Use of the canonical discriminant analysis to select SNP markers for bovine breed assignment and traceability purposes

Genetic Distinctness and Diversity of American Aberdeen Cattle Compared to Common Beef Breeds in the United States

American Aberdeen (AD) cattle in the USA descend from an Aberdeen Angus herd originally brought to the Trangie Agricultural Research Centre, New South Wales, AUS. Although put under specific selection pressure for yearling growth rate, AD remain genomically uncharacterized. The objective was to characterize the genetic diversity and structure of purebred and crossbred AD cattle relative to seven common USA beef breeds using available whole-genome SNP data. A total of 1140 animals consisting of 404 purebred (n = 8 types) and 736 admixed individuals (n = 10 types) was used. Genetic diversity metrics, an analysis of molecular variance, and a discriminant analysis of principal components were employed. When linkage disequilibrium was not accounted for, markers influenced basic diversity parameter estimates, especially for AD cattle. Even so, intrapopulation and interpopulation estimates separate AD cattle from other purebred types (e.g., Latter's pairwise FST ranged from 0.1129 to 0.2209), where AD cattle were less heterozygous and had lower allelic richness than other purebred types. The admixed AD-influenced cattle were intermediate to other admixed types for similar parameters. The diversity metrics separation and differences support strong artificial selection pressures during and after AD breed development, shaping the evolution of the breed and making them genomically distinct from similar breeds.

A Novel Insight into the Identification of Potential SNP Markers for the Genomic Characterization of Buffalo Breeds in Pakistan

Domestic buffaloes (Bubalus bubalis), known as water buffaloes, play a key role as versatile multipurpose agricultural animals in the Asiatic region. Pakistan, with the second-largest buffalo population in the world, holds a rich domestication history of buffaloes. The overall trends in buffalo production demand the genomic characterization of Pakistani buffalo breeds. To this end, the resequencing data of Pakistani breeds, along with buffalo breeds from 13 other countries, were retrieved from our previous study. This dataset, which contained 34,671,886 single-nucleotide polymorphisms (SNPs), was analyzed through a pipeline that was developed to compare possible allele differences among breeds at each SNP position. In contrast, other available tools only check for positional SNP differences for breed-specific markers. In total, 1918, 1549, 404, and 341 breed-specific markers were identified to characterize the Nili, Nili-Ravi, Azakheli, and Kundi breeds of Pakistani buffalo, respectively. Sufficient evidence in the form of phenotypic data, principal component analysis, admixture analysis, and linkage analysis showed that the Nili breed has maintained its distinct breed status despite sharing a close evolutionary relationship with the Nili-Ravi breed of buffalo. In this era of genome science, the conservation of these breeds and the further validation of the given selection markers in larger populations is a pressing need.

Detecting SNP markers discriminating horse breeds by deep learning

The assignment of an individual to the true population of origin using a low-panel of discriminant SNP markers is one of the most important applications of genomic data for practical use. The aim of this study was to evaluate the potential of different Artificial Neural Networks (ANNs) approaches consisting Deep Neural Networks (DNN), Garson and Olden methods for feature selection of informative SNP markers from high-throughput genotyping data, that would be able to trace the true breed of unknown samples. The total of 795 animals from 37 breeds, genotyped by using the Illumina SNP 50k Bead chip were used in the current study and principal component analysis (PCA), log-likelihood ratios (LLR) and Neighbor-Joining (NJ) were applied to assess the performance of different assignment methods. The results revealed that the DNN, Garson, and Olden methods are able to assign individuals to true populations with 4270, 4937, and 7999 SNP markers, respectively. The PCA was used to determine how the animals allocated to the groups using all genotyped markers available on 50k Bead chip and the subset of SNP markers identified with different methods. The results indicated that all SNP panels are able to assign individuals into their true breeds. The success percentage of genetic assignment for different methods assessed by different levels of LLR showed that the success rate of 70% in the analysis was obtained by three methods with the number of markers of 110, 208, and 178 tags for DNN, Garson, and Olden methods, respectively. Also the results showed that DNN performed better than other two approaches by achieving 93% accuracy at the most stringent threshold. Finally, the identified SNPs were successfully used in independent out-group breeds consisting 120 individuals from eight breeds and the results indicated that these markers are able to correctly allocate all unknown samples to true population of origin. Furthermore, the NJ tree of allele-sharing distances on the validation dataset showed that the DNN has a high potential for feature selection. In general, the results of this study indicated that the DNN technique represents an efficient strategy for selecting a reduced pool of highly discriminant markers for assigning individuals to the true population of origin.

