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

Synchronizing inventory and transport within supply chain management

The problem considers synchronized optimization of inventory and transport, and focuses on producer-distributor relations. Particular attention is paid to developing a mathematical model and an optimization problem that can be used to minimize the overall distribution cost by an appropriate placement of warehouses and cross-docking points. Solutions to this problem are explored using genetic algorithms and ideas from graph/network theory. Note: there are three separate reports contained within the uploaded .pdf file

Parallel Fast Möbius (Reed-Muller) Transform and its Implementation with CUDA on GPUs

One of the most important cryptographic characteristics of the Boolean and vector Boolean functions is the algebraic degree which is connected with the Algebraic Normal Form. In this paper, we present an algorithm for computing the Algebraic Normal Form of a Boolean function using binary Fast Möbius (Reed-Muller) Transform implemented in CUDA for parallel execution on GPU. In the end, we give some experimental results

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

Polymorphism of microsatellite loci OarCP549, CSRD247, FCB20 and MAF65 in sheep

Rostov Region is one of the most important regions of sheep breeding in Russia. The Salsk breed of sheep is created as a result of many years work of breeders in the Rostov region. Studies on the genetic diversity of domestic breeds of sheep are a prerequisite for their conservation, further improvement and use for the needs of the modern agro-industrial complex of the Russian Federation. The aim of the work is to study polymorphism of microsatellite loci OarCP549, CSRD247, FCB20, and MAF65 in sheep of Salsk breed. Studies were carried out on sheep of the Salsk breed (n = 95, PZ «Belozernoe», Rostov Region). Analysis of microsatellites was performed on sixteen-capillary genetic analyzer ABI3130xl Genetic Analyzer. To estimate polymorphism of loci it was taken into account: the Number of Alleles per locus (NA); Observed (Ho) and the Expected (He) Heterozygosity, the Effective Number of Alleles per locus (Ae). The highest Number of Alleles (NA = 17) and the Effective Number of Alleles (Ae = 7) was determined from the locus OarCP49 in the sheep of the Salsk breed. At other loci, the Number of Alleles was 15, 12 and 9, and the Effective Number of Alleles was 5.56, 5.33 and 3.85 respectively for CSRD247, FCB20 and MAF65. The heterozygote deficiency in the population is noted at the loci CSRD247 and MAF65. Average index of fixation for four loci was 0.094. The results of the work showed the polymorphism of microsatellite loci OarCP49, CSRD247, FCB20 and MAF65 and the expediency of their inclusion in the panel of DNA markers for assessing the genetic diversity and authenticity of the origin for the sheep of Salsk breed

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

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/

Complete mitochondrial genomes of Karchaev goat (Capra hircus)

Karachaev goat (Capra hircus) is a local breed from North-Caucasus region, Russia. Here we present complete mitochondrial genome of Karachaev goat from the republic of Karachaevo-Cherkessia, Russia. The length of the studied sequence was 16,624 bp in size. It was shown that the studied specimen belonged to haplogroup A

Characteristics of the Gut Microbiota in Regard to Atopic Dermatitis and Food Allergies of Children

The gut microbiota plays an important role in maintaining human health, as well as in the development of various pathologies, as indicated by a large amount of research. One of the manifestations of an imbalance in the gut microbiome composition is the appearance of various diseases or immune reactions, in particular, atopic dermatitis (AD) and/or food allergies (FA). In this research, using 16S NGS sequencing, it was found that the gut microbiome of children with food allergies and children with atopic dermatitis can be characterized as having higher inflammatory potential. Both groups exhibited an abundance of representatives from the Pasteurellaceae and Erysipelotrichaceae families, as well as a decrease in the relative number of representatives from the Barnesiellaceae family compared to healthy participants. In the group of participants with food allergies, there was a decrease in the relative number of Desulfovibrionaceae representatives and Bifidobacteriaceae family enrichment in relatively healthy participants. In addition, when comparing this group with patients with atopic dermatitis, it was revealed that a number of representatives of such families as Erysipelotrichaceae, Ruminococcaceae and Sutterellaceae prevailed. This information confirms that AD and FA correlate with changes in the composition of the gut microbiota. Further research is needed to determine the cause–effect connections and the effect of compounds derived from the microbiota on the AD and FA development and progression, as well as to create new probiotic drugs to prevent and modulate immune responses, including at an early age