Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows

Genetic analysis of milk citrate predicted by milk mid-infrared spectra of Holstein cows in early lactation

Milk citrate is regarded as an early biomarker of negative energy balance in dairy cows during early lactation and serves as a suitable candidate phenotype for genomic selection due to its wide availability across a large number of cows through milk mid-infrared spectra prediction. However, its genetic background is not well known. Therefore, the objectives of this study were to (1) analyze the genetic parameters of milk citrate; (2) identify genomic regions associated with milk citrate; and (3) analyze the functional annotation of candidate genes and quantitative trait loci (QTL) related to milk citrate in Walloon Holstein cows. In total, 134,517 test-day milk-citrate phenotypes (mmol/L) collected within the first 50 d in milk on 52,198 Holstein cows were used. These milk-citrate phenotypes, predicted by milk mid-infrared spectra, were divided into 3 traits according to the first (citrate1), second (citrate2), and third to fifth parity (citrate3+). Genomic information for 566,170 SNPs was available for 4,479 animals. A multiple-trait repeatability model was used to estimate genetic parameters. A single-step GWAS was used to identify candidate genes for citrate and post-GWAS analysis was done to investigate the relationship and function of the identified candidate genes. The heritabilities estimated for citrate1, citrate2, and citrate3+ were 0.40, 0.37, and 0.35, respectively. The genetic correlations among the 3 traits ranged from 0.98 to 0.99. The genomic correlations among the 3 traits were also close to 1.00 across the genomic regions (1 Mb) in the whole genome, which means that citrate can be considered as a single trait in the first 5 parities. In total, 603 significant SNPs located on 3 genomic regions (chromosome 7, 68.569-68.575 Mb; chromosome 14, 0.15-1.90 Mb; and chromosome 20, 54.00-64.28 Mb), were identified to be associated with milk citrate. We identified 89 candidate genes including GPT, ANKH, PPP1R16A, and 32 QTL reported in the literature related to the identified significant SNPs. These identified QTL were mainly reported associated with milk fatty acids and metabolic diseases in dairy cows. This study suggests that milk citrate in Holstein cows is highly heritable and has the potential to be used as an early proxy for the negative energy balance of Holstein cows in a breeding objective.

A Genome-Wide Association Study for Resistance to Tropical Theileriosis in Two Bovine Portuguese Autochthonous Breeds

The control of Tropical Theileriosis, a tick-borne disease with a strong impact on cattle breeding, can be facilitated using marker-assisted selection in breeding programs. Genome-wide association studies (GWAS) using high-density arrays are extremely important for the ongoing process of identifying genomic variants associated with resistance to Theileria annulata infection. In this work, single-nucleotide polymorphisms (SNPs) were analyzed in the Portuguese autochthonous cattle breeds Alentejana and Mertolenga. In total, 24 SNPs suggestive of significance (p ≤ 10-4) were identified for Alentejana cattle and 20 SNPs were identified for Mertolenga cattle. The genomic regions around these SNPs were further investigated for annotated genes and quantitative trait loci (QTLs) previously described by other authors. Regarding the Alentejana breed, the MAP3K1, CMTM7, SSFA2, and ATG13 genes are located near suggestive SNPs and appear as candidate genes for resistance to Tropical Theileriosis, considering its action in the immune response and resistance to other diseases. On the other hand, in the Mertolenga breed, the UOX gene is also a candidate gene due to its apparent link to the pathogenesis of the disease. These results may represent a first step toward the possibility of including genetic markers for resistance to Tropical Theileriosis in current breed selection programs.

Descriptive Statistics and Genome-Wide Copy Number Analysis of Milk Production Traits of Jiangsu Chinese Holstein Cows

Milk production traits are the most important quantitative economic traits in dairy cow production; improving the yield and quality of milk is an important way to ensure the production efficiency of the dairy industry. This study carried out a series of in-depth statistical genetics studies and molecular analyses on the Chinese Holstein cows in the Jiangsu Province, such as descriptive statistics and copy number variation analysis. A genetic correlation, phenotypic correlation, and descriptive statistical analysis of five milk production traits (milk yield, milk fat percentage, milk fat yield, milk protein percentage, and milk protein yield) of the dairy cows were analyzed using the SPSS and DMU software. Through quality control, 4173 cows and their genomes were used for genomic study. Then, SNPs were detected using DNA chips, and a copy number variation (CNV) analysis was carried out to locate the quantitative trait loci (QTL) of the milk production traits by Perl program software Penn CNV and hidden Markov model (HMM). The phenotypic means of the milk yield, milk fat percentage, milk fat mass, milk protein percentage, and milk protein mass at the first trimester were lower than those at the other trimesters by 8.821%, 1.031%, 0.930%, 0.003%, and 0.826%, respectively. The five milk production traits showed a significant phenotypic positive correlation (p < 0.01) and a high genetic positive correlation among the three parities. Based on the GGPBovine 100 K SNP data, QTL-detecting research on the fist-parity milk performance of dairy cows was carried out via the CNV. We identified 1731 CNVs and 236 CNVRs in the 29 autosomes of 984 Holstein dairy cows, and 19 CNVRs were significantly associated with the milk production traits (p < 0.05). These CNVRs were analyzed via a bioinformatics analysis; a total of 13 gene ontology (GO) terms and 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched (p < 0.05), and these terms and pathways are mainly related to lipid metabolism, amino acid metabolism, and cellular catabolic processes. This study provided a theoretical basis for the molecular-marker-assisted selection of dairy cows by developing descriptive statistics on the milk production traits of dairy cows and by locating the QTL and functional genes that affect the milk production traits of first-born dairy cows. The results describe the basic status of the milk production traits of the Chinese Holstein cows in Jiangsu and locate the QTL and functional genes that affect the milk production traits of the first-born cows, providing a theoretical basis for the molecular-marker-assisted selection of dairy cows.

Single-step genome-wide association analyses for selected infrared-predicted cheese-making traits in Walloon Holstein cows

This study aimed to perform genome-wide association study to identify genomic regions associated with milk production and cheese-making properties (CMP) in Walloon Holstein cows. The studied traits were milk yield, fat percentage, protein percentage, casein percentage (CNP), calcium content, somatic cell score (SCS), coagulation time, curd firmness after 30 min from rennet addition, and titratable acidity. The used data have been collected from 2014 to 2020 on 78,073 first-parity (485,218 test-day records), 48,766 second-parity (284,942 test-day records), and 21,948 third-parity (105,112 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA) of 6,617 animals (1,712 males), were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 KB) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Heritability estimates for the studied traits ranged from 0.10 (SCS) to 0.53 (CNP), 0.10 (SCS) to 0.50 (CNP), and 0.12 (SCS) to 0.49 (CNP) in the first, second, and third parity, respectively. Genome-wide association analyses identified 6 genomic regions (BTA1, BTA14 [4 regions], and BTA20) associated with the considered traits. Genes including the SLC37A1 (BTA1), SHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, ADGRB1, GML, LYPD2, JRK (BTA14), and TRIO (BTA20) were identified as positional candidate genes for the studied CMP. The findings of this study help to unravel the genomic background of a cow's ability for cheese production and can be used for the future implementation and use of genomic evaluation to improve the cheese-making traits in Walloon Holstein cows.