A 20-SNP Panel as a Tool for Genetic Authentication and Traceability of Pig Breeds

Simple Summary

Given the high economic and qualitative values of local-breed meat products, it is not uncommon that substitution or mislabeling (either fraudulent or accidental) occurs at the market level. Therefore, to protect the interests of both producers and consumers, a reliable traceability tool should be developed. Nowadays, traceability usually relies on physical labeling systems (e.g., ear tags, tattoos, or electronic transponders). These systems do not, however, have good performances when dealing with carcasses or processed meat products. Molecular markers (i.e., based on the DNA sequence) can be a solution, since DNA is easily extracted from a wide variety of animal products and parts, and is not degraded during processing, even at the high temperatures involved. The aim of this study was to identify a small number of DNA mutations for breed-traceability purposes, in particular of the Italian Nero Siciliano pig and its derived products. A small panel of 12 DNA mutations was enough to discriminate Nero Siciliano pigs from other pig breeds and from wild boars.

Abstract

Food authentication in local breeds has important implications from both an economic and a qualitative point of view. Meat products from autochthonous breeds are of premium value, but can easily incur fraudulent or accidental substitution or mislabeling. The aim of this study was to identify a small number of SNPs using the Illumina PorcineSNP60 BeadChip for breed traceability, in particular of the Italian Nero Siciliano pig and its derived products. A panel of 12 SNPs was sufficient to discriminate Nero Siciliano pig from cosmopolitan breeds and wild boars. After adding 8 SNPs, the final panel of 20 SNPs allowed us to discriminate all the breeds involved in the study, to correctly assign each individual to its breed, and, moreover, to discriminate Nero Siciliano from first-generation hybrids. Almost all livestock breeds are being genotyped with medium- or high-density SNP panels, providing a large amount of information for many applications. Here, we proposed a method to select a reduced SNP panel to be used for the traceability of pig breeds.

Admixture and breed traceability in European indigenous pig breeds and wild boar using genome-wide SNP data

Preserving diversity of indigenous pig (Sus scrofa) breeds is a key factor to (i) sustain the pork chain (both at local and global scales) including the production of high-quality branded products, (ii) enrich the animal biobanking and (iii) progress conservation policies. Single nucleotide polymorphism (SNP) chips offer the opportunity for whole-genome comparisons among individuals and breeds. Animals from twenty European local pigs breeds, reared in nine countries (Croatia: Black Slavonian, Turopolje; France: Basque, Gascon; Germany: Schwabisch-Hällisches Schwein; Italy: Apulo Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano, Sarda; Lithuania: Indigenous Wattle, White Old Type; Portugal: Alentejana, Bísara; Serbia: Moravka, Swallow-Bellied Mangalitsa; Slovenia: Krškopolje pig; Spain: Iberian, Majorcan Black), and three commercial breeds (Duroc, Landrace and Large White) were sampled and genotyped with the GeneSeek Genomic Profiler (GGP) 70 K HD porcine genotyping chip. A dataset of 51 Wild Boars from nine countries was also added, summing up to 1186 pigs (~ 49 pigs/breed). The aim was to: (i) investigate individual admixture ancestries and (ii) assess breed traceability via discriminant analysis on principal components (DAPC). Albeit the mosaic of shared ancestries found for Nero Siciliano, Sarda and Moravka, admixture analysis indicated independent evolvement for the rest of the breeds. High prediction accuracy of DAPC mark SNP data as a reliable solution for the traceability of breed-specific pig products.

