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New Mendelian genetic conditions, which adversely affect livestock, arise all the time. To manage them effectively, some methods need to be devised that are quick and accurate. Until recently, finding the causal genomic site of a new autosomal recessive genetic disease has required a two-stage approach using single-nucleotide polymorphism (SNP) chip genotyping to locate the region containing the new variant. This region is then explored using fine-mapping methods to locate the actual site of the new variant. This study explores bioinformatic methods that can be used to identify the causative variants of recessive genetic disorders with full penetrance with just nine whole genome-sequenced animals to simplify and expedite the process to a one-step procedure. Using whole genome sequencing of only three cases and six carriers, the site of a novel variant causing perinatal mortality in Irish moiled calves was located. Four methods were used to interrogate the variant call format (VCF) data file of these nine animals, they are genotype criteria (GCR), autozygosity-by-difference (ABD), variant prediction scoring, and registered SNP information. From more than nine million variants in the VCF file, only one site was identified by all four methods (Chr4: g.77173487A>T (ARS-UCD1.2 (GCF_002263795.1)). This site was a splice acceptor variant located in the glucokinase gene ( GCK). It was verified on an independent sample of animals from the breed using genotyping by polymerase chain reaction at the candidate site and autozygosity-by-difference using SNP-chips. Both methods confirmed the candidate site. Investigation of the GCR method found that sites meeting the GCR were not evenly spread across the genome but concentrated in regions of long runs of homozygosity. Locating GCR sites was best performed using two carriers to every case, and the carriers should be distantly related to the cases, within the breed concerned. Fewer than 20 animals need to be sequenced when using the GCR and ABD methods together. The genomic site of novel autosomal recessive Mendelian genetic diseases can be located using fewer than 20 animals combined with two bioinformatic methods, autozygosity-by-difference, and genotype criteria. In many instances it may also be confirmed with variant prediction scoring. This should speed-up and simplify the management of new genetic diseases to a single-step process.

There is very little information about how the genome is regulated in domestic pigs (Sus scrofa). This lack of knowledge hinders efforts to define and predict the effects of genetic variants in pig breeding programs. To address this knowledge gap, we need to identify regulatory sequences in the pig genome starting with regions of open chromatin. We used the "Improved Protocol for the Assay for Transposase-Accessible Chromatin (Omni-ATAC-Seq)" to identify putative regulatory regions in flash-frozen semitendinosus muscle from 24 male piglets. We collected samples from the smallest-, average-, and largest-sized male piglets from each litter through five developmental time points. Of the 4661 ATAC-Seq peaks identified that represent regions of open chromatin, >50% were within 1 kb of known transcription start sites. Differential read count analysis revealed 377 ATAC-Seq defined genomic regions where chromatin accessibility differed significantly across developmental time points. We found regions of open chromatin associated with downregulation of genes involved in muscle development that were present in small-sized fetal piglets but absent in large-sized fetal piglets at day 90 of gestation. The dataset that we have generated provides a resource for studies of genome regulation in pigs and contributes valuable functional annotation information to filter genetic variants for use in genomic selection in pig breeding programs.

Additional file 9: Figure S5. Sex assignment for the 1159 goats sampled in VarGoats. Representation of the ratio of read number between the autosomes and the X chromosome and the read number on the Y chromosome for each animal to determine its sex. The horizontal bar corresponds to the threshold of 25 reads from the Y scaffold allowing to differentiate males from females.

Additional file 2: Supplementary Fig. 1. Additional figures supporting the main text of the manuscript. Referred to as Supplementary Figs. 1–3. Supplementary Fig. 1. Volcano plot of differentially expressed genes (padj 1). Supplementary Fig. 2. Outliers detected by sample clustering (WGCNA). Supplementary Fig. 3. Principal component analysis of herd effect. A) with herd effect, B) herd effect removed.

Additional file 6: Figure S4. Tranche plot produced by VariantRecalibrator for SNPs. Description: Partition of the call sets into quality tranches. The tranches correspond to certain levels of sensitivity relative to the truth sets (the highest tranche corresponds to a high accuracy call set but with the lowest value of sensitivity).

Additional file 3: Figure S1. Distribution of the concordance rates. Concordance rates (CR) between sequence variants and 50 k genotypes for 457 individuals. The figure shows a clear disruption in the distribution of CR, thus we found it easy and relevant to discard the samples below a concordance rate of 70% between sequence and chip SNP data.

Additional file 5: Figure S3. Pairwise comparisons of the annotations chosen in VQSR for InDels. Description: Modeling report generated by GATK VariantRecalibrator for every pairwise combination of annotations used (QD, DP, FS, MQRankSum, ReadPosRankSum and SOR), with a 2D projection of the Gaussian mixture model.

