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[Estimating genomic breed composition of individual animals using selected SNPs]
Jun He1,2, Chang Song Qian3, Richard G Tait2
1College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China.
Yi Chuan = Hereditas
|April 29, 2018
Summary
Estimating an animal's genomic breed composition (GBC) using single nucleotide polymorphism (SNP) data is now possible. This method helps understand ancestral contributions and can be applied to commercial SNP chips for cattle.
Area of Science:
- Animal Genetics
- Bioinformatics
- Quantitative Genetics
Background:
- Genetic differentiation among animal populations arises from selection, segregation, and drift.
- Individual animals from mixed ancestries possess composite genomes reflecting their breed origins.
- Genomic Breed Composition (GBC) quantifies the proportional contribution of ancestral breeds to an individual's genome.
Purpose of the Study:
- To review principles and statistical methods for estimating GBC in individual animals using SNP genotype data.
- To demonstrate GBC estimation protocols using linear regression and admixture models.
- To characterize the breed composition of Akaushi cattle.
Main Methods:
- Utilized SNP genotype data from 36,574 cattle across five breeds (Akaushi, Angus, Hereford, Holstein, Jersey).
- Applied linear regression and admixture models for GBC estimation.
- Selected optimal SNP panels from commercial chips for GBC analysis.
Main Results:
- Successfully estimated GBC for 198 purported Akaushi cattle.
- Identified and selected four common SNP panels suitable for GBC estimation.
- Demonstrated the feasibility of using commercial SNP chips for breed composition analysis.
Conclusions:
- SNP genotype data enables accurate estimation of an individual animal's genomic breed composition.
- Optimized SNP panels enhance the utility of commercial SNP chips for GBC analysis.
- Future research should explore integrating GBC into genomic prediction models for improved accuracy in purebred and crossbred animals.
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