Decoding the fish genome opens a new era in important trait research and molecular breeding in China

Aquaculture represents the fastest-growing global food production sector, as it has become an essential component of the global food supply. China has the world's largest aquaculture industry in terms of production volume. However, the sustainable development of fish culture is hindered by several concerns, including germplasm degradation and disease outbreaks. The practice of genomic breeding, which relies heavily on genome information and genotypephenotype relationships, has significant potential for increasing the efficiency of aquaculture production. In 2014, the completion of the genome sequencing and annotation of the Chinese tongue sole signified the beginning of the fish genomics era in China. Since then, domestic researchers have made dramatic progress in functional genomic studies. To date, the genomes of more than 60 species of fish in China have been assembled and annotated. Based on these reference genomes, evolutionary, comparative, and functional genomic studies have revolutionized our understanding of a wide range of biologically and economically important traits of fishes, including growth and development, sex determination, disease resistance, metamorphosis, and pigmentation. Furthermore, genomic tools and breeding techniques such as SNP arrays, genomic selection, and genome editing have greatly accelerated genetic improvement through the incorporation of functional genomic information into breeding activities. This review aims to summarize the current status, advances, and perspectives of the genome resources, genomic study of important traits, and genomic breeding techniques of fish in China. The review will provide aquaculture researchers, fish breeders, and farmers with updated information concerning fish genomic research and breeding technology. The summary will help to promote the genetic improvement of production traits and thus will support the sustainable development of fish aquaculture.

Searching for homozygous haplotype deficiency in Manech Tête Rousse dairy sheep revealed a nonsense variant in the MMUT gene affecting newborn lamb viability

BACKGROUND

Recessive deleterious variants are known to segregate in livestock populations, as in humans, and some may be lethal in the homozygous state.

RESULTS

We used phased 50 k single nucleotide polymorphism (SNP) genotypes and pedigree data to scan the genome of 6845 Manech Tête Rousse dairy sheep to search for deficiency in homozygous haplotypes (DHH). Five Manech Tête Rousse deficient homozygous haplotypes (MTRDHH1 to 5) were identified, with a homozygous deficiency ranging from 84 to 100%. These haplotypes are located on Ovis aries chromosome (OAR)1 (MTRDHH2 and 3), OAR10 (MTRDHH4), OAR13 (MTRDHH5), and OAR20 (MTRDHH1), and have carrier frequencies ranging from 7.8 to 16.6%. When comparing at-risk matings between DHH carriers to safe matings between non-carriers, two DHH (MTRDHH1 and 2) were linked with decreased insemination success and/or increased stillbirth incidence. We investigated the MTRDHH1 haplotype, which substantially increased stillbirth rate, and identified a single nucleotide variant (SNV) inducing a premature stop codon (p.Gln409*) in the methylmalonyl-CoA mutase (MMUT) gene by using a whole-genome sequencing approach. We generated homozygous lambs for the MMUT mutation by at-risk mating between heterozygous carriers, and most of them died within the first 24 h after birth without any obvious clinical symptoms. Reverse transcriptase-qPCR and western blotting on post-mortem liver and kidney biological samples showed a decreased expression of MMUT mRNA in the liver and absence of a full-length MMUT protein in the mutant homozygous lambs.

CONCLUSIONS

We identified five homozygous deficient haplotypes that are likely to harbor five independent deleterious recessive variants in sheep. One of these was detected in the MMUT gene, which is associated with lamb lethality in the homozygous state. A specific management of these haplotypes/variants in the MTR dairy sheep selection program would help enhance the overall fertility and lamb survival.

Genome-wide association studies of parasite resistance, productivity and immunology traits in Scottish Blackface sheep

Gastrointestinal parasitism represents a global problem for grazing ruminants, which can be addressed sustainably by breeding animals to be more resistant against infection by parasites. The aim of this study was to assess the genetic architecture underlying traits associated with gastrointestinal parasite resistance, immunological profile and production in meat sheep, and identify and characterise candidate genes affecting these traits. Data on gastrointestinal parasite infection (faecal egg counts for Strongyles (FECS) and Nematodirus (FECN) and faecal oocyst counts for Coccidia, along with faecal soiling scores (DAG), characterised by the accumulation of faeces around the perineum) and production (live weight (LWT)) were gathered from a flock Scottish Blackface lambs at three and four months of age. Data on the immune profile were also collected from a subset of these lambs at two and five months of age. Immune traits included the production of Interferon-γ (IFN-γ), Interleukin (IL)-4 and IL-10 following stimulation of whole blood with pokeweed mitogen (PWM) or antigen from the gastric parasite Teladorsagia circumcincta (T-ci), and serum levels of T. circumcincta-specific immunoglobulin A (IgA). Animals were genotyped with genome-wide DNA arrays, and a total of 1 766 animals and 45 827 Single Nucleotide Polymorphisms (SNPs) were retained following quality control and imputation. Genome-wide association studies were performed for 24 traits. The effects of individual markers with significant effects were estimated, and the genotypic effect solutions were used to estimate additive and dominance effects, and the proportion of additive genetic variance attributed to each SNP locus. A total of 15 SNPs were associated at least at a suggestive level with FECS, FECN, DAG, IgA, PWM-induced IFN-γ and IL-4, and T-ci-induced IL-10. This study uncovered 52 genes closely related to immune function in proximity to these SNPs. A number of genes encoding C-type lectins and killer cell lectin-like family members were close to a SNP associated with FECN, while several genes encoding IL-1 cytokine family members were found to be associated with IgA. Potential candidate genes belonging to or in close proximity with the Major Histocompatibility Complex (MHC) were revealed, including Homeostatic Iron Regulator and butyrophilin coding genes associated with IFN-γ(PWM), and IL-17 coding genes associated with IgA. Due to the importance of the MHC in the control of immune responses, these genes may play an important role in resistance to parasitic infections. Our results reveal a largely complex and polygenic genetic profile of the studied traits in this Scottish Blackface sheep population.

Review of sheep breeding and genetic research in Türkiye

The livestock industry in Türkiye is vital to the country's agricultural sector and economy. In particular, sheep products are an important source of income and livelihood for many Turkish smallholder farmers in semi-arid and highland areas. Türkiye is one of the largest sheep producers in the world and its sheep production system is heavily dependent on indigenous breeds. Given the importance of the sheep industry in Türkiye, a systematic literature review on sheep breeding and genetic improvement in the country is needed for the development and optimization of sheep breeding programs using modern approaches, such as genomic selection. Therefore, we conducted a comprehensive literature review on the current characteristics of sheep populations and farms based on the most up-to-date census data and breeding and genetic studies obtained from scientific articles. The number of sheep has increased in recent years, mainly due to the state's policy of supporting livestock farming and the increase in consumer demand for sheep dairy products with high nutritional and health benefits. Most of the genetic studies on indigenous Turkish sheep have been limited to specific traits and breeds. The use of genomics was found to be incipient, with genomic analysis applied to only two major breeds for heritability or genome-wide association studies. The scope of heritability and genome-wide association studies should be expanded to include traits and breeds that have received little or no attention. It is also worth revisiting genetic diversity studies using genome-wide single nucleotide polymorphism markers. Although there was no report of genomic selection in Turkish sheep to date, genomics could contribute to overcoming the difficulties of implementing traditional pedigree-based breeding programs that require accurate pedigree recording. As indigenous sheep breeds are better adapted to the local environmental conditions, the proper use of breeding strategies will contribute to increased income, food security, and reduced environmental footprint in a sustainable manner.

