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OBJECTIVE: The use of molecular genetic methods in pig breeding can significantly increase the efficiency of breeding and breeding work. We applied the Fst (fixsacion index) method, the main focus of the work was on the search for common options related to the number of born piglets and the weight of born piglets, since today the urgent task is to prevent a decrease in the weight of piglets at birth while maintaining high fertility of sows. METHODS: One approach is to scan the genome, followed by an assessment of Fst and identification of selectively selected regions. We chose Large White sows (n = 237) with the same conditions of keeping and feeding. The data were collected from the sows across three farrowing. For genotyping, we used GeneSeek GGP Porcine HD Genomic Profiler v1, which included 68,516 single nucleotide polymorphisms evenly distributed with an average spacing of 25 kb (Illumina Inc, San Diego, CA, USA). RESULTS: Based on the results of the Fst analysis, 724 variants representing selection signals for the signs BALWT, BALWT1, NBA, and TNB (weight of piglets born alive, average weight of the 1st piglets born alive, total number born alive, total number born). At the same time, 18 common variants have been identified that are potential markers for both the number of piglets at birth and the weight of piglets at birth, which is extremely important for breeding work to improve reproductive characteristics in sows. CONCLUSION: Ður work resulted in identification of variants associated with the reproductive characteristics of pigs. Moreover, we identified, variants which are potential markers for both the number of piglets at birth and the weight of piglets at birth, which is extremely important for breeding work to improve reproductive performance in sows.
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The neurobiological systems of maintenance and control of behavioral responses result from natural selection. We have analyzed the selection signatures for single nucleotide variants (SNV) of the genes of oxytocin (OXT, OXTR) and vasopressin (AVP, AVPR1A, AVPR1B) systems, which are associated with the regulation of social and emotional behavior in distinct populations. The analysis was performed using original WGS (whole genome sequencing) data on Eastern Slavs (SlEast), as well as publicly available data from the 1000 Genomes Project on GBR, FIN, IBR, PUR, BEB, CHB, and ACB populations (the latter were taken as reference). To identify selection signatures, we rated the integrated haplotype scores (iHS), the numbers of segregating sites by length (nSl), and the integrated haplotype homozygosity pooled (iHH12) measures; the fixation index Fst was implemented to assess genetic differentiation between populations. We revealed that the strongest genetic differentiation of populations was found with respect to the AVPR1B gene, with the greatest differentiation observed in GRB (Fst = 0.316) and CHB (Fst = 0.325) in comparison to ACB. Also, high Fst values were found for SNVs of the AVPR1B gene rs28499431, rs33940624, rs28477649, rs3883899, and rs28452187 in most of the populations. Selection signatures have also been identified in the AVP, AVPR1A, OXT, and OXTR genes. Our analysis shows that the OXT, OXTR, AVP, AVPR1A, and AVPR1B genes were subject to positive selection in a population-specific process, which was likely contributing to the diversity of adaptive emotional response types and social function realizations.
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Ocitocina , Vasopressinas , Humanos , Ocitocina/genética , Genômica , Receptores de Ocitocina/genética , Receptores de Vasopressinas/genéticaRESUMO
One of the most important areas of modern genome research is the search for meaningful relationships between genetic variants and phenotypes. In the livestock field, there has been research demonstrating the influence of copy number variants (CNVs) on phenotypic variation. Despite the wide range in the number and size of detected CNVs, a significant proportion differ between breeds and their functional effects are underestimated in the pig industry. In this work, we focused on the problem of leg defects in pigs (lumps/growths in the area of the hock joint on the hind legs) and focused on searching for molecular genetic predictors associated with this trait for the selection of breeding stock. The study was conducted on Large White pigs using three CNV calling tools (PennCNV, QuantiSNP and R-GADA) and the CNVRanger association analysis tool (CNV-GWAS). As a result, the analysis identified three candidate CNVRs associated with the formation of limb defects. Subsequent functional analysis suggested that all identified CNVs may act as potential predictors of the hock joint phenotype of pigs. It should be noted that the results obtained indicate that all significant regions are localized in genes (CTH, SRSF11, MAN1A1 and LPIN1) responsible for the metabolism of amino acids, fatty acids, glycerolipids and glycerophospholipids, thereby related to the immune response, liver functions, content intramuscular fat and animal fatness. These results are consistent with previously published studies, according to which a predisposition to the formation of leg defects can be realized through genetic variants associated with the functions of the liver, kidneys and hematological characteristics.
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Estudo de Associação Genômica Ampla , Genoma , Suínos/genética , Animais , Genótipo , Fenótipo , Variações do Número de Cópias de DNARESUMO
Non-alcoholic fatty liver disease (NAFLD) is considered the most common chronic liver disease worldwide, affecting nearly 25% of the global adult population. Increasing evidence suggests that functional and compositional changes in the gut microbiota may contribute to the development and promote the progression of NAFLD. 16S rRNA gene next-generation sequencing is widely used to determine specific features of the NAFLD microbiome, but a complex system such as the gut microbiota requires a comprehensive approach. We used three different approaches: MALDI-TOF-MS of bacterial cultures, qPCR, and 16S NGS sequencing, as well as a wide variety of statistical methods to assess the differences in gut microbiota composition between NAFLD patients without significant fibrosis and the control group. The listed methods showed enrichment in Collinsella sp. and Oscillospiraceae for the control samples and enrichment in Lachnospiraceae (and in particular Dorea sp.) and Veillonellaceae in NAFLD. The families, Bifidobacteriaceae, Lactobacillaceae, and Enterococcaceae (particularly Enterococcus faecium and Enterococcus faecalis), were also found to be important taxa for NAFLD microbiome evaluation. Considering individual method observations, an increase in Candida krusei and a decrease in Bacteroides uniformis for NAFLD patients were detected using MALDI-TOF-MS. An increase in Gracilibacteraceae, Chitinophagaceae, Pirellulaceae, Erysipelatoclostridiaceae, Muribaculaceae, and Comamonadaceae, and a decrease in Acidaminococcaceae in NAFLD were observed with 16S NGS, and enrichment in Fusobacterium nucleatum was shown using qPCR analysis. These findings confirm that NAFLD is associated with changes in gut microbiota composition. Further investigations are required to determine the cause-and-effect relationships and the impact of microbiota-derived compounds on the development and progression of NAFLD.