Development of a genomic tool for breed assignment by comparison of different classification models: Application to three local cattle breeds

Assignment of individual cattle to a specific breed can often not rely on pedigree information. This is especially the case for local breeds for which the development of genomic assignment tools is required to allow individuals of unknown origin to be included to their herd books. A breed assignment model can be based on two specific stages: (a) the selection of breed-informative markers and (b) the assignment of individuals to a breed with a classification method. However, the performance of combination of methods used in these two stages has been rarely studied until now. In this study, the combination of 16 different SNP panels with four classification methods was developed on 562 reference genotypes from 12 cattle breeds. Based on their performances, best models were validated on three local breeds of interest. In cross-validation, 14 models had a global cross-validation accuracy higher than 90%, with a maximum of 98.22%. In validation, best models used 7,153 or 2,005 SNPs, based on a partial least squares-discriminant analysis (PLS-DA) and assigned individuals to breeds based on nearest shrunken centroids. The average validation sensitivity of the first two best models for the three local breeds of interest were 98.33% and 97.5%. Moreover, results reported in this study suggest that further studies should consider the PLS-DA method when selecting breed-informative SNPs.

Genome-wide selection of discriminant SNP markers for breed assignment in indigenous sheep breeds

The assignment of an individual to the true population of origin is one of the most important applications of genomic data for practical use in animal breeding. The aim of this study was to develop a statistical method and then, to identify the minimum number of informative SNP markers from high-throughput genotyping data that would be able to trace the true breed of unknown samples in indigenous sheep breeds. The total numbers of 217 animals were genotyped using Illumina OvineSNP50K BeadChip in Zel, Lori-Bakhtiari, Afshari, Moqani, Qezel and a wild-type Iranian sheep breed. After SNP quality check, the principal component analysis (PCA) was used to determine how the animals allocated to the groups using all genotyped markers. The results revealed that the first principal component (PC1) separated out the two domestic and wild sheep breeds, and all domestic breeds were separated from each other for PC2. The genetic distance between different breeds was calculated using FST and Reynold methods and the results showed that the breeds were well differentiated. A statistical method was developed using the stepwise discriminant analysis (SDA) and the linear discriminant analysis (LDA) to reduce the number of SNPs for discriminating 6 different Iranian sheep populations and K-fold cross-validation technique was employed to evaluate the potential of a selected subset of SNPs in assignment success rate. The procedure selected reduced pools of markers into 201 SNPs that were able to exactly discriminate all sheep populations with 100% accuracy. Moreover, a discriminate analysis of principal components (DAPC) developed using 201 linearly independent SNPs revealed that these markers were able to assign all individuals into true breed. Finally, these 201 identified SNPs were successfully used in an independent out-group breed consisting of 96 samples of Baluchi sheep breed and the results indicated that these markers are able to correctly allocate all unknown samples to true population of origin. In general, the results of this study indicated that the combined use of the SDA and LDA techniques represents an efficient strategy for selecting a reduced pool of highly discriminant markers.

Clustering patterns mirror the geographical distribution and genetic history of Lemnos and Lesvos sheep populations

Elucidating the genetic variation and structure of Lemnos and Lesvos sheep is critical for maintaining local genetic diversity, ecosystem integrity and resilience of local food production of the two North Aegean islands. In the present study, we explored genetic diversity and differentiation as well as population structure of the Lemnos and Lesvos sheep. Furthermore, we sought to identify a small panel of markers with the highest discriminatory power to assign animals across islands. A total number of n = 424 (n = 307, Lemnos and n = 117, Lesvos) ewes, sampled from n = 24 herds dispersed at different geographic regions on the two islands, were genotyped with the 50K SNP array. Mean observed heterozygosity was higher (but not statistically significantly different) in Lesvos than in Lemnos population (0.384 vs. 0.377) while inbreeding levels were higher in Lemnos than Lesvos herds (0.065 vs. 0.031). Results of principal components along with that of admixture analysis and estimated genetic distances revealed genetic clusters corresponding to Lesvos and Lemnos origin and the existence of infrastructure within islands that were associated with geographical isolation and genetic history of the studied populations. In particular, genetic analyses highlighted three geographically isolated herds in Lemnos that are located at mountainous areas of the island and are characterized as representatives of the local sheep by historic data and reports. Admixture analysis also showed a shared genetic background between Lemnos and Lesvos sheep attributable to past gene flow. Little overall genetic differentiation was detected between the two island sheep populations, while 150 discriminatory SNPs could accurately assign animals to their origin. Present results are comparable with those reported in the worldwide sheep breeds, suggesting geography related genetic patterns across and within islands and the existence of the local Lemnos sheep.

