scholarly journals Marker-assisted Selection in Fish: A Review

2019 ◽  
pp. 1-11 ◽  
Author(s):  
F. Eze
Keyword(s):  
Marker Assisted Selection ◽  
Quantitative Traits ◽  
Selective Breeding ◽  
Breeding Programme ◽  
Genetic Potential ◽  
Breeding Populations ◽  
Trait Improvement ◽  
Performance Traits ◽  
Selective Breeding Programme ◽  
Selection Of

The important economical traits like body growth, resistance to diseases, meat quality, etc. highly influence the profitability of food animals including fishes. The main target of every selective breeding programme is to produce improved traits offspring’s. However, improvement of performance traits through traditional phenotype-based selection needs several generations to optimise these characters. Marker-Assisted Selection (MAS) is a type of indirect method of selection of better performing breeding individuals. MAS is beneficial when the traits are difficult, expensive to measure and has both low heritability and recessive traits. MAS facilitates the exploitation of existing genetic diversity in breeding populations and can be used to improve desirable traits in livestock. MAS depends on identifying the link between a genetic marker and Quantitative Traits Loci (QTL). The distance between marker and target traits determines the association of the marker with the QTL. After identifying the markers linked to QTL, they can be used in the selective breeding programme to select the brooders having better genetic potential for the targeted trait. Improvement of performance traits through MAS is fast and more accurate and allows us to understand the genetic mechanism affecting performance traits.

scholarly journals Marker-assisted selection using ridge regression

2000 ◽  
Vol 75 (2) ◽  
pp. 249-252 ◽  
Author(s):  
JOHN C. WHITTAKER ◽  
ROBIN THOMPSON ◽  
MIKE C. DENHAM
Keyword(s):  
Linear Regression ◽  
Ridge Regression ◽  
Marker Assisted Selection ◽  
Quantitative Traits ◽  
Selection Response ◽  
Inbred Lines ◽  
Response To Selection ◽  
Satisfactory Method ◽  
The Mean ◽  
Selection Of

In crosses between inbred lines, linear regression can be used to estimate the correlation of markers with a trait of interest; these marker effects then allow marker assisted selection (MAS) for quantitative traits. Usually a subset of markers to include in the model must be selected: no completely satisfactory method of doing this exists. We show that replacing this selection of markers by ridge regression can improve the mean response to selection and reduce the variability of selection response.

scholarly journals Estimation of genetic diversity for quantitative traits of commercial wheat (Triticum aestivum)

2017 ◽  
Vol 1 ◽  
pp. 320
Author(s):  
Adnan Rasheed ◽  
Abdul Malik Solangi
Keyword(s):  
Plant Height ◽  
Quantitative Traits ◽  
Yellow Rust ◽  
Flag Leaf ◽  
Spike Length ◽  
Breeding Programme ◽  
Adult Plant ◽  
Augmented Design ◽  
Peduncle Length ◽  
Selection Of

This research was conducted to assess the performance of wheat genotypes and to detect genotypes with adult plant resistance by evaluating them in a trap nursery. 36 commercial wheat cultivar were used in experiment. Morocco was sown on four sides of trial. Material was sown in augmented design. The data was recorded on different quantitative like Plant height (cm), no. of tillers/plant, spike length (cm), no of spikelets/plant, peduncle length (cm), stem diameter (mm) and flag leaf area. Selection of genotypes could be done from two main clusters to make cross for improvement of traits. No of tillers/plant, peduncle length, spike/length, spikelets/plant and plant height showed large genetic variability in Biplot and these variables could be used as selection criteria. Pak-81, Sindh-81, Mexipak-65, Sarsabaz, Chakwal-86 and Kiran-95 so these cultivars could be potentially used in future breeding programme for improvement of several quantitative traits according to results of Biplot analysis. Following genotypes were found moderate resistant against yellow rust viz. Anmol-9, Bahawalpur-200 and Bakhtawar-92 and could be used further in future breeding programme to stand against yellow ruts pressure. Maxipak-65 and WL-711 need to improve by incorporating yellow rust resistant genes.

scholarly journals Traitmill™: a functional genomics platform for the phenotypic analysis of cereals

2006 ◽  
Vol 4 (1) ◽  
pp. 20-24 ◽  
Author(s):  
Christophe Reuzeau ◽  
Valerie Frankard ◽  
Yves Hatzfeld ◽  
Anabel Sanz ◽  
Wim Van Camp ◽  
...  
Keyword(s):  
High Throughput ◽  
Quantitative Traits ◽  
Cell Cycle Control ◽  
Plant Performance ◽  
Phenotypic Analysis ◽  
Nutrient Metabolism ◽  
Crop Species ◽  
Trait Improvement ◽  
Plant Evaluation ◽  
Selection Of

The improvement of quality and quantitative traits in industrial crops is among the most important goals in plant breeding. Many traits of interest are controlled by multiple genes and improvements have so far only been obtained through conventional breeding. The use of biotechnological tools to modify quantitative traits is highly challenging. CropDesign has developed TraitMill™, an automated plant evaluation platform allowing high-throughput testing of the effect of plant-based transgenes on agronomically valuable traits in crop plants. The focus of the platform is currently on rice, a good model for other important cereals such as maize and wheat. TraitMill™ offers a high-throughput prediction of gene function. Genes of validated function that confer trait improvement can then be transferred to other cereal crop species such as maize, but also to dicots, trees and ornamentals. TraitMill™ involves the following key components: (i) selection of candidate trait improvement genes among genes involved in signal transduction, cell cycle control, transcription, nutrient metabolism, etc.; (ii) a suite of validated constitutive or tissue-specific promoters from rice allowing for the selection of the most appropriate promoter–gene combination in view of the desired trait improvement; (iii) an industrialized plant transformation system generating tens of thousands of transgenic plants annually; and (iv) a robotized trait evaluation set-up for plant evaluation, proprietary image analysis software for measuring plant performance parameters and statistical analysis of results.

