Tracing cattle breeds with principal components analysis ancestry informative SNPs

Machine Learning-Aided Ultra-Low-Density Single Nucleotide Polymorphism Panel Helps to Identify the Tharparkar Cattle Breed: Lessons for Digital Transformation in Livestock Genomics

Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.

Global and Local Ancestry and its Importance: A Review

The fastest way to significantly change the composition of a population is through admixture, an evolutionary mechanism. In animal breeding history, genetic admixture has provided both short-term and long-term advantages by utilizing the phenomenon of complementarity and heterosis in several traits and genetic diversity, respectively. The traditional method of admixture analysis by pedigree records has now been replaced greatly by genome-wide marker data that enables more precise estimations. Among these markers, SNPs have been the popular choice since they are cost-effective, not so laborious, and automation of genotyping is easy. Certain markers can suggest the possibility of a population's origin from a sample of DNA where the source individual is unknown or unwilling to disclose their lineage, which are called Ancestry-Informative Markers (AIMs). Revealing admixture level at the locus-specific level is termed as local ancestry and can be exploited to identify signs of recent selective response and can account for genetic drift. Considering the importance of genetic admixture and local ancestry, in this mini-review, both concepts are illustrated, encompassing basics, their estimation/identification methods, tools/software used and their applications.

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.

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.

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.

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.

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.

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.

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs

BACKGROUND

With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F_ST) to select the most informative SNPs for ancestry inference.

RESULTS

Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof.

CONCLUSIONS

The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

Insight into the Current Genetic Diversity and Population Structure of Domestic Reindeer (Rangifer tarandus) in Russia

Simple Summary

Reindeer herding is the most important agricultural sector of the Russian Far North, representing the local genetic resources that compose original genetic wealth for the indigenous Arctic ethnic groups, which has maintained their life in harsh conditions of the area for many years. Conservation about and further rational use of such resources are very difficult without taking into account genetic diversity. Here, for the first time, the current genetic composition of the four officially recognized reindeer breeds and their ecotypes inhabiting the area from the Kola Peninsula in the west to the Chukotka region in the east are described using a single-nucleotide polymorphism (SNP) array. Our findings reveal the genetic uniqueness of each breed, formed by the consequences of ecological processes, internal gene flow, breeding practices, and geographical features. The obtained results will assist the ongoing breeding policy to develop accurate programs to preserve genetic resources of this essential element of Russia’s Far North ecosystem.

Abstract

To examine the genetic diversity and population structure of domestic reindeer, using the BovineHD BeadChip, we genotyped reindeer individuals belonging to the Nenets breed of the five main breeding regions, the Even breed of the Republic of Sakha, the Evenk breed of the Krasnoyarsk and Yakutia regions, and the Chukotka breed of the Chukotka region and its within-breed ecotype, namely, the Chukotka–Khargin, which is bred in Yakutia. The Chukotka reindeer was shown to have the lowest genetic diversity in terms of the allelic richness and heterozygosity indicators. The principal component analysis (PCA) results are consistent with the neighbor-net tree topology, dividing the reindeer into groups according to their habitat location and origin of the breed. Admixture analysis indicated a genetic structuring of two groups of Chukotka origin, the Even breed and most of the geographical groups of the Nenets breed, with the exception of the Murmansk reindeer, the gene pool of which was comprised of the Nenets and apparently the native Sami reindeer. The presence of a genetic component of the Nenets breed in some reindeer inhabiting the Krasnoyarsk region was detected. Our results provide a deeper insight into the current intra-breeding reindeer genetic diversity, which is an important requirement for future reindeer herding strategies and for animal adaptation to environmental changes.

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.

Ancestry informative markers derived from discriminant analysis of principal components provide important insights into the composition of crossbred cattle

The cost of SNP genotyping to screen different breeds and to estimate the exact proportion of ancestry level is quite high, which can be compensated through deriving a small panel of ancestry informative markers (AIMs). Hence, we carried out the present study to provide an insight into ancestry level inferred from a panel of informative markers in the crossbred Vrindavani population developed at ICAR-IVRI, India. We have performed a new method i.e., discriminant analysis of principal components (DAPC) for the first time on the dataset of Vrindavani cattle. To confirm our method, we had performed DAPC on two other well-known crossbred cattle, i.e., Frieswal and Beefmaster. Three sets of panels (500, 1000 and 2000 markers) were tested for clustering of individuals. Among all the panels, we found the panel (1000 markers) with DAPC based contribution method was of the smallest size and comparatively of the highest accuracy.