Blood biomarkers may be used to detect physiological imbalance and potential disease. However, blood sampling is difficult and expensive, and not applicable in commercial settings. Instead, individual milk samples are readily available at low cost, can be sampled easily and analysed instantly. The present observational study sampled blood and milk from 234 Holstein dairy cows from experimental herds in six European countries. The objective was to compare the use of three different sets of milk biomarkers for identification of cows in physiological imbalance and thus at risk of developing metabolic or infectious diseases. Random forests was used to predict body energy balance (EBAL), index for physiological imbalance (PI-index) and three clusters differentiating the metabolic status of cows created on basis of concentrations of plasma glucose, β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA) and serum IGF-1. These three metabolic clusters were interpreted as cows in balance, physiological imbalance and “intermediate cows” with physiological status in between. The three sets of milk biomarkers used for prediction were: milk Fourier transform mid-IR (FT-MIR) spectra, 19 immunoglobulin G (IgG) N-glycans and 8 milk metabolites and enzymes (MME). Blood biomarkers were sampled twice; around 14 days after calving (days in milk (DIM)) and around 35 DIM. MME and FT-MIR were sampled twice weekly 1-50 DIM whereas IgG N-glycan were measured only four times. Performances of EBAL and PI-index predictions were measured by coefficient of determination (R2cv) and root mean squared error (RMSEcv) from leave-one-cow-out cross-validation (cv). For metabolic clusters, performance was measured by sensitivity, specificity and global accuracy from this cross-validation. Best prediction of PI-index was obtained by MME (R2cv = 0.40 (95% CI: 0.29-0.50) at 14 DIM and 0.35 (0.23-0.44) at 35 DIM) while FT-MIR showed a better performance than MME for prediction of EBAL (R2cv = 0.28 (0.24-0.33) vs 0.21 (0.18-0.25)). Global accuracies of predicting metabolic clusters from MME and FT-MIR were at the same level ranging from 0.54 (95% CI: 0.39-0.68) to 0.65 (0.55-0.75) for MME and 0.51 (0.37-0.65) to 0.68 (0.53-0.81) for FT-MIR. R2cv and accuracies were lower for IgG N-glycans. In conclusion, neither EBAL nor PI-index were sufficiently well predicted to be used as a management tool for identification of risk cows. MME and FT-MIR may be used to predict the physiological status of the cows, while the use of IgG N-glycans for prediction still needs development. Nevertheless, accuracies need to be improved and a larger training data set is warranted.

The aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.

Bovine Tuberculosis (bTB) caused by Mycobacterium bovis is a livestock disease of global economic and public health importance. There are currently no effective vaccines available for livestock and so control relies on animal level surveillance and pasteurisation of dairy products. A new alternative control approach is to exploit the genetic variability of the host; recent studies have demonstrated that breeding Bos taurus cattle for increased resistance to bTB is feasible. The utility of such an approach is still unknown for the Bos indicus cattle population. This study aims to assess genetic variation in bTB resistance and the underlying genomic architecture in Bos indicus breeds in Cameroon. We conducted a cross-sectional study of slaughter cattle in Cameroon and genotyped a sample of 213 cattle. Their genomic diversity was characterised using PCA, hierarchical clustering and admixture analysis. We assessed genetic variation in bTB resistance using heritability analysis and compared quantitative trait loci. Previous studies had found that breed was an important factor in explaining the epidemiology of bTB, with Fulani cattle appearing to be more susceptible than mixed breeds. However, we show that the apparent phenotypic differences in visual appearance between the breeds was not reflected by clear genomic differences. At the genetic level, cattle belonging to different hierarchical genomic clusters differed in their susceptibility to bTB. There was evidence of a genomic association between M. bovis infection status with specific SNPs. We highlight the need to understand the challenges faced by livestock in specific settings both in terms of pathogens and the environment, in addition to their intended purpose and how they fit into a defined management system. It is only at this point livestock keepers can then make informed breeding choices, not only for resistance to disease but also for increasing production

Improving nitrogen efficiency of individual cows has become a key target in dairy production systems, both for environmental and economic reasons. Results of this study demonstrate the potential of MIR analysis of milk to provide information on nitrogen use efficiency. This approach has the potential to allow future large-scale predictions for genetic studies, as well as providing a tool for herd and diet management.Potential of milk mid-infrared spectra to predict nitrogen use efficiency of individual dairy cows in early lactation.