Genetic parameters for milk production and type traits in North American and European Alpine and Saanen dairy goat populations

The development of an across-country genomic evaluation scheme is a promising alternative for enlarging reference populations and successfully implementing genomic selection in small ruminant populations. However, the feasibility of such evaluations depends on the genetic similarity among the populations, and therefore, high connectedness and high genetic correlations between the traits recorded in different countries or populations are needed. In this study, we evaluated the feasibility of performing an across-country genomic evaluation for milk production and type traits in Alpine and Saanen goats from Canada, France, Italy, and Switzerland. Variance components and genetic parameters, including genetic correlations between traits recorded in different countries, were calculated using combined phenotypes, genotypes, and pedigree datasets. The (co)variance component analyses were performed within breed, either based only on pedigree information or also incorporating genomic information. Across-country genetic parameters were calculated for 3 representative traits (i.e., milk yield, fat content, and rear udder attachment). The heritability estimates ranged from 0.10 to 0.50, which are consistent with previous estimates reported in the literature. The genetic correlations for rear udder attachment ranged from 0.75 (between France and Italy, for the Alpine breed without genomic information) to 0.95 (between Canada and France, for the Saanen breed with genomic information), whereas for fat content, between France and Italy, they ranged from 0.75 in the Alpine breed without genomic information to 0.78 in the Alpine breed with genomic information. However, genetic correlations for milk yield were only estimable between France and Italy, with a moderate value of 0.45 for the Alpine breed with or without genomic information, and of 0.22 and 0.26 in the Saanen breed with and without genomic information, respectively. These low genetic correlations for milk yield could be due to several factors, including the trait definition in each country and genotype-by-environment interactions (GxE). The high genetic correlations found for fat content and rear udder attachment indicate that these traits might be more standardized across countries and less affected by GxE effects. Thus, an international genomic evaluation for these traits might be feasible. Further studies should be performed to understand the surprisingly lower genetic correlations between milk yield across countries. Furthermore, additional efforts should be made to increase the genetic connection among the Alpine and Saanen goat populations in the 4 countries included in the analyses.

Field demonstration analyzing the implementation of individual animal electronic identification and genetic testing in western range sheep flocks

Adoption of electronic identification ear tags (EID) and DNA testing by commercial range sheep producers in the Western United States has been low, despite the availability of these technologies for over a decade. Jointly, these technologies offer an approach to provide individual animal performance data to improve flock health, genetic and reproductive management. This project involved a collaboration with five California sheep producers representing a broad geographic range, varying levels of pre-project EID adoption, and diverse operational practices. Tissue samples were collected from, and ear EIDs were placed in, a total of 2,936 rams and their potential lambs. We partnered with a commercial packing company, Superior Farms, to genotype the animals. Superior Farms used a targeted genotyping panel to assign parentage, and link individual animal identification (ID) to camera-graded carcass measurements. This enabled the collection of individual progeny carcass data and provided insight into sire performance, providing for the within-flock identification of prolific sires that were producing lambs with significantly more saleable meat as compared to their flock mates. Overall, almost 91% of lambs were successfully matched to their sire, and prolificacy ranging from 0-135 lambs per ram. There was as much as an $80 difference in the average edible product from camera-graded carcasses derived from lamb groups sired by different rams. A partial budget analysis modeling investment in an EID system coupled with an autodrafter and scale to collect individual weights and improve labor efficiency during processing, and a sheep flip chute to improve worker safety during foot trimmings, yielded a greater than 7:1 return on investment over a five-year time frame. Ideally, the data collection enabled by EIDs and DNA testing would feed into data-driven genetic evaluation programs to enable selection for more productive and profitable animals, and allow the US sheep industry to accelerate the rate of genetic improvement.

Genome-Wide Scan of Wool Production Traits in Akkaraman Sheep

The objective of this study was to uncover the genetic background of wool quality, a production trait, by estimating genomic heritability and implementing GWAS in Akkaraman sheep. The wool characteristics measured included fibre diameter (FD) and staple length (SL) at the age of 8 months and yearling fibre diameter (YFD), yearling staple length (YSL) and yearling greasy fleece weight (YGFW) at 18 months of age. Animals were genotyped using the Axiom 50 K Ovine Genotyping Array. Maximum likelihood estimations of a linear mixed model (LMM) were used to estimate genomic heritability, where GWAS was conducted following a score test of each trait. Genomic heritability estimates for the traits ranged between 0.22 and 0.63, indicating that phenotypes have a moderate range of heritability. One genome- and six chromosome-wide significant SNPs were associated with the wool traits in Akkaraman lambs. Accordingly, TRIM2, MND1, TLR2, RNF175, CEP290, TMTC3, RERE, SLC45A1, SOX2, MORN1, SKI, FAAP20, PRKCZ, GABRD, CFAP74, CALML6 and TMEM52 genes as well as nine uncharacterized regions (LOC101118971, LOC105609137, LOC105603067, LOC101122892, LOC106991694, LOC106991467, LOC106991455, LOC105616534 and LOC105609719) were defined as plausible candidates. The findings of this study shed light on the genetics of wool quality and yield for the Akkaraman breed and suggests targets for breeders during systematic breeding programmes.

Progress and opportunities through use of genomics in animal production

The rearing of farmed animals is a vital component of global food production systems, but its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. Advances in genome sequencing, annotation, and editing offer a means not only to continue that trend, but also, when combined with advanced data collection, analytics, cloud computing, appropriate infrastructure, and regulation, to take precision livestock farming (PLF) and conservation to an advanced level. Such an approach could generate substantial additional benefits in terms of reducing use of resources, health treatments, and environmental impact, while also improving animal health and welfare.

Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization

Depending on excellent prediction ability, machine learning has been considered the most powerful implement to analyze high-throughput sequencing genome data. However, the sophisticated process of tuning hyperparameters tremendously impedes the wider application of machine learning in animal and plant breeding programs. Therefore, we integrated an automatic tuning hyperparameters algorithm, tree-structured Parzen estimator (TPE), with machine learning to simplify the process of using machine learning for genomic prediction. In this study, we applied TPE to optimize the hyperparameters of Kernel ridge regression (KRR) and support vector regression (SVR). To evaluate the performance of TPE, we compared the prediction accuracy of KRR-TPE and SVR-TPE with the genomic best linear unbiased prediction (GBLUP) and KRR-RS, KRR-Grid, SVR-RS, and SVR-Grid, which tuned the hyperparameters of KRR and SVR by using random search (RS) and grid search (Gird) in a simulation dataset and the real datasets. The results indicated that KRR-TPE achieved the most powerful prediction ability considering all populations and was the most convenient. Especially for the Chinese Simmental beef cattle and Loblolly pine populations, the prediction accuracy of KRR-TPE had an 8.73% and 6.08% average improvement compared with GBLUP, respectively. Our study will greatly promote the application of machine learning in GP and further accelerate breeding progress.

