Genetic Selection of Subgraphs for Automatic Reasoning in Design Systems

2011 ◽  
pp. 280-287 ◽  
Author(s):  
Barbara Strug
Keyword(s):  
Genetic Selection ◽  
Automatic Reasoning ◽  
Design Systems ◽  
Selection Of

scholarly journals Prediction of meat quality traits in the abattoir using portable near-infrared spectrometers: heritability of predicted traits and genetic correlations with laboratory-measured traits

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Simone Savoia ◽  
Andrea Albera ◽  
Alberto Brugiapaglia ◽  
Liliana Di Stasio ◽  
Alessio Cecchinato ◽  
...  
Keyword(s):  
Meat Quality ◽  
Near Infrared ◽  
External Validation ◽  
Genetic Correlations ◽  
Genetic Selection ◽  
Quality Traits ◽  
Eye Muscle ◽  
Meat Quality Traits ◽  
Color Traits ◽  
Selection Of

Abstract Background The possibility of assessing meat quality traits over the meat chain is strongly limited, especially in the context of selective breeding which requires a large number of phenotypes. The main objective of this study was to investigate the suitability of portable infrared spectrometers for phenotyping beef cattle aiming to genetically improving the quality of their meat. Meat quality traits (pH, color, water holding capacity, tenderness) were appraised on rib eye muscle samples of 1,327 Piemontese young bulls using traditional (i.e., reference/gold standard) laboratory analyses; the same traits were also predicted from spectra acquired at the abattoir on the intact muscle surface of the same animals 1 d after slaughtering. Genetic parameters were estimated for both laboratory measures of meat quality traits and their spectra-based predictions. Results The prediction performances of the calibration equations, assessed through external validation, were satisfactory for color traits (R2 from 0.52 to 0.80), low for pH and purge losses (R2 around 0.30), and very poor for cooking losses and tenderness (R2 below 0.20). Except for lightness and purge losses, the heritability estimates of most of the predicted traits were lower than those of the measured traits while the genetic correlations between measured and predicted traits were high (average value 0.81). Conclusions Results showed that NIRS predictions of color traits, pH, and purge losses could be used as indicator traits for the indirect genetic selection of the reference quality phenotypes. Results for cooking losses were less effective, while the NIR predictions of tenderness were affected by a relatively high uncertainty of estimate. Overall, genetic selection of some meat quality traits, whose direct phenotyping is difficult, can benefit of the application of infrared spectrometers technology.

Prescreening in large populations as a tool for identifying elevated CO2-responsive genotypes in plants

2019 ◽  
Vol 46 (1) ◽  
pp. 1 ◽  
Author(s):  
Hiroyuki Shimono ◽  
Graham Farquhar ◽  
Matthew Brookhouse ◽  
Florian A. Busch ◽  
Anthony O'Grady ◽  
...  
Keyword(s):  
Genetic Selection ◽  
Co2 Concentration ◽  
Elevated Atmospheric Co2 ◽  
High Performing ◽  
Active Selection ◽  
Wide Range ◽  
Large Populations ◽  
Increase Productivity ◽  
The Cost ◽  
Selection Of

Elevated atmospheric CO2 concentration (e[CO2]) can stimulate the photosynthesis and productivity of C3 species including food and forest crops. Intraspecific variation in responsiveness to e[CO2] can be exploited to increase productivity under e[CO2]. However, active selection of genotypes to increase productivity under e[CO2] is rarely performed across a wide range of germplasm, because of constraints of space and the cost of CO2 fumigation facilities. If we are to capitalise on recent advances in whole genome sequencing, approaches are required to help overcome these issues of space and cost. Here, we discuss the advantage of applying prescreening as a tool in large genome×e[CO2] experiments, where a surrogate for e[CO2] was used to select cultivars for more detailed analysis under e[CO2] conditions. We discuss why phenotypic prescreening in population-wide screening for e[CO2] responsiveness is necessary, what approaches could be used for prescreening for e[CO2] responsiveness, and how the data can be used to improve genetic selection of high-performing cultivars. We do this within the framework of understanding the strengths and limitations of genotype–phenotype mapping.

scholarly journals Biological control of tick populations: review and reflections

1994 ◽  
Vol 10 (1) ◽  
pp. 47-52 ◽  
Author(s):  
Norma V. Labarthe
Keyword(s):  
Biological Control ◽  
Genetic Selection ◽  
Effective Vaccine ◽  
Control Methods ◽  
The Relationship ◽  
Selection Of

This study is concerned with aspects of the relationship between ticks and their hosts that have a bearing on biological control of tick populations. It proposes control methods based on a program that would combine development of an effective vaccine with genetic selection of hosts.

