cluster was associated with grain yield, fertile spikelet number per spike, thousand kernel weight, and heading date. It was located in the flanking region of FT-D1, a homolog gene of Arabidopsis FLOWERING LOCUS T, a major gene that regulates wheat flowering. Abstract Genetic manipulation of yield components is an important approach to increase grain yield in wheat (Triticum aestivum). The present study used a mapping population comprised of 181 doubled haploid lines derived from two high-yielding spring wheat cultivars, UI Platinum and LCS Star. The two cultivars and the derived population were assessed for six traits in eight field trials primarily in Idaho in the USA. The six traits were grain yield, fertile spikelet number per spike, productive tiller number per unit area, thousand kernel weight, heading date, and plant height. Quantitative Trait Locus (QTL) analysis of the six traits was conducted using 14,236 single-nucleotide polymorphism (SNP) markers generated from the wheat 90 K SNP and the exome and promoter capture arrays. Of the 19 QTL detected, 14 were clustered in four chromosomal regions on 4A, 6A, 7B and 7D. Each of the four QTL clusters was associated with multiple yield component traits, and these traits were often negatively correlated with one another. As a result, additional QTL dissection studies are needed to optimize trade-offs among yield component traits for specific production environments. Kompetitive allele-specific PCR markers for the four QTL clusters were developed and assessed in an elite spring wheat panel of 170 lines, and eight of the 14 QTL were validated. The two parents contain complementary alleles for the four QTL clusters, suggesting the possibility of improving grain yield via genetic recombination of yield component loci.

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Validation of Yield Component Traits Identified by Genome‐Wide Association Mapping in a tropical japonica × tropical japonica Rice Biparental Mapping Population

The Plant Genome ◽

10.3835/plantgenome2018.04.0021 ◽

2019 ◽

Vol 12 (1) ◽

pp. 180021 ◽

Cited By ~ 2

Author(s):

Georgia C. Eizenga ◽

Melissa H. Jia ◽

Aaron K. Jackson ◽

Debbie L. Boykin ◽

M. Liakat Ali ◽

...

Keyword(s):

Association Mapping ◽

Mapping Population ◽

Yield Component ◽

Genome Wide Association ◽

Japonica Rice ◽

Tropical Japonica ◽

Genome Wide ◽

Component Traits ◽

Yield Component Traits

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A genome-wide association study identified loci for yield component traits in sugarcane (Saccharum spp.)

PLoS ONE ◽

10.1371/journal.pone.0219843 ◽

2019 ◽

Vol 14 (7) ◽

pp. e0219843 ◽

Cited By ~ 6

Author(s):

Fernanda Zatti Barreto ◽

João Ricardo Bachega Feijó Rosa ◽

Thiago Willian Almeida Balsalobre ◽

Maria Marta Pastina ◽

Renato Rodrigues Silva ◽

...

Keyword(s):

Association Study ◽

Genome Wide Association Study ◽

Yield Component ◽

Genome Wide Association ◽

Saccharum Spp ◽

Genome Wide ◽

A Genome ◽

Component Traits ◽

Yield Component Traits

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Genome-wide association studies for yield component traits in a macadamia breeding population

BMC Genomics ◽

10.1186/s12864-020-6575-3 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Katie O’Connor ◽

Ben Hayes ◽

Craig Hardner ◽

Catherine Nock ◽

Abdul Baten ◽

...

Keyword(s):

Association Studies ◽

Breeding Population ◽

Yield Component ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Component Traits ◽

Yield Component Traits

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Quantitative trait loci identification for yield component traits in an Indonesian local rice variety, Untup Rajab

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/762/1/012011 ◽

2021 ◽

Vol 762 (1) ◽

pp. 012011

Author(s):

M Yunus ◽

D Satyawan ◽

A Dadang ◽

A Warsun

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Rice Variety ◽

Yield Component ◽

Trait Loci ◽

Component Traits ◽

Yield Component Traits

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Evaluating Genotype × Environment Interactions of Yield Traits and Adaptability in Rice Cultivars Grown under Temperate, Subtropical and Tropical Environments

Agriculture ◽

10.3390/agriculture11060558 ◽

2021 ◽

Vol 11 (6) ◽

pp. 558

Author(s):

Xing Huang ◽

Su Jang ◽

Backki Kim ◽

Zhongze Piao ◽

Edilberto Redona ◽

...

Keyword(s):

Yield Component ◽

Yield Potential ◽

High Yield ◽

Environment Interaction ◽

Accurate Identification ◽

Rice Varieties ◽

Panicle Number ◽

Tropical Conditions ◽

Component Traits ◽

Yield Component Traits

Rice yield is a complex trait that is strongly affected by environment and genotype × environment interaction (GEI) effects. Consideration of GEI in diverse environments facilitates the accurate identification of optimal genotypes with high yield performance, which are adaptable to specific or diverse environments. In this study, multiple environment trials were conducted to evaluate grain yield (GY) and four yield-component traits: panicle length, panicle number, spikelet number per panicle, and thousand-grain weight. Eighty-nine rice varieties were cultivated in temperate, subtropical, and tropical regions for two years. The effects of both GEI (12.4–19.6%) and environment (23.6–69.6%) significantly contributed to the variation of all yield-component traits. In addition, 37.1% of GY variation was explained by GEI, indicating that GY performance was strongly affected by the different environmental conditions. GY performance and genotype stability were evaluated using simultaneous selection indexing, and 19 desirable genotypes were identified with high productivity and broad adaptability across temperate, subtropical, and tropical conditions. These optimal genotypes could be recommended for cultivation and as elite parents for rice breeding programs to improve yield potential and general adaptability to climates.

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