Low-density SNP panel for efficient imputation and genomic selection of milk production and technological traits in dairy sheep

The present study aimed to ascertain how different strategies for leveraging genomic information enhance the accuracy of estimated breeding values for milk and cheese-making traits and to evaluate the implementation of a low-density (LowD) SNP chip designed explicitly for that aim. Thus, milk samples from a total of 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra) were collected and analyzed to determine 3 milk production and composition traits and 2 traits related to milk coagulation properties and cheese yield. The 2 studied populations were genotyped with a customized 50K Affymetrix SNP chip (Affymetrix Inc.) containing 55,627 SNP markers. The prediction accuracies were obtained using different multitrait methodologies, such as the BLUP model based on pedigree information, the genomic BLUP (GBLUP), and the BLUP at the SNP level (SNP-BLUP), which are based on genotypic data, and the single-step GBLUP (ssGBLUP), which combines both sources of information. All of these methods were analyzed by cross-validation, comparing predictions of the whole population with the test population sets. Additionally, we describe the design of a LowD SNP chip (3K) and its prediction accuracies through the different methods mentioned previously. Furthermore, the results obtained using the LowD SNP chip were compared with those based on the 50K SNP chip data sets. Finally, we conclude that implementing genomic selection through the ssGBLUP model in the current breeding programs would increase the accuracy of the estimated breeding values compared with the BLUP methodology in the Assaf (from 0.19 to 0.39) and Churra (from 0.27 to 0.44) dairy sheep populations. The LowD SNP chip is cost-effective and has proven to be an accurate tool for estimating genomic breeding values for milk and cheese-making traits, microsatellite imputation, and parentage verification. The results presented here suggest that the routine use of this LowD SNP chip could potentially increase the genetic gains of the breeding selection programs of the 2 Spanish dairy sheep breeds considered here.

Genetic Characterization and Population Connectedness of North American and European Dairy Goats

Genomic prediction of breeding values is routinely performed in several livestock breeding programs around the world, but the size of the training populations and the genetic structure of populations evaluated have, in many instances, limited the increase in the accuracy of genomic estimated breeding values. Combining phenotypic, pedigree, and genomic data from genetically related populations can be a feasible strategy to overcome this limitation. However, the success of across-population genetic evaluations depends on the pedigree connectedness and genetic relationship among individuals from different populations. In this context, this study aimed to evaluate the genetic connectedness and population structure of Alpine and Saanen dairy goats from four countries involved in the European project SMARTER (SMAll RuminanTs Breeding for Efficiency and Resilience), including Canada, France, Italy, and Switzerland. These analyses are paramount for assessing the potential feasibility of an across-country genomic evaluation in dairy goats. Approximately, 9,855 genotyped individuals (with 51% French genotyped animals) and 6,435,189 animals included in the pedigree files were available across all four populations. The pedigree analyses indicated that the exchange of breeding animals was mainly unilateral with flows from France to the other three countries. Italy has also imported breeding animals from Switzerland. Principal component analyses (PCAs), genetic admixture analysis, and consistency of the gametic phase revealed that French and Italian populations are more genetically related than the other dairy goat population pairs. Canadian dairy goats showed the largest within-breed heterogeneity and genetic differences with the European populations. The genetic diversity and population connectedness between the studied populations indicated that an international genomic evaluation may be more feasible, especially for French and Italian goats. Further studies will investigate the accuracy of genomic breeding values when combining the datasets from these four populations.

Assessing Population Structure and Genetic Diversity in U.S. Suffolk Sheep to Define a Framework for Genomic Selection

Long-term sustainability of breeds depends on having sufficient genetic diversity for adaptability to change, whether driven by climatic conditions or by priorities in breeding programs. Genetic diversity in Suffolk sheep in the U.S. was evaluated in four ways: 1) using genetic relationships from pedigree data [(n=64,310 animals recorded in the U.S. National Sheep Improvement Program (NSIP)]; 2) using molecular data (n=304 Suffolk genotyped with the OvineHD BeadChip); 3) comparing Australian (n=109) and Irish (n=55) Suffolk sheep to those in the U.S. using molecular data; and 4) assessing genetic relationships (connectedness) among active Suffolk flocks (n=18) in NSIP. By characterizing genetic diversity, a goal was to define the structure of a reference population for use for genomic selection strategies in this breed. Pedigree-based mean inbreeding level for the most recent year of available data was 5.5%. Ten animals defined 22.8% of the current gene pool. The effective population size (Ne) ranged from 27.5 to 244.2 based on pedigree and was 79.5 based on molecular data. Expected (HE) and observed (HO) heterozygosity were 0.317 and 0.306, respectively. Model-based population structure included 7 subpopulations. From Principal Component Analysis, countries separated into distinct populations. Within the U.S. population, flocks formed genetically disconnected clusters. A decline in genetic diversity over time was observed from both pedigree and genomic-based derived measures with evidence of population substructure as measured by FST. Using these measures of genetic diversity, a framework for establishing a genomic reference population in U.S. Suffolk sheep engaged in NSIP was proposed.

Study of the Fatty Acid Profile of Milk in Different Sheep Breeds: Evaluation by Multivariate Factorial Analysis

Simple Summary

The quality of milk is strongly influenced by its lipid profile. The increase in fats with nutraceutical properties at the expense of those negative for human health, has always been a goal to improve the functional properties of milk. To achieve this goal, it is essential to know the metabolism of the mammary gland and the relationship between the various lipid components. Much is known about bovine milk, while the aspect relating to the sheep species has not been developed. The present work aims to investigate the relationships between the various fatty acids in sheep’s milk through a multivariate approach, which can highlight the mammary role of lipid synthesis.

Abstract

A multivariate analysis was used to investigate the fatty acid (FA) profile in three different Italian sheep breeds: Comisana, Massese, and Sarda. A sample of 852 animals was considered: 118 Massese, 303 Comisana, 431 Sarda. Sarda sheep were divided into two groups, based on their breeding origin (298 and 133 reared in Sardinia and Tuscany, respectively). Sarda sheep, bred both in Sardinia and in Tuscany, were considered in different groups, both because in these two regions most of the sheep of this breed are reared, and because they differ in geographical characteristics and in the farming system. The individual milk FA composition of dairy ewes was analyzed with multivariate factor analysis. The extracted factors were representative of the following eight groups of fatty acids or functions: factor 1 (odd branched fatty acids and long-chain fatty acids), factor 2 (sn3_position), factor 3 (alternative biohydrogenation), factor 4 (SCD_1), factor 5 (SCD_2), factor 6 (SCD_3), factor 7 (fat secretion) and factor 8 (omega-3). A factor analysis suggested the presence of different metabolic pathways for de novo short- and medium-chain fatty acids and Δ9-desaturase products. The ANOVA of factor scores highlighted the significant effects of the breed. The results of the present study showed that breed is an important factor in defining the fatty acid profile of milk, combined with the effect of the diet. Breeds reared in the same farming system (Comisana, Massese and Sarda reared in Tuscany) showed significant differences for all the factors extracted. At the same time, we found differences between the Sarda sheep reared in Sardinia and Tuscany, two different regions of Italy.

