Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition

Identification of candidate genes associated with milk production and mastitis based on transcriptome-wide association study

Genetic research for the assessment of mastitis and milk production traits simultaneously has a long history. The main issue that arises in this context is the known existence of a positive correlation between the risk of mastitis and lactation performance due to selection. The transcriptome-wide association study (TWAS) approach endeavors to combine the expression quantitative trait loci and genome-wide association study summary statistics to decode complex traits or diseases. Accordingly, we used the farmgtex project results as a complete bovine database for mastitis and milk production. The results of colocalization and TWAS approaches were used for the detection of functional associated candidate genes with milk production and mastitis traits on multiple tissue-based transcriptome records. Also, we used the david database for gene ontology to identify significant terms and associated genes. For the identification of interaction networks, the genemania and string databases were used. Also, the available z-scores in TWAS results were used for the calculation of the correlation between tissues. Therefore, the present results confirm that LYNX1, DGAT1, C14H8orf33, and LY6E were identified as significant genes associated with milk production in eight, six, five, and five tissues, respectively. Also, FBXL6 was detected as a significant gene associated with mastitis trait. CLN3 and ZNF34 genes emerged via both the colocalization and TWAS approaches as significant genes for milk production trait. It is expected that TWAS and colocalization can improve our perception of the potential health status control mechanism in high-yielding dairy cows.

Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle

BACKGROUND

Characterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22-150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle.

RESULTS

We identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits.

CONCLUSIONS

This study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.

Association study between SNP markers located in meat quality candidate genes with intramuscular fat content in an endangered dual-purpose cattle population

The aim of this study was to associate single nucleotide polymorphisms (SNP) of the bovine calcium-activated neutral protease µ-calpain, calpastatin, diacylglycerol-O-acyltransferase, adipose fatty acid binding protein, retinoic acid receptor-related orphan receptor C (RORC), and thyroglobulin (TG) gene with intramuscular fat content (IMF). Therefore, 542 animals of the cattle breed "Rotes Höhenvieh" (RHV) were phenotyped for IMF. Genotyping of the animals was performed using polymerase chain reaction-restriction fragment length polymorphism tests for six SNP from candidate genes for meat quality traits. In addition, we calculated allele substitution and dominance effects on IMF. A subgroup of animals (n = 44, reduced dataset) with extraordinary high IMF was analyzed separately. The mean IMF content was 2.5% (SD: 2.8) but ranged from 0.02% to 23.9%, underlining the breeds' potential for quality meat production. Allele and genotype frequencies for all SNP were similar in the complete and reduced dataset. Association analyses in the complete dataset revealed the strongest effects of RORC on IMF (P = 0.075). The log-transformed least-squares mean for IMF of genotype g.3290GG was 0.45 ± 0.16, 0.26 ± 0.14 for genotype g.3290GT, and 0.32 ± 0.14 for genotype g.3290TT. In the reduced dataset, we found a significant effect (P < 0.05) of the g.422C>T-SNP of TG on IMF, with highest IMF for genotype CT (0.91 ± 0.17), lowest IMF for genotype TT (0.37 ± 0.25), and medium IMF for genotype CC (0.59 ± 0.16; log-transformed values). Compared to the complete dataset, allele substitution effects increased in the reduced dataset for most of the SNP, possibly due to the selective genotyping strategy, with focus on animals with highest IMF implying strong phenotypic IMF contrast. Dominance effects were small in both datasets, related to the high heritability of IMF. Results indicated RHV breed particularities regarding the effects of meat quality genes on IMF. An explanation might be the breeding history of RHV with focus on adaptation and resilience in harsh outdoor systems. Consequently, it is imperative to develop breed-specific selection strategies. Allele substitution and dominance effects were in a similar direction in both datasets, suggesting the same breeding approaches for different RHV strains in different regions. Nevertheless, a selective genotyping approach (reduced dataset), contributed to more pronounced genotype effect differences on IMF and dominance values.

GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values

BACKGROUND

Since the very beginning of genomic selection, researchers investigated methods that improved upon SNP-BLUP (single nucleotide polymorphism best linear unbiased prediction). SNP-BLUP gives equal weight to all SNPs, whereas it is expected that many SNPs are not near causal variants and thus do not have substantial effects. A recent approach to remedy this is to use genome-wide association study (GWAS) findings and increase the weights of GWAS-top-SNPs in genomic predictions. Here, we employ a genome-wide approach to integrate GWAS results into genomic prediction, called GWABLUP.

RESULTS

GWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. Using high-density genotypes and milk, fat, protein and somatic cell count phenotypes on dairy cows, GWABLUP was compared to GBLUP, GBLUP (topSNPs) with extra weights for GWAS top-SNPs, and BayesGC, i.e. a Bayesian variable selection model. The GWAS resulted in six, five, four, and three genome-wide significant peaks for milk, fat and protein yield and somatic cell count, respectively. GWABLUP genomic predictions were 10, 6, 7 and 1% more reliable than those of GBLUP for milk, fat and protein yield and somatic cell count, respectively. It was also more reliable than GBLUP (topSNPs) for all four traits, and more reliable than BayesGC for three of the traits. Although GWABLUP showed a tendency towards inflation bias for three of the traits, this was not statistically significant. In a multitrait analysis, GWABLUP yielded the highest accuracy for two of the traits. However, for SCC, which was relatively unrelated to the yield traits, including yield trait GWAS-results reduced the reliability compared to a single trait analysis.