Review: genetic background of milk fatty acid synthesis in bovines

Milk fat composition is an important trait for the dairy industry as it directly influences the nutritional and technological properties of milk and other dairy products. The synthesis of milk fat is a complex process regulated by a network of genes. Thus, understanding the genetic variation and molecular mechanisms regulating milk fat synthesis will help to improve the nutritional quality of dairy products. In this review, we provide an overview of milk fat synthesis in bovines along with the candidate genes involved in the pathway. We also discuss de novo synthesis of fatty acids (ACSS, ACACA, FASN), uptake of FAs (FATP, FAT, LPL), intracellular activation and channelling of FAs (ACSL, FABP), elongation (EVOLV6), desaturation (SCD, FADS), formation of triglycerides (GPAM, AGPAT, LIPIN, DGAT), and milk lipid secretion (BTN1A1, XDH, PLIN2). The genetic variability of individual fatty acids will help to develop selection strategies for obtaining a healthier milk fat profile in bovines. Thus, this review will offer a potential understanding of the molecular mechanisms that regulate milk fat synthesis in bovines.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Genomic diversity and relationship analyses of endangered German Black Pied cattle (DSN) to 68 other taurine breeds based on whole-genome sequencing

German Black Pied cattle (Deutsches Schwarzbuntes Niederungsrind, DSN) are an endangered dual-purpose cattle breed originating from the North Sea region. The population comprises about 2,500 cattle and is considered one of the ancestral populations of the modern Holstein breed. The current study aimed at defining the breeds closest related to DSN cattle, characterizing their genomic diversity and inbreeding. In addition, the detection of selection signatures between DSN and Holstein was a goal. Relationship analyses using fixation index (F_ST), phylogenetic, and admixture analyses were performed between DSN and 68 other breeds from the 1000 Bull Genomes Project. Nucleotide diversity, observed heterozygosity, and expected heterozygosity were calculated as metrics for genomic diversity. Inbreeding was measured as excess of homozygosity (F_Hom) and genomic inbreeding (F_RoH) through runs of homozygosity (RoHs). Region-wide F_ST and cross-population-extended haplotype homozygosity (XP-EHH) between DSN and Holstein were used to detect selection signatures between the two breeds, and RoH islands were used to detect selection signatures within DSN and Holstein. DSN showed a close genetic relationship with breeds from the Netherlands, Belgium, Northern Germany, and Scandinavia, such as Dutch Friesian Red, Dutch Improved Red, Belgian Red White Campine, Red White Dual Purpose, Modern Angler, Modern Danish Red, and Holstein. The nucleotide diversity in DSN (0.151%) was higher than in Holstein (0.147%) and other breeds, e.g., Norwegian Red (0.149%), Red White Dual Purpose (0.149%), Swedish Red (0.149%), Hereford (0.145%), Angus (0.143%), and Jersey (0.136%). The F_Hom and F_RoH values in DSN were among the lowest. Regions with high F_ST between DSN and Holstein, significant XP-EHH regions, and RoH islands detected in both breeds harbor candidate genes that were previously reported for milk, meat, fertility, production, and health traits, including one QTL detected in DSN for endoparasite infection resistance. The selection signatures between DSN and Holstein provide evidence of regions responsible for the dual-purpose properties of DSN and the milk type of Holstein. Despite the small population size, DSN has a high level of diversity and low inbreeding. F_ST supports its relatedness to breeds from the same geographic origin and provides information on potential gene pools that could be used to maintain diversity in DSN.

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from -0.61 to -0.41 and from -0.55 to -0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.

Genome-Wide Association Study for Udder Conformation Traits in Chinese Holstein Cattle

Udder conformation traits are one of the most economic traits in dairy cows, greatly affecting animal health, milk production, and producer profitability in the dairy industry. Genetic analysis of udder structure and scores have been developed in Holstein cattle. In our research, we conducted a genome-wide association study for five udder traits, including anterior udder attachment (AUA), central suspensory ligament (CSL), posterior udder attachment height (PUAH), posterior udder attachment width (PUAW), and udder depth (UD), in which the fixed and random model circulating probability unification (FarmCPU) model was applied for the association analysis. The heritability and the standard errors of these five udder traits ranged from 0.04 ± 0.00 to 0.49 ± 0.03. Phenotype data were measured from 1000 Holstein cows, and the GeneSeek Genomic Profiler (GGP) Bovine 100 K SNP chip was used to analyze genotypic data in Holstein cattle. For GWAS analysis, 984 individual cows and 84,407 single-nucleotide polymorphisms (SNPs) remained after quality control; a total of 18 SNPs were found at the GW significant threshold (p < 5.90 × 10−7). Many candidate genes were identified within 200kb upstream or downstream of the significant SNPs, which include MGST1, MGST2, MTUS1, PRKN, STXBP6, GRID2, E2F8, CDH11, FOXP1, SLF1, TMEM117, SBF2, GC, ADGRB3, and GCLC. Pathway analysis revealed that 58 Gene Ontology (GO) terms and 18 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched with adjusted p values, and these GO terms and the KEGG pathway analysis were associated with biological information, metabolism, hormonal growth, and development processes. These results could give valuable biological information for the genetic architecture of udder conformation traits in dairy Holstein cattle.

Genome-wide elucidation of CNV regions and their association with production and reproduction traits in composite Vrindavani cattle

The present study was aimed to analyze the genome-wide copy number variations (CNVs) in Vrindavani composite cattle and concatenate them into CNV regions (CNVRs), and finally test the association of CNVRs with different production and reproduction traits. Genotypic data, generated on BovineSNP50 Beadchip (v3) array for 96 Vrindavani animals, was used to elucidate the CNVs at the genome level. Intensity data covering over 53,218 SNP genotypes on bovine genome was used. Algorithm based on Hidden Markov Model was employed in PennCNV program to detect, normalize and filter CNVs across the genome. 252 putative CNVs, detected via PennCNV program, in different individuals were concatenated into 71 CNV regions (CNVRs) using CNVRuler program. Association of CNVRs with important (re)production traits in Vrindavani animals was assessed using linear regression. Five CNVRs were found to be significantly associated with ten important (re)production traits. The genes harbored in these regions provided useful insights into the association of CNVRs with genes and ultimately the variation at phenotype level. Important genes that overlapped with CNVRs included WASHC4, HS6ST3, MBNL2, TOLLIP, PIDD1 and TSPAN4. Furthermore, the CNVRs were found to overlap with important QTLs available in AnimalQTL database which affect milk yield and composition along with reproduction and immune function traits. The copy number states of three enes were validated using digital droplet PCR technique. The results from the present study significantly enhance the understanding about CNVs in Vrindavani cattle and should help establish its CNV map. The study will also enable further investigation on association of these variants with important traits of economic interest including disease incidence.

