Oilseed rape () is an economically important oil crop, yet the genetic architecture of its complex traits remain largely unknown. Here, genome-wide association study was conducted for eight yield-related traits to dissect the genetic architecture of additive, dominance, epistasis, and their environment interaction. Additionally, the optimal genotype combination and the breeding value of superior line, superior hybrid and existing best line in mapping population were predicted for each trait in two environments based on the predicted genotypic effects. As a result, 17 quantitative trait SNPs (QTSs) were identified significantly for target traits with total heritability varied from 58.47 to 87.98%, most of which were contributed by dominance, epistasis, and environment-specific effects. The results indicated that non-additive effects were large contributions to heritability and epistasis, and also noted that environment interactions were important variants for oilseed breeding. Our study facilitates the understanding of genetic basis of rapeseed yield trait, helps to accelerate rapeseed breading, and also offers a roadmap for precision plant breeding via marker-assisted selection.
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http://dx.doi.org/10.3389/fgene.2017.00015 | DOI Listing |
Theor Appl Genet
January 2025
College of Agriculture, State Key Laboratory of Crop Stress Biology in Arid Areas, Northwest A&F University, Yangling, 712100, China.
QTL mapping of two RIL populations in multiple environments revealed a consistent QTL for bristle length, and combined with RNA-seq, a potential candidate gene influencing bristle length was identified. Foxtail millet bristles play a vital role in increasing yields and preventing bird damage. However, there is currently limited research on the molecular regulatory mechanisms underlying foxtail millet bristle formation, which constrains the genetic improvement and breeding of new foxtail millet varieties.
View Article and Find Full Text PDFGenetics
January 2025
School of BioSciences, The University of Melbourne, Royal Parade, Parkville, VIC 3010, Australia.
Genomic prediction applies to any agro- or ecologically relevant traits, with distinct ontologies and genetic architectures. Selecting the most appropriate model for the distribution of genetic effects and their associated allele frequencies in the training population is crucial. Linear regression models are often preferred for genomic prediction.
View Article and Find Full Text PDFDevelopment
January 2025
Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
Heterozygous variants in SOX10 cause congenital syndromes affecting pigmentation, digestion, hearing, and neural development, primarily attributable to failed differentiation or loss of non-skeletal neural crest derivatives. We report here an additional, previously undescribed requirement for Sox10 in bone mineralization. Neither crest- nor mesoderm-derived bones initiate mineralization on time in zebrafish sox10 mutants, despite normal osteoblast differentiation and matrix production.
View Article and Find Full Text PDFMol Biol Evol
January 2025
Institut de Biologie, École Normale Supérieure, CNRS UMR 8197, Inserm U1024, PSL Research University, Paris 75005, France.
Modifiers of recombination rates have been described but the selective pressures acting on them and their effect on adaptation to novel environments remain unclear. We performed experimental evolution in the nematode Caenorhabditis elegans using alternative rec-1 alleles modifying the position of meiotic crossovers along chromosomes without detectable direct fitness effects. We show that adaptation to a novel environment is impaired by the allele that decreases recombination rates in the genomic regions containing fitness variation.
View Article and Find Full Text PDFArXiv
December 2024
James Franck Institute, University of Chicago, Chicago, United States.
All biological systems are subject to perturbations: due to thermal fluctuations, external environments, or mutations. Yet, while biological systems are composed of thousands of interacting components, recent high-throughput experiments show that their response to perturbations is surprisingly low-dimensional: confined to only a few stereotyped changes out of the many possible. Here, we explore a unifying dynamical systems framework - soft modes - to explain and analyze low-dimensionality in biology, from molecules to eco-systems.
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