Genetic selection of lambda phage clones from a gene clonotheque

1986 ◽  
Vol 203 (2) ◽  
pp. 312-315
Author(s):  
Anatolij A. Melnikov ◽  
Istvan Fodor
Keyword(s):  
Genetic Selection ◽  
Lambda Phage ◽  
Selection Of

Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model

2015 ◽  
Vol 17 ◽  
pp. 38-46 ◽  
Author(s):  
Alejandro Rosales-Pérez ◽  
Carlos A. Reyes-García ◽  
Jesus A. Gonzalez ◽  
Orion F. Reyes-Galaviz ◽  
Hugo Jair Escalante ◽  
...  
Keyword(s):  
Genetic Selection ◽  
Fuzzy Model ◽  
Infant Cry ◽  
Selection Of

Multiple-trait BLUP: a suitable strategy for genetic selection of Eucalyptus

2018 ◽  
Vol 14 (5) ◽  
Author(s):  
Rodrigo Silva Alves ◽  
João Romero do Amaral Santos de Carvalho Rocha ◽  
Paulo Eduardo Teodoro ◽  
Marcos Deon Vilela de Resende ◽  
Eduardo Pinheiro Henriques ◽  
...  
Keyword(s):  
Genetic Selection ◽  
Multiple Trait ◽  
Selection Of

PSVII-28 Genetic analysis of frame score and performance traits in Nellore cattle–growth, carcass and efficiency traits

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 238-239
Author(s):  
Maria Paula Negreiros ◽  
Elisa Peripolli ◽  
Claudio U Magnabosco ◽  
Nayanny Guimarães ◽  
Raysildo Lôbo ◽  
...  
Keyword(s):  
Animal Model ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Genetic Selection ◽  
Heritability Estimate ◽  
Score System ◽  
Adult Weight ◽  
Nellore Cattle ◽  
And Performance ◽  
Selection Of

Abstract We evaluated a novel frame score system for Nellore cattle raised on pasture (Guimarães et al., 2020 J, Anim. Sci. 98 (Suppl. 4)). Growth traits included age-adjusted weights (kg) at birth (W0), 120 (W120), 210 (W210) and 450 (W450) days of age, and adult weight (AW). Carcass traits (by ultrasound) included ribeye area (REA, cm2), 12th-13th rib backfat (BF, mm), rump fat (RF, mm), and intramuscular fat (IMF, %). Feed efficiency traits included residual feed intake (RFI, kg of DM/d) and DM intake (DMI, kg/d). We applied the previously developed equations (that also include hip height (HH, cm) and age to 12,049 records from the National Association of Breeders and Researchers (ANCP) database:FSmales= -20.35 + 0.1305*REA + 0.2633*BF - 0.5901*RF+ 0.1139*HH + 0.0056*AGEFSfemales = -11.87 + 0.1316*REA - 0.2457*BF - 0.6218*RF + 0.1139*HH + 0.0009507*AGEThe (co)variance components and genetic parameters were estimated using a linear animal model or a threshold animal model depending on the trait. The heritability estimate for frame score was moderate (0.30). Estimated genetic correlations were moderate for growth traits (W0, 0.51; W120, 0.41; W210, 0.35; W450, 0.29; AW, 0.39). Estimated genetic correlations for carcass and efficiency traits ranged from low to high (REA, 0.50; BF, -0.25; RF, -0.84; IMF, -0.15; DMI, 0.29; RFI, 0.10). The estimates support the notion that larger frame animals are heavier, leaner and later maturing, and less feed efficient. The new frame score may be a useful tool for genetic selection of animals that are best suited to their environment.

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