Genetic architecture of a composite beef cattle population

Composite breeds are widely used in the beef industry. Composites allow producers to combine desirable traits from the progenitor breeds and simplify herd management, without repeated crossbreeding and maintenance of purebreds. In this study, genomic information was used to evaluate the genetic composition and characteristics of a three-breed beef cattle composite. This composite population referred to as Composite Gene Combination (CGC) consisted of 50% Red Angus, 25% Charolais, 25% Tarentaise. A total of 248 animals were used in this study CGC (n=79), Red Angus (n=61), Charolais (n=79) and Tarentaise (n=29). All animals were genotyped with 777k HD panel. Principal component and ADMIXTURE analyses were carried out to evaluate the genetic structure of CGC animals. The ADMIXTURE revealed the proportion of Tarentaise increased to approximately 57% while Charolais decreased to approximately 5%, and Red Angus decreased to 38% across generations. To evaluate these changes in the genomic composition across different breeds and in CGC across generations runs of homozygosity (ROH) were conducted. This analysis showed Red Angus to have the highest total length of ROH segments per animal with a mean of 349.92 Mb and lowest in CGC with a mean of 141.10 Mb. Furthermore, it showed the formation of new haplotypes in CGC around the sixth generation. Selection signatures were evaluated through Fst and HapFlk analyses. Several selection sweeps in CGC were identified especially in chromosomes 5 and 14 which have previously been reported to be associated with coat color and growth traits. The study supports our previous findings that progenitor combinations are not stable over generations and that either direct or natural selection plays a role in modifying the progenitor proportions. Furthermore, the results showed that Tarentaise contributed useful attributes to the composite in a cool semi-arid environment and suggests a re-exploration of this breed's role may be warranted.

Genome-wide analyses reveal a strong association between LEPR gene variants and body fat reserves in ewes

BACKGROUND

Among the adaptive capacities of animals, the management of energetic body reserves (BR) through the BR mobilization and accretion processes (BR dynamics, BRD) has become an increasingly valuable attribute for livestock sustainability, allowing animals to cope with more variable environments. BRD has previously been reported to be heritable in ruminants. In the present work, we conducted genome-wide studies (GWAS) in sheep to determine genetic variants associated with BRD. BR (i.e. levels) and BRD (i.e. changes over time) were obtained through body condition score measurements at eight physiological stages throughout each productive cycle in Romane ewes (n = 1034) and were used as phenotypes for GWAS. After quality controls and imputation, 48,593 single nucleotide polymorphisms (SNP) were included in the GWAS.

RESULTS

Among the 23 QTL regions identified, a major QTL associated with BR during pregnancy and lactation was identified on chromosome 1. In this region, several significant SNPs mapped to the leptin receptor gene (LEPR), among which one SNP mapped to the coding sequence. The point mutation induces the p.P1019S substitution in the cytoplasmic domain, close to tyrosine phosphorylation sites. The frequency of the SNP associated with increased BR levels was 32%, and the LEPR genotype explained up to 5% of the variance of the trait. Higher fatness levels in ewes carrying the LEPR p.P1019S mutation were observed all along the productive cycle.

CONCLUSIONS

These results provide strong evidences for involvement of LEPR in the regulation of BR in sheep and highlight it as a major candidate gene for improving adaptive capacities by genetic selection.

Multivariate and Genome-Wide Analysis of Mid-Infrared Spectra of Non-Coagulating Milk of Sarda Sheep Breed

Milk coagulation ability is crucial for the dairy sheep industry since the whole amount of milk is processed into cheese. Non-coagulating milk (NCM) is defined as milk not forming a curd within the testing time. In sheep milk, it has been reported in literature that up to 20% of milk is NCM. Although the clotting properties of individual milk have been widely studied, little attention has been given to NCM and genomic dissection of this trait. Mid-infrared (MIR) spectra can be exploited both to predict cheese-making aptitude and to discriminate between coagulating milk and NCM. The main goals of this work were (i) to assess the predictivity of MIR spectra for NCM classification and (ii) to conduct a genome-wide association study on coagulation ability. Milk samples from 949 Sarda ewes genotyped and phenotyped for milk coagulation properties (MCPs) served as the training dataset. The validation dataset included 662 ewes. Three classical MCPs were measured: rennet coagulation time (RCT), curd firmness (a30), and curd firming time (k20). Moreover, MIR spectra were acquired and stored in the region between 925.92 and 5,011.54 cm−1. The probability of a sample to be NCM was modeled by step-wise logistic regression on milk spectral information (LR-W), logistic regression on principal component (LR-PC), and canonical discriminant analysis of spectral wave number (DA-W). About 9% of the samples did not coagulate at 30 min. The use of LR-W gave a poorer classification of NCM. The use of LR-PC improved the percentage of correct assignment (45 ± 9%). The DA-W method allows us to reach 75.1 ± 10.3 and 76.5 ± 18.4% of correct assignments of the inner and external validation datasets, respectively. As far as GWA of NCM, 458 SNP associations and 45 candidate genes were detected. The genes retrieved from public databases were mostly linked to mammary gland metabolism, udder health status, and a milk compound also known to affect the ability of milk to coagulate. In particular, the potential involvement of CAPNs deserves further investigation.

Opportunities for genomic selection of cheese-making traits in Montbéliarde cows

As part of the From'MIR project, traits related to the composition and cheese-making properties (CMP) of milk were predicted from 6.6 million mid-infrared spectra taken from 410,622 Montbéliarde cows (19,862 with genotypes). Genome-wide association studies of imputed whole-genome sequences highlighted candidate SNPs that were then added to the EuroG10K BeadChip, which is routinely used in genomic selection. In the present study, we (1) assessed the reliability of single-step genomic BLUP breeding values (ssEBVs) for cheese yields, coagulation traits, and casein and calcium content generated from test-day records of the first 3 lactations, (2) estimated realized genetic trends for these traits over the last decade, and (3) simulated different cheese-making breeding objectives and estimated the responses for CMP as well as for other traits currently selected in the Montbéliarde breed. To estimate the reliability of ssEBVs, the available data were split into 2 independent training and validation sets that respectively contained cows with the oldest and the most recent lactation data. The training set included 155,961 cows (12,850 with genotypes) and was used to predict ssEBVs of 2,125 genotyped cows in the validation set. We first tested 4 models that included either lactation (LACT) or test-day (TD) records from the first (1) or the first 3 (3) lactations, giving equal weight to all 50K SNP effects. Mean reliabilities were 61%, 62%, 63%, and 64% for the LACT1, LACT3, TD1, and TD3 models, respectively. Using the most accurate model (TD3), we then compared the reliabilities of 3 scenarios with: SNPs from the Illumina BovineSNP50 BeadChip only, equally weighted (50K); 50K SNPs plus additional candidate SNPs, equally weighted (50K+); and 50K and candidate SNPs with additional weight given to 7 to 14 candidate SNPs, depending on the trait (CAND). The 50K+ and CAND scenarios led to similar mean reliabilities (67%) and both outperformed the 50K scenario (64%), whereas the CAND scenario generated the less biased ssEBVs. To assess genetic trends, SNP effects were estimated with a single-step GBLUP based on the TD3 model and the 50K scenario applied to the whole population (2.6 million performance records from 190,261 cows and 423,348 animals in the pedigree, of which 21,874 were genotyped) and then applied to 50K genotypes of 21,171 males and 311,761 females. We detected a positive genetic trend for all CMP during the last decade, probably due to selection for an increase in milk protein and fat content in Montbéliarde cows. Finally, we compared the selection responses to 3 different breeding objectives: the current Montbéliarde total merit index (TMI) and 2 alternative scenarios that gave a weight of 70% to TMI and the remaining 30% to either milk casein content (TMI-COMP) or a combination of 3 CMP (TMI-Cheese). The TMI-Cheese scenario yielded the best responses for all the CMP analyzed, whereas values in the TMI-COMP scenario were intermediate, with a slight effect on other traits currently included in TMI. Based on these results, a program of genomic evaluation for CMP predicted from mid-infrared spectra was designed and implemented for the Montbéliarde breed.