SNPs with intermediate minor allele frequencies facilitate accurate breed assignment of Indian Tharparkar cattle

Tharparkar cattle breed is widely known for its superior milch quality and hardiness attributes. This study aimed to develop an ultra-low density breed-specific single nucleotide polymorphism (SNP) genotype panel to accurately quantify Tharparkar populations in biological samples. In this study, we selected and genotyped 72 Tharparkar animals randomly from Cattle & Buffalo Farm of IVRI, India. This Bovine SNP50 BeadChip genotypic datum was merged with the online data from six indigenous cattle breeds and five taurine breeds. Here, we used a combination of pre-selection statistics and the MAF-LD method developed in our laboratory to analyze the genotypic data obtained from 317 individuals of 12 distinct breeds to identify breed-informative SNPs for the selection of Tharparkar cattle. This methodology identified 63 unique Tharparkar-specific SNPs near intermediate gene frequencies. We report several informative SNPs in genes/QTL regions affecting phenotypes or production traits that might differentiate the Tharparkar breed.

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

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.

Use of the Multivariate Discriminant Analysis for Genome-Wide Association Studies in Cattle

Simple Summary

In the traditional single marker regression approach for genome-wide association studies, if the number of involved individuals is small and the number of single nucleotide polymorphisms (SNPs) to be tested is very large, the probability of getting a significant association simply due to chance becomes enormous. Other techniques, such as the Bayesian methods, require several a priori assumptions, as an a priori posterior inclusion probability threshold, that can limit their effectiveness. In the present study, a multivariate algorithm able to partially overcome this problem was proposed. On simulated data, with 3000 individuals, only 13 and 3 quantitative trait loci (QTLs) were obtained with the single marker regression and the Bayesian approaches, respectively. On the other hand, the multivariate algorithm detected 65 QTLs in the same scenario. The gap between the single marker regression and the multivariate methods slowly decreased as the number of animals increased. This figure was also confirmed on real data.

Abstract

Genome-wide association studies (GWAS) are traditionally carried out by using the single marker regression model that, if a small number of individuals is involved, often lead to very few associations. The Bayesian methods, such as BayesR, have obtained encouraging results when they are applied to the GWAS. However, these approaches, require that an a priori posterior inclusion probability threshold be fixed, thus arbitrarily affecting the obtained associations. To partially overcome these problems, a multivariate statistical algorithm was proposed. The basic idea was that animals with different phenotypic values of a specific trait share different allelic combinations for genes involved in its determinism. Three multivariate techniques were used to highlight the differences between the individuals assembled in high and low phenotype groups: the canonical discriminant analysis, the discriminant analysis and the stepwise discriminant analysis. The multivariate method was tested both on simulated and on real data. The results from the simulation study highlighted that the multivariate GWAS detected a greater number of true associated single nucleotide polymorphisms (SNPs) and Quantitative trait loci (QTLs) than the single marker model and the Bayesian approach. For example, with 3000 animals, the traditional GWAS highlighted only 29 significantly associated markers and 13 QTLs, whereas the multivariate method found 127 associated SNPs and 65 QTLs. The gap between the two approaches slowly decreased as the number of animals increased. The Bayesian method gave worse results than the other two. On average, with the real data, the multivariate GWAS found 108 associated markers for each trait under study and among them, around 63% SNPs were also found in the single marker approach. Among the top 118 associated markers, 76 SNPs harbored putative candidate genes.

