scholarly journals Genomic prediction of growth in a commercially, recreationally, and culturally important marine resource, the Australian snapper (Chrysophrys auratus)

2021 ◽  
Author(s):  
Jonathan Sandoval-Castillo ◽  
Luciano B. Beheregaray ◽  
Maren Wellenreuther
Keyword(s):  
Genomic Prediction ◽  
Growth Traits ◽  
Anthropogenic Pressures ◽  
Bioinformatic Tools ◽  
Reduced Genome ◽  
High Quality Snps ◽  
Breeding Programmes ◽  
Cost Efficient ◽  
The Right ◽  
Genome Representation

AbstractGrowth is one of the most important traits of an organism. For exploited species, this trait has ecological and evolutionary consequences as well as economical and conservation significance. Rapid changes in growth rate associated with anthropogenic stressors have been reported for several marine fishes, but little is known about the genetic basis of growth traits in teleosts. We used reduced genome representation data and genome-wide association approaches to identify growth-related genetic variation in the commercially, recreationally, and culturally important Australian snapper (Chrysophrys auratus, Sparidae). Based on 17,490 high-quality SNPs and 363 individuals representing extreme growth phenotypes from 15,000 fish of the same age and reared under identical conditions in a sea pen, we identified 100 unique candidates that were annotated to 51 proteins. We documented a complex polygenic nature of growth in the species that included several loci with small effects and a few loci with larger effects. Overall heritability was high (75.7%), reflected in the high accuracy of the genomic prediction for the phenotype (small vs large). Although the SNPs were distributed across the genome, most candidates (60%) clustered on chromosome 16, which also explains the largest proportion of heritability (16.4%). This study demonstrates that reduced genome representation SNPs and the right bioinformatic tools provide a cost-efficient approach to identify growth-related loci and to describe genomic architectures of complex quantitative traits. Our results help to inform captive aquaculture breeding programmes and are of relevance to monitor growth-related evolutionary shifts in wild populations in response to anthropogenic pressures.

scholarly journals Genomic prediction using low density marker panels in aquaculture: performance across species, traits, and genotyping platforms

2019 ◽  
Author(s):  
Christina Kriaridou ◽  
Smaragda Tsairidou ◽  
Ross D. Houston ◽  
Diego Robledo
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Species Traits ◽  
Breeding Values ◽  
Genotyping Platform ◽  
Genome Wide ◽  
Breeding Programmes ◽  
Cost Efficient ◽  
Snp Panels ◽  
Different Characteristics

ABSTRACTGenomic selection increases the rate of genetic gain in breeding programmes, which results in significant cumulative improvements in commercially important traits such as disease resistance. Genomic selection currently relies on collecting genome-wide genotype data accross a large number of individuals which requires substantial economic investment. However, global aquaculture production predominantly occurs in small and medium sized enterprises for whom this technology can be prohibitively expensive. For genomic selection to benefit these aquaculture sectors more cost-efficient genotyping is necessary. In this study the utility of low and medium density SNP panels (ranging from 100 to 9000 SNPs) to accurate predict breeding values was tested and compared in four aquaculture datasets with different characteristics (species, genome size, genotyping platform, family number and size, total population size, and target trait). A consistent pattern of genomic prediction accuracy was observed across species, with little or no reduction until SNP density was reduced below 1,000 SNPs. Below this SNP density, heritability estimates and genomic prediction accuracies tended to be lower and more variable (93 % of maximum accuracy achieved with 1,000 SNPs, 89 % with 500 SNPs, and 70% with 100 SNPs). Now that a multitude of studies have highlighted the benefits of genomic over pedigree-based prediction of breeding values in aquaculture species, the results of the current study highlight that these benefits can be achieved at lower SNP densities and at lower cost, raising the possibility of a broader application of genetic improvement in smaller and more fragmented aquaculture settings.