Integrated analysis of expression profiles with meat quality traits in cattle

MicroRNAs (miRNAs) play a vital role in improving meat quality by binding to messenger RNAs (mRNAs). We performed an integrated analysis of miRNA and mRNA expression profiling between bulls and steers based on the differences in meat quality traits. Fat and fatty acids are the major phenotypic indices of meat quality traits to estimate between-group variance. In the present study, 90 differentially expressed mRNAs (DEGs) and 18 differentially expressed miRNAs (DEMs) were identified. Eighty-three potential DEG targets and 18 DEMs were used to structure a negative interaction network, and 75 matching target genes were shown in this network. Twenty-six target genes were designated as intersection genes, screened from 18 DEMs, and overlapped with the DEGs. Seventeen of these genes enriched to 19 terms involved in lipid metabolism. Subsequently, 13 DEGs and nine DEMs were validated using quantitative real-time PCR, and seven critical genes were selected to explore the influence of fat and fatty acids through hub genes and predict functional association. A dual-luciferase reporter and Western blot assays confirmed a predicted miRNA target (bta-miR-409a and PLIN5). These findings provide substantial evidence for molecular genetic controls and interaction among genes in cattle.

Unraveling Admixture, Inbreeding, and Recent Selection Signatures in West African Indigenous Cattle Populations in Benin

The Dwarf Lagune and the Savannah Somba cattle in Benin are typical representatives of the endangered West African indigenous Shorthorn taurine. The Lagune was previously exported to African and European countries and bred as Dahomey cattle, whereas the Somba contributed to the formation of two indigenous hybrids known as Borgou and Pabli cattle. These breeds are affected by demographic, economic, and environmental pressures in local production systems. Considering current and historical genomic data, we applied a formal test of admixture, estimated admixture proportions, and computed genomic inbreeding coefficients to characterize the five breeds. Subsequently, we unraveled the most recent selection signatures using the cross-population extended haplotype homozygosity approach, based on the current and historical genotypes. Results from principal component analyses and high proportion of Lagune ancestry confirm the Lagune origin of the European Dahomey cattle. Moreover, the Dahomey cattle displayed neither indicine nor European taurine (EUT) background, but they shared on average 40% of autozygosity from common ancestors, dated approximately eight generations ago. The Lagune cattle presented inbreeding coefficients larger than 0.13; however, the Somba and the hybrids (Borgou and Pabli) were less inbred (≤0.08). We detected evidence of admixture in the Somba and Lagune cattle, but they exhibited a similar African taurine (AFT) ancestral proportion (≥96%) to historical populations, respectively. A moderate and stable AFT ancestral proportion (62%) was also inferred for less admixed hybrid cattle including the Pabli. In contrast, the current Borgou samples displayed a lower AFT ancestral proportion (47%) than historical samples (63%). Irrespective of the admixture proportions, the hybrid populations displayed more selection signatures related to economic traits (reproduction, growth, and milk) than the taurine. In contrast, the taurine, especially the Somba, presented several regions known to be associated with adaptive traits (immunity and feed efficiency). The identified subregion of bovine leukocyte antigen (BoLA) class IIb (including DSB and BOLA-DYA) in Somba cattle is interestingly uncommon in other African breeds, suggesting further investigations to understand its association with specific adaptation to endemic diseases in Benin. Overall, our study provides deeper insights into recent evolutionary processes in the Beninese indigenous cattle and their aptitude for conservation and genetic improvement.

Genome-wide association study with imputed whole-genome sequence variants including large deletions for female fertility in 3 Nordic dairy cattle breeds

Fertility is an economically important trait in livestock. Poor fertility in dairy cattle can be due to loss-of-function variants affecting any essential gene that causes early embryonic mortality in homozygotes. To identify fertility-associated quantitative trait loci, we performed single-marker association analyses for 8 fertility traits in Holstein, Jersey, and Nordic Red Dairy cattle using imputed whole-genome sequence variants including SNPs, indels, and large deletion. We then performed stepwise selection of independent markers from GWAS loci using conditional and joint association analyses. From single-marker analyses for fertility traits, we reported genome-wide significant associations of 30,384 SNPs, 178 indels, and 3 deletions in Holstein; 23,481 SNPs, 189 indels, and 13 deletions in Nordic Red; and 17 SNPs in Jersey cattle. Conditional and joint association analyses identified 37 and 23 independent associations in Holstein and Nordic Red Dairy cattle, respectively. Fertility-associated GWAS loci were enriched for developmental and cellular processes (Gene Ontology enrichment, false discovery rate < 0.05). For these quantitative trait loci regions (top marker and 500 kb of surrounding regions), we proposed several candidate genes with functional annotations corresponding to embryonic lethality and various fertility-related phenotypes in mouse and cattle. The inclusion of these top markers in future releases of the custom SNP chip used for genomic evaluations will enable their validation in independent populations and improve the accuracy of genomic predictions.

A Co-Association Network Analysis Reveals Putative Regulators for Health-Related Traits in Pigs

In recent years, the increase in awareness of antimicrobial resistance together with the societal demand of healthier meat products have driven attention to health-related traits in livestock production. Previous studies have reported medium to high heritabilities for these traits and described genomic regions associated with them. Despite its genetic component, health- and immunity-related traits are complex and its study by association analysis with genomic markers may be missing some information. To analyse multiple phenotypes and gene-by-gene interactions, systems biology approaches, such as the association weight matrix (AWM), allows combining genome wide association study results with network inference algorithms. The present study aimed to identify gene networks, key regulators and candidate genes associated to immunocompetence in pigs by integrating multiple health-related traits, enriched for innate immune phenotypes, using the AWM approach. The co-association network analysis unveiled a network comprised of 3,636 nodes (genes) and 451,407 edges (interactions), including a total of 246 regulators. From these, five genes (ARNT2, BRMS1L, MED12L, SUPT3H and TRIM25) were selected as key regulators as they were associated with the maximum number of genes with the minimum overlapping (1,827 genes in total). The five regulators were involved in pathways related to immunity such as lymphocyte differentiation and activation, platelet activation and degranulation, megakaryocyte differentiation, FcγR-mediated phagocytosis and response to nitric oxide, among others, but also in immunometabolism. Furthermore, we identified genes co-associated with the key regulators previously reported as candidate genes (e.g., ANGPT1, CD4, CD36, DOCK1, PDE4B, PRKCE, PTPRC and SH2B3) for immunity traits in humans and pigs, but also new candidate ones (e.g., ACSL3, CXADR, HBB, MMP12, PTPN6, WLS) that were not previously described. The co-association analysis revealed new regulators associated with health-related traits in pigs. This approach also identified gene-by-gene interactions and candidate genes involved in pathways related to cell fate and metabolic and immune functions. Our results shed new light in the regulatory mechanisms involved in pig immunity and reinforce the use of the pig as biomedical model.