Identification of Ancestry Informative Marker (AIM) Panels to Assess Hybridisation between Feral and Domestic Sheep

Simple Summary

Once present in the entirety of Europe, mouflon (wild sheep) became extinct due to intense hunting, but remnant populations survived and became feral on the Mediterranean islands of Corsica and Sardinia. Although now protected by regional laws, Sardinian mouflon is threatened by crossbreeding with domestic sheep causing genetic hybridisation. The spread of domestic genes can be detrimental for wild populations as it dilutes the genetic features that characterise them. This work aimed to identify diagnostic tools that could be applied to monitor the level of hybridisation between mouflon and domestic sheep. Tens of thousands of genetic markers known as single nucleotide polymorphisms (SNPs) were screened and we identified the smallest number of SNPs necessary to discriminate between pure mouflon and sheep. We produced four SNP panels of different sizes which were able to assess the hybridisation level of a mouflon and we verified that the SNP panels efficacy is independent of the domestic sheep breed involved in the hybrid. The implementation of these results into actual diagnostic tools will help the conservation of this unique and irreplaceable mouflon population, and the methodology applied can easily be transferred to other case studies of interest.

Abstract

Hybridisation of wild populations with their domestic counterparts can lead to the loss of wildtype genetic integrity, outbreeding depression, and loss of adaptive features. The Mediterranean island of Sardinia hosts one of the last extant autochthonous European mouflon (Ovis aries musimon) populations. Although conservation policies, including reintroduction plans, have been enforced to preserve Sardinian mouflon, crossbreeding with domestic sheep has been documented. We identified panels of single nucleotide polymorphisms (SNPs) that could act as ancestry informative markers able to assess admixture in feral x domestic sheep hybrids. The medium-density SNP array genotyping data of Sardinian mouflon and domestic sheep (O. aries aries) showing pure ancestry were used as references. We applied a two-step selection algorithm to this data consisting of preselection via Principal Component Analysis followed by a supervised machine learning classification method based on random forest to develop SNP panels of various sizes. We generated ancestry informative marker (AIM) panels and tested their ability to assess admixture in mouflon x domestic sheep hybrids both in simulated and real populations of known ancestry proportions. All the AIM panels recorded high correlations with the ancestry proportion computed using the full medium-density SNP array. The AIM panels proposed here may be used by conservation practitioners as diagnostic tools to exclude hybrids from reintroduction plans and improve conservation strategies for mouflon populations.

Analysis of genome-wide DNA arrays reveals the genomic population structure and diversity in autochthonous Greek goat breeds

Goats play an important role in the livestock sector in Greece. The national herd consists mainly of two indigenous breeds, the Eghoria and Skopelos. Here, we report the population structure and genomic profiles of these two native goat breeds using Illumina’s Goat SNP50 BeadChip. Moreover, we present a panel of candidate markers acquired using different genetic models for breed discrimination. Quality control on the initial dataset resulted in 48,841 SNPs kept for downstream analysis. Principal component and admixture analyses were applied to assess population structure. The rate of inbreeding within breed was evaluated based on the distribution of runs of homozygosity in the genome and respective coefficients, the genomic relationship matrix, the patterns of linkage disequilibrium, and the historic effective population size. Results showed that both breeds exhibit high levels of genetic diversity. Level of inbreeding between the two breeds estimated by the Wright’s fixation index F_ST was low (Fst = 0.04362), indicating the existence of a weak genetic differentiation between them. In addition, grouping of farms according to their geographical locations was observed. This study presents for the first time a genome-based analysis on the genetic structure of the two indigenous Greek goat breeds and identifies markers that can be potentially exploited in future selective breeding programs for traceability purposes, targeted genetic improvement schemes and conservation strategies.