Single-step genomic evaluation of milk production traits in Canadian Alpine and Saanen dairy goats

Genomic evaluations are routine in most plant and livestock breeding programs but are used infrequently in dairy goat breeding schemes. In this context, the purpose of this study was to investigate the use of the single-step genomic BLUP method for predicting genomic breeding values for milk production traits (milk, protein, and fat yields; protein and fat percentages) in Canadian Alpine and Saanen dairy goats. There were 6,409 and 12,236 Alpine records and 3,434 and 5,008 Saanen records for each trait in first and later lactations, respectively, and a total of 1,707 genotyped animals (833 Alpine and 874 Saanen). Two validation approaches were used, forward validation (i.e., animals born after 2013 with an average estimated breeding value accuracy from the full data set ≥0.50) and forward cross-validation (i.e., subsets of all animals included in the forward validation were used in successive replications). The forward cross-validation approach resulted in similar validation accuracies (0.55 to 0.66 versus 0.54 to 0.61) and biases (-0.01 to -0.07 versus -0.03 to 0.11) to the forward validation when averaged across traits. Additionally, both single and multiple-breed analyses were compared, and similar average accuracies and biases were observed across traits. However, there was a small gain in accuracy from the use of multiple-breed models for the Saanen breed. A small gain in validation accuracy for genomically enhanced estimated breeding values (GEBV) relative to pedigree-based estimated breeding values (EBV) was observed across traits for the Alpine breed, but not for the Saanen breed, possibly due to limitations in the validation design, heritability of the traits evaluated, and size of the training populations. Trait-specific gains in theoretical accuracy of GEBV relative to EBV for the validation animals ranged from 17 to 31% in Alpine and 35 to 55% in Saanen, using the cross-validation approach. The GEBV predicted from the full data set were 12 to 16% more accurate than EBV for genotyped animals, but no gains were observed for nongenotyped animals. The largest gains were found for does without lactation records (35-41%) and bucks without daughter records (46-54%), and consequently, the implementation of genomic selection in the Canadian dairy goat population would be expected to increase selection accuracy for young breeding candidates. Overall, this study represents the first step toward implementation of genomic selection in Canadian dairy goat populations.

ssGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca

Simple Summary

Alpaca breeding takes place in the most entrenched areas of the Andes, where the conditions to implement genetic improvement programs are very difficult. Likewise, taking phenotypic records is limited in its ability to predict genetic merit accurately. For this reason, genomic information is shown as an alternative that helps to predict the genetic values of fiber traits more precisely. This study showed how genomic information increased precision by 2.623% for the fiber diameter, 6.442% for the standard deviation of the fiber diameter, and 1.471% for the percentage of medullation compared to traditional methods for predicting genetic merit, suggesting that adding genomic data in prediction models could be beneficial for alpaca breeding programs in the future.

Abstract

Improving textile characteristics is the main objective of alpaca breeding. A recently developed SNP chip for alpacas could potentially be used to implement genomic selection and accelerate genetic progress. Therefore, this study aimed to compare the increase in prediction accuracy of three important fiber traits: fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) in Huacaya alpacas. The data contains a total pedigree of 12,431 animals, 24,169 records for FD and SD, and 8386 records for PM and 60,624 SNP markers for each of the 431 genotyped animals of the Pacomarca Genetic Center. Prediction accuracy of breeding values was compared between a classical BLUP and a single-step Genomic BLUP (ssGBLUP). Deregressed phenotypes were predicted. The accuracies of the genetic and genomic values were calculated using the correlation between the predicted breeding values and the deregressed values of 100 randomly selected animals from the genotyped ones. Fifty replicates were carried out. Accuracies with ssGBLUP improved by 2.623%, 6.442%, and 1.471% on average for FD, SD, and PM, respectively, compared to the BLUP method. The increase in accuracy was relevant, suggesting that adding genomic data could benefit alpaca breeding programs.

Application of Genomic Data for Reliability Improvement of Pig Breeding Value Estimates

Replacement pigs' genomic prediction for reproduction (total number and born alive piglets in the first parity), meat, fatness and growth traits (muscle depth, days to 100 kg and backfat thickness over 6-7 rib) was tested using single-step genomic best linear unbiased prediction ssGBLUP methodology. These traits were selected as the most economically significant and different in terms of heritability. The heritability for meat, fatness and growth traits varied from 0.17 to 0.39 and for reproduction traits from 0.12 to 0.14. We confirm from our data that ssGBLUP is the most appropriate method of genomic evaluation. The validation of genomic predictions was performed by calculating the correlation between preliminary GEBV (based on pedigree and genomic data only) with high reliable conventional estimates (EBV) (based on pedigree, own phenotype and offspring records) of validating animals. Validation datasets include 151 and 110 individuals for reproduction, meat and fattening traits, respectively. The level of correlation (r) between EBV and GEBV scores varied from +0.44 to +0.55 for meat and fatness traits, and from +0.75 to +0.77 for reproduction traits. Average breeding value (EBV) of group selected on genomic evaluation basis exceeded the group selected on parental average estimates by 22, 24 and 66% for muscle depth, days to 100 kg and backfat thickness over 6-7 rib, respectively. Prediction based on SNP markers data and parental estimates showed a significant increase in the reliability of low heritable reproduction traits (about 40%), which is equivalent to including information about 10 additional descendants for sows and 20 additional descendants for boars in the evaluation dataset.

Comparison of genetic parameters and estimated breeding values for worm resistance in meat sheep obtained using traditional and genomic models

Gastrointestinal nematode infections have caused expressive losses in sheep production worldwide. The improvement of host genetic resistance to worms has been used as a strategy to mitigate this problem. In this sense, the inclusion of genomic information has shown potential to increase the accuracy of prediction of breeding values and speed up selection. In this study, we aimed to compare estimates of genetic parameters and breeding values for traits that indicate the resistance to gastrointestinal nematode infection in Santa Inês sheep using the pedigree-based BLUP or including genomic information. There were 1478 animals in the pedigree, of which 271 were genotyped using the OvineSNP50 BeadChip (Illumina, Inc.). The host resistance was assessed using the following traits: fecal nematode egg counts (FEC); FAMACHA score (FAMACHA); and resistance to gastrointestinal nematode infection (RGNI) as a combination of FEC, FAMACHA, body condition score, and hematocrit. The genetic parameters and breeding values were estimated using single- and multi-trait analyses. For RGNI, the heritability estimates ranged from 0.25 using the single-trait genomic model (S-H) to 0.54 using the traditional multi-trait model (M-A). The heritability estimates for FEC ranged from 0.06 to 0.36, using the single-trait pedigree-based model (S-A) and the multi-trait genomic model (M-H), respectively. For FAMACHA, the heritability estimates ranged from 0.46 (M-H) to 0.54 (M-A). Estimates of genetic correlation ranged from 0.22 to 0.69. The inclusion of genomic information provided gain in accuracy for all traits. All estimates of predictive ability obtained using genomic data in a multi-trait setting were higher than those obtained using single-trait models. The estimates of predictive ability ranged from 0.03 (S-A) to 0.46 (M-H). The heritability estimates obtained using genomic information showed that all traits evaluated are suitable for genomic selection. Despite the low accuracies obtained, the use of the genomic model provided more accurate estimates of breeding values in comparison to the pedigree-based model.

Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

Genomic selection has been implemented over the years in several livestock species, due to the achievable higher genetic progress. The use of genomic information in evaluations provides better prediction accuracy than do pedigree-based evaluations, and the makeup of the genotyped population is a decisive point. The aim of this work is to compare the effect of different genotyping strategies (number and type of animals) on the prediction accuracy for dairy sheep Latxa breeds. A simulation study was designed based on the real data structure of each population, and the phenotypic and genotypic data obtained were used in genetic (BLUP) and genomic (single-step genomic BLUP) evaluations of different genotyping strategies. The genotyping of males was beneficial when they were genetically connected individuals and if they had daughters with phenotypic records. Genotyping females with their own lactation records increased prediction accuracy, and the connection level has less relevance. The differences in genotyping females were independent of their estimated breeding value. The combined genotyping of males and females provided intermediate accuracy results regardless of the female selection strategy. Therefore, assuming that genotyping rams is interesting, the incorporation of genotyped females would be beneficial and worthwhile. The benefits of genotyping individuals from various generations were highlighted, although it was also possible to gain prediction accuracy when historic individuals were not considered. Greater genotyped population sizes resulted in more accuracy, even if the increase seems to reach a plateau.

The levels of artificial insemination and missing sire information make genomic selection not always beneficial in meat sheep

Numerous meat sheep breeding programs in developed and developing countries are characterized by incomplete sire information and a predominant use of natural matings. These two parameters potentially affect the benefit of genomic selection (GS), especially for the selection of a late-in-life trait. Using stochastic simulations, the genetic gains obtained using genomic and conventional strategies for a maternal trait were evaluated in meat sheep population. Natural mating and artificial insemination (AI)-based designs, inspired by the current diversity of designs used for French meat sheep breeds, were modeled and three genomic strategies were tested and compared with a conventional selection strategy: parentage assignment, GS based on a male or a male and female reference population. Genomic selection based on a male reference population did not always outperform conventional selection. Its benefit depended on the design, the level of missing information on dam sires, and the level of AI. Genomic selection based on a male and female reference population always outperformed the conventional selection strategy, even if only 25 % of the females in the nucleus were genotyped.

Genomic predictions based on haplotypes fitted as pseudo-SNP for milk production and udder type traits and SCS in French dairy goats

The development of statistical methods aiming to improve the accuracy of genomic predictions is of utmost value for dairy goat breeding programs. In this context, the use of haplotypes, instead of individual SNP, could improve the accuracy of genomic predictions by better capturing the effect of causal variants, instead of relying solely on linkage disequilibrium with individual SNP. Haplotypes can be included in genomic evaluation models in various ways, such as fitting them as pseudo-SNP (i.e., haplotypes converted into biallelic SNP format). This can be easily incorporated in the software already available for single-step genomic predictions (ssGBLUP). Therefore, the aim of this study was to compare the predictive performances of ssGBLUP and weighted ssGBLUP (WssGBLUP) based on individual SNP or on haplotypes fitted as pseudo-SNP. Performance was compared in terms of accuracy, bias, and weights for SNP versus pseudo-SNP. Genomic predictions were performed on 5 milk production traits, 5 udder type traits, and somatic cell score (SCS). The training population was formed by 307 Alpine and 247 Saanen progeny-tested bucks, genotyped using the Illumina Goat SNP50 BeadChip (Illumina, San Diego, CA). The validation population included 205 Alpine and 146 Saanen young bucks. The accuracy of genomic predictions was evaluated in the validation population as the Pearson correlation between genomic estimated breeding values (GEBV), predicted based on various methods, and daughter deviation (DD) based on the official genetic evaluation of January 2016. Haplotype-based models were shown to improve the performance of genomic predictions for some traits. Gains in accuracy of up to +19% (0.310 to 0.368 for fat yield) in Alpine and up to +3% (0.361 to 0.373 for udder shape) in Saanen were observed with ssGBLUP. The ssGBLUP accuracies averaged across all traits and methods were equal to 0.467 (SNP) versus 0.471 (pseudo-SNP) in Alpine and 0.528 (SNP) versus 0.523 (pseudo-SNP) in Saanen. With WssGBLUP, gains in accuracy of up to 24% (0.298 to 0.370 for fat yield) in Alpine and 14% (0.431 to 0.490 for SCS) in Saanen were observed with WssGBLUP. Accuracies of WssGBLUP averaged across all traits and methods were equal to 0.455 (SNP and pseudo-SNP) in Alpine and 0.542 (SNP) versus 0.528 (pseudo-SNP) in Saanen. The average (±SD) slope of the regression of DD on GEBV for the validation animals, across all breeds, traits and scenarios, were equal to 0.82 ± 0.20 (SNP) and 0.83 ± 0.18 (pseudo-SNP) for ssGBLUP and 0.67 ± 0.16 (SNP) and 0.65 ± 0.16 (pseudo-SNP) for WssGBLUP, which suggest that haplotype-based models and ssGBLUP_SNP were similarly biased. However, WssGBLUP was more biased than ssGBLUP, and its gains in accuracies were limited to milk production traits. Despite the fact that genomic predictions based on haplotypes require additional steps and time, the observed gains in GEBV predictive performance indicate that haplotype-based methods could be recommended for some traits.

Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90

Single-step genomic evaluation became a standard procedure in livestock breeding, and the main reason is the ability to combine all pedigree, phenotypes, and genotypes available into one single evaluation, without the need of post-analysis processing. Therefore, the incorporation of data on genotyped and non-genotyped animals in this method is straightforward. Since 2009, two main implementations of single-step were proposed. One is called single-step genomic best linear unbiased prediction (ssGBLUP) and uses single nucleotide polymorphism (SNP) to construct the genomic relationship matrix; the other is the single-step Bayesian regression (ssBR), which is a marker effect model. Under the same assumptions, both models are equivalent. In this review, we focus solely on ssGBLUP. The implementation of ssGBLUP into the BLUPF90 software suite was done in 2009, and since then, several changes were made to make ssGBLUP flexible to any model, number of traits, number of phenotypes, and number of genotyped animals. Single-step GBLUP from the BLUPF90 software suite has been used for genomic evaluations worldwide. In this review, we will show theoretical developments and numerical examples of ssGBLUP using SNP data from regular chips to sequence data.

Detecting novel Indel variants within the GHR gene and their associations with growth traits in Luxi Blackhead sheep

The growth hormone is important in the regulation of metabolism and energy homeostasis and acts through a growth hormone receptor (GHR). In this work, genetic variations within the ovine GHR gene were identified and tested for associations with body morphometric traits in Chinese Luxi Blackhead (LXBH) sheep. Novel deletion loci in the LXBH GHR gene included P2-del-23 bp and P8-del-23 bp indel variants. The polymorphic information content (PIC) was 0.329 in P2-del-23 bp and 0.257 in P8-del-23 bp. Moreover, both indel polymorphisms were not at Hardy-Weinberg equilibrium (p < 0.05) in the LXBH population. Statistical analyses revealed that the P2-del-23 bp and P8-del-23 bp indels were significantly associated (p < 0.05) with several growth traits in rams and ewes, including body weight, body height, chest depth, chest width, chest circumference, cannon circumference, paunch girth and hip width. Among the tested sheep, the body traits of those with genotype DD were superior to those with II and ID genotypes, suggesting that the 'D' allele was responsible for the positive effects on growth traits. Thus, these results indicate that the P2-del-23 bp and P8-del-23 bp indel sites and the DD genotype can be useful in marker-assisted selection in sheep.

