Genomic selection: Status in different species and challenges for breeding

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

The Omics Revolution in Understanding Chicken Reproduction: A Comprehensive Review

Omics approaches have significantly contributed to our understanding of several aspects of chicken reproduction. This review paper gives an overview of the use of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics to elucidate the mechanisms of chicken reproduction. Genomics has transformed the study of chicken reproduction by allowing the examination of the full genetic makeup of chickens, resulting in the discovery of genes associated with reproductive features and disorders. Transcriptomics has provided insights into the gene expression patterns and regulatory mechanisms involved in reproductive processes, allowing for a better knowledge of developmental stages and hormone regulation. Furthermore, proteomics has made it easier to identify and quantify the proteins involved in reproductive physiology to better understand the molecular mechanisms driving fertility, embryonic development, and egg quality. Metabolomics has emerged as a useful technique for understanding the metabolic pathways and biomarkers linked to reproductive performance, providing vital insights for enhancing breeding tactics and reproductive health. The integration of omics data has resulted in the identification of critical molecular pathways and biomarkers linked with chicken reproductive features, providing the opportunity for targeted genetic selection and improved reproductive management approaches. Furthermore, omics technologies have helped to create biomarkers for fertility and embryonic viability, providing the poultry sector with tools for effective breeding and reproductive health management. Finally, omics technologies have greatly improved our understanding of chicken reproduction by revealing the molecular complexities that underpin reproductive processes.

PC Gene Affects Milk Production Traits in Dairy Cattle

In previous work, we found that PC was differentially expressed in cows at different lactation stages. Thus, we deemed that PC may be a candidate gene affecting milk production traits in dairy cattle. In this study, we found the polymorphisms of PC by resequencing and verified their genetic associations with milk production traits by using an animal model in a cattle population. In total, we detected six single-nucleotide polymorphisms (SNPs) in PC. The single marker association analysis showed that all SNPs were significantly associated with the five milk production traits (p < 0.05). Additionally, we predicted that allele G of 29:g.44965658 in the 5' regulatory region created binding sites for TF GATA1 and verified that this allele inhibited the transcriptional activity of PC by the dual-luciferase reporter assay. In conclusion, we proved that PC had a prominent genetic effect on milk production traits, and six SNPs with prominent genetic effects could be used as markers for genomic selection (GS) in dairy cattle, which is beneficial for accelerating the improvement in milk yield and quality in Chinese Holstein cows.

Genome-wide scans for selection signatures in indigenous chickens reveal candidate genes associated with local adaptation

Population growth and climate change pose challenges to the sustainability of poultry farming. The emphasis on high-yield traits in commercialized breeds has led to a decline in their adaptability. Chicken varieties adapted to the local environment, possessing traits that facilitate adaptation to climate change, such as disease resistance and tolerance to extreme weather conditions, can improve hybridization outcomes. In this study, we conducted an analysis of the population structure and genetic diversity of 110 chickens representing indigenous breeds from southern China and two different commercial breeds. Further, we performed comparative population genomics, utilizing nucleotide diversity and fixation statistics, to characterize genomic features of natural selection and to identify unique genetic traits and potential selection markers developed by indigenous breeds after adapting to the local environment. Results based on genetic diversity and population structure analyses showed that indigenous varieties exhibited high levels of genetic diversity. Commercial breeds that have been indigenously bred demonstrated higher levels of genetic diversity than those that have not, and breeds with different selection practices displayed significant differences in genetic structure. Additionally, we further searched for potential genomic regions in native chicken ecotypes, uncovering several candidate genes related to ecological adaptations affecting local breeds, such as IKBKB, S1PR1, TSHR, IL1RAPL1 and AMY2A, which are involved in disease resistance, heat tolerance, immune regulation and behavioral traits. This work provides important insights into the genomic characterization of ecotypes of native chicken in southern China. The identification of candidate genes associated with traits such as disease resistance, heat tolerance, immunomodulation, and behavioral traits is a significant outcome. These candidate genes may contribute to the understanding of the molecular basis of the adaptation of the southern native chicken to the local environment. It is recommended that these genes be integrated into chicken breeding programs to enhance sustainable agriculture and promote effective conservation and utilization strategies.

