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

Abstract

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.

Screening Discriminating SNPs for Chinese Indigenous Pig Breeds Identification Using a Random Forests Algorithm

“…Ramos et al obtained a total of 193 breed-specific SNPs in five pig breeds, which were used to assign an additional 490 individuals from the same breeds, showing that >99% of animals were correctly assigned, and demonstrating the high utility of breed-specific markers for breed assignment and traceability [ 17 ]. Several other approaches, including delta statistics, the fixation index ( F ST ), principal component analysis (PCA), and machine learning algorithms have been proposed to identify informative markers in livestock and poultry populations [ 2 , 18 , 19 , 20 , 21 ]. All of these methods had a high accuracy in breed assignments.…”

“…All of these methods had a high accuracy in breed assignments. For example, Seo et al [ 20 ] used the RF model to select 44 SNPs as the minimum number of markers to classify 283 chicken individuals from 20 populations, and the accuracy reached up to 98.0%. However, when the number of breeds reaches dozens or more, or when there are relatively close genetic evolutionary relationships among some local breeds, a moderate increase in the number of highly informative SNPs may be more necessary to accurately identify and assign these breeds, although this may increase the cost of genotyping.…”

Identification of Ancestry Informative Markers in Mediterranean Trout Populations of Molise (Italy): A Multi-Methodological Approach with Machine Learning

“…Four statistical methods were used for the identification of informative SNP panels (i.e., Delta, F ST , PCA and RF statistics), according to Schiavo et al [ 26 ]. Several approaches have been proposed in literature for the identification of population-informative markers [ 40 ] and it is known that the choice of a specific approach can affect the results for a particular population [ 49 ]. As explained by Bertolini et al [ 50 ], the main problems for the identification of fully informative SNP markers are due by the high level of linkage disequilibrium (LD) that is present in most livestock populations.…”

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach

“…We applied machine learning algorithms to identify SNP marker combinations for Yeonsan Ogye classification through GWAS and LD pruning. Machine learning has been used to select SNP markers for various livestock species [29][30][31][32]. Moreover, applying feature selection to GWAS results can reduce dimensionality and overfitting errors when identifying markers, resulting in more accurate predictions [33].…”