scholarly journals Genotyping USDA Rice Mini-Core Collection With Functional Markers for Important Agronomic Traits

2021 ◽  
Author(s):  
Kehu Li ◽  
Yongyi Ge ◽  
Lily Yan Wang
Keyword(s):  
Physicochemical Properties ◽  
Core Collection ◽  
Biotic Resistance ◽  
Agronomic Traits ◽  
Brown Planthopper ◽  
Functional Markers ◽  
Rice Breeding ◽  
Breeding Programs ◽  
Exon 2 ◽  
Mini Core Collection

Abstract The USDA rice mini-core collection was established to capture the diversity of an entire collection of over 18,700 accessions of global origins for efficient germplasm evaluation and exploration. Previous studies have investigated its genetic diversity and population structure using genome-wide SSR markers. Many important agronomic traits that are fundamental to rice breeding programs, however, remain to be explored. Functional markers can be developed based on polymorphic sites within genes affecting phenotypic variation in, e.g., starch physicochemical properties, nutritional qualities and biotic resistance. These markers can be used for genotyping and hence differentiating phenotypes among rice accessions. In this study, we employed 12 pairs of functional markers (SNP and Indel) to genotype all 217 accessions constituting the USDA rice mini-core. These markers are highly associated with starch physicochemical properties (intron 1 G/C SNP, 23bp duplication in exon 2, exon 6 C/A SNP, exon 10 C/T SNP of Waxy gene, GC/TT SNPs of SSIIa gene, G/C SNP of SBE3 gene), glutelin content (3.5 kb deletion in Lgc1 gene), grain length (C/A SNP in GS3 gene), brown planthopper resistance (InDel in Bph 14 gene) and rice blast resistance (InDel in Pi54 and Pit gene). Using these functional markers, all the 217 accessions of the mini-core are characterized for aforementioned agronomic traits associated alleles/genes. The results of this study will help breeders select parental materials with desirable allele/gene combinations and phenotypes among mini-core accessions for rice breeding programs.

QTL analysis of yield-related traits and their association with functional markers in Brassica napus L.

2007 ◽  
Vol 58 (8) ◽  
pp. 759 ◽  
Author(s):  
Yuanyuan Li ◽  
Jinxiong Shen ◽  
Tonghua Wang ◽  
Qingfang Chen ◽  
Xingguo Zhang ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Agronomic Traits ◽  
High Yield ◽  
Functional Markers ◽  
Multiple Traits ◽  
Brassica Napus L ◽  
Breeding Programs ◽  
F2 Population ◽  
Expressed Sequence ◽  
Simple Sequence

Yield is one of the most important traits in Brassica napus breeding programs. Quantitative trait loci (QTLs) for yield-related traits based on genetic mapping would help breeders to develop high-yield cultivars. In this study, a genetic linkage map of B. napus, containing 142 sequence-related amplified polymorphism (SRAP) markers, 163 functional markers, 160 simple sequence repeat (SSR) markers, and 117 amplified fragment length polymorphism (AFLP) markers, was constructed in an F2 population of 184 individuals resulting from the cross SI-1300 × Eagle. This map covered 2054.51 cM with an average marker interval of 3.53 cM. Subsequently, QTLs were detected for 12 yield-related traits in Wuhan and Jingmen. In total, 133 QTLs were identified, including 14 consistent ones across the 2 locations. Fifteen of 20 linkage groups (LGs) were found to have QTLs for the 12 traits investigated, and most of the QTLs were clustered, especially on LGs N2 and N7, where similar QTL positions were identified for multiple traits. Eight of 10 QTLs for yield per plant (YP) were also associated with number of seeds per silique (SS), number of siliques per plant (SP), and/or 1000-seed weight (SW). In addition, 45 functional markers involved in 39 expressed sequence tags (ESTs) were linked to the QTLs of 12 traits. The present results may serve as a valuable basis for further molecular dissection of agronomic traits in B. napus, and the markers related to QTLs may offer promising possible makers for marker assisted selection.

scholarly journals Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods

2020 ◽  
Author(s):  
Shuai Liu ◽  
Hua Zhong ◽  
Xiaoxi Meng ◽  
Tong Sun ◽  
Yangsheng Li ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Candidate Genes ◽  
Metal Contamination ◽  
Core Collection ◽  
Heavy Metal Contamination ◽  
Agronomic Traits ◽  
Association Studies ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Mini Core Collection

Abstract BackgroundRice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers. ResultsTo identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value <1.53×10-8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~20%) of the 250 QTLs were coinciding with previous reported QTLs/genes, about 201 (~80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods. ConclusionsWhile 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.

scholarly journals Response of U.S. Rice Cultivars Grown under Non-Flooded Irrigation Management

Agronomy ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 55
Author(s):  
Anna M. McClung ◽  
Jai S. Rohila ◽  
Christopher G. Henry ◽  
Argelia Lorence
Keyword(s):  
Soil Moisture ◽  
Environmental Sustainability ◽  
Agronomic Traits ◽  
Phenotypic Variability ◽  
Irrigation Management ◽  
Field Capacity ◽  
Japonica Rice ◽  
Rice Breeding ◽  
Rice Varieties ◽  
Breeding Programs