Selection and direct biomarkers of reproductive capacity of breeding boars

Efficient management of pig reproduction is paramount for the sustainability and productivity of the global pork industry. Modern artificial insemination (AI) breeding programs have greatly benefited from the integration of advanced selection methods and biomarkers to enhance the reproductive performance of boars. While traditional selection methods have relied soley on boar phenotype, such as growth rate and conformation, modern pig breeding has shifted more and more toward molecular and genetic tools, which are still complemented by phenotypic traits. These methods encompass genomics, transcriptomics, and proteomics. Biomarkers serve as critical indicators of boar reproductive capacity. They can help to identify individuals with superior fertility and aid in the early identification of potential fertility issues, allowing for proactive management strategies. This review summarizes current knowledge of various biomarkers associated with semen quality, sperm function, and overall reproductive fitness in boars. Furthermore, we explore advanced technologies and their potential applications in uncovering novel selection methods and biomarkers for predicting boar fertility. A comprehensive understanding of selection criteria and biomarkers governing boar reproductive capacity is essential for developing effective breeding programs to enhance swine reproductive performance.

Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits

The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.

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.

Genetic polymorphisms of PKLR gene and their associations with milk production traits in Chinese Holstein cows

Our previous work had confirmed that pyruvate kinase L/R (PKLR) gene was expressed differently in different lactation periods of dairy cattle, and participated in lipid metabolism through insulin, PI3K-Akt, MAPK, AMPK, mTOR, and PPAR signaling pathways, suggesting that PKLR is a candidate gene to affect milk production traits in dairy cattle. Here, we verified whether this gene has significant genetic association with milk yield and composition traits in a Chinese Holstein cow population. In total, we identified 21 single nucleotide polymorphisms (SNPs) by resequencing the entire coding region and partial flanking region of PKLR gene, in which, two SNPs were located in 5′ promoter region, two in 5′ untranslated region (UTR), three in introns, five in exons, six in 3′ UTR and three in 3′ flanking region. The single marker association analysis displayed that all SNPs were significantly associated with milk yield, fat and protein yields or protein percentage (p ≤ 0.0497). The haplotype block containing all the SNPs, predicted by Haploview, had a significant association with fat yield and protein percentage (p ≤ 0.0145). Further, four SNPs in 5′ regulatory region and eight SNPs in UTR and exon regions were predicted to change the transcription factor binding sites (TFBSs) and mRNA secondary structure, respectively, thus affecting the expression of PKLR, leading to changes in milk production phenotypes, suggesting that these SNPs might be the potential functional mutations for milk production traits in dairy cattle. In conclusion, we demonstrated that PKLR had significant genetic effects on milk production traits, and the SNPs with significant genetic effects could be used as candidate genetic markers for genomic selection (GS) in dairy cattle.

Single-step genomic evaluation of Russian dairy cattle using internal and external information

Genomic data are widely used in predicting the breeding values of dairy cattle. The accuracy of genomic prediction depends on the size of the reference population and how related the candidate animals are to it. For populations with limited numbers of progeny-tested bulls, the reference populations must include cows and data from external populations. The aim of this study was to implement state-of-the-art single-step genomic evaluations for milk and fat yield in Holstein and Russian Black & White cattle in the Leningrad region (LR, Russia), using only a limited number of genotyped animals. We complemented internal information with external pseudo-phenotypic and genotypic data of bulls from the neighbouring Danish, Finnish and Swedish Holstein (DFS) population. Three data scenarios were used to perform single-step GBLUP predictions in the LR dairy cattle population. The first scenario was based on the original LR reference population, which constituted 1,080 genotyped cows and 427 genotyped bulls. In the second scenario, the genotypes of 414 bulls related to the LR from the DFS population were added to the reference population. In the third scenario, LR data were further augmented with pseudo-phenotypic data from the DFS population. The inclusion of foreign information increased the validation reliability of the milk yield by up to 30%. Suboptimal data recording practices hindered the improvement of fat yield. We confirmed that the single-step model is suitable for populations with a low number of genotyped animals, especially when external information is integrated into the evaluations. Genomic prediction in populations with a low number of progeny-tested bulls can be based on data from genotyped cows and on the inclusion of genotypes and pseudo-phenotypes from the external population. This approach increased the validation reliability of the implemented single-step model in the milk yield, but shortcomings in the LR data recording scheme prevented improvements in fat yield.

