Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

Saini, Dinesh Kumar; Chopra, Yuvraj; Singh, Jagmohan; Sandhu, Karansher S.; Kumar, Anand; Bazzer, Sumandeep K.; Srivastava, Puja

doi:10.1007/s11032-021-01272-7

Cited by 89 publications

(60 citation statements)

References 344 publications

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“…Wheat (Triticum aestivum L.) is one of the most important staple crops worldwide, providing 18% of the caloric intake (Awika, 2011;Saini et al, 2022). Hexaploid wheat is categorized into soft and hard wheat classes based on protein strength, kernel texture, water absorption, and milling quality (Kiszonas et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

et al. 2022

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Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait–based GS models. This study’s main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait– and multi-trait–based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait–based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.

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Section: Introductionmentioning

confidence: 99%

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

et al. 2022

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“…In these conditions, maintaining the genetic progress will be a huge challenge. This will rely on the optimal use of all recent breeding tools (high-throughput genotyping and phenotyping, genomic and phenomic selection) [ 140 , 141 ] and the exploitation of all available genetic diversity that could best be conducted within large consortia associating public and private research.…”

Section: Discussionmentioning

confidence: 99%

Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection

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Lafarge

Balfourier

et al. 2022

Biology

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There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker–trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.

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“…Sears et al Sears et al, (1993) created aneuploid stocks of wheat, which were later used for several mapping and genome sequence studies. The last 3 decades witnessed a profound improvement in understanding wheat genomics and genetics due to the rapid adoption of DNAbased molecular markers such as RFLP, SSR, AFLP, DArT and SNPs from the early 1990s (Saini et al, 2022). These molecular markers have aided in conducting several QTL mapping studies using interval mapping, single-marker analysis, and GWAS (Muhu-Din Ahmed et al, 2020).…”

Section: Overview Of Six Crops Used In This Studymentioning

confidence: 99%

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

et al. 2022

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The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.

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Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

Cited by 89 publications

References 344 publications

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

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