Comparative analysis of five different methods to design a breed-specific SNP panel for cattle

Single nucleotide polymorphisms (SNPs) have now replaced microsatellite markers in several species for various genetic investigations like parentage assignment, genetic breed composition, assessment for individuality and, most popularly, as a useful tool in genomic selection. However, such a resource, which can offer to assist breed identification in a cost-effective manner is still not explored in cattle breeding programs. In our study, we have tried to describe methods for reducing the number of SNPs to develop a breed-specific panel. We have used SNP data from Dryad open public access repository. Starting from a global dataset of 178 animals belonging to 10 different breeds, we selected five panels each comprising of similar number of SNPs using different methods i.e., Delta, Pairwise Wright's F_ST, informativeness for assignment, frequent item feature selection (FIFS) and minor allele frequency-linkage disequilibrium (MAF-LD) based method. MAF-LD based method has been recently developed by us for construction of breed-specific SNP panels. The STRUCTURE software analysis of MAF-LD based method showed appropriate clustering in comparison to other panels. Later, the panel of 591 breed-specific SNPs was called to their respective breeds using Venny 2.1.0 and UGent web tools software. Breed-specific SNPs were later annotated by using various Bioinformatics softwares.

Genomic diversity and population structure of three autochthonous Greek sheep breeds assessed with genome-wide DNA arrays

In the present study, genome-wide genotyping was applied to characterize the genetic diversity and population structure of three autochthonous Greek breeds: Boutsko, Karagouniko and Chios. Dairy sheep are among the most significant livestock species in Greece numbering approximately 9 million animals which are characterized by large phenotypic variation and reared under various farming systems. A total of 96 animals were genotyped with the Illumina's OvineSNP50K microarray beadchip, to study the population structure of the breeds and develop a specialized panel of single-nucleotide polymorphisms (SNPs), which could distinguish one breed from the others. Quality control on the dataset resulted in 46,125 SNPs, which were used to evaluate the genetic structure of the breeds. Population structure was assessed through principal component analysis (PCA) and admixture analysis, whereas inbreeding was estimated based on runs of homozygosity (ROHs) coefficients, genomic relationship matrix inbreeding coefficients (F_GRM) and patterns of linkage disequilibrium (LD). Associations between SNPs and breeds were analyzed with different inheritance models, to identify SNPs that distinguish among the breeds. Results showed high levels of genetic heterogeneity in the three breeds. Genetic distances among breeds were modest, despite their different ancestries. Chios and Karagouniko breeds were more genetically related to each other compared to Boutsko. Analysis revealed 3802 candidate SNPs that can be used to identify two-breed crosses and purebred animals. The present study provides, for the first time, data on the genetic background of three Greek indigenous dairy sheep breeds as well as a specialized marker panel that can be applied for traceability purposes as well as targeted genetic improvement schemes and conservation programs.

Early diagnosis using canonical discriminant analysis of innate immune receptor gene expression profiles in a murine infectious or sterile systemic inflammation model

BACKGROUND

Infection in patients with systemic inflammation is difficult to diagnose with a single biomarker. We aimed to clarify the time course of change in the gene expression profile of innate immune receptors in infectious or sterile inflammation and to establish an early diagnostic method using canonical discriminant analysis (CDA) of the gene expression profile.

METHODS

To compare infectious and sterile inflammation, we used cecal ligation and puncture (CLP) and 20% full-thickness burn injury (Burn) models. C57BL/6 mice underwent sham treatment (n = 9 × three groups), CLP (n = 12 × three groups), or Burn (n = 12 × three groups) injury. Mice were killed at 6, 12, and 24 hours after injury, and total RNA was extracted from whole blood. We used quantitative real-time polymerase chain reaction to investigate gene expression of innate immune receptors Toll-like receptor 2 (TLR2), TLR4, TLR9, NLRP3 (nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3), and retinoic acid-inducible gene I. To evaluate all gene expression together as patterns, each value was standardized, and CDA was performed at each time point.

RESULTS

Gene expression of TLR2 and TLR4 was already significantly increased in both CLP and Burn compared with sham mice at 6 hours after injury (p < 0.05). Gene expression of TLR9 was significantly decreased in CLP compared with sham and Burn mice at 12 hours and 24 hours after injury (p < 0.05) but not at 6 hours. Gene expression of NLRP3 was significantly increased in CLP and Burn compared with sham mice at 6 hours and 24 hours after injury (p < 0.05). In the CDA, each group showed distinctive gene expression patterns at only 6 hours after injury. Each group was clearly classified, and the classification error rates were 0% at all of the time points.