Towards a Cost-Effective Implementation of Genomic Prediction Based on Low Coverage Whole Genome Sequencing in Dezhou Donkey

Low-coverage whole genome sequencing is a low-cost genotyping technology. Combined with genotype imputation approaches, it is likely to become a critical component of cost-effective genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low-coverage whole genome sequence data and genomic prediction based on the imputed genotype data. The specific aims were as follows: 1) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, minor allele frequency (MAF), and imputation pipelines and 2) to assess the accuracy of genomic prediction under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single-vs. multi-trait models. We found that a high imputation accuracy (>0.95) can be achieved for sequence data with a sequencing depth as low as 1x and the number of sequenced individuals ≥400. For genomic prediction, the best performance was obtained by using a marker density of 410K and a G matrix constructed using expected marker dosages. Multi-trait genomic best linear unbiased prediction (GBLUP) performed better than single-trait GBLUP. Our study demonstrates that low-coverage whole genome sequencing would be a cost-effective approach for genomic prediction in Dezhou donkey.

Genomic prediction in family bulks using different traits and cross-validations in pine

Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5–20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations.

Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?

Due to the steadily increasing need for seafood and the plateauing output of fisheries, more fish need to be produced by aquaculture production. In parallel with the improvement of farming methods, elite food fish lines with superior traits for production must be generated by selection programs that utilize cutting-edge tools of genomics. The purpose of this review is to provide a historical overview and status report of a selection program performed on a catadromous predator, the Asian seabass (Lates calcarifer, Bloch 1790) that can change its sex during its lifetime. We describe the practices of wet lab, farm and lab in detail by focusing onto the foundations and achievements of the program. In addition to the approaches used for selection, our review also provides an inventory of genetic/genomic platforms and technologies developed to (i) provide current and future support for the selection process; and (ii) improve our understanding of the biology of the species. Approaches used for the improvement of terrestrial farm animals are used as examples and references, as those processes are far ahead of the ones used in aquaculture and thus they might help those working on fish to select the best possible options and avoid potential pitfalls.

Which Individuals To Choose To Update the Reference Population? Minimizing the Loss of Genetic Diversity in Animal Genomic Selection Programs

Genomic selection (GS) is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study, we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for GS, our findings are of major economic interest. This study aims to answer the following questions: how would decisions on the reference population affect the breeding population, and how to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation, and optimal contribution (OC) strategies. OC maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over 10 generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.

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.

Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model

Background

In recent years, with the development of high-throughput sequencing technology and the commercial availability of genotyping bead chips, more attention is being directed towards the utilization of abundant genetic markers in animal and plant breeding programs, human disease risk prediction and personal medicine. Several useful approaches to accomplish genomic prediction have been developed and used widely, but still have room for improvement to gain more accuracy. In this study, an improved Bayesian approach, termed BayesBπ, which differs from the original BayesB in priors assigning, is proposed. An effective method for calculating the locus-specific π by converting p-values from association between SNPs and traits’ phenotypes is given and systemically validated using a German Holstein dairy cattle population. Furthermore, the new method is applied to a loblolly pine (Pinus taeda) dataset.

Results

Compared with the original BayesB, BayesBπ can improve the accuracy of genomic prediction up to 7.62 % for milk fat percentage, a trait which shows a large effect of quantitative trait loci (QTL). For milk yield, which is controlled by small to moderate effect genes, the accuracy of genomic prediction can be improved up to 4.94 %. For somatic cell score, of which no large effect QTL has been reported, GBLUP performs better than Bayesian methods. BayesBπ outperforms BayesCπ in 10 out of 12 scenarios in the dairy cattle population, especially in small to moderate population sizes where accuracy of BayesCπ are dramatically low. Results of the loblolly pine dataset show that BayesBπ outperforms BayesB in 14 out of 17 traits and BayesCπ in 8 out of 17 traits, respectively.

Conclusions

For traits controlled by large effect genes, BayesBπ can improve the accuracy of genomic prediction and unbiasedness of BayesB in moderate size populations. Knowledge of traits’ genetic architectures can be integrated into practices of genomic prediction by assigning locus-specific priors to markers, which will help Bayesian approaches perform better in variable selection and marker effects shrinkage.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0278-9) contains supplementary material, which is available to authorized users.