Genetic structure of six cattle populations revealed by transcriptome-wide SNPs and gene expression

There are abundant cattle breeds/populations in China, and the systematic discovery of genomic variants is essential for performing the marker assisted selection and conservation of genetic resources. In the present study, we employed whole transcriptome sequencing (RNA-Seq) technology for revealing genetic structure among six Chinese cattle populations according to transcriptome-wide SNPs and gene expression. A total of 68,094 variants consisting of 61,754 SNPs and 6340 InDels were detected and widely distributed among all chromosomes, by which the clear patterns of population structures were revealed. We also found the significantly differential density of variant distribution among genes. Additionally, we totally assembled 15,992 genes and detected obvious differences on the expression profiles among populations. In contrast to genomic variants, the measure of gene expression levels failed to support the expected population structure. Here, we provided a global landscape on the differential expression genes among these cattle populations.

The Bos taurus-Bos indicus balance in fertility and milk related genes

Numerical approaches to high-density single nucleotide polymorphism (SNP) data are often employed independently to address individual questions. We linked independent approaches in a bioinformatics pipeline for further insight. The pipeline driven by heterozygosity and Hardy-Weinberg equilibrium (HWE) analyses was applied to characterize Bos taurus and Bos indicus ancestry. We infer a gene co-heterozygosity network that regulates bovine fertility, from data on 18,363 cattle with genotypes for 729,068 SNP. Hierarchical clustering separated populations according to Bos taurus and Bos indicus ancestry. The weights of the first principal component were subjected to Normal mixture modelling allowing the estimation of a gene’s contribution to the Bos taurus-Bos indicus axis. We used deviation from HWE, contribution to Bos indicus content and association to fertility traits to select 1,284 genes. With this set, we developed a co-heterozygosity network where the group of genes annotated as fertility-related had significantly higher Bos indicus content compared to other functional classes of genes, while the group of genes associated with milk production had significantly higher Bos taurus content. The network analysis resulted in capturing novel gene associations of relevance to bovine domestication events. We report transcription factors that are likely to regulate genes associated with cattle domestication and tropical adaptation. Our pipeline can be generalized to any scenarios where population structure requires scrutiny at the molecular level, particularly in the presence of a priori set of genes known to impact a phenotype of evolutionary interest such as fertility.

Inference of population structure of purebred dairy and beef cattle using high-density genotype data

Information on the genetic diversity and population structure of cattle breeds is useful when deciding the most optimal, for example, crossbreeding strategies to improve phenotypic performance by exploiting heterosis. The present study investigated the genetic diversity and population structure of the most prominent dairy and beef breeds used in Ireland. Illumina high-density genotypes (777 962 single nucleotide polymorphisms; SNPs) were available on 4623 purebred bulls from nine breeds; Angus (n=430), Belgian Blue (n=298), Charolais (n=893), Hereford (n=327), Holstein-Friesian (n=1261), Jersey (n=75), Limousin (n=943), Montbéliarde (n=33) and Simmental (n=363). Principal component analysis revealed that Angus, Hereford, and Jersey formed non-overlapping clusters, representing distinct populations. In contrast, overlapping clusters suggested geographical proximity of origin and genetic similarity between Limousin, Simmental and Montbéliarde and to a lesser extent between Holstein, Friesian and Belgian Blue. The observed SNP heterozygosity averaged across all loci was 0.379. The Belgian Blue had the greatest mean observed heterozygosity (HO=0.389) among individuals within breed while the Holstein-Friesian and Jersey populations had the lowest mean heterozygosity (HO=0.370 and 0.376, respectively). The correlation between the genomic-based and pedigree-based inbreeding coefficients was weak (r=0.171; P<0.001). Mean genomic inbreeding estimates were greatest for Jersey (0.173) and least for Hereford (0.051). The pair-wise breed fixation index (F st) ranged from 0.049 (Limousin and Charolais) to 0.165 (Hereford and Jersey). In conclusion, substantial genetic variation exists among breeds commercially used in Ireland. Thus custom-mating strategies would be successful in maximising the exploitation of heterosis in crossbreeding strategies.