Differential Induction of Glutathione Transferases and Glucosyltransferases in Wheat, Maize and Arabidopsis thaliana by Herbicide Safeners

2005 ◽  
Vol 60 (3-4) ◽  
pp. 307-316 ◽  
Author(s):  
Robert Edwards ◽  
Daniele Del Buono ◽  
Michael Fordham ◽  
Mark Skipsey ◽  
Melissa Brazier ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Crop Protection ◽  
Detoxification Enzymes ◽  
Glutathione Transferases ◽  
Wide Range ◽  
Breeding Programmes ◽  
Model Plant Arabidopsis Thaliana ◽  
Herbicide Safeners ◽  
Species Specific ◽  
The Right

Abstract By learning lessons from weed science we have adopted three approaches to make plants more effective in phytoremediation: 1. The application of functional genomics to identify key components involved in the detoxification of, or tolerance to, xenobiotics for use in subsequent genetic engineering/breeding programmes. 2. The rational metabolic engineering of plants through the use of forced evolution of protective enzymes, or alternatively transgenesis of detoxification pathways. 3. The use of chemical treatments which protect plants from herbicide injury. In this paper we examine the regulation of the xenome by herbicide safeners, which are chemicals widely used in crop protection due to their ability to enhance herbicide selectivity in cereals. We demonstrate that these chemicals act to enhance two major groups of phase 2 detoxification enzymes, notably the glutathione transferases and glucosyltransferases, in both cereals and the model plant Arabidopsis thaliana, with the safeners acting in a chemical- and species-specific manner. Our results demonstrate that by choosing the right combination of safener and plant it should be possible to enhance the tolerance of diverse plants to a wide range of xenobiotics including pollutants.

scholarly journals Genomic Prediction of Growth and Stem Quality Traits in Eucalyptus globulus Labill. at Its Southernmost Distribution Limit in Chile

Forests ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 779 ◽  
Author(s):  
Paulina Ballesta ◽  
Nicolle Serra ◽  
Fernando Guerra ◽  
Rodrigo Hasbún ◽  
Freddy Mora
Keyword(s):  
Genomic Prediction ◽  
Eucalyptus Globulus ◽  
Growth Traits ◽  
Principal Component ◽  
Local Economy ◽  
Range Limit ◽  
Distribution Limit ◽  
Genome Wide ◽  
A Genome ◽  
Stem Straightness

The present study was undertaken to examine the ability of different genomic selection (GS) models to predict growth traits (diameter at breast height, tree height and wood volume), stem straightness and branching quality of Eucalyptus globulus Labill. trees using a genome-wide Single Nucleotide Polymorphism (SNP) chip (60 K), in one of the southernmost progeny trials of the species, close to its southern distribution limit in Chile. The GS methods examined were Ridge Regression-BLUP (RRBLUP), Bayes-A, Bayes-B, Bayesian least absolute shrinkage and selection operator (BLASSO), principal component regression (PCR), supervised PCR and a variant of the RRBLUP method that involves the previous selection of predictor variables (RRBLUP-B). RRBLUP-B and supervised PCR models presented the greatest predictive ability (PA), followed by the PCR method, for most of the traits studied. The highest PA was obtained for the branching quality (~0.7). For the growth traits, the maximum values of PA varied from 0.43 to 0.54, while for stem straightness, the maximum value of PA reached 0.62 (supervised PCR). The study population presented a more extended linkage disequilibrium (LD) than other populations of E. globulus previously studied. The genome-wide LD decayed rapidly within 0.76 Mbp (threshold value of r2 = 0.1). The average LD on all chromosomes was r2 = 0.09. In addition, the 0.15% of total pairs of linked SNPs were in a complete LD (r2 = 1), and the 3% had an r2 value >0.5. Genomic prediction, which is based on the reduction in dimensionality and variable selection may be a promising method, considering the early growth of the trees and the low-to-moderate values of heritability found in the traits evaluated. These findings provide new understanding of how develop novel breeding strategies for tree improvement of E. globulus at its southernmost range limit in Chile, which could represent new opportunities for forest planting that can benefit the local economy.

scholarly journals Variance Component Decomposition for Growth Traits of the Bay Scallop (Argopecten irradians irradians)

2021 ◽  
Vol 8 ◽  
Author(s):  
Hengde Li ◽  
Yangfan Wang ◽  
Qiang Xing ◽  
Qifan Zeng ◽  
Liang Zhao ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Prediction Model ◽  
Genomic Prediction ◽  
Mate Selection ◽  
Cross Validation ◽  
Growth Traits ◽  
Shell Height ◽  
Mass Selection ◽  
Argopecten Irradians ◽  
Bay Scallop