Short communication: Genetic parameter estimates for caprine arthritis encephalitis in dairy goats

Caprine arthritis encephalitis (CAE) is a chronic disease caused by a retrovirus from the Lentivirus genus. No effective vaccines or treatments exist, and therefore genetic selection for CAE resistance might be a feasible alternative. To our best knowledge, no other studies have investigated the genetic architecture of CAE resistance in dairy goats. In this context, this study was designed to estimate genetic parameters for CAE infection in Alpine and Saanen goats using a Bayesian threshold model. A total of 542 adult goats (and >3-generation pedigree), which were group-housed in a population with high CAE prevalence, were tested based on a serological infection assessment test (negative = 1 or positive = 2) and used for this study. Genetic parameters were estimated using the BLUPF90 family programs. There was considerable genetic variability for CAE resistance, and pedigree-based heritability was significantly different from zero (0.026 < heritability < 0.128). Our findings indicate that the prevalence of CAE in goat herds can be reduced or eliminated through direct genetic selection for CAE resistance in addition to proper management strategies.

Genomic selection strategies to improve maternal traits in Norwegian White Sheep

This study tested and compared different implementation strategies for genomic selection for Norwegian White Sheep, aiming to increase genetic gain for maternal traits. These strategies were evaluated for their genetic gain ingrowth, carcass and maternal traits, total genetic gain, a weighted sum of the gain in each trait and rates of inbreeding through a full-scale stochastic simulation. Results showed genomic selection schemes to increase genetic gain for maternal traits but reduced genetic gain for other traits. This could also be obtained by selecting rams for artificial selection at a higher age. Implementation of genomic selection in the current breeding structure increased genetic gain for maternal traits up to 57%, outcompeted by reducing the generation interval for artificial insemination rams from current 3 to 2 years. Then, total genetic gain for maternal traits increased by 65%-77% and total genetic gain by18%-20%, but at increased rates of inbreeding.

On-farm net benefit of genotyping candidate female replacement cattle and sheep

The net benefit from investing in any technology is a function of the cost of implementation and the expected return in revenue. The objective of the present study was to quantify, using deterministic equations, the net monetary benefit from investing in genotyping of commercial females. Three case studies were presented reflecting dairy cows, beef cows and ewes based on Irish population parameters; sensitivity analyses were also performed. Parameters considered in the sensitivity analyses included the accuracy of genomic evaluations, replacement rate, proportion of female selection candidates retained as replacements, the cost of genotyping, the sire parentage error rate and the age of the female when it first gave birth. Results were presented as an annualised monetary net benefit over the lifetime of an individual, after discounting for the timing of expressions. In the base scenarios, the net benefit was greatest for dairy, followed by beef and then sheep. The net benefit improved as the reliability of the genomic evaluations improved and, in fact, a negative net benefit of genotyping was less frequent when the reliability of the genomic evaluations was high. The impact of a 10% point increase in genomic reliability was, however, greatest in sheep, followed by beef and then dairy. The net benefit of genotyping female selection candidates reduced as replacement rate increased. As genotyping costs increased, the net benefit reduced irrespective of the percentage of selection candidates kept, the replacement rate or even the population considered. Nonetheless, the association between the genotyping cost and the net benefit of genotyping differed by the percentage of selection candidates kept. Across all replacement rates evaluated, retaining 25% of the selection candidates resulted in the greatest net benefit when genotyping cost was low but the lowest net benefit when genotyping cost was high. Genotyping breakeven cost was non-linearly associated with the percentage of selection candidates retained, reaching a maximum when 50% of selection candidates were retained, irrespective of replacement rate, genomic reliability or the population. The genotyping breakeven cost was also non-linearly associated with replacement rate. The approaches outlined within provide the back-end framework for a decision support tool to quantify the net benefit of genotyping, once parameterised by the relevant population metrics.

Genetic and nongenetic factors associated with the fate of maiden ewe lambs: slaughtered without ever lambing versus retained for breeding

The decision on which ewe lamb to retain versus which to sell is likely to vary by producer based on personal preference. What is not known, however, is if any commonality exists among producers in the characteristics of ewe lambs that influence their eventual fate. The objective of the present study was to determine what genetic and nongenetic factors associate with the fate of maiden ewe lambs. The fate of each ewe lamb born in the present study was defined as either subsequently: 1) having lambed in the flock, or 2) was slaughtered without any recorded lambing event. A total of 9,705 ewe lamb records from 41 crossbred flocks were used. The logit of the odds of the ewe lamb being retained for lambing was modeled using logistic regression. Variance components were then estimated for the binary trait representing the fate of the ewe lamb using animal linear and threshold mixed models. The genetic correlations between fate of the ewe lamb and preweaning, weaning, or postweaning liveweight were also estimated. From the edited data set, 45% of ewe lambs born entered the mature flock as ewes. Ewe lambs reared as singles, with greater levels of heterosis but lower levels of recombination loss, born to dams that lambed for the first time as hoggets, with greater breed proportion of the Belclare, Suffolk, Texel, and Llyen breeds were more likely (P < 0.001) to eventually lamb in the flock than be slaughtered without ever lambing. Irrespective of the age of the animal when weighed, heavier ewe lambs were more likely to eventually lamb (P < 0.001). The genetic SD and direct heritability of fate of the ewe lamb estimated in the univariate linear model was 26.58 percentage units and 0.31 (SE = 0.03), respectively; the heritability was 0.30 when estimated using the threshold model. The corresponding direct heritability of fate of the ewe lamb estimated in the bivariate analyses with liveweight ranged from 0.29 (SE = 0.03; preweaning weight) to 0.35 (SE = 0.04; postweaning weight). The genetic correlations estimated between fate of the ewe lamb and the liveweight traits were weak to moderate but strengthened as the age of the ewe lamb at weighing increased. Results from this study provide an understanding of the factors producers consider when selecting females for retention versus slaughter which may form useful parameters in the development of a decision support tool to identify suitable ewe lambs for retention.

A Mini-Atlas of Gene Expression for the Domestic Goat (Capra hircus)

Goats (Capra hircus) are an economically important livestock species providing meat and milk across the globe. They are of particular importance in tropical agri-systems contributing to sustainable agriculture, alleviation of poverty, social cohesion, and utilisation of marginal grazing. There are excellent genetic and genomic resources available for goats, including a highly contiguous reference genome (ARS1). However, gene expression information is limited in comparison to other ruminants. To support functional annotation of the genome and comparative transcriptomics, we created a mini-atlas of gene expression for the domestic goat. RNA-Seq analysis of 17 transcriptionally rich tissues and 3 cell-types detected the majority (90%) of predicted protein-coding transcripts and assigned informative gene names to more than 1000 previously unannotated protein-coding genes in the current reference genome for goat (ARS1). Using network-based cluster analysis, we grouped genes according to their expression patterns and assigned those groups of coexpressed genes to specific cell populations or pathways. We describe clusters of genes expressed in the gastro-intestinal tract and provide the expression profiles across tissues of a subset of genes associated with functional traits. Comparative analysis of the goat atlas with the larger sheep gene expression atlas dataset revealed transcriptional similarities between macrophage associated signatures in the sheep and goats sampled in this study. The goat transcriptomic resource complements the large gene expression dataset we have generated for sheep and contributes to the available genomic resources for interpretation of the relationship between genotype and phenotype in small ruminants.