Investigation of β-hydroxybutyrate in early lactation of Simmental cows: Genetic parameters and genomic predictions

Genomic information allows for a more accurate calculation of relationships among animals than the pedigree information, leading to an increase in accuracy of breeding values. Here, we used pedigree-based and single-step genomic approaches to estimate variance components and breeding values for β-hydroxybutyrate milk content (BHB). Additionally, we performed a genome-wide association study (GWAS) to depict its genetic architecture. BHB concentrations within the first 90 days of lactation, estimated from milk medium infrared spectra, were available for 30,461 cows (70,984 records). Genotypes at 42,152 loci were available for 9,123 animals. Low heritabilities were found for BHB using pedigree-based (0.09 ± 0.01) and genomic (0.10 ± 0.01) approaches. Genetic correlation between BHB and milk traits ranged from -0.27 ± 0.06 (BHB and protein percentage) to 0.13 ± 0.07 (BHB and fat-to-protein ratio) using pedigree and from -0.26 ± 0.05 (BHB and protein percentage) to 0.13 ± 0.06 (BHB and fat-to-protein ratio) using genomics. Breeding values were validated for 344 genotyped cows using linear regression method. The genomic EBV (GEBV) had greater accuracy (0.51 vs. 0.45) and regression coefficient (0.98 vs. 0.95) compared to EBV. The correlation between two subsequent evaluations, without and with phenotypes for validation cows, was 0.85 for GEBV and 0.82 for EBV. Predictive ability (correlation between (G)EBV and adjusted phenotypes) was greater when genomic information was used (0.38) than in the pedigree-based approach (0.31). Validation statistics in the pairwise two-trait models (milk yield, fat and protein percentage, urea, fat/protein ratio, lactose and logarithmic transformation of somatic cells count) were very similar to the ones highlighted for the single-trait model. The GWAS allowed discovering four significant markers located on BTA20 (57.5-58.2 Mb), where the ANKH gene is mapped. This gene has been associated with lactose, alpha-lactalbumin and BHB. Results of this study confirmed the usefulness of genomic information to provide more accurate variance components and breeding values, and important insights about the genomic determination of BHB milk content.

Study on the concordance between different SNP-genotyping platforms in sheep

Different SNP genotyping technologies are commonly used in multiple studies to perform QTL detection, genotype imputation, and genomic predictions. Therefore, genotyping errors cannot be ignored, as they can reduce the accuracy of different procedures applied in genomic selection, such as genomic imputation, genomic predictions, and false-positive results in genome-wide association studies. Currently, whole-genome resequencing (WGR) also offers the potential for variant calling analysis and high-throughput genotyping. WGR might overshadow array-based genotyping technologies due to the larger amount and precision of the genomic information provided; however, its comparatively higher price per individual still limits its use in larger populations. Thus, the objective of this work was to evaluate the accuracy of the two most popular SNP-chip technologies, namely, Affymetrix and Illumina, for high-throughput genotyping in sheep considering high-coverage WGR datasets as references. Analyses were performed using two reference sheep genome assemblies, the popular Oar_v3.1 reference genome and the latest available version Oar_rambouillet_v1.0. Our results demonstrate that the genotypes from both platforms are suggested to have high concordance rates with the genotypes determined from reference WGR datasets (96.59% and 99.51% for Affymetrix and Illumina technologies, respectively). The concordance results provided in the current study can pinpoint low reproducible markers across multiple platforms used for sheep genotyping data. Comparing results using two reference genome assemblies also informs how genome assembly quality can influence genotype concordance rates among different genotyping platforms. Moreover, we describe an efficient pipeline to test the reliability of markers included in sheep SNP-chip panels against WGR datasets available on public databases. This pipeline may be helpful for discarding low-reliability markers before exploiting genomic information for gene mapping analyses or genomic prediction.

Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds

This study aimed to perform a GWAS to identify genomic regions associated with milk and cheese-making traits in Assaf and Churra dairy sheep breeds; second, it aimed to identify possible positional and functional candidate genes and their interactions through post-GWAS studies. For 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra), milk samples were collected and analyzed to determine 6 milk production and composition traits and 6 traits related to milk coagulation properties and cheese yield. The genetic profiles of the ewes were obtained using a genotyping chip array that included 50,934 SNP markers. For both milk and cheese-making traits, separate single-breed GWAS were performed using GCTA software. The set of positional candidate genes identified via GWAS was subjected to guilt-by-association-based prioritization analysis with ToppGene software. Totals of 84 and 139 chromosome-wise significant associations for the 6 milk traits and the 6 cheese-making traits were identified in this study. No significant SNPs were found in common between the 2 studied breeds, possibly due to their genetic heterogeneity of the phenotypes under study. Additionally, 63 and 176 positional candidate genes were located in the genomic intervals defined as confidence regions in relation to the significant SNPs identified for the analyzed traits for Assaf and Churra breeds. After the functional prioritization analysis, 71 genes were identified as promising positional and functional candidate genes and proposed as targets of future research to identify putative causative variants in relation to the traits under examination. In addition, this multitrait study allowed us to identify variants that have a pleiotropic effect on both milk production and cheese-related traits. The incorporation of variants among the proposed functional and positional candidate genes into genomic selection strategies represent an interesting approach for achieving rapid genetic gains, specifically for those traits difficult to measure, such as cheese-making traits.

Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle

BACKGROUND

Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk.

RESULTS

Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.

CONCLUSIONS

This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.

Effect of genomic X-chromosome regions on Nelore bull fertility

Scrotal circumference (SC) is a commonly used trait related to sexual precocity in bulls. Genome-wide association studies have uncovered a lot of genes related to this trait, however, only those present on autosomes. The inclusion of the second biggest chromosome (BTAX) can improve the knowledge of the genetic architecture of this trait. In this study, we performed a weighted, single-step, genome-wide association study using a 777 k BovineHD BeadChip (IllumHD) to analyze the association between SNPs and SC in Brazilian Nelore cattle. Phenotypes from 79,300 males and 3263 genotypes (2017 from females and 1246 from males)-(39,367 SNPs markers located at ChrX) were used. We identified eight regions on chromosome X that displayed important associations with SC. The results showed that together the genomic windows explained 28.52% of the genetic variance for the examined trait. Genes with potential functions in reproduction and fertility regulation were highlighted as candidates for sexual precocity rates in Nelore cattle (AFF2 and PJA1). Moreover, we found 10 genes that had not previously been identified as being associated with sexual precocity traits in cattle. These findings will further advance our understanding of the genetic architecture, considering mainly the presence of the chromosome X, for indicine cattle reproductive traits, being useful in the context of genomic prediction in beef cattle.

