About this Research Topic
Crossbreeding is a common strategy to enhance animal production because it can capitalize on breed complementary and heterosis. To date, crossbreeding has been a significant form of producing commercial animals in pigs and chickens. There are also composite animals, such as Brangus cattle, which combined “superior” characters of Angus and Brahman and retain some heterosis (hybrid vigor) in future generations without crossbreeding and maintained like purebred animals.
In the past decades, advances in animal genomics have fundamentally revolutionized all the aspects of animal breeding and production, including crossbreeding. Crossbred animals differ considerably from purebred animals, and their genome is a mosaic of genome regions inherited from the different parental breeds. Therefore, genomics solutions for crossbred animals need to be taken differently from those for purebred animals. So far, many milestone discoveries have been made in genomics for crossbreeding research, but there are far more questions still unanswered.
This Research Topic is intended to provide a forum on all aspects of the genomics of crossbred farm animals. We welcome both review and original articles that contribute to enhancing the understanding and the applications of genomics applied to crossbred animals. Potential topics include, but not limited to, the following:
1. Ancestry estimation and genomic breed composition (GBC) in crossbred animals;
- Definition, interpretation, and validation of estimated GBC for crossbred animals, obtained using various statistical methods;
- Noval strategies and statistical methods for estimating GBC of crossbred animals, such as sparsely regularized admixture models, structure equation models (including path analysis), and machine learning algorithms;
- Impact of feature (SNP) selection and relevant strategies on estimated GBC for crossbred animals;
- Applications of estimated GBC in the breeding, production, and management of crossbred animals;
2. Genomic prediction and selection for crossbreeding;
- Breeding objectives in pure lines or breeds for crossbreeding: selection in pure lines yet the target is to obtain crosses of high performance or economic values;
- Training on purebred lines versus training on crossbred data (or a training set including purebred and crossbred animals);
- Statistical models and modeling strategies: additive versus non-additive (e.g. dominance) effect models, various model assumptions about SNP effects across multiple breeds, and BOA (breed-of-origin of alleles) models for the genetic evaluation in two-way, three-way or four-way crossbreeding systems;
- GBLUP or single-step GBLUP: compatibility between the pedigree-based and the genetic relationship matrixes, the definition of relationships between different pure lines, and appropriate definition of base generations (including the use of meta-founders);
- Machine learning algorithms (including deep-learning) for predicting crossbred performance;
3. Predicting heterosis and innovative genomic crossbreeding;
- The genetic basis for heterosis in the view of functional genomics and beyond.
- Various measures for heterosis (or retained heterosis) and their correlations with crossbred performance;
- Use of BOA-GCA to select combinations of pure lines (breeds) for optimal crossbreeding schemes, which will replace the need for conducting the expensive and time-consuming diallel cross experiments.
Xiao-Lin Wu is the Director of Biostatistics and Bioinformatics, Neogen GeneSeek, and also an adjunct faculty member at the University of Wisconsin, Madison. All other topic editors declare no competing interests with regard to the Research Topic subject.
In the past decades, advances in animal genomics have fundamentally revolutionized all the aspects of animal breeding and production, including crossbreeding. Crossbred animals differ considerably from purebred animals, and their genome is a mosaic of genome regions inherited from the different parental breeds. Therefore, genomics solutions for crossbred animals need to be taken differently from those for purebred animals. So far, many milestone discoveries have been made in genomics for crossbreeding research, but there are far more questions still unanswered.
This Research Topic is intended to provide a forum on all aspects of the genomics of crossbred farm animals. We welcome both review and original articles that contribute to enhancing the understanding and the applications of genomics applied to crossbred animals. Potential topics include, but not limited to, the following:
1. Ancestry estimation and genomic breed composition (GBC) in crossbred animals;
- Definition, interpretation, and validation of estimated GBC for crossbred animals, obtained using various statistical methods;
- Noval strategies and statistical methods for estimating GBC of crossbred animals, such as sparsely regularized admixture models, structure equation models (including path analysis), and machine learning algorithms;
- Impact of feature (SNP) selection and relevant strategies on estimated GBC for crossbred animals;
- Applications of estimated GBC in the breeding, production, and management of crossbred animals;
2. Genomic prediction and selection for crossbreeding;
- Breeding objectives in pure lines or breeds for crossbreeding: selection in pure lines yet the target is to obtain crosses of high performance or economic values;
- Training on purebred lines versus training on crossbred data (or a training set including purebred and crossbred animals);
- Statistical models and modeling strategies: additive versus non-additive (e.g. dominance) effect models, various model assumptions about SNP effects across multiple breeds, and BOA (breed-of-origin of alleles) models for the genetic evaluation in two-way, three-way or four-way crossbreeding systems;
- GBLUP or single-step GBLUP: compatibility between the pedigree-based and the genetic relationship matrixes, the definition of relationships between different pure lines, and appropriate definition of base generations (including the use of meta-founders);
- Machine learning algorithms (including deep-learning) for predicting crossbred performance;
3. Predicting heterosis and innovative genomic crossbreeding;
- The genetic basis for heterosis in the view of functional genomics and beyond.
- Various measures for heterosis (or retained heterosis) and their correlations with crossbred performance;
- Use of BOA-GCA to select combinations of pure lines (breeds) for optimal crossbreeding schemes, which will replace the need for conducting the expensive and time-consuming diallel cross experiments.
Xiao-Lin Wu is the Director of Biostatistics and Bioinformatics, Neogen GeneSeek, and also an adjunct faculty member at the University of Wisconsin, Madison. All other topic editors declare no competing interests with regard to the Research Topic subject.
Keywords: ancestry estimation, crossbred animals, genomic breed composition, genomic prediction, SNP
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