A machine learning approach for the identification of population-informative markers from high-throughput genotyping data: application to several pig breeds

Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds

Large genotyping datasets, obtained from high-density single nucleotide polymorphism (SNP) arrays, developed for different livestock species, can be used to describe and differentiate breeds or populations. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this study, we applied the Boruta algorithm, a wrapper of the machine learning random forest algorithm, on a database of 23 European pig breeds (20 autochthonous and three cosmopolitan breeds) genotyped with a 70k SNP chip, to pre-select informative SNPs. To identify different sets of SNPs, these pre-selected markers were then ranked with random forest based on their mean decrease accuracy and mean decrease gene indexes. We evaluated the efficiency of these subsets for breed classification and the usefulness of this approach to detect candidate genes affecting breed-specific phenotypes and relevant production traits that might differ among breeds. The lowest overall classification error (2.3%) was reached with a subpanel including only 398 SNPs (ranked based on their mean decrease accuracy), with no classification error in seven breeds using up to 49 SNPs. Several SNPs of these selected subpanels were in genomic regions in which previous studies had identified signatures of selection or genes associated with morphological or production traits that distinguish the analysed breeds. Therefore, even if these approaches have not been originally designed to identify signatures of selection, the obtained results showed that they could potentially be useful for this purpose.

Natural clines and human management impact the genetic structure of Algerian honey bee populations

BACKGROUND

The Algerian honey bee population is composed of two described subspecies A. m. intermissa and A. m. sahariensis, of which little is known regarding population genomics, both in terms of genetic differentiation and of possible contamination by exogenous stock. Moreover, the phenotypic differences between the two subspecies are expected to translate into genetic differences and possible adaptation to heat and drought in A. m. sahariensis. To shed light on the structure of this population and to integrate these two subspecies in the growing dataset of available haploid drone sequences, we performed whole-genome sequencing of 151 haploid drones.

RESULTS

Integrated analysis of our drone sequences with a similar dataset of European reference populations did not detect any significant admixture in the Algerian honey bees. Interestingly, most of the genetic variation was not found between the A. m. intermissa and A. m. sahariensis subspecies; instead, two main genetic clusters were found along an East-West axis. We found that the correlation between genetic and geographic distances was higher in the Western cluster and that close-family relationships were mostly detected in the Eastern cluster, sometimes at long distances. In addition, we selected a panel of 96 ancestry-informative markers to decide whether a sampled bee is Algerian or not, and tested this panel in simulated cases of admixture.

CONCLUSIONS

The differences between the two main genetic clusters suggest differential breeding management between eastern and western Algeria, with greater exchange of genetic material over long distances in the east. The lack of detected admixture events suggests that, unlike what is seen in many places worldwide, imports of queens from foreign countries do not seem to have occurred on a large scale in Algeria, a finding that is relevant for conservation purposes. In addition, the proposed panel of 96 markers was found effective to distinguish Algerian from European honey bees. Therefore, we conclude that applying this approach to other taxa is promising, in particular when genetic differentiation is difficult to capture.

Genetic diversity and breed-informative SNPs identification in domestic pig populations using coding SNPs

Background: The use of breed-informative genetic markers, specifically coding Single Nucleotide Polymorphisms (SNPs), is crucial for breed traceability, authentication of meat and dairy products, and the preservation and improvement of pig breeds. By identifying breed informative markers, we aimed to gain insights into the genetic mechanisms that influence production traits, enabling informed decisions in animal management and promoting sustainable pig production to meet the growing demand for animal products. Methods: Our dataset consists of 300 coding SNPs genotyped from three Italian commercial pig populations: Landrace, Yorkshire, and Duroc. Firstly, we analyzed the genetic diversity among the populations. Then, we applied a discriminant analysis of principal components to identify the most informative SNPs for discriminating between these populations. Lastly, we conducted a functional enrichment analysis to identify the most enriched pathways related to the genetic variation observed in the pig populations. Results: The alpha diversity indexes revealed a high genetic diversity within the three breeds. The higher proportion of observed heterozygosity than expected revealed an excess of heterozygotes in the populations that was supported by negative values of the fixation index (FIS) and deviations from the Hardy-Weinberg equilibrium. The Euclidean distance, the pairwise FST, and the pairwise Nei's GST genetic distances revealed that Yorkshire and Landrace breeds are genetically the closest, with distance values of 2.242, 0.029, and 0.033, respectively. Conversely, Landrace and Duroc breeds showed the highest genetic divergence, with distance values of 2.815, 0.048, and 0.052, respectively. We identified 28 significant SNPs that are related to phenotypic traits and these SNPs were able to differentiate between the pig breeds with high accuracy. The Functional Enrichment Analysis of the informative SNPs highlighted biological functions related to DNA packaging, chromatin integrity, and the preparation of DNA into higher-order structures. Conclusion: Our study sheds light on the genetic underpinnings of phenotypic variation among three Italian pig breeds, offering potential insights into the mechanisms driving breed differentiation. By prioritizing breed-specific coding SNPs, our approach enables a more focused analysis of specific genomic regions relevant to the research question compared to analyzing the entire genome.

