Strategies for accelerating genetic gains in crop plants: special focus on speed breeding

Gudi, Santosh; Kumar, Pradeep; Singh, Surjan; Tanin, Mohammad Jafar; Sharma, Achla

doi:10.1007/s12298-022-01247-8

Cited by 13 publications

(3 citation statements)

References 95 publications

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“…Mostly, these gene families coincided with the CGs mined within the hcMQTL regions in the present study. These MQTLs are promising and could be used for effective MAS for maize root traits [ 73 , 74 ].…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

Karnatam

Chhabra

Saini

et al. 2023

IJMS

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Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.

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Section: Discussionmentioning

confidence: 99%

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

Karnatam

Chhabra

Saini

et al. 2023

IJMS

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“…Cumin researchers should focus on developing climate-resilient high-yielding varieties through climate-smart technologies and practices to deal with the climatic constraints [7] (Figure 3). Plant breeders combine the important yield component and quality traits [32] by utilizing modern breeding tools [45,110,111] such as speed breeding [33] should continue for cumin production. Advanced machineries and low-cost equipment for performing different field operations in cumin cultivation needs to be developed by agricultural engineers to overcome the challenges of high labour requirement, high cost and drudgery in operation and low profitability due to untimely operations.…”

Section: Future Perspectivementioning

confidence: 99%

Tackling the Constraints of Cumin Cultivation and Management Practices

Kumar¹,

Sahoo²,

Kushwaha³

et al. 2023

annagriccropsci

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Cumin (Cuminum cyminum L.) is an important seed spice crop, having good medicinal properties and grown predominately in arid and semi-arid region of the world. India is the leading country with share of 70% in the world production of cumin. Two states of India (Rajasthan and Gujarat) cover nearly 80% of the total area under cumin cultivation. The current scenario and status of area, production and productivity in India has been reviewed to identify the various factors affecting cumin production and productivity. Apart from these major constraints related to climate, soil, agronomical, crop input, extension, marketing and farm mechanization in cumin production and their management are discussed thoroughly. Due to low productivity of cumin there is need to focus on integrated approaches such as utilizing modern technologies, advance agronomic practises, farm mechanization interventions and advanced breeding tools to enhance the cumin production. The study reveals alternative solutions to overcome these problems and evidenced that there is huge scope of farm mechanization to improve the production of cumin crop and reduce drudgery of farmers.

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“…Plant breeders are concerned with combining multiple yield‐related traits (Singh, Gudi, et al., 2022; Singh, Kaur, et al., 2022). A paradigm shift may occur in the near future if the conventional breeding (pre‐breeding, and alien gene introgression) approaches are integrated with modern breeding tools (such as genomics, phenomics, speed breeding, genetic engineering, haplotype‐based breeding, allele mining, and genome editing) (Bakala et al., 2020; Gudi, Kumar, et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Meta‐analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.)

et al. 2023

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A meta‐analysis of quantitative trait loci (QTLs), associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits was conducted for the first time in pigeonpea (Cajanus cajan L.). Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 biparental populations). Of these 498, 203 QTLs were projected onto “PigeonPea_ConsensusMap_2022,” saturated with 10,522 markers, which resulted in the prediction of 34 meta‐QTLs (MQTLs). The average confidence interval (CI) of these MQTLs (2.54 cM) was 3.37 times lower than the CI of the initial QTLs (8.56 cM). Of the 34 MQTLs, 12 high‐confidence MQTLs with CI (≤5 cM) and a greater number of initial QTLs (≥5) were utilized to extract 2255 gene models, of which 105 were believed to be associated with different traits under study. Furthermore, eight of these MQTLs were observed to overlap with several marker‐trait associations or significant SNPs identified in previous genome‐wide association studies. Furthermore, synteny and ortho‐MQTL analyses among pigeonpea and four related legumes crops, such as chickpea, pea, cowpea, and French bean, led to the identification of 117 orthologous genes from 20 MQTL regions. Markers associated with MQTLs can be employed for MQTL‐assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Additionally, MQTLs may be subjected to fine mapping, and some of the promising candidate genes may serve as potential targets for positional cloning and functional analysis to elucidate the molecular mechanisms underlying the target traits.

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Strategies for accelerating genetic gains in crop plants: special focus on speed breeding

Cited by 13 publications

References 95 publications

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

Tackling the Constraints of Cumin Cultivation and Management Practices

Meta‐analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.)

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