Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4, 122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.
Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher’s fundamental theorem of natural selection both of which are trait-dependent. Fisher’s theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to 2*SNP effect.
Foot-and-mouth disease (FMD) is a highly contagious disease affecting cloven-hoofed animals and causes severe economic loss and devastating effect on international trade of animal or animal products. Since FMD outbreaks have recently occurred in some Asian countries, it is important to understand the relationship between diverse immunogenomic structures of host animals and the immunity to foot-and-mouth disease virus (FMDV). We performed genome wide association study based on high-density bovine single nucleotide polymorphism (SNP) chip for identifying FMD resistant loci in Holstein cattle. Among 624532 SNP after quality control, we found that 11 SNPs on 3 chromosomes (chr17, 22, and 15) were significantly associated with the trait at the p.adjust <0.05 after PERMORY test. Most significantly associated SNPs were located on chromosome 17, around the genes Myosin XVIIIB and Seizure related 6 homolog (mouse)-like, which were associated with lung cancer. Based on the known function of the genes nearby the significant SNPs, the FMD resistant animals might have ability to improve their innate immune response to FMDV infection.
Using next-generation sequencing, we conducted a genome-wide scan of selective sweeps associated with selection toward genetic improvement in Thoroughbreds. We investigated potential phenotypic consequence of putative candidate loci by candidate gene association mapping for the finishing time in 240 Thoroughbred horses. We found a significant association with the trait for Ral GApase alpha 2 (RALGAP2) that regulates a variety of cellular processes of signal trafficking. Neighboring genes around RALGAP2 included insulinoma-associated 1 (INSM1), pallid (PLDN), and Ras and Rab interactor 2 (RIN2) genes have similar roles in signal trafficking, suggesting that a co-evolving gene cluster located on the chromosome 22 is under strong artificial selection in racehorses.
A genome wide association study was conducted using estimated breeding value (EBV) for milk production traits from 1st to 4th lactation. Significant single nucleotide polymorphism (SNP) markers were selected for each trait and the differences were compared by lactation. DNA samples were taken from 456 animals with EBV which are Holstein proven bulls whose semen is being sold or the daughters of old proven bulls whose semen is no longer being sold in Korea. High density genome wide SNP genotype was investigated and the significance of markers associated with traits was tested using the breeding value estimated by a multiple lactation model as a dependent variant. As the result of significance comparisons by lactations, several differences were found between the first lactation and subsequent lactations (from second to 4th lactation). A similar trend was noted in mean deviation and correlation of the estimated effects by lactation. Since there was a difference in the genes associated with EBV for each trait between first and subsequent lactations, a multi-lactation model in which lactation is considered as a different trait is genetically useful. Also, significant markers in all lactations and common markers for different traits were detected, which can be used as markers for quantitative trait loci exploration and marker assisted selection in milk production traits.
Natural and artificial selection following domestication has led to the existence of more than a hundred pig breeds, as well as incredible variation in phenotypic traits. Berkshire pigs are regarded as having superior meat quality compared to other breeds. As the meat production industry seeks selective breeding approaches to improve profitable traits such as meat quality, information about genetic determinants of these traits is in high demand. However, most of the studies have been performed using trained sensory panel analysis without investigating the underlying genetic factors. Here we investigate the relationship between genomic composition and this phenotypic trait by scanning for signatures of positive selection in whole-genome sequencing data.
We generated genomes of 10 Berkshire pigs at a total of 100.6 coverage depth, using the Illumina Hiseq2000 platform. Along with the genomes of 11 Landrace and 13 Yorkshire pigs, we identified genomic variants of 18.9 million SNVs and 3.4 million Indels in the mapped regions. We identified several associated genes related to lipid metabolism, intramuscular fatty acid deposition, and muscle fiber type which attribute to pork quality (TG, FABP1, AKIRIN2, GLP2R, TGFBR3, JPH3, ICAM2, and ERN1) by applying between population statistical tests (XP-EHH and XP-CLR). A statistical enrichment test was also conducted to detect breed specific genetic variation. In addition, de novo short sequence read assembly strategy identified several candidate genes (SLC25A14, IGF1, PI4KA, CACNA1A) as also contributing to lipid metabolism.
