scholarly journals Mapping of Genomic Regions (Quantitative Trait Loci) Controlling Production and Quality in Industrial Cultures of the Edible Basidiomycete Pleurotus ostreatus

2003 ◽  
Vol 69 (6) ◽  
pp. 3617-3625 ◽  
Author(s):  
Luis M. Larraya ◽  
Mikel Alfonso ◽  
Antonio G. Pisabarro ◽  
Luc�a Ram�rez
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Pleurotus Ostreatus ◽  
Production Traits ◽  
Trial And Error ◽  
Trait Loci ◽  
Industrial Strains ◽  
Culture Production ◽  
Culture Process ◽  
Genomic Regions

ABSTRACT Industrial production of the edible basidiomycete Pleurotus ostreatus (oyster mushroom) is based on a solid fermentation process in which a limited number of selected strains are used. Optimization of industrial mushroom production depends on improving the culture process and breeding new strains with higher yields and productivities. Traditionally, fungal breeding has been carried out by an empirical trial and error process. In this study, we used a different approach by mapping quantitative trait loci (QTLs) controlling culture production and quality within the framework of the genetic linkage map of P. ostreatus. Ten production traits and four quality traits were studied and mapped. The production QTLs identified explain nearly one-half of the production variation. More interestingly, a single QTL mapping to the highly polymorphic chromosome VII appears to be involved in control of all the productivity traits studied. Quality QTLs appear to be scattered across the genome and to have less effect on the variation of the corresponding traits. Moreover, some of the new hybrid strains constructed in the course of our experiments had production or quality values higher than those of the parents or other commercial strains. This approach opens the possibility of marker-assisted selection and breeding of new industrial strains of this fungus.

scholarly journals Identification of quantitative trait loci associated with canopy temperature in soybean

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sumandeep K. Bazzer ◽  
Larry C. Purcell
Keyword(s):  
Quantitative Trait Loci ◽  
Phenotypic Variation ◽  
Quantitative Trait ◽  
Recombinant Inbred Lines ◽  
Canopy Temperature ◽  
Growth And Yield ◽  
Indirect Measure ◽  
Trait Loci ◽  
Genomic Regions ◽  
The Impact

Abstract A consistent risk for soybean (Glycine max L.) production is the impact of drought on growth and yield. Canopy temperature (CT) is an indirect measure of transpiration rate and stomatal conductance and may be valuable in distinguishing differences among genotypes in response to drought. The objective of this study was to map quantitative trait loci (QTLs) associated with CT using thermal infrared imaging in a population of recombinant inbred lines developed from a cross between KS4895 and Jackson. Heritability of CT was 35% when estimated across environments. QTL analysis identified 11 loci for CT distributed on eight chromosomes that individually explained between 4.6 and 12.3% of the phenotypic variation. The locus on Gm11 was identified in two individual environments and across environments and explained the highest proportion of phenotypic variation (9.3% to 11.5%) in CT. Several of these CT loci coincided with the genomic regions from previous studies associated with canopy wilting, canopy temperature, water use efficiency, and other morpho-physiological traits related with drought tolerance. Candidate genes with biological function related to transpiration, root development, and signal transduction underlie these putative CT loci. These genomic regions may be important resources in soybean breeding programs to improve tolerance to drought.

Revisiting the quantitative trait loci for milk production traits on BTA6

2011 ◽  
Vol 43 (3) ◽  
pp. 318-323 ◽  
Author(s):  
R. Weikard ◽  
P. Widmann ◽  
J. Buitkamp ◽  
R. Emmerling ◽  
C. Kuehn
Keyword(s):  
Quantitative Trait Loci ◽  
Milk Production ◽  
Quantitative Trait ◽  
Production Traits ◽  
Milk Production Traits ◽  
Trait Loci

scholarly journals Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

2017 ◽  
Author(s):  
Fanny Bonnafous ◽  
Ghislain Fievet ◽  
Nicolas Blanchet ◽  
Marie-Claude Boniface ◽  
Sébastien Carrère ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Flowering Time ◽  
Genetic Control ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Additive Models ◽  
Genome Wide Association Studies ◽  
Trait Loci ◽  
Genomic Regions

AbstractGenome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

scholarly journals Identification of main-effect quantitative trait loci (QTLs) for low-temperature stress tolerance germination- and early seedling vigor-related traits in rice (Oryza sativa L.)

2020 ◽  
Vol 40 (1) ◽  
Author(s):  
S. Najeeb ◽  
J. Ali ◽  
A. Mahender ◽  
Y.L. Pang ◽  
J. Zilhas ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Low Temperature ◽  
Quantitative Trait ◽  
Root Length ◽  
Stress Index ◽  
Multiple Traits ◽  
Main Effect ◽  
Trait Loci ◽  
Early Seedling ◽  
Genomic Regions

AbstractAn attempt was made in the current study to identify the main-effect and co-localized quantitative trait loci (QTLs) for germination and early seedling growth traits under low-temperature stress (LTS) conditions in rice. The plant material used in this study was an early backcross population of 230 introgression lines (ILs) in BCIF7 generation derived from the Weed Tolerant Rice-1 (WTR-1) (as the recipient) and Haoannong (HNG) (as the donor). Genetic analyses of LTS tolerance revealed a total of 27 main-effect quantitative trait loci (M-QTLs) mapped on 12 chromosomes. These QTLs explained more than 10% of phenotypic variance (PV), and average PV of 12.71% while employing 704 high-quality SNP markers. Of these 27 QTLs distributed on 12 chromosomes, 11 were associated with low-temperature germination (LTG), nine with low-temperature germination stress index (LTGS), five with root length stress index (RLSI), and two with biomass stress index (BMSI) QTLs, shoot length stress index (SLSI) and root length stress index (RLSI), seven with seed vigor index (SVI), and single QTL with root length (RL). Among them, five significant major QTLs (qLTG(I)1, qLTGS(I)1–2, qLTG(I)5, qLTGS(I)5, and qLTG(I)7) mapped on chromosomes 1, 5, and 7 were associated with LTG and LTGS traits and the PV explained ranged from 16 to 23.3%. The genomic regions of these QTLs were co-localized with two to six QTLs. Most of the QTLs were growth stage-specific and found to harbor QTLs governing multiple traits. Eight chromosomes had more than four QTLs and were clustered together and designated as promising LTS tolerance QTLs (qLTTs), as qLTT1, qLTT2, qLTT3, qLTT5, qLTT6, qLTT8, qLTT9, and qLTT11. A total of 16 putative candidate genes were identified in the major M-QTLs and co-localized QTL regions distributed on different chromosomes. Overall, these significant genomic regions of M-QTLs are responsible for multiple traits and this suggested that these could serve as the best predictors of LTS tolerance at germination and early seedling growth stages. Furthermore, it is necessary to fine-map these regions and to find functional markers for marker-assisted selection in rice breeding programs for cold tolerance.

scholarly journals Quantitative trait loci linked to PRNP gene controlling health and production traits in INRA 401 sheep

2007 ◽  
Vol 39 (4) ◽  
pp. 421 ◽  
Author(s):  
Zulma G Vitezica ◽  
Carole R Moreno ◽  
Frederic Lantier ◽  
Isabelle Lantier ◽  
Laurent Schibler ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Prnp Gene ◽  
Production Traits ◽  
Trait Loci
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