Genetic analysis on infrared-predicted milk minerals for Danish dairy cattle

A group of milk components that has shown potential to be predicted with milk spectra is milk minerals. Milk minerals are important for human health and cow health. Having an inexpensive and fast way to measure milk mineral concentrations would open doors for research, herd management, and selective breeding. The first aim of this study was to predict milk minerals with infrared milk spectra. Additionally, milk minerals were predicted with infrared-predicted fat, protein, and lactose content. The second aim was to perform a genetic analysis on infrared-predicted milk minerals, to identify QTL, and estimate variance components. For training and validating a multibreed prediction model for individual milk minerals, 264 Danish Jersey cows and 254 Danish Holstein cows were used. Partial least square regression prediction models were built for Ca, Cu, Fe, K, Mg, Mn, Na, P, Se, and Zn based on 80% of the cows, selected randomly. Prediction models were externally validated with 8 herds based on the remaining 20% of the cows. The prediction models were applied on a population of approximately 1,400 Danish Holstein cows with 5,600 infrared spectral records and 1,700 Danish Jersey cows with 7,200 infrared spectral records. Cows from this population had 50k imputed genotypes. Prediction accuracy was good for P and Ca, with external R² ≥ 0.80 and a relative prediction error of 5.4% for P and 6.3% for Ca. Prediction was moderately good for Na with an external R² of 0.63, and a relative error of 18.8%. Prediction accuracies of milk minerals based on infrared-predicted fat, protein, and lactose content were considerably lower than those based on the infrared milk spectra. This shows that the milk infrared spectrum contains valuable information on milk minerals, which is currently not used. Heritability for infrared-predicted Ca, Na, and P varied from low (0.13) to moderate (0.36). Several QTL for infrared-predicted milk minerals were observed that have been associated with gold standard milk minerals previously. In conclusion, this study has shown infrared milk spectra were good at predicting Ca, Na, and P in milk. Infrared-predicted Ca, Na, and P had low to moderate heritability estimates.

Nutrigenomic analyses reveal miRNAs and mRNAs affected by feed restriction in the mammary gland of midlactation dairy cows

The objective of this study was to investigate the effects of feed restriction on mammary miRNAs and coding gene expression in midlactation cows. Five Holstein cows and 6 Montbéliarde cows underwent 6 days of feed restriction, during which feed allowance was reduced to meet 50% of their net energy for lactation requirements. Mammary biopsies were performed before and at the end of the restriction period. Mammary miRNA and mRNA analyses were performed using high-throughput sequencing and microarray analyses, respectively. Feed restriction induced a negative energy balance and decreased milk production and fat and protein yields in both breeds. Feed restriction modified the expression of 27 miRNAs and 374 mRNAs in mammary glands from Holstein cows, whereas no significant miRNA change was observed in Montbéliarde cows. Among the 27 differentially expressed miRNAs, 8 miRNAs were associated with dairy QTL. Analysis of target genes indicate that the 8 most abundantly expressed miRNAs control transcripts related to lipid metabolism, mammary remodeling and stress response. A comparison between the mRNAs targeted by the 8 most strongly expressed miRNAs and 374 differentially expressed mRNAs identified 59 mRNAs in common. The bioinformatic analyses of these 59 mRNAs revealed their implication in lipid metabolism and endothelial cell proliferation. These effects of feed restriction on mammary miRNAs and mRNAs observed in Holstein cows suggest a potential role of miRNAs in mammary structure and lipid biosynthesis that could explain changes in milk production and composition.

Sequence-based GWAS and post-GWAS analyses reveal a key role of SLC37A1, ANKH, and regulatory regions on bovine milk mineral content

The mineral composition of bovine milk plays an important role in determining its nutritional and cheese-making value. Concentrations of the main minerals predicted from mid-infrared spectra produced during milk recording, combined with cow genotypes, provide a unique opportunity to decipher the genetic determinism of these traits. The present study included 1 million test-day predictions of Ca, Mg, P, K, Na, and citrate content from 126,876 Montbéliarde cows, of which 19,586 had genotype data available. All investigated traits were highly heritable (0.50-0.58), with the exception of Na (0.32). A sequence-based genome-wide association study (GWAS) detected 50 QTL (18 affecting two to five traits) and positional candidate genes and variants, mostly located in non-coding sequences. In silico post-GWAS analyses highlighted 877 variants that could be regulatory SNPs altering transcription factor (TF) binding sites or located in non-coding RNA (mainly lncRNA). Furthermore, we found 47 positional candidate genes and 45 TFs highly expressed in mammary gland compared to 90 other bovine tissues. Among the mammary-specific genes, SLC37A1 and ANKH, encoding proteins involved in ion transport were located in the most significant QTL. This study therefore highlights a comprehensive set of functional candidate genes and variants that affect milk mineral content.

A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep

Sheep milk is mainly intended to manufacture a wide variety of high-quality cheeses. The ovine cheese industry would benefit from an improvement, through genetic selection, of traits related to the milk coagulation properties (MCPs) and cheese yield-related traits, broadly denoted as "cheese-making traits." Considering that routine measurements of these traits needed for genetic selection are expensive and time-consuming, this study aimed to evaluate the accuracy of a cheese-making phenotype imputation method based on the information from official milk control records combined with the pH of the milk. For this study, we analyzed records of milk production traits, milk composition traits, and measurements of cheese-making traits available from a total of 1,145 dairy ewes of the Spanish Assaf sheep breed. Cheese-making traits included five related to the MCPs and two cheese yield-related traits. The milk and cheese-making phenotypes were adjusted for significant effects based on a general linear model. The adjusted phenotypes were used to define a multiple-phenotype imputation procedure for the cheese-making traits based on multivariate normality and Markov chain Monte Carlo sampling. Five of the seven cheese-making traits considered in this study achieved a prediction accuracy of 0.60 computed as the correlation between the adjusted phenotypes and the imputed phenotypes. Particularly the logarithm of curd-firming time since rennet addition (logK20) (0.68), which has been previously suggested as a potential candidate trait to improve the cheese ability in this breed, and the logarithm of the ratio between the rennet clotting time and the curd firmness at 60 min (logRCT/A60) (0.65), which has been defined by other studies as an indicator trait of milk coagulation efficiency. This study represents a first step toward the possible use of the phenotype imputation of cheese-making traits to develop a practical methodology for the dairy sheep industry to impute cheese-making traits only based on the analysis of a milk sample without the need of pedigree information. This information could be also used in future planning of specific breeding programs considering the importance of the cheese-making efficiency in dairy sheep and highlights the potential of phenotype imputation to leverage sample size on expensive, hard-to-measure phenotypes.

