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

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.

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

BMC Plant Biology ◽

10.1186/s12870-020-02603-0 ◽

2020 ◽

Vol 20 (1) ◽

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 ◽

Rice Genome ◽

Genome Wide Association Studies ◽

Mini Core Collection

Abstract Background Rice 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. Results To 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 previously 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. Conclusions While 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.

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

10.21203/rs.2.22202/v2 ◽

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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Genome-wide association studies of ionomic and agronomic traits in USDA minicore collection of rice and comparative analyses of different mapping methods

10.21203/rs.2.22202/v1 ◽

2020 ◽

Author(s):

Shuai Liu ◽

Hua Zhong ◽

Xiaoxi Meng ◽

Tong Sun ◽

Yangsheng Li ◽

...

Keyword(s):

Heavy Metal ◽

Metal Contamination ◽

Heavy Metal Contamination ◽

Crop Yields ◽

Agronomic Traits ◽

Association Studies ◽

High Yield ◽

Multivariate Methods ◽

Genome Wide Association Studies ◽

Growth Environment

Abstract 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. In this report, a comprehensive GWAS analyses for ionomic and agronomic traits based on 3,259,478 SNPs were performed using two univariate methods and two multivariate methods. Under the criterium p-value <1.53×10-8, 106, 47, and 97 QTLs were identified for ionomics in flooded environment, unflooded environment, and agronomic traits, respectively. Detailed analysis of the QTLs revealed that many of the identified QTLs are co-localized with the QTLs reported in prior ionomic and agronomic studies or posited near the genes with known functions in the related traits, suggesting that our GWAS analyses are reliable. 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 and some methods may be more powerful with less false discovery rate. In addition, several candidate genes involved in ionomic and agronomic traits control were identified by sequence analysis of the QTL regions. 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.

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Genome-level diversification of eight ancient tea populations in the Guizhou and Yunnan regions identifies candidate genes for core agronomic traits

Horticulture Research ◽

10.1038/s41438-021-00617-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Litang Lu ◽

Hufang Chen ◽

Xiaojing Wang ◽

Yichen Zhao ◽

Xinzhuan Yao ◽

...

Keyword(s):

Genetic Diversity ◽

Candidate Genes ◽

Agronomic Traits ◽

Association Studies ◽

Functional Characterization ◽

Principal Component ◽

Tea Plant ◽

Guizhou Province ◽

Genome Wide Association Studies ◽

Tea Plants

AbstractThe ancient tea plant, as a precious natural resource and source of tea plant genetic diversity, is of great value for studying the evolutionary mechanism, diversification, and domestication of plants. The overall genetic diversity among ancient tea plants and the genetic changes that occurred during natural selection remain poorly understood. Here, we report the genome resequencing of eight different groups consisting of 120 ancient tea plants: six groups from Guizhou Province and two groups from Yunnan Province. Based on the 8,082,370 identified high-quality SNPs, we constructed phylogenetic relationships, assessed population structure, and performed genome-wide association studies (GWAS). Our phylogenetic analysis showed that the 120 ancient tea plants were mainly clustered into three groups and five single branches, which is consistent with the results of principal component analysis (PCA). Ancient tea plants were further divided into seven subpopulations based on genetic structure analysis. Moreover, it was found that the variation in ancient tea plants was not reduced by pressure from the external natural environment or artificial breeding (nonsynonymous/synonymous = 1.05). By integrating GWAS, selection signals, and gene function prediction, four candidate genes were significantly associated with three leaf traits, and two candidate genes were significantly associated with plant type. These candidate genes can be used for further functional characterization and genetic improvement of tea plants.

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Genome-wide Association Study-based Identification Genes Influencing Agronomic Traits in Rice (Oryza Sativa L.)

10.21203/rs.3.rs-74749/v1 ◽

2020 ◽

Author(s):

aijun wang ◽

Xinyue Shu ◽

yuqi Jiang ◽

Li Ma ◽

xiaomei jia ◽

...