Evaluation of the Genetic Diversity, Population Structure and Selection Signatures of Three Native Chinese Pig Populations

Indigenous pig populations in Hainan Province live in tropical climate conditions and a relatively closed geographical environment, which has contributed to the formation of some excellent characteristics, such as heat tolerance, strong disease resistance and excellent meat quality. Over the past few decades, the number of these pig populations has decreased sharply, largely due to a decrease in growth rate and poor lean meat percentage. For effective conservation of these genetic resources (such as heat tolerance, meat quality and disease resistance), the whole-genome sequencing data of 78 individuals from 3 native Chinese pig populations, including Wuzhishan (WZS), Tunchang (TC) and Dingan (DA), were obtained using a 150 bp paired-end platform, and 25 individuals from two foreign breeds, including Landrace (LR) and Large White (LW), were downloaded from a public database. A total of 28,384,282 SNPs were identified, of which 27,134,233 SNPs were identified in native Chinese pig populations. Both genetic diversity statistics and linkage disequilibrium (LD) analysis indicated that indigenous pig populations displayed high genetic diversity. The result of population structure implied the uniqueness of each native Chinese pig population. The selection signatures were detected between indigenous pig populations and foreign breeds by using the population differentiation index (F_ST) method. A total of 359 candidate genes were identified, and some genes may affect characteristics such as immunity (IL-2, IL-21 and ZFYVE16), adaptability (APBA1), reproduction (FGF2, RNF17, ADAD1 and HIPK4), meat quality (ABCA1, ADIG, TLE4 and IRX5), and heat tolerance (VPS13A, HSPA4). Overall, the findings of this study will provide some valuable insights for the future breeding, conservation and utilization of these three Chinese indigenous pig populations.

Genome-wide scans for selection signatures in indigenous pigs revealed candidate genes relating to heat tolerance

Heat stress is a major problem that constrains pig productivity. Understanding and identifying adaptation to heat stress has been the focus of recent studies, and the identification of genome-wide selection signatures can provide insights into the mechanisms of environmental adaptation. Here, we generated whole-genome re-sequencing data from six Chinese indigenous pig populations to identify genomic regions with selection signatures related to heat tolerance using multiple methods: three methods for intra-population analyses (Integrated Haplotype Score, Runs of Homozygosity and Nucleotide diversity Analysis) and three methods for inter-population analyses (Fixation index (F_ST), Cross-population Composite Likelihood Ratio and Cross-population Extended Haplotype Homozygosity). In total, 1 966 796 single nucleotide polymorphisms were identified in this study. Genetic structure analyses and F_ST indicated differentiation among these breeds. Based on information on the location environment, the six breeds were divided into heat and cold groups. By combining two or more approaches for selection signatures, outlier signals in overlapping regions were identified as candidate selection regions. A total of 163 candidate genes were identified, of which, 29 were associated with heat stress injury and anti-inflammatory effects. These candidate genes were further associated with 78 Gene Ontology functional terms and 30 Kyoto Encyclopedia of Genes and Genomes pathways in enrichment analysis (P < 0.05). Some of these have clear relevance to heat resistance, such as the AMPK signalling pathway and the mTOR signalling pathway. The results improve our understanding of the selection mechanisms responsible for heat resistance in pigs and provide new insights of introgression in heat adaptation.

Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data

BACKGROUND

Breed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, F_ST, and I_n) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities.

RESULTS

We found that all combinations performed quite well with identification accuracies over 95% in all scenarios. However, there was no combination which performed the best and robust across all scenarios. We proposed to integrate the three breed-informative detection methods, named DFI, and integrate the three machine learning methods, KNN, SVM, and RF, named KSR. We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99% in most cases and was very robust in all scenarios. The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.