Revisiting demographic processes in cattle with genome-wide population genetic analysis

The domestication of the aurochs took place approximately 10,000 years ago giving rise to the two main types of domestic cattle known today, taurine (Bos taurus) domesticated somewhere on or near the Fertile Crescent, and indicine (Bos indicus) domesticated in the Indus Valley. However, although cattle have historically played a prominent role in human society the exact origin of many extant breeds is not well known. Here we used a combination of medium and high-density Illumina Bovine SNP arrays (i.e., ~54,000 and ~770,000 SNPs, respectively), genotyped for over 1300 animals representing 56 cattle breeds, to describe the relationships among major European cattle breeds and detect patterns of admixture among them. Our results suggest modern cross-breeding and ancient hybridisation events have both played an important role, including with animals of indicine origin. We use these data to identify signatures of selection reflecting both domestication (hypothesized to produce a common signature across breeds) and local adaptation (predicted to exhibit a signature of selection unique to a single breed or group of related breeds with a common history) to uncover additional demographic complexity of modern European cattle.

Genetic differentiation of Mexican Holstein cattle and its relationship with Canadian and U.S. Holsteins

The Mexican Holstein (HO) industry has imported Canadian and US (CAN + USA) HO germplasm for use in two different production systems, the conventional (Conv) and the low income (Lowi) system. The objective of this work was to study the genetic composition and differentiation of the Mexican HO cattle, considering the production system in which they perform and their relationship with the Canadian and US HO populations. The analysis included information from 149, 303, and 173 unrelated or with unknown pedigree HO animals from the Conv, Lowi, and CAN + USA populations, respectively. Canadian and US Jersey (JE) and Brown Swiss (BS) genotypes (162 and 86, respectively) were used to determine if Mexican HOs were hybridized with either of these breeds. After quality control filtering, a total of 6,617 out of 6,836 single nucleotide polymorphism markers were used. To describe the genetic diversity across the populations, principal component (PC), admixture composition, and linkage disequilibrium (LD; r²) analyses were performed. Through the PC analysis, HO × JE and HO × BS crossbreeding was detected in the Lowi system. The Conv system appeared to be in between Lowi and CAN + USA populations. Admixture analysis differentiated between the genetic composition of the Conv and Lowi systems, and five ancestry groups associated to sire’s country of origin were identified. The minimum distance between markers to estimate a useful LD was found to be 54.5 kb for the Mexican HO populations. At this average distance, the persistence of phase across autosomes of Conv and Lowi systems was 0.94, for Conv and CAN + USA was 0.92 and for the Lowi and CAN + USA was 0.91. Results supported the flow of germplasm among populations being Conv a source for Lowi, and dependent on migration from CAN + USA. Mexican HO cattle in Conv and Lowi populations share common ancestry with CAN + USA but have different genetic signatures.

A comparison of principal component regression and genomic REML for genomic prediction across populations

BACKGROUND

Genomic prediction faces two main statistical problems: multicollinearity and n ≪ p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction.

METHODS

The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK.

RESULTS

In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC.

CONCLUSIONS

On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge.

Signatures of selection in five Italian cattle breeds detected by a 54K SNP panel

In this study we used a medium density panel of SNP markers to perform population genetic analysis in five Italian cattle breeds. The BovineSNP50 BeadChip was used to genotype a total of 2,935 bulls of Piedmontese, Marchigiana, Italian Holstein, Italian Brown and Italian Pezzata Rossa breeds. To determine a genome-wide pattern of positive selection we mapped the F st values against genome location. The highest F st peaks were obtained on BTA6 and BTA13 where some candidate genes are located. We identified selection signatures peculiar of each breed which suggest selection for genes involved in milk or meat traits. The genetic structure was investigated by using a multidimensional scaling of the genetic distance matrix and a Bayesian approach implemented in the STRUCTURE software. The genotyping data showed a clear partitioning of the cattle genetic diversity into distinct breeds if a number of clusters equal to the number of populations were given. Assuming a lower number of clusters beef breeds group together. Both methods showed all five breeds separated in well defined clusters and the Bayesian approach assigned individuals to the breed of origin. The work is of interest not only because it enriches the knowledge on the process of evolution but also because the results generated could have implications for selective breeding programs.