scholarly journals Marker assisted selection of xa5, xa13 and Xa21 gene in breeding populations derived from Karma Mahsuri x IRBB 59

2021 ◽  
Vol 4 (1) ◽  
pp. 108-116
Author(s):  
A. J. Kotasthane ◽  
N. J. Gaikwad
Keyword(s):  
Marker Assisted Selection ◽  
Bacterial Blight ◽  
Durable Resistance ◽  
Gene Pyramiding ◽  
Effective Means ◽  
Rice Cultivar ◽  
Breeding Populations ◽  
Broad Spectrum Resistance ◽  
Serious Disease ◽  
Selection Of

Bacterial leaf blight, caused by the Gram negative bacterium Xanthomonas oryzae pv. oryzae (Xoo), is a serious disease throughout the rice growing world. Resistant cultivars are the primary and most effective means of control. Marker assisted selection (MAS) can help in screening more efficiently for the presence or absence of resistant genes. Molecular markers have made it possible to identify and pyramid valuable genes of agronomic importance in resistance rice breeding. In the present study, to incorporate durable resistance against bacterial blight three resistance genes, xa 5, xa13 and Xa21, from an indica donor IRBB 59 were introgressed into high yielding susceptible rice cultivar Karma Mahsuri. Karma Mahsuri is one of the most popular varieties of Chhattisgarh and mega varieties of India. These three genes were pyramided through marker-assisted breeding. For MAS of xa5:- RG556, RM122, RM390, RM13;  xa13:-RG136 and RM 230 and  Xa21: Xa21 and RM21 are the known linked markers. Markers xa5R and xa5S specific for xa5 resistant and susceptible genes respectively, xa13Pro for xa13 gene and PT248 for Xa21 gene obtained from Dr Sundaram (DRR, Hyderabad) were also used in the present study for MAS. High-resolution maps generated in silico around xa5 and xa13 will be useful for the precise placement of a gene of interest and the analysis of regional and sub-regional rates of recombination and appropriate combinations of markers for marker assisted selection in plant-breeding. In Karma Mahsuri X IRBB 59 cross we got Three lines (03)containing three gene (xa5, xa13 and Xa21), Twenty three (23) line contain a combination of xa5 & xa13,  only one (01) with xa5 and Xa21. There were eight lines with xa5 gene Seventeen (17) lines with xa13 gene. We therefore report herein the development of nil, two and three gene pyramids of  xa5, xa13 and Xa21 in the background of Karma Mahsuri. Key words: bacterial blight (BB), Broad-spectrum resistance, Gene pyramiding marker-assisted selection (MAS), Rice.

Evolution and Selection of Quantitative Traits

2018 ◽  
Author(s):  
Bruce Walsh ◽  
Michael Lynch
Keyword(s):  
Mathematical Models ◽  
Quantitative Genetics ◽  
Complex Traits ◽  
Evolutionary Biology ◽  
Quantitative Traits ◽  
Specific Gene ◽  
Real World Data ◽  
Holistic Treatment ◽  
Genetics And Genomics ◽  
Selection Of

Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.

scholarly journals Mapping QTL for spike fertility and related traits in two doubled haploid wheat (Triticum aestivum L.) populations

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nicole Pretini ◽  
Leonardo S. Vanzetti ◽  
Ignacio I. Terrile ◽  
Guillermo Donaire ◽  
Fernanda G. González
Keyword(s):  
Doubled Haploid ◽  
Marker Assisted Selection ◽  
Triticum Aestivum L ◽  
Physiological Model ◽  
Yield Potential ◽  
Breeding Programs ◽  
Per Se ◽  
Genomic Regions ◽  
Fertility Traits ◽  
Selection Of

Abstract Background In breeding programs, the selection of cultivars with the highest yield potential consisted in the selection of the yield per se, which resulted in cultivars with higher grains per spike (GN) and occasionally increased grain weight (GW) (main numerical components of the yield). In this study, quantitative trait loci (QTL) for GW, GN and spike fertility traits related to GN determination were mapped using two doubled haploid (DH) populations (Baguette Premium 11 × BioINTA 2002 and Baguette 19 × BioINTA 2002). Results In total 305 QTL were identified for 14 traits, out of which 12 QTL were identified in more than three environments and explained more than 10% of the phenotypic variation in at least one environment. Eight hotspot regions were detected on chromosomes 1A, 2B, 3A, 5A, 5B, 7A and 7B in which at least two major and stable QTL sheared confidence intervals. QTL on two of these regions (R5A.1 and R5A.2) have previously been described, but the other six regions are novel. Conclusions Based on the pleiotropic analysis within a robust physiological model we conclude that two hotspot genomic regions (R5A.1 and R5A.2) together with the QGW.perg-6B are of high relevance to be used in marker assisted selection in order to improve the spike yield potential. All the QTL identified for the spike related traits are the first step to search for their candidate genes, which will allow their better manipulation in the future.

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