CONCLUSIONS

The current study showed that the combination of DFI and KSR was the optimal strategy. Using sequence data resulted in higher accuracies than using chip data in most cases. However, the differences were generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification.

Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information

Introduction: The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition. Methods: All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle. Results: When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels <2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction. Discussion: Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software.

A web tool for the global identification of pig breeds

BACKGROUND

Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration.

RESULTS

We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds.

CONCLUSIONS

In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.

Screening Discriminating SNPs for Chinese Indigenous Pig Breeds Identification Using a Random Forests Algorithm

Chinese indigenous pig breeds have unique genetic characteristics and a rich diversity; however, effective breed identification methods have not yet been well established. In this study, a genotype file of 62,822 single-nucleotide polymorphisms (SNPs), which were obtained from 1059 individuals of 18 Chinese indigenous pig breeds and 5 cosmopolitan breeds, were used to screen the discriminating SNPs for pig breed identification. After linkage disequilibrium (LD) pruning filtering, this study excluded 396 SNPs on non-constant chromosomes and retained 20.92~-27.84% of SNPs for each of the 18 autosomes, leaving a total of 14,823 SNPs. The principal component analysis (PCA) showed the largest differences between cosmopolitan and Chinese pig breeds (PC1 = 10.452%), while relatively small differences were found among the 18 indigenous pig breeds from the Yangtze River Delta region of China. Next, a random forest (RF) algorithm was used to filter these SNPs and obtain the optimal number of decision trees (ntree = 1000) using corresponding out-of-bag (OOB) error rates. By comparing two different SNP ranking methods in the RF analysis, the mean decreasing accuracy (MDA) and mean decreasing Gini index (MDG), the effects of panels with different numbers of SNPs on the assignment accuracy, and the statistics of SNP distribution on each chromosome in the panels, a panel of 1000 of the most breed-discriminative tagged SNPs were finally selected based on the MDA screening method. A high accuracy (>99.3%) was obtained by the breed prediction of 318 samples in the RF test set; thus, a machine learning classification method was established for the multi-breed identification of Chinese indigenous pigs based on a low-density panel of SNPs.

Species-informative SNP markers for characterising freshwater prawns of genus Macrobrachium in Cameroon

Single Nucleotide Polymorphisms (SNPs) are now popular for a myriad of applications in animal and plant species including, ancestry assignment, conservation genetics, breeding, and traceability of animal products. The objective of this study was to develop a customized cost-effective SNP panel for genetic characterisation of Macrobrachium species in Cameroon. The SNPs identified in a previous characterization study were screened as viable candidates for the reduced panel. Starting from a full set of 1,814 SNPs, a total of 72 core SNPs were chosen using conventional approaches: allele frequency differentials, minor allele frequency profiles, and Wright’s Fst statistics. The discriminatory power of reduced set of informative SNPs were then tested using the admixture analysis, principal component analysis, and discriminant analysis of principal components. The panel of prioritised SNP markers (i.e., N = 72 SNPs) distinguished Macrobrachium species with 100% accuracy. However, large sample size is needed to identify more informative SNPs for discriminating genetically closely related species, including M. macrobrachion versus M. vollenhovenii and M. sollaudii versus M. dux. Overall, the findings in this study show that we can accurately characterise Macrobrachium using a small set of core SNPs which could be useful for this economically important species in Cameroon. Given the results obtained in this study, a larger independent validation sample set will be needed to confirm the discriminative capacity of this SNP panel for wider commercial and research applications.

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

Identification of Ancestry Informative Markers in Mediterranean Trout Populations of Molise (Italy): A Multi-Methodological Approach with Machine Learning

Brown trout (Salmo trutta), like many other freshwater species, is threated by the release in its natural environment of alien species and the restocking with allochthonous conspecific stocks. Many conservation projects are ongoing and several morphological and genetic tools have been proposed to support activities aimed to restore genetic integrity status of native populations. Nevertheless, due to the complexity of degree of introgression reached up after many generations of crossing, the use of dichotomous key and molecular markers, such as mtDNA, LDH-C1* and microsatellites, are often not sufficient to discriminate native and admixed specimens at individual level. Here we propose a reduced panel of ancestry-informative SNP markers (AIMs) to support on field activities for Mediterranean trout management and conservation purpose. Starting from the genotypes data obtained on specimens sampled in the main two Molise’s rivers (Central-Southern Italy), a 47 AIMs panel was identified and validated on simulated and real hybrid population datasets, mainly through a Machine Learning approach based on Random Forest classifier. The AIMs panel proposed may represent an interesting and cost-effective tool for monitoring the level of introgression between native and allochthonous trout population for conservation purpose and this methodology could be also applied in other species.