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Microbioma Gastrointestinal , Microbiota , Hepatopatia Gordurosa não Alcoólica , Adulto , Humanos , Hepatopatia Gordurosa não Alcoólica/patologia , Microbioma Gastrointestinal/genética , RNA Ribossômico 16S/genética , Fibrose , Bacteroidetes , Fígado/patologiaRESUMO
Pigs are strategically important animals for the agricultural industry. An assessment of genetic differentiation between pigs, undergone and not undergone to selection intensification, is of particular interest. Our research was conducted on two groups of Large White pigs grown on the same farm but in different years. A total of 165 samples were selected with 78 LW_Ð (n = 78, the Russian selection) and LW_B (n = 87, a commercial livestock). For genotyping, we used GeneSeek® GGP Porcine HD Genomic Profiler v1 (Illumina Inc, San Diego, CA, USA). To define breeding characteristics of selection, we used smoothing FST and segment identification of HBD (Homozygous-by-Descent). The results of smoothing FST showed 20 areas of a genome with strong ejection regions of the genome located on all chromosomes except SSC2, SSC3, and SSC8. The average realized autozygosity in Large White pigs of native selection was in (LW_A)-0.21, in LW_Ð-0.29. LW_Ð showed 13,338 HBD segments, 171 per one animal, and LW_B-15,747 HBD segments, 181 per one animal. The ejections found by the smoothing FST method were partially localized in the HBD regions. In these areas, the genes ((NCBP1, PLPPR1, GRIN3A, NBEA, TRPC4, HS6ST3, NALCN, SMG6, TTC3, KCNJ6, IKZF2, OBSL1, CARD10, ETV6, VWF, CCND2, TSPAN9, CDH13, CEP128, SERPINA11, PIK3CG, COG5, BCAP29, SLC26A4) were defined. The revealed genes can be of special interest for further studying their influence on an organism of an animal since they can act as candidate genes for selection-significant traits.
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Genoma , Genômica , Animais , Genômica/métodos , Homozigoto , Fenótipo , Federação Russa , Suínos/genéticaRESUMO
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 (âLR × âLW) (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.
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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.
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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/.
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Capped hock affects the exterior of pedigree pigs, making them unsalable and resulting in a negative impact on the efficiency of pig-breeding centers. The purpose of this paper was to carry out pilot studies aimed at finding genomic regions and genes linked to the capped hock in pigs. The studies were carried out on Landrace pigs (n = 75) and Duroc pigs (n = 70). To identify genomic regions linked to capped hock in pigs, we used smoothing FST statistics. Genotyping was performed with GeneSeek® GGP Porcine HD Genomic Profiler v1 (Illumina Inc, USA). The research results showed 70 SNPs linked to capped hock in Landrace (38 SNPs) and Duroc (32 SNPs). The identified regions overlapped with QTLs related with health traits (blood parameters) and meat and carcass traits (fatness). In total, 31 genes were identified (i.e., 17 genes in Landrace, 14 genes in Durocs). Three genes appeared in both the Landrace and Duroc groups, including A2ML1 (SSC5), ROBO2 (SSC13), and MSI1 (SSC14). We identified genomic regions directly or indirectly linked to capped hock, which thus might contribute to identifying genetic variants and using them as genetic markers in pig breeding.
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The Large White pig is the most commonly raised commercial pig breed in the world. The aim of this work was to investigate D-loop mtDNA in Large White pigs (n = 402) of various selections bred in the Russian Federation from 2000 to 2019. The general sample consisted of three groups: Old (n = 78) (Russian selection, 2000-2010); Imp (n = 123) (imported to Russia in 2008-2014); New (n = 201) (2015-2019). The synthesized score (Fz) was calculated by analyzing the main PCA (principal component analysis components). An affiliation to Asian or European haplogroups was determined according to the NCBI (National Center for Biotechnology Information). In the study, we defined 46 polymorphic sites and 42 haplotypes. Significant distinctions between groups Old, Imp and New in frequencies of haplotypes and haplogroups were established. The distribution of Asian and European haplotypes in the groups was Old: 50%/50%, Imp: 43%/57%, New: 75%/25%, respectively. The variety of haplotypes and haplogroups in the pigs of the group New is related to the farms in which they breed. Haplotype frequencies significantly differ between the clusters Old_Center, Old_Siberia and Old_South. This study will provide information on the genetic diversity of Large White breed pigs. The results will be useful for the conservation and sustainable use of these resources.
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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.
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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.
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Tamanho da Ninhada de Vivíparos/genética , Locos de Características Quantitativas/genética , Reprodução/genética , Suínos/genética , Animais , Cruzamento , Cromossomos/genética , Feminino , Estudo de Associação Genômica Ampla , Genótipo , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Gravidez , Sus scrofa/genéticaRESUMO
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.