Genome-wide analysis of Italian sheep diversity reveals a strong geographic pattern and cryptic relationships between breeds

Italy counts several sheep breeds, arisen over centuries as a consequence of ancient and recent genetic and demographic events. To finely reconstruct genetic structure and relationships between Italian sheep, 496 subjects from 19 breeds were typed at 50K single nucleotide polymorphism loci. A subset of foreign breeds from the Sheep HapMap dataset was also included in the analyses. Genetic distances (as visualized either in a network or in a multidimensional scaling analysis of identical by state distances) closely reflected geographic proximity between breeds, with a clear north-south gradient, likely because of high levels of past gene flow and admixture all along the peninsula. Sardinian breeds diverged more from other breeds, a probable consequence of the combined effect of ancient sporadic introgression of feral mouflon and long-lasting genetic isolation from continental sheep populations. The study allowed the detection of previously undocumented episodes of recent introgression (Delle Langhe into the endangered Altamurana breed) as well as signatures of known, or claimed, historical introgression (Merino into Sopravissana and Gentile di Puglia; Bergamasca into Fabrianese, Appenninica and, to a lesser extent, Leccese). Arguments that would question, from a genomic point of view, the current breed classification of Bergamasca and Biellese into two separate breeds are presented. Finally, a role for traditional transhumance practices in shaping the genetic makeup of Alpine sheep breeds is proposed. The study represents the first exhaustive analysis of Italian sheep diversity in an European context, and it bridges the gap in the previous HapMap panel between Western Mediterranean and Swiss breeds.

Genetic footprints of Iberian cattle in America 500 years after the arrival of Columbus

Background

American Creole cattle presumably descend from animals imported from the Iberian Peninsula during the period of colonization and settlement, through different migration routes, and may have also suffered the influence of cattle directly imported from Africa. The introduction of European cattle, which began in the 18th century, and later of Zebu from India, has threatened the survival of Creole populations, some of which have nearly disappeared or were admixed with exotic breeds. Assessment of the genetic status of Creole cattle is essential for the establishment of conservation programs of these historical resources.

Methodology/Principal Findings

We sampled 27 Creole populations, 39 Iberian, 9 European and 6 Zebu breeds. We used microsatellite markers to assess the origins of Creole cattle, and to investigate the influence of different breeds on their genetic make-up. The major ancestral contributions are from breeds of southern Spain and Portugal, in agreement with the historical ports of departure of ships sailing towards the Western Hemisphere. This Iberian contribution to Creoles may also include some African influence, given the influential role that African cattle have had in the development of Iberian breeds, but the possibility of a direct influence on Creoles of African cattle imported to America can not be discarded. In addition to the Iberian influence, the admixture with other European breeds was minor. The Creoles from tropical areas, especially those from the Caribbean, show clear signs of admixture with Zebu.

Conclusions/Significance

Nearly five centuries since cattle were first brought to the Americas, Creoles still show a strong and predominant signature of their Iberian ancestors. Creole breeds differ widely from each other, both in genetic structure and influences from other breeds. Efforts are needed to avoid their extinction or further genetic erosion, which would compromise centuries of selective adaptation to a wide range of environmental conditions.

Beef cattle breeding in Australia with genomics: opportunities and needs

Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.

The impact of the rank of marker variance-covariance matrix in principal component evaluation for genomic selection applications

In genomic selection (GS) programmes, direct genomic values (DGV) are evaluated using information provided by high-density SNP chip. Being DGV accuracy strictly dependent on SNP density, it is likely that an increase in the number of markers per chip will result in severe computational consequences. Aim of present work was to test the effectiveness of principal component analysis (PCA) carried out by chromosome in reducing the marker dimensionality for GS purposes. A simulated data set of 5700 individuals with an equal number of SNP distributed over six chromosomes was used. PCs were extracted both genome-wide (ALL) and separately by chromosome (CHR) and used to predict DGVs. In the ALL scenario, the SNP variance-covariance matrix (S) was singular, positive semi-definite and contained null information which introduces 'spuriousness' in the derived results. On the contrary, the S matrix for each chromosome (CHR scenario) had a full rank. Obtained DGV accuracies were always better for CHR than ALL. Moreover, in the latter scenario, DGV accuracies became soon unsettled as the number of animals decreases, whereas in CHR, they remain stable till 900-1000 individuals. In real applications where a 54k SNP chip is used, the largest number of markers per chromosome is approximately 2500. Thus, a number of around 3000 genotyped animals could lead to reliable results when the original SNP variables are replaced by a reduced number of PCs.