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.

Single-marker and haplotype-based genome-wide association studies for the number of teats in two heavy pig breeds

The number of teats is a reproductive‐related trait of great economic relevance as it affects the mothering ability of the sows and thus the number of properly weaned piglets. Moreover, genetic improvement of this trait is fundamental to parallelly help the selection for increased litter size. We present the results of single‐marker and haplotypes‐based genome‐wide association studies for the number of teats in two large cohorts of heavy pig breeds (Italian Large White and Italian Landrace) including 3990 animals genotyped with the 70K GGP Porcine BeadChip and other 1927 animals genotyped with the Illumina PorcineSNP60 BeadChip. In the Italian Large White population, genome scans identified three genome regions (SSC7, SSC10, and SSC12) that confirmed the involvement of the VRTN gene (as we previously reported) and highlighted additional loci known to affect teat counts, including the FRMD4A and HOXB1 gene regions. A different picture emerged in the Italian Landrace population, with a total of 12 genome regions in eight chromosomes (SSC3, SSC6, SSC8, SSC11, SSC13, SSC14, SSC15, and SSC16) mainly detected via the haplotype‐based genome scan. The most relevant QTL was close to the ARL4C gene on SSC15. Markers in the VRTN gene region were not significant in the Italian Landrace breed. The use of both single‐marker and haplotype‐based genome‐wide association analyses can be helpful to exploit and dissect the genome of the pigs of different populations. Overall, the obtained results supported the polygenic nature of the investigated trait and better elucidated its genetic architecture in Italian heavy pigs.

Redefinition of the Mora Romagnola Pig Breed Herd Book Standard Based on DNA Markers Useful to Authenticate Its "Mono-Breed" Products: An Example of Sustainable Conservation of a Livestock Genetic Resource

Simple Summary

Autochthonous breeds are, in general, well adapted to their production systems in which they have been constituted but they are usually less efficient than commercial breeds. Therefore, conservation strategies of livestock genetic resources should be designed to assure profitability to the farmers. The development of “mono-breed” brand products is one of the most effective actions towards this aim. These products are usually sold at a higher price compared to undifferentiated ones, as the consumers consider positively the link between these breeds and the perceived quality of their products. The premium price, however, also attracts fraudsters that unscrupulously see an economic advantage by selling mis-labelled products to obtain an unjustified additional economic gain. These frauds undermine the whole strategy designed to support a sustainable conservation of autochthonous genetic resources. Mora Romagnola is a local pig breed raised in the north of Italy. Mono-breed pork products derived from this breed are part of an important niche value chain that is intrinsically linked to the conservation of this local genetic resource. In this study we present how the Mora Romagnola Herd Book standard integrated information of DNA markers of two genes (MC1R and NR6A1), affecting morphological traits, to allow the authentication of mono-breed products of this breed. This is one of the first examples of sustainable conservation of a pig genetic resource designed starting from the genotype of the animals registered to the breed herd book, with the specific purpose to combat frauds.

Abstract

Mora Romagnola is an autochthonous pig breed, raised in the north of Italy. Mono-breed pork products of this breed are part of important niche value chain that is intrinsically linked to the conservation of this local genetic resources that can only survive due to the premium price that these products can obtain on the market. However, the added value attracts fraudsters that unscrupulously sell mis-labelled Mora Romagnola products, causing consumer distrust that, in turn, undermines the conservation strategy of this breed. To monitor and better characterise this local breed, we phenotyped 826 Mora Romagnola pigs for three breed-specific traits. Then, we genotyped almost all living sows and boars registered to the Herd Book (n. = 357 animals) for polymorphisms in the MC1R and NR6A1 genes (affecting coat colour and vertebral number, respectively). The results were used to re-define the breed descriptors of the Mora Romagnala breed that included information on the allowed genotypes at these two genes. A few pigs that did not carry the allowed genotypes were excluded from its Herd Book. Finally, we evaluated the usefulness of these DNA markers to authenticate Mora Romagnola meat against meat derived from other 11 pig breeds and wild boars. To our knowledge, the Mora Romagnola Herd Book is one of the first examples that established a direct link between a genetic standard of a breed with the possibility to authenticate mono-breed products using DNA markers with the specific purpose to combat frauds and, indirectly, support the conservation of a livestock genetic resource.

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.

