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| http://dx.doi.org/10.3389/fgene.2023.1267334 | DOI Listing |
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Developing genomic tools to assist turnip rape [ (L.) subsp. (DC.) Metzg.] breeding.
Front Genet
August 2024
Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
Introduction: Turnip rape is recognized as an oilseed crop contributing to environmentally sustainable agriculture via integration into crop rotation systems. Despite its various advantages, the crop's cultivation has declined globally due to a relatively low productivity, giving way to other crops. The use of genomic tools could enhance the breeding process and accelerate genetic gains.
View Article and Find Full Text PDFSparse testing designs for optimizing predictive ability in sugarcane populations.
Front Plant Sci
July 2024
Agronomy Department, University of Florida, Gainesville, FL, United States.
Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction.
View Article and Find Full Text PDFMetabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study.
Front Genet
March 2024
South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States.
Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS).
View Article and Find Full Text PDFImproving predictive ability in sparse testing designs in soybean populations.
Front Genet
November 2023
Agronomy Department, University of Florida, Gainesville, FL, United States.
Article Synopsis
- The study explores the use of genome-based prediction models (GP) to enhance soybean breeding programs through a method called GP-based sparse testing, which optimizes testing capacity and reduces costs.
- It examines various training set compositions of recombinant inbred lines (RILs) derived from 39 bi-parental populations, testing their predictive abilities across nine environments.
- Results indicate that maximizing genetic diversity and including overlapping RILs in training sets improves prediction accuracy, while reducing training set size generally lowers predictive ability, underlining the importance of diverse environmental testing for selecting superior soybean genotypes.
Editorial: Genotype-by-environment interaction in farm animals: from measuring to understanding.
Front Genet
August 2023
Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain.
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