Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS

Kumar, Jitendra; Saripalli, Gautam; Gahlaut, Vijay; Goel, Neha; Meher, Prabina Kumar; Mishra, Kaushlesh Kumar; Mishra, Prafulla Chandra; Sehgal, Deepmala; Vikram, Prashant; Sansaloni, Carolina; Singh, Sukhwinder; Sharma, Pradeep; Gupta, P. K.

doi:10.1007/s10681-018-2284-2

Cited by 72 publications

(59 citation statements)

References 73 publications

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“…Association analysis is increasingly used for identification of chromosomal regions affecting mineral accumulations (Alomari et al , ; Kumar et al , ), and ionomic traits (Shakoor et al , ; Ziegler et al , ). Yet, quantitative trait loci (QTL) analysis, based on segregating mapping populations, remains an important approach for genetic dissection of elemental accumulation (Gu et al , ; Huang et al , ; Velu et al , ).…”

Section: Introductionmentioning

confidence: 99%

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within the wheat grain ionome

et al. 2019

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Dissection of the genetic basis of wheat ionome is crucial for understanding the physiological and biochemical processes underlying mineral accumulation in seeds, as well as for efficient crop breeding. Most of the elements essential for plants are metals stored in seeds as chelate complexes with phytic acid or sulfur-containing compounds. We assume that the involvement of phosphorus and sulfur in metal chelation is the reason for strong phenotypic correlations within ionome. Adjustment of element concentrations for the effect of variation in phosphorus and sulfur seed content resulted in drastic change of phenotypic correlations between the elements. The genetic architecture of wheat grain ionome was characterized by quantitative trait loci (QTL) analysis using a cross between durum and wild emmer wheat. QTL analysis of the adjusted traits and two-trait analysis of the initial traits paired with either P or S considerably improved QTL detection power and accuracy, resulting in the identification of 105 QTLs and 617 QTL effects for 11 elements. Candidate gene search revealed some potential functional associations between QTLs and corresponding genes within their intervals. Thus, we have shown that accounting for variation in P and S is crucial for understanding of the physiological and genetic regulation of mineral composition of wheat grain ionome and can be implemented for other plants.

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Section: Introductionmentioning

confidence: 99%

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within the wheat grain ionome

et al. 2019

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“…Moreover, many loci associated with Ca concentration (Alomari et al, 2017), Cu and Manganese (Mg) concentration in wheat grains (Cu et al, 2020), and Se and molybdenum (Mo) in chickpea (Ozkuru et al, 2019) were identified by GWAS. Interestingly, Kumar et al, (2018) and Cu et al, (2020) reported loci controlling multiple grain micronutrients, phosphorus content, and yield-related traits.…”

Section: Gwas For Mineral Contentmentioning

confidence: 99%

Genome-wide Association Studies of Agronomic Traits Consisting of Field- and Molecular-based Phenotypes

Nakano

Kobayashi

2020

RAS

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Genome-wide association study (GWAS) is a powerful approach to identify the genetic factors underlying the intraspecific phenotypic variations. Recent advances in DNA sequencing technology, including next generation sequencing has enabled us to easily genotype high density genome-wide SNPs. In addition, many accessions of various plant species have been widely collected in recent years. These genetic resources have made GWAS a markedly more popular approach for investigation of natural variations occurring in various traits using large populations. In addition to genotyping technology, advances in high-throughput phenotyping technologies have enabled us to acquire variation data on a large number of accessions characterized for various traits, including not only the field traits (e.g., yield and disease resistance) but also molecular traits (e.g., gene expression level and metabolite content). Thus, it is possible to expand the range of application of GWAS and enhance the detection power of genomic association. In this review, we summarize recent GWAS of various agronomic traits at field and molecular scale, following which we highlight the integration approach involving GWAS and high-throughput phenotyping technologies including transcriptome, ionome and metabolome.

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“…In recent years, a large number of multivariate GWAS methods have been developed, including MLMM (multi-locus mixed-model) [26], FarmCPU (Fixed and random model Circulating Probability Unification) [27], mrMLM (multi-locus random-SNP-effect MLM) [28], FASTmrMLM (fast mrMLM) [29], FASTmrEMMA (fast multi-locus random-SNP-effect efficient mixed model analysis) [30], pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes) [31], 6 pKWmEB (integration of Kruskal-Wallis test with empirical Bayes) [32], ISIS EM-BLASSO (iterative modified-sure independence screening expectation-maximization-Bayesian least absolute shrinkage and selection operator) [33], and GPWAS (Genome-Phenome Wide Association Study) [34]. The MLMM [26] [35], rice [36,37], foxtail millet [38], soybean [39,40], maize [41,42], and wheat [43,44].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Zhong

Meng

et al. 2020

Preprint

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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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Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS

Cited by 72 publications

References 73 publications

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within the wheat grain ionome

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within the wheat grain ionome

Genome-wide Association Studies of Agronomic Traits Consisting of Field- and Molecular-based Phenotypes

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

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