The bay scallop (Argopecten irradians irradians) is one of the most important shellfish species in China. Since their introduction into China, only mass selection has been used in bay scallop breeding. With its gradual expansion and shortage of mate selection, population homozygosity increased, and fitness decreased. To investigate the effects of inbreeding and provide reference for improving breeding strategies and mating management, the variance components of the growth traits of the bay scallop were decomposed with genomic relationship matrices. The results indicated that the genetic variations in shell height and length were mainly accounted for by the additive effects. The genetic variation in shell width was mainly caused by dominance or dominance-by-dominance epistasis. The genetic variation in body weight was accounted for by dominance. No significant directional dominances were detected for all growth traits. Cross-validation for genomic prediction showed that including insignificant inbreeding in the genomic prediction model is not necessary, and we suggest that the genomic prediction model should be optimized with both likelihood ratio tests and cross-validation before utilization in practice.

Knowledge Management Toolkit for SMEs

2011 ◽  
pp. 49-63 ◽  
Author(s):  
Kerstin Fink ◽  
Christian Ploder
Keyword(s):  
Knowledge Management ◽  
Social Cognitive ◽  
Software Tool ◽  
Software Products ◽  
Knowledge Processes ◽  
The Social ◽  
Cost Efficient ◽  
Knowledge Preservation ◽  
The Right ◽  
Efficient Software

The discipline of knowledge management is no longer emerging in large organizations, but also small and medium-sized enterprises (SMEs) are focusing on finding the right process that will allow them to make advantages of their intellectual capital. Using survey data from 219 small and medium-sized enterprises in Austria and Switzerland, this article illustrates the four key knowledge processes (1) knowledge identification, (2) knowledge acquisition, (3) knowledge distribution, and (4) knowledge preservation for SMEs and also reports the findings of the empirical study designed to allocate cost-efficient software products to each of the four knowledge processes. As a result a knowledge toolkit for SMEs that integrates knowledge processes, methods and software tool for decision support making is given. Finally, the social view of knowledge management to SMEs is discussed, showing that the use of information technology is currently far more important than the integration of a social-cognitive perspective.

Knowledge Management Toolkit for SMEs

2009 ◽  
pp. 1136-1150
Author(s):  
Kerstin Fink ◽  
Christian Ploder
Keyword(s):  
Knowledge Management ◽  
Social Cognitive ◽  
Software Tool ◽  
Software Products ◽  
Knowledge Processes ◽  
The Social ◽  
Cost Efficient ◽  
Knowledge Preservation ◽  
The Right ◽  
Efficient Software

The discipline of knowledge management is no longer emerging in large organizations, but also small and medium-sized enterprises (SMEs) are focusing on finding the right process that will allow them to make advantages of their intellectual capital. Using survey data from 219 small and medium-sized enterprises in Austria and Switzerland, this chapter illustrates the four key knowledge processes (1) knowledge identification, (2) knowledge acquisition, (3) knowledge distribution, and (4) knowledge preservation for SMEs and also reports the findings of the empirical study designed to allocate cost-efficient software products to each of the four knowledge processes. As a result a knowledge toolkit for SMEs that integrates knowledge processes, methods and software tool for decision support making is given. Finally, the social view of knowledge management to SMEs is discussed, showing that the use of information technology is currently far more important than the integration of a social-cognitive perspective.

Genomic prediction of growth traits in scallop using convolutional neural networks

Aquaculture ◽  
2021 ◽  
pp. 737171
Author(s):  
Xinghai Zhu ◽  
Ping Ni ◽  
Qiang Xing ◽  
YangfanWang ◽  
Xiaoting Huang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Genomic Prediction ◽  
Growth Traits

scholarly journals Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1591
Author(s):  
Sebastian Michel ◽  
Franziska Löschenberger ◽  
Ellen Sparry ◽  
Christian Ametz ◽  
Hermann Bürstmayr
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Wheat Breeding ◽  
Single Step ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Record Keeping ◽  
Line Breeding ◽  
Cost Efficient

The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain.

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