Results revealed several candidate genes involved in Berkshire meat quality; most of these genes are involved in lipid metabolism and intramuscular fat deposition. These results can provide a basis for future research on the genomic characteristics of Berkshire pigs."
Poultry contamination can be largely attributed to the presence of chicken feces during the production process. Fecal contamination is often found in raw chicken products sold for human consumption. Quantitative analysis of the fecal microbial community of chickens using next-generation sequencing techniques is the focus of this study. Fecal samples were collected from 30 broiler chickens at two time points: days 1 and 35 of development. 454 pyrosequencing was conducted on 16S rRNA extracted from each sample, and microbial population dynamics were investigated using various automated bioinformatics pipelines. Diversity of the microbial community at the genus level increased during the 5-week growth period. Despite this growth, only a few dominant bacteria groups (over 80%) were identified in each fecal sample, with most groups being unique and only a few were shared between samples. Population analysis at the genus level showed that microbial diversity increased with chicken growth and development. Classification and phylogenetic analysis of highly represented microbes (over 1%) clearly showed high levels of sequence similarity between groups such as Firmicutes and Proteobacteria. These results suggest that the chicken fecal excreted microbiome is a dynamic system with a differentiated population structure that harbors a highly restricted number of higher taxa.
At least 150 indigenous African cattle breeds have been named, but the majority of African cattle populations remain largely uncharacterized. As cattle breeds and populations in Africa adapted to various local environmental conditions, they acquired unique features. We know now that the history of African cattle was particularly complex and while several of its episodes remain debated, there is no doubt that African cattle population evolved dramatically over time. Today, we find a mosaic of genetically diverse population from the purest Bos taurus to the nearly pure Bos indicus. African cattle are now found all across the continent, with the exception of the Sahara and the river Congo basin. They are found on the rift valley highlands as well as below sea level in the Afar depression. These unique livestock genetic resources are in danger to disappear rapidly following uncontrolled crossbreeding and breed replacements with exotic breeds. Breeding improvement programs of African indigenous livestock remain too few while paradoxically the demand of livestock products is continually increasing. Many African indigenous breeds are endangered now, and their unique adaptive traits may be lost forever. This paper reviews the unique known characteristics of indigenous African cattle populations while describing the opportunities, the necessity and urgency to understand and utilize these resources to respond to the needs of the people of the continent and to the benefit of African farmers.
Mitochondrial genomes were sequenced from five Raja pulchra individuals, and single-nucleotide polymorphisms (SNPs) were identified by comparing previously announced sequences in this study. Total 117 SNPs were detected and they were present in 2 rRNA genes, 9 tRNA genes, 13 protein coding genes and non-coding region. One deleted polymorphic site, which was located in 16S rRNA gene, was observed in two individuals. Six polymorphic sites were non-synonymous SNPs, which were distributed in ND1, ND2, ATP6 and ND4 gene. Phylogenic analysis validated current taxa. The genome sequences of R. pulchra mitochondria could be comparable information for understanding species divergence and genomic variation among the populations.
The missing heritability has been a major problem in the analysis of best linear unbiased prediction (BLUP). We introduced the traditional genome-wide association study (GWAS) into the BLUP to improve the heritability estimation. We analyzed eight pork quality traits of the Berkshire breeds using GWAS and BLUP. GWAS detects the putative quantitative trait loci regions given traits. The single nucleotide polymorphisms (SNPs) were obtained using GWAS results with p value <0.01. BLUP analyzed with significant SNPs was much more accurate than that with total genotyped SNPs in terms of narrow-sense heritability. It implies that genomic estimated breeding values (GEBVs) of pork quality traits can be calculated by BLUP via GWAS. The GWAS model was the linear regression using PLINK and BLUP model was the G-BLUP and SNP-GBLUP. The SNP-GBLUP uses SNP-SNP relationship matrix. The BLUP analysis using preprocessing of GWAS can be one of the possible alternatives of solving the missing heritability problem and it can provide alternative BLUP method which can find more accurate GEBVs.