A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

BACKGROUND

Genetic pressure in animal breeding is sparking the interest of breeders for selecting elite boars with higher sperm quality to optimize ejaculate doses and fertility rates. However, the molecular basis of sperm quality is not yet fully understood. Our aim was to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analysing 25 sperm-related phenotypes and integrating genome-wide association studies (GWAS) and RNA-seq under a systems biology framework.

RESULTS

By GWAS, we identified 12 quantitative trait loci (QTL) associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 and ABCA1. By RNA-seq, we identified a wide repertoire of mRNAs (e.g. PRM1, OAZ3, DNAJB8, TPPP2 and TNP1) and miRNAs (e.g. ssc-miR-30d, ssc-miR-34c, ssc-miR-30c-5p, ssc-miR-191, members of the let-7 family and ssc-miR-425-5p) with functions related to sperm biology. We detected 6128 significant correlations (P-value ≤ 0.05) between sperm traits and mRNA abundances. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ, ACTR2 and HARS. Using the GWAS and RNA-seq data, we built a gene interaction network. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality and used them to build a robust gene interaction network. In addition, in the final network we included genes with RNA abundances correlated with more than four semen traits and miRNAs interacting with the genes on the network. The final network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. Finally, we designed a panel of 73 SNPs based on the GWAS, eGWAS and final network data, that explains between 5% (for sperm cell concentration) and 36% (for percentage of neck abnormalities) of the phenotypic variance of the sperm traits.

CONCLUSIONS

By applying a systems biology approach, we identified genes that potentially affect sperm quality and constructed a SNP panel that explains a substantial part of the phenotypic variance for semen quality in our study and that should be tested in other swine populations to evaluate its relevance for the pig breeding sector.

Investigating the accuracy of imputing autosomal variants in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

Background

Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to the empirical imputation accuracy.

Results

The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R²dose) was almost identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R²dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy increased from 0.903 to 0.913, and the marker density in the imputed data increased from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals. The model-based imputation quality scores from Minimac3 (Rsq) were systematically higher than empirically estimated accuracies. However, both metrics were positively correlated and the correlation increased with the size of the reference panel and MAF of imputed variants.

Conclusions

Accurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel and MAF of the imputed variants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07184-8.

Gene networks for three feed efficiency criteria reveal shared and specific biological processes

Background

French beef producers suffer from the decrease in profitability of their farms mainly because of the continuous increase in feed costs. Selection for feed efficiency in beef cattle represents a relevant solution to face this problem. However, feed efficiency is a complex trait that can be assessed by three major criteria: residual feed intake (RFI), residual gain (RG) and feed efficiency ratio (FE), which involve different genetic determinisms. An analysis that combines phenotype and whole-genome sequence data provides a unique framework for genomic studies. The aim of our study was to identify the gene networks and the biological processes that are responsible for the genetic determinism that is shared between these three feed efficiency criteria.

Results

A population of 1477 French Charolais young bulls was phenotyped for feed intake (FI), average daily gain (ADG) and final weight (FW) to estimate RFI, RG and FE. A subset of 789 young bulls was genotyped on the BovineSNP50 single nucleotide polymorphism (SNP) array and imputed at the sequence level using RUN6 of the 1000 Bull Genomes Project. We conducted a genome-wide association study (GWAS) to estimate the individual effect of 8.5 million SNPs and applied an association weight matrix (AWM) approach to analyse the results, one for each feed efficiency criterion. The results highlighted co-association networks including 626 genes for RFI, 426 for RG and 564 for FE. Enrichment assessment revealed the biological processes that show the strongest association with RFI, RG and FE, i.e. digestive tract (salivary, gastric and mucin secretion) and metabolic processes (cellular and cardiovascular). Energetic functions were more associated with RFI and FE and cardio-vascular and cellular processes with RG. Several hormones such as apelin, glucagon, insulin, aldosterone, the gonadotrophin releasing hormone and the thyroid hormone were also identified, and these should be tested in future studies as candidate biomarkers for feed efficiency.

Conclusions

The combination of network and pathway analyses at the sequence level led to the identification of both common and specific mechanisms that are involved in RFI, RG and FE, and to a better understanding of the genetic determinism underlying these three criteria. The effects of the genes involved in each of the identified processes need to be tested in genomic evaluations to confirm the potential gain in reliability of using functional variants to select animals for feed efficiency.

Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle

Background

Over the last years, genome-wide association studies (GWAS) based on imputed whole-genome sequences (WGS) have been used to detect quantitative trait loci (QTL) and highlight candidate genes for important traits. However, in general this approach does not allow to validate the effects of candidate mutations or determine if they are truly causative for the trait(s) in question. To address these questions, we applied a two-step, within-breed GWAS approach on 15 traits (5 linked with milk production, 2 with udder health, and 8 with udder morphology) in Montbéliarde (MON), Normande (NOR), and Holstein (HOL) cattle. We detected the most-promising candidate variants (CV) using imputed WGS of 2515 MON, 2203 NOR, and 6321 HOL bulls, and validated their effects in three younger populations of 23,926 MON, 9400 NOR, and 51,977 HOL cows.

Results

Bull sequence-based GWAS detected 84 QTL: 13, 10, and 30 for milk production traits; 3, 0, and 2 for somatic cell score (SCS); and 8, 2 and 16 for udder morphology traits, in MON, NOR, and HOL respectively. Five genomic regions with effects on milk production traits were shared among the three breeds whereas six (2 for production and 4 for udder morphology and health traits) had effects in two breeds. In 80 of these QTL, 855 CV were highlighted based on the significance of their effects and functional annotation. The subsequent GWAS on MON, NOR, and HOL cows validated 8, 9, and 23 QTL for production traits; 0, 0, and 1 for SCS; and 4, 1, and 8 for udder morphology traits, respectively. In 47 of the 54 confirmed QTL, the CV identified in bulls had more significant effects than single nucleotide polymorphisms (SNPs) from the standard 50K chip. The best CV for each validated QTL was located in a gene that was functionally related to production (36 QTL) or udder (9 QTL) traits.

Conclusions

Using this two-step GWAS approach, we identified and validated 54 QTL that included CV mostly located within functional candidate genes and explained up to 6.3% (udder traits) and 37% (production traits) of the genetic variance of economically important dairy traits. These CV are now included in the chip used to evaluate French dairy cattle and can be integrated into routine genomic evaluation.

Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds

Background

Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision.

Results

To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a large-scale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10⁻⁸) 138 QTL for fat percentage and 176 QTL for protein percentage. This was more than the number of QTL detected in all within-population GWAS together (124 QTL for fat percentage and 104 QTL for protein percentage). Among all the lead variants, 100 QTL for fat percentage and 114 QTL for protein percentage had the same direction of effect in all within-population GWAS. This indicates either persistence of the linkage phase between the causal variant and the lead variant across breeds or that some of the lead variants might indeed be causal or tightly linked with causal variants. The percentage of intergenic variants was substantially lower for significant variants than for non-significant variants, and significant variants had mostly moderate to high minor allele frequencies. Significant variants were also clustered in genes that are known to be relevant for fat and protein percentages in milk.

Conclusions

Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.