Whole-genome sequencing of European autochthonous and commercial pig breeds allows the detection of signatures of selection for adaptation of genetic resources to different breeding and production systems

Background

Natural and artificial directional selection in cosmopolitan and autochthonous pig breeds and wild boars have shaped their genomes and resulted in a reservoir of animal genetic diversity. Signatures of selection are the result of these selection events that have contributed to the adaptation of breeds to different environments and production systems. In this study, we analysed the genome variability of 19 European autochthonous pig breeds (Alentejana, Bísara, Majorcan Black, Basque, Gascon, Apulo-Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano, Sarda, Krškopolje pig, Black Slavonian, Turopolje, Moravka, Swallow-Bellied Mangalitsa, Schwäbisch-Hällisches Schwein, Lithuanian indigenous wattle and Lithuanian White old type) from nine countries, three European commercial breeds (Italian Large White, Italian Landrace and Italian Duroc), and European wild boars, by mining whole-genome sequencing data obtained by using a DNA-pool sequencing approach. Signatures of selection were identified by using a single-breed approach with two statistics [within-breed pooled heterozygosity (H_P) and fixation index (F_ST)] and group-based F_ST approaches, which compare groups of breeds defined according to external traits and use/specialization/type.

Results

We detected more than 22 million single nucleotide polymorphisms (SNPs) across the 23 compared populations and identified 359 chromosome regions showing signatures of selection. These regions harbour genes that are already known or new genes that are under selection and relevant for the domestication process in this species, and that affect several morphological and physiological traits (e.g. coat colours and patterns, body size, number of vertebrae and teats, ear size and conformation, reproductive traits, growth and fat deposition traits). Wild boar related signatures of selection were detected across all the genome of several autochthonous breeds, which suggests that crossbreeding (accidental or deliberate) occurred with wild boars.

Conclusions

Our findings provide a catalogue of genetic variants of many European pig populations and identify genome regions that can explain, at least in part, the phenotypic diversity of these genetic resources.

Genome-wide detection of copy number variants in European autochthonous and commercial pig breeds by whole-genome sequencing of DNA pools identified breed-characterising copy number states

In this study, we identified copy number variants (CNVs) in 19 European autochthonous pig breeds and in two commercial breeds (Italian Large White and Italian Duroc) that represent important genetic resources for this species. The genome of 725 pigs was sequenced using a breed-specific DNA pooling approach (30-35 animals per pool) obtaining an average depth per pool of 42×. This approach maximised CNV discovery as well as the related copy number states characterising, on average, the analysed breeds. By mining more than 17.5 billion reads, we identified a total of 9592 CNVs (~683 CNVs per breed) and 3710 CNV regions (CNVRs; 1.15% of the reference pig genome), with an average of 77 CNVRs per breed that were considered as private. A few CNVRs were analysed in more detail, together with other information derived from sequencing data. For example, the CNVR encompassing the KIT gene was associated with coat colour phenotypes in the analysed breeds, confirming the role of the multiple copies in determining breed-specific coat colours. The CNVR covering the MSRB3 gene was associated with ear size in most breeds. The CNVRs affecting the ELOVL6 and ZNF622 genes were private features observed in the Lithuanian Indigenous Wattle and in the Turopolje pig breeds respectively. Overall, the genome variability unravelled here can explain part of the genetic diversity among breeds and might contribute to explain their origin, history and adaptation to a variety of production systems.

Development of a 64 SNV panel for breed authentication in Iberian pigs and their derived meat products

Spanish legislation regulates the labelling of Iberian pig meat and dry-cured products, which are labelled as "Ibérico" or "100% Ibérico" when they come from Duroc x Iberian crossbred or Iberian purebred pigs. Although the analytical authentication of breed origin is not mandatory, a genetic diagnostic tool is demanded by producers and consumers. We have designed a 64 Single Nucleotide Variant genotyping panel displaying extreme allelic frequencies between Duroc and Iberian purebred samples. Average proportions of Iberian alleles of 0.99, 0.01, 0.77 and 0.48 were estimated by admixture clustering analysis of known origin samples, for Iberian and Duroc purebred, 75% Iberian and 50% Iberian classes, respectively. A supervised analysis with 1419 samples showed some overlapping between contiguous classes, but the calculated degrees of separability ranged from 0.800 to 0.996, exceeding the threshold value (0.70) for considering suitable for prediction. Therefore, this panel is a useful genetic tool to infer purebred or crossbred Iberian origin of live animals, meat and dry-cured products.