scholarly journals DNA fingerprints applied to gene introgression in breeding programs.

Genetics ◽  
1990 ◽  
Vol 124 (3) ◽  
pp. 783-789 ◽  
Author(s):  
J Hillel ◽  
T Schaap ◽  
A Haberfeld ◽  
A J Jeffreys ◽  
Y Plotzky ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Farm Animals ◽  
Gene Introgression ◽  
Dna Fingerprints ◽  
Breeding Programs ◽  
Introgression Breeding ◽  
Donor Line ◽  
Genomic Similarity

Abstract An application of DNA fingerprints (DFP) for gene introgression in breeding programs of both farm animals and plants is proposed. DFP loci, detectable by minisatellite probes, are extremely polymorphic. Individuals have unique patterns of DFP and thus can be selected for maximal genomic similarity to the recipient line, and minimal similarity to the donor line, using their DFP patterns as the criterion for similarity. This genomic selection (GS) can be performed at generations BC1, BC2 or both, and thus significantly reduce the required number of backcross generations in introgression breeding programs. The association between genomic and DFP similarity is demonstrated. Theoretical distributions and variances of the relative percentages of the donor and recipient genomes as the basis for the GS approach are presented.

scholarly journals Using markers in gene introgression breeding programs.

Genetics ◽  
1992 ◽  
Vol 132 (4) ◽  
pp. 1199-1210 ◽  
Author(s):  
F Hospital ◽  
C Chevalet ◽  
P Mulsant
Keyword(s):  
Population Size ◽  
Marker Assisted Selection ◽  
Gene Introgression ◽  
Selection Intensity ◽  
Breeding Programs ◽  
Introgression Breeding ◽  
Size Number ◽  
Theoretical Predictions ◽  
Two Generations ◽  
High Selection

Abstract We investigate the use of markers to hasten the recovery of the recipient genome during an introgression breeding program. The effects of time and intensity of selection, population size, number and position of selected markers are studied for chromosomes either carrying or not carrying the introgressed gene. We show that marker assisted selection may lead to a gain in time of about two generations, an efficiency below previous theoretical predictions. Markers are most useful when their map position is known. In the early generations, it is shown that increasing the number of markers over three per non-carrier chromosome is not efficient, that the segment surrounding the introgressed gene is better controlled by rather distant markers unless high selection intensity can be applied, and that selection on this segment first can reduce the selection intensity available for selection on non-carrier chromosomes. These results are used to propose an optimal strategy for selection on the whole genome, making the most of available material and conditions (e.g., population size and fertility, genetic map).

scholarly journals Why and How to Switch to Genomic Selection: Lessons From Plant and Animal Breeding Experience

2021 ◽  
Vol 12 ◽  
Author(s):  
◽  
Aline Fugeray-Scarbel ◽  
Catherine Bastien ◽  
Mathilde Dupont-Nivet ◽  
Stéphane Lemarié
Keyword(s):  
Genetic Diversity ◽  
Genomic Selection ◽  
Plant Species ◽  
Animal Species ◽  
Animal Breeding ◽  
Breeding Cycle ◽  
Breeding Programs ◽  
The Third ◽  
Wide Range ◽  
Selection For

The present study is a transversal analysis of the interest in genomic selection for plant and animal species. It focuses on the arguments that may convince breeders to switch to genomic selection. The arguments are classified into three different “bricks.” The first brick considers the addition of genotyping to improve the accuracy of the prediction of breeding values. The second consists of saving costs and/or shortening the breeding cycle by replacing all or a portion of the phenotyping effort with genotyping. The third concerns population management to improve the choice of parents to either optimize crossbreeding or maintain genetic diversity. We analyse the relevance of these different bricks for a wide range of animal and plant species and sought to explain the differences between species according to their biological specificities and the organization of breeding programs.

scholarly journals Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

2021 ◽  
Vol 12 ◽  
Author(s):  
Jana Obšteter ◽  
Janez Jenko ◽  
Gregor Gorjanc
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Breeding Program ◽  
Progeny Testing ◽  
Initial Training ◽  
Training Population ◽  
Breeding Programs ◽  
Use Of Resources ◽  
Dairy Cattle Breeding ◽  
Close Relatives

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

Characterisation of genetic diversity and DNA fingerprinting of Australian chickpea (Cicer arietinum L.) cultivars using MFLP markers

2008 ◽  
Vol 59 (8) ◽  
pp. 707 ◽  
Author(s):  
R. Lin ◽  
H. Yang ◽  
T. N. Khan ◽  
K. H. M. Siddique ◽  
G. Yan
Keyword(s):  
Genetic Diversity ◽  
Cicer Arietinum ◽  
Dna Markers ◽  
Fragment Length Polymorphism ◽  
Grain Legume ◽  
Dna Fingerprints ◽  
Breeding Programs ◽  
Cicer Arietinum L ◽  
The World ◽  
Chickpea Cultivars

Chickpea (Cicer arietinum L.) is one of the major grain legume crops in the world. In this study, the genetic diversity of 24 Australian chickpea cultivars released between 1987 and 2005 was investigated with microsatellite-anchored fragment length polymorphism (MFLP) DNA markers. Among the cultivars examined, 30 cultivar-specific markers were identified and all were unequivocally identified using the DNA fingerprints developed in this study. Most of the cultivars were grouped into two major clusters; cv. Flipper was separated from the rest based on total character differences of DNA polymorphism. The MFLP approach proved suitable in the analysis of genetic diversity among the chickpea cultivars studied and the genetic relationship identified will be useful for chickpea breeding programs in selecting parent materials.

scholarly journals Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program

2020 ◽  
Author(s):  
Sikiru Adeniyi Atanda ◽  
Michael Olsen ◽  
Juan Burgueño ◽  
Jose Crossa ◽  
Daniel Dzidzienyo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Primary Objective ◽  
Breeding Program ◽  
Breeding Cycle ◽  
Training Set ◽  
Maize Breeding ◽  
Phenotypic Data ◽  
Breeding Programs

Abstract Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. Abstract The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

scholarly journals How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding

2020 ◽  
Vol 11 ◽  
Author(s):  
Christian R. Werner ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
John M. Hickey ◽  
Tobias Kox ◽  
...  
Keyword(s):  
Family Structure ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Careful Analysis ◽  
Critical Approach ◽  
Crop Species ◽  
Breeding Programs ◽  
Mendelian Sampling

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.

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