Achieving food security along with environmental sustainability requires high yields with reduced demands on irrigation resources for rice production systems. The goal of the present investigation was to identify traits and germplasms for rice breeding programs that target effective grain production (EGP) under non-flooded field systems where the crop can be subjected to intermittent water stress throughout the growing season. A panel of 15 cultivars was evaluated over three years regarding phenological and agronomic traits under four soil moisture levels ranging from field capacity (29% volumetric water content; VWC) to just above the wilting point (16% VWC) using subsurface drip irrigation. An average of 690 ha-mm ha−1 water was applied for the 30% VWC treatment compared to 360 ha-mm ha−1 for the 14% VWC treatment. The average soil moisture content influenced several traits, including grain quality. Regression analysis identified six traits that explained 35% of the phenotypic variability of EGP. Four varieties (PI 312777, Francis, Zhe 733, and Mars) were found possessing significant slopes for 10 or more traits that respond to a range in soil moisture levels, indicating that they may offer promise for future rice breeding programs. Furthermore, based on the contrasting responses of four parent cultivars, two mapping populations were identified as potential genetic resources for identifying new quantitative trait loci/genes for improving EGP of tropical japonica rice varieties.

scholarly journals Genomic tools and germplasm diversity for chickpea improvement

2011 ◽  
Vol 9 (01) ◽  
pp. 45-58 ◽  
Author(s):  
Hari D. Upadhyaya ◽  
Mahendar Thudi ◽  
Naresh Dronavalli ◽  
Neha Gujaria ◽  
Sube Singh ◽  
...  
Keyword(s):  
Core Collection ◽  
Large Scale ◽  
Agronomic Traits ◽  
Genetic Maps ◽  
Genetic Enhancement ◽  
Grain Legume ◽  
Genomic Resources ◽  
Mini Core Collection ◽  
Reference Set ◽  
Germplasm Accessions

Chickpea is the third most important grain legume grown in the arid and semi-arid regions of the world. In spite of vast germplasm accessions available in different genebanks, there has been very limited use of these accessions in genetic enhancement of chickpea. However, in recent years, specialized germplasm subsets such as global composite collection, core collection, mini core collection and reference set have been developed. In parallel, significant genomic resources such as molecular markers including simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs), diversity arrays technology (DArT) and transcript sequences, e.g. expressed sequence tags, short transcript reads, have been developed. By using SSR, SNP and DArT markers, integrated genetic maps have been developed. It is anticipated that the use of genomic resources and specialized germplasm subsets such as mini core collection and reference set will facilitate identification of trait-specific germplasm, trait mapping and allele mining for resistance to biotic and abiotic stresses and for agronomic traits. Advent of the next generation sequencing technologies coupled with advances in bioinformatics offers the possibility of undertaking large-scale sequencing of germplasm accessions so that modern breeding approaches such as genomic selection and breeding by design can be realized in near future for chickpea improvement.

Association Analysis of Agronomic Traits and Resistance to Aspergillus flavus in the ICRISAT Peanut Mini-Core Collection

2013 ◽  
Vol 38 (6) ◽  
pp. 935-946 ◽  
Author(s):  
Li HUANG ◽  
Xiao-Ping REN ◽  
Xiao-Jie ZHANG ◽  
Yu-Ning CHEN ◽  
Hui-Fang JIANG
Keyword(s):  
Association Analysis ◽  
Aspergillus Flavus ◽  
Core Collection ◽  
Agronomic Traits ◽  
Mini Core Collection

scholarly journals Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods

2020 ◽  
Author(s):  
Shuai Liu ◽  
Hua Zhong ◽  
Xiaoxi Meng ◽  
Tong Sun ◽  
Yangsheng Li ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Contamination ◽  
Core Collection ◽  
Heavy Metal Contamination ◽  
Agronomic Traits ◽  
Rice Genome ◽  
P Value ◽  
Multivariate Methods ◽  
Genome Wide Association Studies ◽  
Mini Core Collection

Abstract Background: Rice is an important human staple food vulnerable to heavy metal contamination due to its unique physiology and growth environment. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge. To identify candidate QTLs for rice yield and heavy metal content, sixteen ionomic traits and thirteen agronomic traits of the USDA Rice mini-core collection were analyzed using both univariate and multivariate GWAS methods in this study. The USDA Rice Mini-Core Collection contains about 1% of the whole Rice Collection of the National Small Grains Collection (NSGC), USA.Results: Using the p-value <1.53×10-8, this criterium p-value was determined by the Bonferroni correction for p-value of 0.05, 106, 47, and 97 QTLs were identified for ionomics in flooded environment, unflooded environment, and agronomic traits, respectively. A large number of QTLs coincide well with previous report results while many of the QTLs are new QTLs, suggesting the efficiency of GWAS methods and the reliability of this study. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs. When univariate methods failed to identify QTLs for a trait, the multivariate methods frequently detected QTLs. However, when many QTLs were detected by univariate methods, the number of QTLs detected by multivariate methods were reduced in many cases. These analyses suggest that using multiple GWAS methods can complement each other in QTL identification. In addition, several candidate genes involved in ionomic and agronomic traits control were identified by analyzing the sequences of the candidate QTL regions.Conclusions: Significant QTLs for heavy metal, mineral, and agronomic traits are presented in the rice genome and some of them have been fine mapped in the rice genome in this study. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing an important foundation for further studies on reducing heavy-metal contamination and improving crop yields. In addition, the comparison analysis of the GAWS methods showed that each method has unique feature and different method can complement each other.

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