Improving nitrogen use efficiency (NUE) at both the individual cow and the herd level has become a key target in dairy production systems, for both environmental and economic reasons. Cost-effective and large-scale phenotyping methods are required to improve NUE through genetic selection and by feeding and management strategies. The aim of this study was to evaluate the possibility of using mid-infrared (MIR) spectra of milk to predict individual dairy cow NUE during early lactation. Data were collected from 129 Holstein cows, from calving until 50 d in milk, in 3 research herds (Denmark, Ireland, and the UK). In 2 of the herds, diets were designed to challenge cows metabolically, whereas a diet reflecting local management practices was offered in the third herd. Nitrogen intake (kg/d) and nitrogen excreted in milk (kg/d) were calculated daily. Nitrogen use efficiency was calculated as the ratio between nitrogen in milk and nitrogen intake, and expressed as a percentage. Individual daily values for NUE ranged from 9.7 to 81.7%, with an average of 36.9% and standard deviation of 10.4%. Milk MIR spectra were recorded twice weekly and were standardized into a common format to avoid bias between apparatus or sampling periods. Regression models predicting NUE using milk MIR spectra were developed on 1,034 observations using partial least squares or support vector machines regression methods. The models were then evaluated through (1) a cross-validation using 10 subsets, (2) a cow validation excluding 25% of the cows to be used as a validation set, and (3) a diet validation excluding each of the diets one by one to be used as validation sets. The best statistical performances were obtained when using the support vector machines method. Inclusion of milk yield and lactation number as predictors, in combination with the spectra, also improved the calibration. In cross-validation, the best model predicted NUE with a coefficient of determination of cross-validation of 0.74 and a relative error of 14%, which is suitable to discriminate between low- and high-NUE cows. When performing the cow validation, the relative error remained at 14%, and during the diet validation the relative error ranged from 12 to 34%. In the diet validation, the models showed a lack of robustness, demonstrating difficulties in predicting NUE for diets and for samples that were not represented in the calibration data set. Hence, a need exists to integrate more data in the models to cover a maximum of variability regarding breeds, diets, lactation stages, management practices, seasons, MIR instruments, and geographic regions. Although the model needs to be validated and improved for use in routine conditions, these preliminary results showed that it was possible to obtain information on NUE through milk MIR spectra. This could potentially allow large-scale predictions to aid both further genetic and genomic studies, and the development of farm management tools.

The aim of this study was to describe metabolism of early lactation dairy cows by clustering cows based on glucose, insulin like growth factor I (IGF-I), free fatty acid (FFA), and β- hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using three sets of milk biomarkers (milk metabolites and enzymes,glycans on the immuno-gamma globulin (IgG) fraction of milk, and Fourier transformed mid-infrared spectra (FT-MIR) of milk). Metabolic clusters are used to identify dairy cows with a(n) (im)balanced metabolic profile. Around 14 and 35 days in milk, serum or plasma concentrations of BHB, FFA, glucose and IGF-I, were determined. Cows with a favorable metabolic profile were grouped in BALANCED (n=43) and compared with OTHERBAL (n=64). Cows with an unfavorable metabolic profile were grouped in IMBALANCED (n=19) and compared with OTHERIMBAL (n=88). Glucose and IGF-I were higher in BALANCED compared with OTHERBAL. FFA and BHB were lower in BALANCED compared with OTHERBAL. Glucose and IGF-I were lower in IMBALANCED compared with OTHERIMBAL. FFA and BHB were higher in IMBALANCED. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat and protein corrected milk yield (FPCM) in BALANCED while FPCM of IMBALANCED was higher. Dry matter intake (DMI) and the daily increase in DMI were higher in BALANCED and lower in IMBALANCED. Energy balance was continuously higher in BALANCED and lower in IMBALANCED. Weekly or bi- weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6- phosphate, BHB, lactate dehydrogenase, N-acetyl-β-D-glucosaminidase, isocitrate), IgGglycans (19 peaks) and FT-MIR (1,060 wavelengths reduced to 15 principal components) determined. Milk biomarkers with or without additional cow information (DIM, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of BALANCED (80%) and IMBALANCED (88%) was highest using milk metabolites and enzymes combined with DIM and parity. The results and models of the present study are part of the GplusE project and identify novel milk based phenotypes that may be used as predictors for metabolic and performance traits in early lactation dairy cows.

BACKGROUND Since their domestication 10,500��years ago, goat populations with distinctive genetic backgrounds have adapted to a broad variety of environments and breeding conditions. The VarGoats project is an international 1000-genome resequencing program designed to understand the consequences of domestication and breeding on the genetic diversity of domestic goats and to elucidate how speciation and hybridization have modeled the genomes of a set of species representative of the genus Capra. FINDINGS A dataset comprising 652 sequenced goats and 507 public goat sequences, including 35 animals representing eight wild species, has been collected worldwide. We identified 74,274,427 single nucleotide polymorphisms (SNPs) and 13,607,850 insertion-deletions (InDels) by aligning these sequences to the latest version of the goat reference genome (ARS1). A Neighbor-joining tree based on Reynolds genetic distances showed that goats from Africa, Asia and Europe tend to group into independent clusters. Because goat breeds from Oceania and Caribbean (Creole) all derive from imported animals, they are distributed along the tree according to their ancestral geographic origin. CONCLUSIONS We report on an unprecedented international effort to characterize the genome-wide diversity of domestic goats. This large range of sequenced individuals represents a unique opportunity to ascertain how the demographic and selection processes associated with post-domestication history have shaped the diversity of this species. Data generated for the project will also be extremely useful to identify deleterious mutations and polymorphisms with causal effects on complex traits, and thus will contribute to new knowledge that could be used in genomic prediction and genome-wide association studies.