Genome-wide analysis of genetic diversity and artificial selection in Large White pigs in Russia

Breeding practices adopted at different farms are aimed at maximizing the profitability of pig farming. In this work, we have analyzed the genetic diversity of Large White pigs in Russia. We compared genomes of historic and modern Large White Russian breeds using 271 pig samples. We have identified 120 candidate regions associated with the differentiation of modern and historic pigs and analyzed genomic differences between the modern farms. The identified genes were associated with height, fitness, conformation, reproductive performance, and meat quality

PigLeg: prediction of swine phenotype using machine learning

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics

Survey of SNPs Associated with Total Number Born and Total Number Born Alive in Pig

Reproductive productivity depend on a complex set of characteristics. The number of piglets at birth (Total number born, Litter size, TNB) and the number of alive piglets at birth (Total number born alive, NBA) are the main indicators of the reproductive productivity of sows in pig breeding. Great hopes are pinned on GWAS (Genome-Wide Association Studies) to solve the problems associated with studying the genetic architecture of reproductive traits of pigs. This paper provides an overview of international studies on SNP (Single nucleotide polymorphism) associated with TNB and NBA in pigs presented in PigQTLdb as “Genome map association”. Currently on the base of Genome map association results 306 SNPs associated with TNB (218 SNPs) and NBA (88 SNPs) have been identified and presented in the Pig QTLdb database. The results are based on research of pigs such as Large White, Yorkshire, Landrace, Berkshire, Duroc and Erhualian. The presented review shows that most SNPs found in chromosome areas where candidate genes or QTLs (Quantitative trait locus) have been identified. Further research in the given direction will allow to obtain new data that will become an impulse for creating breakthrough breeding technologies and increase the production efficiency in pig farming

PigLeg: prediction of swine phenotype using machine learning

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics

Analysis of Homozygous-by-Descent (HBD) Segments for Purebred and Crossbred Pigs in Russia

Intensive selection raises the efficiency of pig farming considerably, but it also promotes the accumulation of homozygosity, which can lead to an increase in inbreeding and the accumulation of deleterious variation. The analysis of segments homozygous-by-descent (HBD) and non-HBD segments in purebred and crossbred pigs is of great interest. Research was carried out on 657 pigs, of which there were Large White (LW, n = 280), Landrace (LR, n = 218) and F1 female (♂LR × ♀LW) (F1, n = 159). Genotyping was performed using the GeneSeek® GGP Porcine HD Genomic Profiler v1 (Illumina Inc., USA). To identify HBD segments and estimate autozygosity (inbreeding coefficient), we used the multiple HBD classes model. LW pigs exhibited 50,420 HBD segments, an average of 180 per animal; LR pigs exhibited 33,586 HBD segments, an average of 154 per animal; F1 pigs exhibited 21,068 HBD segments, an average of 132 per animal. The longest HBD segments in LW were presented in SSC1, SSC13 and SSC15; in LR, in SSC1; and in F1, in SSC15. In these segments, 3898 SNPs localized in 1252 genes were identified. These areas overlap with 441 QTLs (SSC1—238 QTLs; SSC13—101 QTLs; and SSC15—102 QTLs), including 174 QTLs for meat and carcass traits (84 QTLs—fatness), 127 QTLs for reproduction traits (100 QTLs—litter traits), 101 for production traits (69 QTLs—growth and 30 QTLs—feed intake), 21 QTLs for exterior traits (9 QTLs—conformation) and 18 QTLs for health traits (13 QTLs—blood parameters). Thirty SNPs were missense variants. Whilst estimating the potential for deleterious variation, six SNPs localized in the NEDD4, SEC11C, DCP1A, CCT8, PKP4 and TENM3 genes were identified, which may show deleterious variation. A high frequency of potential deleterious variation was noted for LR in DCP1A, and for LW in TENM3 and PKP4. In all cases, the genotype frequencies in F1 were intermediate between LR and LW. The findings presented in our work show the promise of genome scanning for HBD as a strategy for studying population history, identifying genomic regions and genes associated with important economic traits, as well as deleterious variation

Detection of genomic regions associated malformations in newborn piglets: a machine-learning approach

Background A significant proportion of perinatal losses in pigs occurs due to congenital malformations. The purpose of this study is the identification of genomic loci associated with fetal malformations in piglets. Methods The malformations were divided into two groups: associated with limb defects (piglet splay leg) and associated with other congenital anomalies found in newborn piglets. 148 Landrace and 170 Large White piglets were selected for the study. A genome-wide association study based on the gradient boosting machine algorithm was performed to identify markers associated with congenital anomalies and piglet splay leg. Results Forty-nine SNPs (23 SNPs in Landrace pigs and 26 SNPs in Large White) were associated with congenital anomalies, 22 of which were localized in genes. A total of 156 SNPs (28 SNPs in Landrace; 128 in Large White) were identified for piglet splay leg, of which 79 SNPs were localized in genes. We have demonstrated that the gradient boosting machine algorithm can identify SNPs and their combinations associated with significant selection indicators of studied malformations and productive characteristics. Data availability Genotyping and phenotyping data are available at http://www.compubioverne.group/data-and-software/

Assessing the Effect of SNPs on Litter Traits in Pigs

The reproductive ability of sows is the principle of continuous and efficient production, based on such traits as the number of piglets, the total number of parities, and the period of economic use. Currently, SNPs associated with the TNB and NBA are presented in the PigQTLdb. The aim of this work was the assessment of the SNP effects on the litter traits in Large White (LW, n = 502) and Landrace (LN, n = 432) sow breeds in a farm in Russia. 9 SNPs (SNP_1: rs80956812; SNP_2: rs81471381; SNP_3: rs80891106; SNP_4: rs81399474; SNP_5: rs81421148; SNP_6: rs81242222; SNP_7: rs81319839; SNP_8: rs81312912; SNP_9: rs80962240) were selected for the study. Associative analysis was performed using the GLM procedure in R version 3.5.1. The analysis of reproductive traits was carried out according to the results of the first parity, the second and subsequent parities, and totals for lifetime of sows. The significant effect on litter traits in LW was determined for SNP rs80956812, SNP rs81471381, SNP rs81421148, and SNP rs81399474. The significant effect on litter traits in LN was determined for SNP rs81421148 and SNP rs81319839. AKT3 gene was identified as perspective candidate gene, whose biological functions, as well as the results obtained in our work and in other studies, indicate its potential role in the reproductive process regulation in pigs. In general, the data obtained help to explain the genetic mechanisms of reproductive traits