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

• Published in: Agronomy (Basel) Vol. 12; no. 10; p. 2442
• Main Authors: Kumar, Rahul, Saini, Dinesh Kumar, Kumar, Mukesh, Priyanka, Veerala, Akhatar, Javed, Kaushik, Deepak, Sharma, Amit, Dhanda, Parmdeep Singh, Kaushik, Prashant
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Agricultural production; Analysis; Brassica; Brassica juncea; Breeding; candidate genes; Clustering; expression analysis; Functional analysis; Genes; Genetic engineering; Genetically modified organisms; Genome editing; Genome-wide association studies; Genomes; meta-QTL analysis; Mustard; Oilseeds; Proteins; quality traits; Quantitative genetics; Quantitative trait loci; Seeds; Singh, Vishwanath Pratap; Single-nucleotide polymorphism; Source studies; yield-related traits; Israel; India
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12102442

A meta-QTL analysis was conducted in Indian mustard to identify robust and stable meta-QTLs (MQTLs) by utilizing 1504 available QTLs, which included 891 QTLs for yield-related traits and 613 QTLs for quality traits. For yield-related traits, a total of 57 MQTLs (YRTs_MQTLs) were uncovered from the clustering of 560 projected QTLs, which had a 4.18-fold smaller confidence interval (CI) than that of the initial QTLs, whereas, for quality traits, as many as 51 MQTLs (Quality_MQTLs) were derived from 324 projected QTLs, which had a 2.65-fold smaller CI than that of the initial QTLs. Sixteen YRTs_MQTLs were observed to share chromosomal positions with 16 Quality_MQTLs. Moreover, four most promising YRTs_MQTLs and eight Quality-MQTLs were also selected and recommended for use in breeding programs. Four of these selected MQTLs were also validated with significant SNPs that were identified in previously published genome-wide association studies. Further, in silico functional analysis of some promising MQTLs allowed the detection of as many as 1435 genes, which also involved 15 high-confidence candidate genes (CGs) for yield-related traits and 46 high-confidence CGs for quality traits. After validation, the identified CGs can also be exploited to model the plant architecture and to improve quality traits through marker-assisted breeding, genetic engineering, and genome editing approaches.