Revealing the Genetic Architecture of Yield-Related and Quality Traits in Indian Mustard [Brassica juncea (L.) Czern. and Coss.] Using Meta-QTL Analysis

Kumar, Rahul; Saini, Dinesh Kumar; Kumar, Mukesh; Priyanka, Veerala; Akhatar, Javed; Kaushik, Deepak; Sharma, Amit; Dhanda, Parmdeep Singh; Kaushik, Prashant

doi:10.3390/agronomy12102442

Cited by 9 publications

(7 citation statements)

References 83 publications

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“…There were hundreds of shared markers present in these genetic maps, albeit at different genetic positions, which were considered when ordering the markers in the consensus chromosomal maps (Supplementary Table 11 ). The steps involved in the construction of consensus map are well-described in a recent study [ 101 ].…”

Section: Methodsmentioning

confidence: 99%

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat

et al. 2023

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Background Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. Results Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker–trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. Conclusion The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

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Section: Methodsmentioning

confidence: 99%

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat

et al. 2023

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“…Furthermore, all the markers flanking the QTLs extracted from different studies were integrated into this consensus map, facilitating the projection of a greater number of QTLs. The construction of the consensus map was accomplished using the LPMerge package within the R environment as described by Kumar R et al (2022) .…”

Section: Methodsmentioning

confidence: 99%

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

et al. 2023

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Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population’s hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2 Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing.

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“…Common candidate genes, viz., AP2/ERF, SANT4, MYB, zinc finger domain, Protein kinases, glutathione s transferase, and homeobox domains were observed among heterotic ILs developed in different genetic backgrounds (Tables S6 and S7). Previous studies have confirmed that these candidate genes have an important role in the regulation of yield-related traits [52][53][54]. These candidate genes can be deployed in parental lines through marker-assisted selection to improve yield-related traits in hybrids after identifying suitable molecular markers.…”

Section: Candidate Genes Associated With Heterotic Genomic Segmentsmentioning

confidence: 80%

“…Candidate genes, viz., seed storage albumin 1 (SESA1), SANT 4, CYP72A13, zinc finger/ring type, Glycoside hydrolase family 9, potassium transporter, UDP-glucosyl transferase 73B3, and 3-ketoacyl-acyl carrier protein synthase III were reported in heterotic PM30_ILs (Table S9). These candidate genes were reported to have an association with oil content in B. juncea [52]. Common candidate genes, viz., AP2/ERF, SANT4, MYB, zinc finger domain, Protein kinases, glutathione s transferase, and homeobox domains were observed among heterotic ILs developed in different genetic backgrounds (Tables S6 and S7).…”

Section: Candidate Genes Associated With Heterotic Genomic Segmentsmentioning

confidence: 99%

Introgression of Heterotic Genomic Segments from Brassica carinata into Brassica juncea for Enhancing Productivity

Vasisth

Singh

Limbalkar

et al. 2023

Plants

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Interspecific hybridization resulted in the creation of B. juncea introgression lines (ILs) generated from B. carinata with increased productivity and adaptability. Forty ILs were crossed with their respective B. juncea recipient parents to generate introgression line hybrids (ILHs) and the common tester (SEJ 8) was used to generate test hybrids (THs). Mid-parent heterosis in ILHs and standard heterosis in THs were calculated for eight yield and yield-related traits. Heterotic genomic regions were dissected using ten ILs with significant mid-parent heterosis in ILHs and standard heterosis in THs for seed yield. A high level of heterosis for seed yield was contributed by 1000 seed weight (13.48%) in D31_ILHs and by total siliquae/plant (14.01%) and siliqua length (10.56%) in PM30_ILHs. The heterotic ILs of DRMRIJ 31 and Pusa Mustard 30 were examined using polymorphic SNPs between the parents, and a total of 254 and 335 introgressed heterotic segments were identified, respectively. This investigation discovered potential genes, viz., PUB10, glutathione S transferase, TT4, SGT, FLA3, AP2/ERF, SANT4, MYB, and UDP-glucosyl transferase 73B3 that were previously reported to regulate yield-related traits. The heterozygosity of the FLA3 gene significantly improved siliqua length and seeds per siliqua in ILHs of Pusa Mustard 30. This research proved that interspecific hybridization is an effective means of increasing the diversity of cultivated species by introducing new genetic variants and improving the level of heterosis.

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Revealing the Genetic Architecture of Yield-Related and Quality Traits in Indian Mustard [Brassica juncea (L.) Czern. and Coss.] Using Meta-QTL Analysis

Cited by 9 publications

References 83 publications

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

Introgression of Heterotic Genomic Segments from Brassica carinata into Brassica juncea for Enhancing Productivity

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