Keyword(s):

Oryza Sativa ◽

Candidate Genes ◽

Human Population ◽

Agronomic Traits ◽

Association Studies ◽

Rice Genome ◽

Genome Wide Association ◽

High Yield ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract BackgroundRice (Oryza sativa L.) is one of the most important cereal crops, providing the daily dietary intake for approximately 50% of the global human population. To needs of the rapidly increasing human population worldwide, cultivation of rice varieties with high yield and quality, more genes or QTLs association with yield traits are required.ResultsCurrently, correlations among different traits and gene interactions both affect the rice breeding. Here, we re-sequenced 259 rice accessions, generating 1, 371.65 Gb of raw data. Furthermore, we performed genome-wide association studies (GWAS) on 13 agronomic traits using 2.8 million single nucleotide polymorphisms (SNPs) characterized in 259 rice accessions. Phenotypic data and best linear unbiased prediction (BLUP) values of each of the 13 traits over two years of each trait were used for GWAS. The result showed that 816 SNP signals were significantly associated (−log10P≥5) with the 13 agronomic traits. We detected candidate genes related to target traits within 200 kb upstream and downstream of the associated SNP loci, based on linkage disequilibrium (LD) blocks in the whole rice genome. These candidate genes were further identified though haplotype block construction. ConclusionsThis study provides an important genomic resource and valuable new information for breeding high yielding breeding rice cultivars through genomic selection.

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Genome-Wide Association Studies of Soybean Seed Hardness in the Chinese Mini Core Collection

Plant Molecular Biology Reporter ◽

10.1007/s11105-018-1102-2 ◽

2018 ◽

Vol 36 (4) ◽

pp. 605-617 ◽

Cited By ~ 1

Author(s):

Xing Zhang ◽

Jinming Zhao ◽

Yuanpeng Bu ◽

Dong Xue ◽

Zhangxiong Liu ◽

...

Keyword(s):

Core Collection ◽

Association Studies ◽

Soybean Seed ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Mini Core Collection ◽

Genome Wide ◽

Seed Hardness

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Dissection of the Genetic Basis of Yield-Related Traits in the Chinese Peanut Mini-Core Collection Through Genome-Wide Association Studies

Frontiers in Plant Science ◽

10.3389/fpls.2021.637284 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaojing Zhou ◽

Jianbin Guo ◽

Manish K. Pandey ◽

Rajeev K. Varshney ◽

Li Huang ◽

...

Keyword(s):

Core Collection ◽

Genetic Basis ◽

Diagnostic Marker ◽

Gene Annotation ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Mini Core Collection ◽

Genome Wide ◽

Population Structure Analysis

Peanut is an important legume crop worldwide. To uncover the genetic basis of yield features and assist breeding in the future, we conducted genome-wide association studies (GWAS) for six yield-related traits of the Chinese peanut mini-core collection. The seed (pod) size and weight of the population were investigated under four different environments, and these traits showed highly positive correlations in pairwise combinations. We sequenced the Chinese peanut mini-core collection using genotyping-by-sequencing approach and identified 105,814 high-quality single-nucleotide polymorphisms (SNPs). The population structure analysis showed essentially subspecies patterns in groups and obvious geographical distribution patterns in subgroups. A total of 79 significantly associated loci (P < 4.73 × 10–7) were detected for the six yield-related traits through GWAS. Of these, 31 associations were consistently detected in multiple environments, and 15 loci were commonly detected to be associated with multiple traits. Two major loci located on chromosomal pseudomolecules A06 and A02 showed pleiotropic effects on yield-related traits, explaining ∼20% phenotypic variations across environments. The two genomic regions were found 46 putative candidate genes based on gene annotation and expression profile. The diagnostic marker for the yield-related traits from non-synonymous SNP (Aradu-A06-107901527) was successfully validated, achieving a high correlation between nucleotide polymorphism and phenotypic variation. This study provided insights into the genetic basis of yield-related traits in peanut and verified one diagnostic marker to facilitate marker-assisted selection for developing high-yield peanut varieties.

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