Selection strategies for marker-assisted backcrossing with high-throughput marker systems. 2011

Eva Herzog, and Matthias Frisch
Institute of Agronomy and Plant Breeding II, Justus Liebig University, Giessen, Germany.

Application of marker-assisted backcrossing for gene introgression is still limited by the high costs of marker analysis. High-throughput (HT) assays promise to reduce these costs, but new selection strategies are required for their efficient implementation in breeding programs. The objectives of our study were to investigate the properties of HT marker systems compared to single-marker (SM) assays, and to develop optimal selection strategies for marker-assisted backcrossing with HT assays. We employed computer simulations with a genetic model consisting of 10 chromosomes of 160 cM length to investigate the introgression of a dominant target gene. We found that a major advantage of HT marker systems is that they can provide linkage maps with equally spaced markers, whereas the possibility to provide linkage maps with high marker densities smaller than 10 cM is only of secondary use in marker-assisted backcrossing. A three-stage selection strategy that combines selection for recombinants at markers flanking the target gene with SM assays and genome-wide background selection with HT markers in the first backcross generation was more efficient than genome-wide background selection with HT markers alone. Selection strategies that combine SM and HT assays were more efficient than genome-wide background selection with HT assays alone. This result was obtained for a broad range of cost ratios of HT and SM assays. A further considerable reduction of the costs could be achieved if the population size in the first backcross generation was twice the population size in generations BC(2) and BC(3) of a three-generation backcrossing program. We conclude that selection strategies combining SM and HT assays have the potential to greatly increase the efficiency and flexibility of marker-assisted backcrossing.

UI MeSH Term Description Entries
D007178 Inbreeding The mating of plants or non-human animals which are closely related genetically. Backcrossing,Half-Sib Mating,Sib Mating,Genetic Inbreeding,Backcrossings,Genetic Inbreedings,Half Sib Mating,Half-Sib Matings,Inbreeding, Genetic,Mating, Half-Sib,Mating, Sib,Matings, Half-Sib,Matings, Sib,Sib Matings
D008040 Genetic Linkage The co-inheritance of two or more non-allelic GENES due to their being located more or less closely on the same CHROMOSOME. Genetic Linkage Analysis,Linkage, Genetic,Analyses, Genetic Linkage,Analysis, Genetic Linkage,Genetic Linkage Analyses,Linkage Analyses, Genetic,Linkage Analysis, Genetic
D008957 Models, Genetic Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment. Genetic Models,Genetic Model,Model, Genetic
D002874 Chromosome Mapping Any method used for determining the location of and relative distances between genes on a chromosome. Gene Mapping,Linkage Mapping,Genome Mapping,Chromosome Mappings,Gene Mappings,Genome Mappings,Linkage Mappings,Mapping, Chromosome,Mapping, Gene,Mapping, Genome,Mapping, Linkage,Mappings, Chromosome,Mappings, Gene,Mappings, Genome,Mappings, Linkage
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D003313 Zea mays A plant species of the family POACEAE. It is a tall grass grown for its EDIBLE GRAIN, corn, used as food and animal FODDER. Corn,Indian Corn,Maize,Teosinte,Zea,Corn, Indian
D003362 Cost-Benefit Analysis A method of comparing the cost of a program with its expected benefits in dollars (or other currency). The benefit-to-cost ratio is a measure of total return expected per unit of money spent. This analysis generally excludes consideration of factors that are not measured ultimately in economic terms. In contrast a cost effectiveness in general compares cost with qualitative outcomes. Cost and Benefit,Cost-Benefit Data,Benefits and Costs,Cost Benefit,Cost Benefit Analysis,Cost-Utility Analysis,Costs and Benefits,Economic Evaluation,Marginal Analysis,Analyses, Cost Benefit,Analysis, Cost Benefit,Analysis, Cost-Benefit,Analysis, Cost-Utility,Analysis, Marginal,Benefit and Cost,Cost Benefit Analyses,Cost Benefit Data,Cost Utility Analysis,Cost-Benefit Analyses,Cost-Utility Analyses,Data, Cost-Benefit,Economic Evaluations,Evaluation, Economic,Marginal Analyses
D003433 Crosses, Genetic Deliberate breeding of two different individuals that results in offspring that carry part of the genetic material of each parent. The parent organisms must be genetically compatible and may be from different varieties or closely related species. Cross, Genetic,Genetic Cross,Genetic Crosses
D015415 Biomarkers Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, ENVIRONMENTAL EXPOSURE and its effects, disease diagnosis; METABOLIC PROCESSES; SUBSTANCE ABUSE; PREGNANCY; cell line development; EPIDEMIOLOGIC STUDIES; etc. Biochemical Markers,Biological Markers,Biomarker,Clinical Markers,Immunologic Markers,Laboratory Markers,Markers, Biochemical,Markers, Biological,Markers, Clinical,Markers, Immunologic,Markers, Laboratory,Markers, Serum,Markers, Surrogate,Markers, Viral,Serum Markers,Surrogate Markers,Viral Markers,Biochemical Marker,Biologic Marker,Biologic Markers,Clinical Marker,Immune Marker,Immune Markers,Immunologic Marker,Laboratory Marker,Marker, Biochemical,Marker, Biological,Marker, Clinical,Marker, Immunologic,Marker, Laboratory,Marker, Serum,Marker, Surrogate,Serum Marker,Surrogate End Point,Surrogate End Points,Surrogate Endpoint,Surrogate Endpoints,Surrogate Marker,Viral Marker,Biological Marker,End Point, Surrogate,End Points, Surrogate,Endpoint, Surrogate,Endpoints, Surrogate,Marker, Biologic,Marker, Immune,Marker, Viral,Markers, Biologic,Markers, Immune
D059014 High-Throughput Nucleotide Sequencing Techniques of nucleotide sequence analysis that increase the range, complexity, sensitivity, and accuracy of results by greatly increasing the scale of operations and thus the number of nucleotides, and the number of copies of each nucleotide sequenced. The sequencing may be done by analysis of the synthesis or ligation products, hybridization to preexisting sequences, etc. High-Throughput Sequencing,Illumina Sequencing,Ion Proton Sequencing,Ion Torrent Sequencing,Next-Generation Sequencing,Deep Sequencing,High-Throughput DNA Sequencing,High-Throughput RNA Sequencing,Massively-Parallel Sequencing,Pyrosequencing,DNA Sequencing, High-Throughput,High Throughput DNA Sequencing,High Throughput Nucleotide Sequencing,High Throughput RNA Sequencing,High Throughput Sequencing,Massively Parallel Sequencing,Next Generation Sequencing,Nucleotide Sequencing, High-Throughput,RNA Sequencing, High-Throughput,Sequencing, Deep,Sequencing, High-Throughput,Sequencing, High-Throughput DNA,Sequencing, High-Throughput Nucleotide,Sequencing, High-Throughput RNA,Sequencing, Illumina,Sequencing, Ion Proton,Sequencing, Ion Torrent,Sequencing, Massively-Parallel,Sequencing, Next-Generation

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