Applications of the Soil, Plant and Rumen Microbiomes in Pastoral Agriculture

The production of dairy, meat, and fiber by ruminant animals relies on the biological processes occurring in soils, forage plants, and the animals' rumens. Each of these components has an associated microbiome, and these have traditionally been viewed as distinct ecosystems. However, these microbiomes operate under similar ecological principles and are connected via water, energy flows, and the carbon and nitrogen nutrient cycles. Here, we summarize the microbiome research that has been done in each of these three environments (soils, forage plants, animals' rumen) and investigate what additional benefits may be possible through understanding the interactions between the various microbiomes. The challenge for future research is to enhance microbiome function by appropriate matching of plant and animal genotypes with the environment to improve the output and environmental sustainability of pastoral agriculture.

Chromosomal Localization of Candidate Genes for Fiber Growth and Color in Alpaca (Vicugna pacos)

The alpaca (Vicugna pacos) is an economically important and cultural signature species in Peru. Thus, molecular genomic information about the genes underlying the traits of interest, such as fiber properties and color, is critical for improved breeding and management schemes. Current knowledge about the alpaca genome, particularly the chromosomal location of such genes of interest is limited and lags far behind other livestock species. The main objective of this work was to localize alpaca candidate genes for fiber growth and color using fluorescence in situ hybridization (FISH). We report the mapping of candidate genes for fiber growth COL1A1, CTNNB1, DAB2IP, KRT15, KRTAP13-1, and TNFSF12 to chromosomes 16, 17, 4, 16, 1, and 16, respectively. Likewise, we report the mapping of candidate genes for fiber color ALX3, NCOA6, SOX9, ZIC1, and ZIC5 to chromosomes 9, 19, 16, 1, and 14, respectively. In addition, since KRT15 clusters with five other keratin genes (KRT31, KRT13, KRT9, KRT14, and KRT16) in scaffold 450 (Vic.Pac 2.0.2), the entire gene cluster was assigned to chromosome 16. Similarly, mapping NCOA6 to chromosome 19, anchored scaffold 34 with 8 genes, viz., RALY, EIF2S2, XPOTP1, ASIP, AHCY, ITCH, PIGU, and GGT7 to chromosome 19. These results are concordant with known conserved synteny blocks between camelids and humans, cattle and pigs.

Effects of genomic selection for intramuscular fat content in breast muscle in Chinese local chickens

Improvements in living standards have resulted in consumers having higher expectations for chicken meat quality. This is particularly true in Asia, where there is high consumer preference for local breeds. Nothing is presently known about the effectiveness of using genomic selection (GS) strategies in chickens to genetically improve meat quality traits that cannot be measured in living potential parents. In this study, 724 Beijing-You chickens were used as a training population; all were genotyped using Illumina 60K SNP chips, and intramuscular fat content in breast muscle (IMFbr ) was measured. Birds in the GS line were selected based on genomic estimated breeding values, IMFbr being the sole trait. Genetic progress in one generation was compared to that from conventional family-based selection, and both were evaluated against random-bred controls. Results showed that relative to the random-bred controls, IMF percentage was improved 9.62% using GS, comparable to the 10.38% improvement using family-based selection. We quantified the effectiveness of GS when applied to a meat quality trait with low heritability in chickens. We plan to introduce custom SNP chips, appropriate for native chicken breeds in China, to assist in applying GS in local breeding and accelerate genetic gain.

New mastitis phenotypes suitable for genomic selection in meat sheep and their genetic relationships with udder conformation and lamb live weights

Mastitis can prove expensive in sheep reared for meat production due to costs associated with treatment methods, poor lamb growth and premature culling of ewes. The most commonly used method to detect mastitis, in dairy systems, is somatic cell counts. However, in many meat-producing sheep flocks ewes are not routinely handled, thus regular milk sampling is not always possible. It is, therefore, worthwhile to investigate alternative phenotypes, such as those associated with udder conformation and methods of evaluating somatic cell counts in the milk, such as the California Mastitis Test. The main objectives of this study were therefore: (a) to estimate genetic parameters of traits relating to mastitis and udder conformation in a meat sheep breed; (b) estimate the level of association between somatic cell counts and the California Mastitis Test and (c) assess the relationships between mastitis and both udder conformation and lamb live weights. Data were collected from Texel ewes based on 29 flocks, throughout the UK, during 2015 and 2016. The ewes were scored twice each year, at mid- and late-lactation. Eight different conformation traits, relating to udder and teat characteristics, and milk samples were recorded. The data set comprised of data available for 2957 ewes. The pedigree file used contained sire and dam information for 31 775 individuals. The animal models used fitted relevant fixed and random effects. Heritability estimates for traits relating to mastitis (somatic cell score and the California Mastitis Test), ranged from 0.08 to 0.11 and 0.07 to 0.11, respectively. High genetic correlations were observed between somatic cell score and the California Mastitis Test (0.76 to 0.98), indicating the California Mastitis Test to be worthwhile for assessing infection levels, particularly at mid-lactation. The strongest correlations observed between the mastitis traits and the udder conformation traits were associated with udder depth (0.61 to 0.75) also at mid-lactation. Negative phenotypic correlations were estimated between mastitis and the weight of lamb reared by the ewe (-0.15 to -0.23), suggesting that lamb weights fell as infection levels rose. Genetic correlations were not significantly different from zero. Reducing mastitis will lead to improvements in flock productivity and the health and welfare of the animals. It will also improve the efficiency of production and the resilience to disease challenge. The economic benefits, therefore, of these results combined could be substantial not only in this breed but also in the overall meat sheep industry.

Invited review: Genomic selection for small ruminants in developed countries: how applicable for the rest of the world?

Improved management and use of estimated breeding values in breeding programmes, have resulted in rapid genetic progress for small ruminants (SR) in Europe and other developed countries. The development of single nucleotide polymorphisms chips opened opportunities for genomic selection (GS) in SR in these countries. Initially focused on production traits (growth and milk), GS has been extended to functional traits (reproductive performance, disease resistance and meat quality). The GS systems have been characterized by smaller reference populations compared with those of dairy cattle and consisting mostly of cross- or multi-breed populations. Molecular information has resulted in gains in accuracy of between 0.05 and 0.27 and proved useful in parentage verification and the identification of QTLs for economically important traits. Except for a few established breeds with some degree of infrastructure, the basic building blocks to support conventional breeding programmes in small holder systems are lacking in most developing countries. In these systems, molecular data could offer quick wins in undertaking parentage verification and genetic evaluations using G matrix, and determination of breed composition. The development of next-generation molecular tools has prompted investigations on genome-wide signatures of selection for mainly adaptive and reproduction traits in SR in developing countries. Here, the relevance of the developments and application of GS and other molecular tools in developed countries to developing countries context is examined. Worth noting is that in the latter, the application of GS in SR will not be a 'one-size fits all' scenario. For breeds with some degree of conventional genetic improvement, classical GS may be feasible. In small holder systems, where production is key, community-based breeding programmes can provide the framework to implement GS. However, in fragile growth systems, for example those found in marginal environments, innovative GS to maximize adaptive diversity will be required. A cost-benefit analysis should accompany any strategy of implementing GS in these systems.

Using a very low-density SNP panel for genomic selection in a breeding program for sheep

Background

Building an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs).

Methods

A population under selection for a maternal trait was simulated using real genotypes. Genetic gains realized from classical selection and genomic selection designs were compared. Genomic selection scenarios that differed in reference population structure (whether or not dams were included in the reference) and genotype quality (medium-density or imputed to medium-density from very low-density) were evaluated.

