Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique

Pasupa, Kitsuchart; Rathasamuth, Wanthanee; Tongsima, Sissades

doi:10.1186/s12859-020-3471-4

Cited by 11 publications

(11 citation statements)

References 38 publications

(62 reference statements)

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“…With a model based on an SVM, Pasupa et al. (2020) obtained an accuracy of 95.12% with only 164 SNPs to discriminate 21 pig breeds that seemed less genomically related than the 12 cattle breeds under study. Moreover, they used an iterative combination of algorithms to select breed‐informative SNPs, and they tuned the SVM to be radial, which may take time to parameterize as they highlighted.…”

Section: Discussionmentioning

confidence: 99%

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds

Wilmot

Bormann²,

Soyeurt

et al. 2021

J Animal Breeding Genetics

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Assignment of individual cattle to a specific breed can often not rely on pedigree information. This is especially the case for local breeds for which the development of genomic assignment tools is required to allow individuals of unknown origin to be included to their herd books. A breed assignment model can be based on two specific stages: (a) the selection of breed‐informative markers and (b) the assignment of individuals to a breed with a classification method. However, the performance of combination of methods used in these two stages has been rarely studied until now. In this study, the combination of 16 different SNP panels with four classification methods was developed on 562 reference genotypes from 12 cattle breeds. Based on their performances, best models were validated on three local breeds of interest. In cross‐validation, 14 models had a global cross‐validation accuracy higher than 90%, with a maximum of 98.22%. In validation, best models used 7,153 or 2,005 SNPs, based on a partial least squares‐discriminant analysis (PLS‐DA) and assigned individuals to breeds based on nearest shrunken centroids. The average validation sensitivity of the first two best models for the three local breeds of interest were 98.33% and 97.5%. Moreover, results reported in this study suggest that further studies should consider the PLS‐DA method when selecting breed‐informative SNPs.

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Section: Discussionmentioning

confidence: 99%

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds

Wilmot

Bormann²,

Soyeurt

et al. 2021

J Animal Breeding Genetics

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show abstract

“…Purebred pigs are commercially important, and many breeders have requested them in their cross-breeding programs. Cross-breeding helps breeders discover new breeds with desirable traits, such as disease resistance and heat tolerance (Pasupa et al 2020 ). When crossing native pigs in Korea with western commercial pig breeds, we aimed to determine the breed-specific variants of native pigs in Korea, which could help improve the conservation of the genetic resources of native breeds.…”

Section: Introductionmentioning

confidence: 99%

Elucidating breed-specific variants of native pigs in Korea: insights into pig breeds’ genomic characteristics

Lee

Son

Lee

et al. 2022

Animal Cells and Systems

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Although conserving native pig breeds is important in Korea, research on the genomic aspects to identify breed-specific variations in native pig breeds is uncommon. Single nucleotide polymorphisms (SNPs) can be a powerful source for identifying breed-specific variants. We used whole genome sequencing data, including Jeju Native Pig (JNP), Korean Native Pig (KNP), Korean Wild Boar (KWB), and other western commercial pig breeds to determine native pig breed-specific SNPs. Furthermore, the goal was not only to determine the genomic specificity of native pig breeds but also to identify SNPs that carry breed-specific information (breed-informative SNPs) that can be related to breed characteristics. The representative characteristics of native pigs are their unique meat quality and disease resistance. We surveyed the gene ontology (GO) of native pigs with breed-specific SNPs. Examining the genes associated with GO may contribute to revealing the reasons for the unique characteristics of native pig breeds. The enriched GOs terms were neuron projection development, cell surface receptor signaling pathway, ion homeostasis in JNP, cell adhesion and wound healing in KNP, and DNA repair and reproduction in KWB. We expect that this study of breed-specific SNPs will enable us to gain a deeper understanding of native pigs in Korea.

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“…Machine learning algorithms are rarely used in genetic diversity studies. However, a recent study compared classification performance, based on high-density SNPs, between support vector machine (SVM) and Random Forest (RF) models [ 20 , 21 , 22 , 23 ]. In the classification of populations based on genomic information, previous studies have compared the F statistic, delta statistic, and eigenvalue of principal component analysis (PCA) to assess classification models derived from machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

Seo

Cho

Manjula

et al. 2021

Animals

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A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

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Discovery of significant porcine SNPs for swine breed identification by a hybrid of information gain, genetic algorithm, and frequency feature selection technique

Cited by 11 publications

References 38 publications

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds

Elucidating breed-specific variants of native pigs in Korea: insights into pig breeds’ genomic characteristics

Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

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