CONCLUSIONS

Canonical discriminant analysis of the gene expression profile of innate immune receptors could be a novel approach for diagnosing the pathophysiology of complicated systemic inflammation from the early stage of injury.

Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds

The identification of individuals' breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the minimum number of single nucleotide polymorphisms (SNPs) from high-throughput genotyping data for individual assignment to dairy sheep breeds reared in Sicily. The combined use of penalized multinomial regression and stability selection reduced the number of SNPs required to 48. A final validation step on an independent population was carried out obtaining 100% correctly classified individuals. The results using independent analysis, such as admixture, F st, principal component analysis and random forest, confirmed the ability of these methods in selecting distinctive markers. The identified SNPs may constitute a starting point for the development of a SNP based identification test as a tool for breed assignment and traceability of animal products.

Use of canonical discriminant analysis to study signatures of selection in cattle

BACKGROUND

Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of "signatures of selection" is a useful approach to study the evolutionary pressures experienced throughout history. In the present study, signatures of selection were investigated in five cattle breeds farmed in Italy using a multivariate approach.

METHODS

A total of 4094 bulls from five breeds with different production aptitudes (two dairy breeds: Italian Holstein and Italian Brown Swiss; two beef breeds: Piemontese and Marchigiana; and one dual purpose breed: Italian Simmental) were genotyped using the Illumina BovineSNP50 v.1 beadchip. Canonical discriminant analysis was carried out on the matrix of single nucleotide polymorphisms (SNP) genotyping data, separately for each chromosome. Scores for each canonical variable were calculated and then plotted in the canonical space to quantify the distance between breeds. SNPs for which the correlation with the canonical variable was in the 99th percentile for a specific chromosome were considered to be significantly associated with that variable. Results were compared with those obtained using an FST-based approach.

RESULTS

Based on the results of the canonical discriminant analysis, a large number of signatures of selection were detected, among which several had strong signals in genomic regions that harbour genes known to have an impact on production and morphological bovine traits, including MSTN, LCT, GHR, SCD, NCAPG, KIT, and ASIP. Moreover, new putative candidate genes were identified, such as GCK, B3GALNT1, MGAT1, GALNTL1, PRNP, and PRND. Similar results were obtained with the FST-based approach.

CONCLUSIONS

The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that maximize the separation between breeds. This approach is quite straightforward, it can compare more than two groups simultaneously, and relative distances between breeds can be visualized. The genes that were highlighted in the canonical discriminant analysis were in concordance with those obtained using the FST index.

Recent development of allele frequencies and exclusion probabilities of microsatellites used for parentage control in the German Holstein Friesian cattle population

Background

Methods for parentage control in cattle have changed since their initial implementation in the late 1950’s from blood group typing to more current single nucleotide polymorphism determination. In the early 1990’s, 12 microsatellites were selected by the International Society for Animal Genetics based on their informativeness and robustness in a variety of different cattle breeds. Since then this panel is used as standard in cattle herd book breeding and its application is accompanied by recurrent international comparison tests ensuring permanent validity for the most common commercial dairy and beef cattle breeds for example Holstein Friesian, Simmental, Angus, and Hereford. Although, nearly every parentage can be resolved using these microsatellites, cases with very close relatives became an emerging resolution problem during recent years. This is mainly due to an increase of monomorphism and a trend to the fixation of alleles, although no direct selection against their variability was applied. Thus other effects must be presumed resulting in a loss of polymorphism information content, heterozygosity, and exclusion probabilities.