Results

The genomic design increased genetic gain by 26% when the reference population was based on sire medium-density genotypes and by 54% when the reference population included both sire and dam medium-density genotypes. When medium-density genotypes of male candidates and dams were replaced by imputed genotypes from very low-density SNP genotypes (1000 SNPs), the increase in gain was 22% for the sire reference population and 42% for the sire and dam reference population. The rate of increase in inbreeding was lower (from − 20 to − 34%) for the genomic design than for the classical design regardless of the genomic scenario.

Conclusions

We show that very low-density genotypes of male candidates and dams combined with an imputation process result in a substantial increase in genetic gain for small sheep breeding programs.

BREEDING AND GENETICS SYMPOSIUM: Breeding for resilience to heat stress effects in dairy ruminants. A comprehensive review

Selection for heat tolerant (HT) animals in dairy production has been so far linked to estimation of declines in production using milk recording and meteorological information on the day of control using reaction norms. Results from these models show that there is a reasonable amount of genetic variability in the individual response to high heat loads, which makes feasible selection of HT animals at low costs. However, the antagonistic relationship between level of production and response to heat stress (HS) implies that selection for HT animals under this approach must be done with caution so that productivity is not damaged. Decomposition of the genetic variability in principal components (PC) can provide selection criteria independent of milk production level although biological interpretation of PC is difficult. Moreover, given that response to heat stress for each animal is estimated with very sparse information collected under different physiological and management circumstances, biased (normally underestimation) and lack of accuracy may be expected. Alternative phenotypic characterization of HT can come from the use of physiological traits, which have also shown moderate heritability. However, costs of a large scale implementation based on physiological characteristics has precluded its use. Another alternative is the use of biomarkers that define heat tolerance. A review of biomarkers of HS from more recent studies is provided. Of particular interest are milk biomarkers, which together with infrared spectra prediction equations can provide useful tools for genetic selection. In the 'omics' era, genomics, transcriptomics, proteomics and metabolomics have been already used to detect genes affecting HT. A review of findings in these areas is also provided. Except for the slick hair gene, there are no other genes for which variants have been clearly associated with HT. However, integration of omics information could help in pointing at knots of the HS control network and, in the end, to a panel of markers to be used in the selection of HT animals. Overall, HT is a complex phenomenon that requires integration of fine phenotypes and omics information to provide accurate tools for selection without damaging productivity. Technological developments to make on-farm implementation feasible and with greater insight into the key biomarkers and genes involved in HT are needed.

Goat domestication and breeding: a jigsaw of historical, biological and molecular data with missing pieces

Domestic goats (Capra hircus) are spread across the five continents with a census of 1 billion individuals. The worldwide population of goats descends from a limited number of bezoars (Capra aegagrus) domesticated 10 000 YBP (years before the present) in the Fertile Crescent. The extraordinary adaptability and hardiness of goats favoured their rapid spread over the Old World, reaching the Iberian Peninsula and Southern Africa 7000 YBP and 2000 YBP respectively. Molecular studies have revealed one major mitochondrial haplogroup A and five less frequent haplogroups B, C, D, F and G. Moreover, the analysis of autosomal and Y-chromosome markers has evidenced an appreciable geographic differentiation. The implementation of new molecular technologies, such as whole-genome sequencing and genome-wide genotyping, allows for the exploration of caprine diversity at an unprecedented scale, thus providing new insights into the evolutionary history of goats. In spite of a number of pitfalls, the characterization of the functional elements of the goat genome is expected to play a key role in understanding the genetic determination of economically relevant traits. Genomic selection and genome editing also hold great potential, particularly for improving traits that cannot be modified easily by traditional selection.

Imputation of non-genotyped sheep from the genotypes of their mates and resulting progeny

Accurate genomic analyses are predicated on access to a large quantity of accurately genotyped and phenotyped animals. Because the cost of genotyping is often less than the cost of phenotyping, interest is increasing in generating genotypes for phenotyped animals. In some instances this may imply the requirement to genotype older animals with greater phenotypic information content. Biological material for these older informative animals may, however, no longer exist. The objective of the present study was to quantify the ability to impute 11 129 single nucleotide polymorphism (SNP) genotypes of non-genotyped animals (in this instance sires) from the genotypes of their progeny with or without including the genotypes of the progenys' dams (i.e. mates of the sire to be imputed). The impact on the accuracy of genotype imputation by including more progeny (and their dams') genotypes in the imputation reference population was also quantified. When genotypes of the dams were not available, genotypes of 41 sires with at least 15 genotyped progeny were used for the imputation; when genotypes of the dams were available, genotypes of 21 sires with at least 10 genotyped progeny were used for the imputation. Imputation was undertaken exploiting family and population level information. The mean and variability in the proportion of genotypes per individual that could not be imputed reduced as the number of progeny genotypes used per individual increased. Little improvement in the proportion of genotypes that could not be imputed was achieved once genotypes of seven progeny and their dams were used or genotypes of 11 progeny without their respective dam's genotypes were used. Mean imputation accuracy per individual (depicted by both concordance rates and correlation between true and imputed) increased with increasing progeny group size. Moreover, the range in mean imputation accuracy per individual reduced as more progeny genotypes were used in the imputation. If the genotype of the mate of the sire was also used, high accuracy of imputation (mean genotype concordance rate per individual of 0.988), with little additional benefit thereafter, was achieved with seven genotyped progeny. In the absence of genotypes on the dam, similar imputation accuracy could not be achieved even using genotypes on up to 15 progeny. Results therefore suggest, at least for the SNP density used in the present study, that it is possible to accurately impute the genotypes of a non-genotyped parent from the genotypes of its progeny and there is a benefit of also including the genotype of the sire's mate (i.e. dam of the progeny).

Enhancing genetic gain in the era of molecular breeding

As one of the important concepts in conventional quantitative genetics and breeding, genetic gain can be defined as the amount of increase in performance that is achieved annually through artificial selection. To develop pro ducts that meet the increasing demand of mankind, especially for food and feed, in addition to various industrial uses, breeders are challenged to enhance the potential of genetic gain continuously, at ever higher rates, while they close the gaps that remain between the yield potential in breeders' demonstration trials and the actual yield in farmers' fields. Factors affecting genetic gain include genetic variation available in breeding materials, heritability for traits of interest, selection intensity, and the time required to complete a breeding cycle. Genetic gain can be improved through enhancing the potential and closing the gaps, which has been evolving and complemented with modern breeding techniques and platforms, mainly driven by molecular and genomic tools, combined with improved agronomic practice. Several key strategies are reviewed in this article. Favorable genetic variation can be unlocked and created through molecular and genomic approaches including mutation, gene mapping and discovery, and transgene and genome editing. Estimation of heritability can be improved by refining field experiments through well-controlled and precisely assayed environmental factors or envirotyping, particularly for understanding and controlling spatial heterogeneity at the field level. Selection intensity can be significantly heightened through improvements in the scale and precision of genotyping and phenotyping. The breeding cycle time can be shortened by accelerating breeding procedures through integrated breeding approaches such as marker-assisted selection and doubled haploid development. All the strategies can be integrated with other widely used conventional approaches in breeding programs to enhance genetic gain. More transdisciplinary approaches, team breeding, will be required to address the challenge of maintaining a plentiful and safe food supply for future generations. New opportunities for enhancing genetic gain, a high efficiency breeding pipeline, and broad-sense genetic gain are also discussed prospectively.