Breed identification usually involves a two-stage approach, namely (a) detection of breed-informative SNPs based on a reference population consisting of multiple known breeds and (b) assignment of individuals of unknown breed to their corresponding breeds based on the breed-informative SNPs. SNPs are becoming increasingly popular as breed identification markers because they are abundant and widely distributed in the genome. Genome-wide SNP markers can be discovered and genotyped using SNP arrays or genome sequencing. Many commercial SNP microarrays have been used to capture breed-informative markers that can be used for a variety of applications. However, breed identification studies based on whole genome sequencing data are scarce. Several statistical methods have been proposed to obtain highly breed-informative SNPs in genome-wide abundance markers.
Fig. 1. Pipeline for identification and characterization of breed-specific markers (BSMs) of the Nili–Ravi, Nili, Kundi, and Azakheli breeds using resequencing genotypic data. (Anas et al., 2023)
Our breed identification solution
Crossbreeding helps breeders to identify new varieties with desired traits, such as disease resistance and heat tolerance. Genetic diversity in animals and plants results from genetic differences, most notably single nucleotide polymorphisms (SNPs). SNPs can classify an animal into different breeds. CD Genomics offers SNP detection services to identify animal breeds. Our lab offers three methods (Delta, FST, and In) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we can evaluate the accuracy of breed identification using SNP chip data of different densities. We provide the following information for your breed identification program.
Identify SNPs that are significantly informative in livestock.
Estimate the potential utility of these markers in animal identification and parentage testing.
Identification of polymorphisms in DNA surrounding the target single nucleotide polymorphism.
Identification of population structure and selection traits in livestock, and allocation information.
Impact of minor allele frequency (MAF) and linkage disequilibrium (LD) on breed-informative SNP selection.
We can combine simple sequence repeat(SSR) markers with high-resolution melting analysis for effective use in animal breed identification. The combination of SSR marker analysis with other informative genetic markers can improve the accuracy of genetic distances between animal breeds and the estimation of diversity within breeds.
SNP is widely used for breed identification
Management of livestock genetic resources.
Understanding and assessing the breeding history and breed purity of specific animal breeds.
Implementation strategies and breeding plans.
Inferring product origin to improve supply chain integrity.
Conservation of specific local species.
Our strengths and features
Our solution integrates the best SNP detection methods with machine learning algorithms. This integration ensures that species identification accuracy exceeds 99% in most cases, providing our customers with the highest level of confidence in their results.
Whether you are dealing with different reference population sizes or different numbers of SNPs for breed information, our solution delivers consistently.
CD Genomics is committed to SNP-based animal breed identification. We provide the optimal combination of breed-informative SNP detection methods and machine-learning classification strategies to ensure recognition accuracy of 95% or more in all scenarios. If you are interested, please feel free to contact us.
Reference
Anas, Muhammad, et al. "A Novel Insight into the Identification of Potential SNP Markers for the Genomic Characterization of Buffalo Breeds in Pakistan." Animals. 13.15 (2023): 2543.
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OUR MISSION
CD Genomics is propelling the future of agriculture by employing cutting-edge sequencing and genotyping technologies to predict and enhance multiple complex polygenic traits within breeding populations.
Fig. 1. Pipeline for identification and characterization of breed-specific markers (BSMs) of the Nili–Ravi, Nili, Kundi, and Azakheli breeds using resequencing genotypic data. (Anas et al., 2023)