Results

To determine changes of allele frequencies and exclusion probabilities, we analyzed the development of these parameters for the 12 microsatellites from 2004 to 2014. One hundred sixty eight thousand recorded Holstein Friesian cattle genotypes were evaluated. During this period certain alleles of nine microsatellites increased significantly (t-values >5). When calculating the exclusion probabilities for 11 microsatellites, reduction was determined for the three situations, i.e. one parent is wrongly identified (p = 0.01), both parents are wrongly identified (p = 0.005), and the genotype of one parent is missing (p = 0.048). With the addition of BM1818 to the marker set in 2009, this development was corrected leading to significant increases in exclusion probabilities. Although, the exclusion probabilities for the three family situations using the 12 microsatellites are >99 %, the clarification of 142 relationships in 40,000 situations where one parent is missing will still be impossible.

Twenty-five sires were identified that are responsible for the most significant microsatellite allele increases in the population. The corresponding alleles are mainly associated with milk protein and fat yield, body weight at birth and weaning, as well as somatic cell score, milk fat percentage, and longissimus muscle area.

Conclusions

Our data show that most of the microsatellites used for parentage control in cattle show directional changes in allele frequencies consistent with the history of artificial selection in the German Holstein population.

Application of LogitBoost Classifier for Traceability Using SNP Chip Data

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.

Effectiveness of a 95 SNP panel for the screening of breed label fraud in the Chinese meat market

Breed assignment has proved to be useful to control meat trade and protect the value of special productions. Meat-related frauds have been detected in China; therefore, 95 SNPs selected from the ISAG core panel were evaluated to develop an automated and technologically updated tool to screen breed label fraud in the Chinese meat market. A total of 271 animals from four Chinese yellow cattle (CYC) populations, six Bos taurus breeds, two Bos indicus and one composite were used. The allocation test distinguished European, Japanese and Zebu breeds, and two Chinese genetic components. It correctly allocated Japanese Black, Zebu and British breeds in 100, 90 and 89% of samples, respectively. CYC evidenced the Zebu, Holstein and Limousin introgression. The test did not detect CYC components in any of the 25 samples from Argentinean butchers. The method could be useful to certify Angus, Hereford and Japanese Black meat, but a modification in the panel would be needed to differentiate other breeds.

A SNP test to identify Africanized honeybees via proportion of 'African' ancestry

The honeybee, Apis mellifera, is the world's most important pollinator and is ubiquitous in most agricultural ecosystems. Four major evolutionary lineages and at least 24 subspecies are recognized. Commercial populations are mainly derived from subspecies originating in Europe (75-95%). The Africanized honeybee is a New World hybrid of A. m. scutellata from Africa and European subspecies, with the African component making up 50-90% of the genome. Africanized honeybees are considered undesirable for bee-keeping in most countries, due to their extreme defensiveness and poor honey production. The international trade in honeybees is restricted, due in part to bans on the importation of queens (and semen) from countries where Africanized honeybees are extant. Some desirable strains from the United States of America that have been bred for traits such as resistance to the mite Varroa destructor are unfortunately excluded from export to countries such as Australia due to the presence of Africanized honeybees in the USA. This study shows that a panel of 95 single nucleotide polymorphisms, chosen to differentiate between the African, Eastern European and Western European lineages, can detect Africanized honeybees with a high degree of confidence via ancestry assignment. Our panel therefore offers a valuable tool to mitigate the risks of spreading Africanized honeybees across the globe and may enable the resumption of queen and bee semen imports from the Americas.

Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis

Background

SNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific haplotypes. It is therefore convenient to have a practical statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we present a procedure combining variable selection (i.e. the selection of predictive SNPs) and linear discriminant analysis for the identification of carriers of a haplotype on BTA19 (Bos taurus autosome 19) known to be associated with reduced cow fertility. A population of 3645 Brown Swiss cows and bulls genotyped with the 54K SNP-chip was available for the analysis.

Results

The overall error rate for the prediction of haplotype carriers was on average very low (∼≤1%). The error rate was found to depend on the number of SNPs in the model and their density around the region of the haplotype on BTA19. The minimum set of SNPs to still achieve accurate predictions was 5, with a total test error rate of 1.59.

Conclusions

The paper describes a procedure to accurately identify haplotype carriers from SNP genotypes in cattle populations. Very few misclassifications were observed, which indicates that this is a very reliable approach for potential applications in cattle breeding.