De Novo Assembly of the Donkey White Blood Cell Transcriptome and a Comparative Analysis of Phenotype-Associated Genes between Donkeys and Horses

Prior to the mechanization of agriculture and labor-intensive tasks, humans used donkeys (Equus africanus asinus) for farm work and packing. However, as mechanization increased, donkeys have been increasingly raised for meat, milk, and fur in China. To maintain the development of the donkey industry, breeding programs should focus on traits related to these new uses. Compared to conventional marker-assisted breeding plans, genome- and transcriptome-based selection methods are more efficient and effective. To analyze the coding genes of the donkey genome, we assembled the transcriptome of donkey white blood cells de novo. Using transcriptomic deep-sequencing data, we identified 264,714 distinct donkey unigenes and predicted 38,949 protein fragments. We annotated the donkey unigenes by BLAST searches against the non-redundant (NR) protein database. We also compared the donkey protein sequences with those of the horse (E. caballus) and wild horse (E. przewalskii), and linked the donkey protein fragments with mammalian phenotypes. As the outer ear size of donkeys and horses are obviously different, we compared the outer ear size-associated proteins in donkeys and horses. We identified three ear size-associated proteins, HIC1, PRKRA, and KMT2A, with sequence differences among the donkey, horse, and wild horse loci. Since the donkey genome sequence has not been released, the de novo assembled donkey transcriptome is helpful for preliminary investigations of donkey cultivars and for genetic improvement.

The effect of rare alleles on estimated genomic relationships from whole genome sequence data

BACKGROUND

Relationships between individuals and inbreeding coefficients are commonly used for breeding decisions, but may be affected by the type of data used for their estimation. The proportion of variants with low Minor Allele Frequency (MAF) is larger in whole genome sequence (WGS) data compared to Single Nucleotide Polymorphism (SNP) chips. Therefore, WGS data provide true relationships between individuals and may influence breeding decisions and prioritisation for conservation of genetic diversity in livestock. This study identifies differences between relationships and inbreeding coefficients estimated using pedigree, SNP or WGS data for 118 Holstein bulls from the 1000 Bull genomes project. To determine the impact of rare alleles on the estimates we compared three scenarios of MAF restrictions: variants with a MAF higher than 5%, variants with a MAF higher than 1% and variants with a MAF between 1% and 5%.

RESULTS

We observed significant differences between estimated relationships and, although less significantly, inbreeding coefficients from pedigree, SNP or WGS data, and between MAF restriction scenarios. Computed correlations between pedigree and genomic relationships, within groups with similar relationships, ranged from negative to moderate for both estimated relationships and inbreeding coefficients, but were high between estimates from SNP and WGS (0.49 to 0.99). Estimated relationships from genomic information exhibited higher variation than from pedigree. Inbreeding coefficients analysis showed that more complete pedigree records lead to higher correlation between inbreeding coefficients from pedigree and genomic data. Finally, estimates and correlations between additive genetic (A) and genomic (G) relationship matrices were lower, and variances of the relationships were larger when accounting for allele frequencies than without accounting for allele frequencies.

CONCLUSIONS

Using pedigree data or genomic information, and including or excluding variants with a MAF below 5% showed significant differences in relationship and inbreeding coefficient estimates. Estimated relationships and inbreeding coefficients are the basis for selection decisions. Therefore, it can be expected that using WGS instead of SNP can affect selection decision. Inclusion of rare variants will give access to the variation they carry, which is of interest for conservation of genetic diversity.

On the History of Cattle Genetic Resources

Cattle are our most important livestock species because of their production and role in human culture. Many breeds that differ in appearance, performance and environmental adaptation are kept on all inhabited continents, but the historic origin of the diverse phenotypes is not always clear. We give an account of the history of cattle by integrating archaeological record and pictorial or written sources, scarce until 300 years ago, with the recent contributions of DNA analysis. We describe the domestication of their wild ancestor, migrations to eventually all inhabited continents, the developments during prehistory, the antiquity and the Middle Ages, the relatively recent breed formation, the industrial cattle husbandry in the Old and New World and the current efforts to preserve the cattle genetic resources. Surveying the available information, we propose three main and overlapping phases during the development of the present genetic diversity: (i) domestication and subsequent wild introgression; (ii) natural adaptation to a diverse agricultural habitat; and (iii) breed development.