Soybean time of flowering and maturity are genetically controlled by genes. Different allelic combinations of these genes determine soybean adaptation to a specific latitude. The paper describes the first attempt to assess adaptation of soybean genotypes developed and realized at Institute of Field and Vegetable Crops, Novi Sad, Serbia [Novi Sad (NS) varieties and breeding lines] based on gene variation, as well as to comparatively assess gene variation in North-American (NA), Chinese, and European genotypes, as most of the studies published so far deal with North-American and Chinese cultivars and breeding material. Allelic variation and distribution of the major maturity genes (, and ) has been determined in 445 genotypes from soybean collections of NA ancestral lines, Chinese germplasm, and European varieties, as well as NS varieties and breeding lines. The study showed that allelic combinations of - genes significantly determined the adaptation of varieties to different geographical regions, although they have different impacts on maturity. In general, each collection had one major genotype haplogroup, comprising over 50% of the lines. The exceptions were European varieties that had two predominant haplogroups and NA ancestral lines distributed almost evenly among several haplogroups. As was the most common genotype in NS population, present in the best-performing genotypes in terms of yield, this specific allele combination was proposed as the optimal combination for the environments of Central-Eastern Europe.
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http://dx.doi.org/10.3389/fpls.2018.01286 | DOI Listing |
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NHO Tokyo Medical Center, National Institute of Sensory Organs, Tokyo, Japan.
Age-related macular degeneration (AMD) is one of the leading causes of severe irreversible blindness worldwide in the elderly population. AMD is a multifactorial disease mainly caused by advanced age, environmental factors, and genetic variations. Genome-wide association studies (GWAS) have strongly supported the link between locus on chromosome 10q26 and AMD development, encompassing multiple variants, rs10490924 (c.
View Article and Find Full Text PDFMalar J
January 2025
Centre for Biotechnology Research and Development, Kenya Medical Research Institute (KEMRI), Nairobi, Kenya.
Background: The current study sought to re-evaluate malaria prevalence, susceptibility to artemisinin-based combination therapy (ACT), transmission patterns and the presence of malaria vectors in the Kikuyu area of the Kenyan Central highlands, a non-traditional/low risk malaria transmission zone where there have been anecdotal reports of emerging malaria infections.
Methods: Sampling of adult mosquitoes was done indoors, while larvae were sampled outdoors in June 2019. The malaria clinical study was an open label non-randomized clinical trial where the efficacy of one ACT drug, was evaluated in two health facilities.
Gene
January 2025
Department of Immunology, Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, China. Electronic address:
J Exp Bot
January 2025
Department of Plant, Soil and Microbial Science, Michigan State University, East Lansing, MI 48824, USA.
Sorghum is emerging as an ideal genetic model for designing high-biomass bioenergy crops. Biomass yield, a complex trait influenced by various plant architectural characteristics, is typically regulated by numerous genes. This study aimed to dissect the genetic regulators underlying fourteen plant architectural traits and ten biomass yield traits in the Sorghum Association Panel across two growing seasons.
View Article and Find Full Text PDFNat Genet
January 2025
Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7-40.
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