About this Research Topic
Crop improvement traits significant to agriculture are genetically complex and regulated by polygenes. These poly genes spread across crop genomes mapped as genomic targets known as quantitative trait loci (QTLs). Genome-Wide Association (GWA) mapping is an effective plant breeding strategy for detecting natural allelic variation and associating haplotype polymorphism to valuable agronomic traits such as yield, (a) biotic resistance, and nutritional quality traits. GWA gained momentum over traditional mapping by documenting alleles/QTLs with a higher resolution by addressing the population structure and linkage disequilibrium (LD). The success of the GWA application relies on the choice of germplasm, population size/structure, molecular marker density, accurate phenotypic data, and appropriate statistical analyses. The biological value of genomic regions identified by QTL or GWA warrants validation through diverse functional genomic approaches that drives crop improvement in commercial crops. Combining mapping and functional genomic strategies will enhance the use of genetic variation towards improving economically valuable traits in crop plants.
Association mapping successfully identifies polymorphism in the DNA sequence (loci, alleles) associated with the trait phenotypic variations observed. Association mapping studies in oilseed crops are in the early stage and gaining acceleration at a faster pace. This Research Topic aims to publish recent discoveries made with GWA studies in oilseed crops to dissect complex traits of economic value. Globally, several edible oilseed crops like groundnut, sesame, soybean, mustard, sunflower, soybean, safflower, rapeseed, and other minor oilseeds like niger, coconut, and palm kernel contribute to the significant trades for plant-based oil. Castor, linseed, and cottonseed oil are categorized as non-edible oilseed crops. Sal, mahua, simarouba, kokum, olive, wild apricot, karanja, jatropha, neem, jojoba, cherua, walnut, and tung are minor oilseeds gaining popularity in recent times. These plant-based oil crops contribute a substantial share in Asian and South American tropical countries adding to the oil economy. Yield is an extensively researched trait, then biotic and abiotic stress resistance/tolerance studies. Association mapping would unquestionably find genomic solutions to mitigate the losses caused by both biotic and abiotic factors.
The success of an AM study to identify true associations depends on the ability to discern LD of the marker with a QTL from LD due to other causes. Population structure, family relatedness, selection, and genetic drift are the primary causes of false positive associations. The false positives which are a major problem in AM also arise from the most recent common ancestry. This could be controlled by adding a kinship matrix into the linear model. Similarly, false negatives are generated due to the overfitting of these complex models. Multi-locus AM models like the FarmCPU model are recommended against single-locus models since they consider the information of all loci simultaneously and so they can control both false positives and false negatives. We are also open to further options of protection against false positives suggested by researchers.
This Research Topic aims to publish genetic research involving GWA on oilseeds. In specific, research focusing on oilseed crops such as soybean, groundnut, rapeseed, mustard, sunflower, and safflower are highly encouraged for submission, along with other emerging crops with the potential for commercial oil production. We wish to compile research from diverse expertise on approaches and technologies in crop improvement. GWA studies with diverse germplasm to dissect major complex traits and associate a key phenotypic characteristic(s) with genetic components, QTL, or haplotype with potential as a molecular marker(s) are valuable for publishing in this Research Topic. Application of functional genomic tools alongside association analysis, and the inclusion of any first-time report or innovative statistical methods related to oilseed crops are welcome. Brief research reports, data reports, general commentary, hypothesis & theory, methods, reviews, opinions, original research, and perspectives can be submitted.
Association mapping successfully identifies polymorphism in the DNA sequence (loci, alleles) associated with the trait phenotypic variations observed. Association mapping studies in oilseed crops are in the early stage and gaining acceleration at a faster pace. This Research Topic aims to publish recent discoveries made with GWA studies in oilseed crops to dissect complex traits of economic value. Globally, several edible oilseed crops like groundnut, sesame, soybean, mustard, sunflower, soybean, safflower, rapeseed, and other minor oilseeds like niger, coconut, and palm kernel contribute to the significant trades for plant-based oil. Castor, linseed, and cottonseed oil are categorized as non-edible oilseed crops. Sal, mahua, simarouba, kokum, olive, wild apricot, karanja, jatropha, neem, jojoba, cherua, walnut, and tung are minor oilseeds gaining popularity in recent times. These plant-based oil crops contribute a substantial share in Asian and South American tropical countries adding to the oil economy. Yield is an extensively researched trait, then biotic and abiotic stress resistance/tolerance studies. Association mapping would unquestionably find genomic solutions to mitigate the losses caused by both biotic and abiotic factors.
The success of an AM study to identify true associations depends on the ability to discern LD of the marker with a QTL from LD due to other causes. Population structure, family relatedness, selection, and genetic drift are the primary causes of false positive associations. The false positives which are a major problem in AM also arise from the most recent common ancestry. This could be controlled by adding a kinship matrix into the linear model. Similarly, false negatives are generated due to the overfitting of these complex models. Multi-locus AM models like the FarmCPU model are recommended against single-locus models since they consider the information of all loci simultaneously and so they can control both false positives and false negatives. We are also open to further options of protection against false positives suggested by researchers.
This Research Topic aims to publish genetic research involving GWA on oilseeds. In specific, research focusing on oilseed crops such as soybean, groundnut, rapeseed, mustard, sunflower, and safflower are highly encouraged for submission, along with other emerging crops with the potential for commercial oil production. We wish to compile research from diverse expertise on approaches and technologies in crop improvement. GWA studies with diverse germplasm to dissect major complex traits and associate a key phenotypic characteristic(s) with genetic components, QTL, or haplotype with potential as a molecular marker(s) are valuable for publishing in this Research Topic. Application of functional genomic tools alongside association analysis, and the inclusion of any first-time report or innovative statistical methods related to oilseed crops are welcome. Brief research reports, data reports, general commentary, hypothesis & theory, methods, reviews, opinions, original research, and perspectives can be submitted.
Keywords: Quantitative trait loci, Genome-wide association studies, Oilseed crops, Haplotype
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