Gene Networks Driving Genetic Variation in Milk and Cheese-Making Traits of Spanish Assaf Sheep

Most of the milk produced by sheep is used for the production of high-quality cheese. Consequently, traits related to milk coagulation properties and cheese yield are economically important to the Spanish dairy industry. The present study aims to identify candidate genes and their regulators related to 14 milk and cheese-making traits and to develop a low-density panel of markers that could be used to predict an individual's genetic potential for cheese-making efficiency. In this study, we performed a combination of the classical genome-wide association study (GWAS) with a stepwise regression method and a pleiotropy analysis to determine the best combination of the variants located within the confidence intervals of the potential candidate genes that may explain the greatest genetic variance for milk and cheese-making traits. Two gene networks related to milk and cheese-making traits were created using the genomic relationship matrices built through a stepwise multiple regression approach. Several co-associated genes in these networks are involved in biological processes previously found to be associated with milk synthesis and cheese-making efficiency. The methodology applied in this study enabled the selection of a co-association network comprised of 374 variants located in the surrounding of genes showing a potential influence on milk synthesis and cheese-making efficiency.

Mid-infrared spectrometry prediction of the cheese-making properties of raw Montbéliarde milks from herds and cheese dairy vats used for the production of Protected Designation of Origin and Protected Geographical Indication cheeses in Franche-Comté

Franche-Comté is the primary producing region of Protected Designation of Origin cheeses in France. Normally, mid-infrared (MIR) prediction models for cheese-making property (CMP) traits are developed using individual bovine milks. However, considering the requests of all actors in the dairy sector, the present study aimed to assess the feasibility of MIR spectroscopy to develop CMP equations of Montbéliarde herd and dairy vat milks. For this purpose, 22 CMP traits were analyzed on samples collected in 2016 (half in February-March and half in May-June) from 100 commercial herds and 70 dairy vats (55 cheese dairies) located in Franche-Comté. These characteristics included 11 rennet coagulation traits and 8 lactic acidification traits measured in either soft cheese or pressed cooked cheese conditions and 3 laboratory curd yields. Models of MIR prediction for each of the 22 CMP traits were built using partial least squares regression with external validation by dividing the data set into calibration (70%) and validation (30%) sets. We confirmed that the variability of milk traits depends largely on the production scale and is higher for individual milk than for herd milk and even higher for vat milk. The best prediction models were obtained in herd milk samples for curd yields expressed in dry matter or fresh, with a coefficient of determination (R²) in external validation of 0.78 and 0.77, respectively. As with individual milk, these traits are closely related to the gross composition of the milk and therefore easier to predict by MIR spectroscopy. However, these curd yield traits were poorly predicted (R² = 0.58) in vat milk samples due to their lower variability. In herd milk samples, prediction models of other CMP traits were poorly accurate except for the ratio of the time to obtain a standard firmness to the rennet coagulation time in soft cheese or pressed cooked cheese conditions, which showed R² > 0.66 in external validation. Such trait is important in qualifying the behavior of milk during cheese production. Prediction models of other CMP traits for either herd or vat milk samples had poor accuracy, and further work is needed to improve their performance.

Identification of the ABCC4, IER3, and CBFA2T2 candidate genes for resistance to paratuberculosis from sequence-based GWAS in Holstein and Normande dairy cattle

Background

Bovine paratuberculosis is a contagious disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), with adverse effects on animal welfare and serious economic consequences. Published results on host genetic resistance to MAP are inconsistent, mainly because of difficulties in characterizing the infection status of cows. The objectives of this study were to identify quantitative trait loci (QTL) for resistance to MAP in Holstein and Normande cows with an accurately defined status for MAP.

Results

From MAP-infected herds, cows without clinical signs of disease were subjected to at least four repeated serum ELISA and fecal PCR tests over time to determine both infected and non-infected statuses. Clinical cases were confirmed using PCR. Only cows that had concordant results for all tests were included in further analyses. Positive and control cows were matched within herd according to their birth date to ensure a same level of exposure to MAP. Cows with accurate phenotypes, i.e. unaffected (control) or affected (clinical or non-clinical cases), were genotyped with the Illumina BovineSNP50 BeadChip. Genotypes were imputed to whole-genome sequences using the 1000 Bull Genomes reference population (run6). A genome-wide association study (GWAS) of MAP status of 1644 Holstein and 649 Normande cows, using either two (controls versus cases) or three classes of phenotype (controls, non-clinical and clinical cases), revealed three regions, on Bos taurus (BTA) chromosomes 12, 13, and 23, presenting significant effects in Holstein cows, while only one of those was identified in Normande cows (BTA23). The most significant effect was found on BTA13, in a short 8.5-kb region. Conditional analyses revealed that only one causal variant may be responsible for the effects observed on each chromosome with the ABCC4 (BTA12), CBFA2T2 (BTA13), and IER3 (BTA23) genes as good functional candidates.

Conclusions

A sequence-based GWAS on cows for which resistance to MAP was accurately defined, was able to identify candidate variants located in genes that were functionally related to resistance to MAP; these explained up to 28% of the genetic variance of the trait. These results are very encouraging for efforts towards implementation of a breeding strategy aimed at improving resistance to paratuberculosis in Holstein cows.

Gene polymorphisms influencing yield, composition and technological properties of milk from Czech Simmental and Holstein cows

Objective

The aim of the study was to evaluate the influence of polymorphic loci and other factors on milk performance and the technological properties of milk.

Methods

The analysis was performed on Simmental and Holstein cows in field conditions (n = 748). Milk yield in kg, fat and protein percentage and yield were evaluated. Technological properties were evaluated by milk fermentation ability, renneting, and an alcohol test. Polymorphisms in the acyl-CoA diacylgycerol transferase 1 (DGAT1), leptin (LEP), fatty acid synthase (FASN), stearoyl CoA desaturase 1 (SCD1), casein beta (CSN2), casein kappa (CSN3), and lactoglobulin beta genes were genotyped, and association analysis was performed.

Results

The DGAT1 AA genotype was associated with higher milk, protein and fat yields (p<0.05). The MM genotype in the LEP gene was associated with a lower protein percentage and the W allele with a higher protein percentage (p<0.05). In cows with the FASN GG genotype, the protein percentage was higher, but the A allele was associated with higher milk, protein and fat yields than the G allele. The TT genotype in SCD1 was associated with the lowest milk, protein and fat yields and with the highest milk protein percentage (p<0.01). The T allele had higher values than the C allele (p<0.05) except for fat percentage. The genotype CSN3 AA was associated with a significantly heightened milk yield; BB was associated with a high protein percentage. The effect of the alleles on the technological properties was not significant. The CSN2 BB genotype was associated with the best alcohol test (p<0.01), and the renneting order was inverse. Milk from cows with the CSN2 A¹A¹ genotype was best in the milk fermentation ability. CSN3 significantly affected the technological properties.

Conclusion

The findings revealed the potential of some polymorphic loci for use in dairy cattle breeding and for the management of milk quality. In field research, the pivotal role of farms in milk yield, composition and technological properties was confirmed.