CONCLUSIONS

GWABLUP uses GWAS results to differentially weigh all the SNPs in a weighted GBLUP genomic prediction analysis. GWABLUP yielded up to 10% and 13% more reliable genomic predictions than GBLUP for single and multitrait analyses, respectively. Extension of GWABLUP to single-step analyses is straightforward.

AGIDB: a versatile database for genotype imputation and variant decoding across species

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.

A Million-Cow Validation of a Chromosome 14 Region Interacting with All Chromosomes for Fat Percentage in U.S. Holstein Cows

A genome-wide association study (GWAS) of fat percentage (FPC) using 1,231,898 first lactation cows and 75,198 SNPs confirmed a previous result that a Chr14 region about 9.38 Mb in size (0.14-9.52 Mb) had significant inter-chromosome additive × additive (A×A) effects with all chromosomes and revealed many new such effects. This study divides this 9.38 Mb region into two sub-regions, Chr14a at 0.14-0.88 Mb (0.74 Mb in size) with 78% and Chr14b at 2.21-9.52 Mb (7.31 Mb in size) with 22% of the 2761 significant A×A effects. These two sub-regions were separated by a 1.3 Mb gap at 0.9-2.2 Mb without significant inter-chromosome A×A effects. The PPP1R16A-FOXH1-CYHR1-TONSL (PFCT) region of Chr14a (29 Kb in size) with four SNPs had the largest number of inter-chromosome A×A effects (1141 pairs) with all chromosomes, including the most significant inter-chromosome A×A effects. The SLC4A4-GC-NPFFR2 (SGN) region of Chr06, known to have highly significant additive effects for some production, fertility and health traits, specifically interacted with the PFCT region and a Chr14a region with CPSF1, ADCK5, SLC52A2, DGAT1, SMPD5 and PARP10 (CASDSP) known to have highly significant additive effects for milk production traits. The most significant effects were between an SNP in SGN and four SNPs in PFCT. The CASDSP region mostly interacted with the SGN region. In the Chr14b region, the 2.28-2.42 Mb region (138.46 Kb in size) lacking coding genes had the largest cluster of A×A effects, interacting with seventeen chromosomes. The results from this study provide high-confidence evidence towards the understanding of the genetic mechanism of FPC in Holstein cows.

Developing flexible models for genetic evaluations in smallholder crossbred dairy farms

The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.

Changes in colostrum ingredients of Hu sheep, as well as the missense mutation genes associated with colostrum yield

This study focused on the changes in the composition and immune evolution in milk from birth to 144 h postpartum and the genes associated with the colostrum yield of Hu sheep. Twelve Hu sheep, which were bred carefully under animal health standards and have a litter size of two kids and similar gestation length (149 ± 1 days), were used. Lambs were transferred into their own cots to avoid interference. The compositional content (i.e., fat, protein, and lactose) and some other properties, including daily colostrum yield, DM, and SNF, were determined. In addition, immunity molecules (IgG, IgA, and IgM concentrations) received remarkable attention. The DM, SNF, fat, and protein contents were higher in the first days postpartum and then dropped quickly from the time of birth to 144 h postpartum. However, the lactose content displayed an increasing pattern and reached normal milk percentage at 48 h. The highest IgG (103.17 mg/mL), IgA (352.82 μg/mL), and IgM (2.79 mg/mL) colostrum concentrations were observed at partum, decreased quickly, and finally stabilized. The change law of concentration of IgA and IgM in colostrum are the same with IgG. Furthermore, the whole-genome resequencing was performed, and a missense variant locus in the SRC gene and two missense locus variants in the HIF1A gene were significantly associated with the colostrum yield of sheep by using the whole-genome selection signal detection analysis. In conclusions, colostrum contains abundant nutrients especially immunoglobulin, and the HIF1A gene may be used as candidate genes for colostrum yield, which has important information as a basic knowledge for the Hu sheep breeding program.

Analysis of the impact of DGAT1 p.M435L and p.K232A variants on pre-mRNA splicing in a full-length gene assay

DGAT1 is playing a major role in fat metabolism and triacylglyceride synthesis. Only two DGAT1 loss-of-function variants altering milk production traits in cattle have been reported to date, namely p.M435L and p.K232A. The p.M435L variant is a rare alteration and has been associated with skipping of exon 16 which results in a non-functional truncated protein, and the p.K232A-containing haplotype has been associated with modifications of the splicing rate of several DGAT1 introns. In particular, the direct causality of the p.K232A variant in decreasing the splicing rate of the intron 7 junction was validated using a minigene assay in MAC-T cells. As both these DGAT1 variants were shown to be spliceogenic, we developed a full-length gene assay (FLGA) to re-analyse p.M435L and p.K232A variants in HEK293T and MAC-T cells. Qualitative RT-PCR analysis of cells transfected with the full-length DGAT1 expression construct carrying the p.M435L variant highlighted complete skipping of exon 16. The same analysis performed using the construct carrying the p.K232A variant showed moderate differences compared to the wild-type construct, suggesting a possible effect of this variant on the splicing of intron 7. Finally, quantitative RT-PCR analyses of cells transfected with the p.K232A-carrying construct did not show any significant modification on the splicing rate of introns 1, 2 and 7. In conclusion, the DGAT1 FLGA confirmed the p.M435L impact previously observed in vivo, but invalidated the hypothesis whereby the p.K232A variant strongly decreased the splicing rate of intron 7.

Assessing Genomic Diversity and Signatures of Selection in Chinese Red Steppe Cattle Using High-Density SNP Array

Chinese Red Steppe Cattle (CRS), a composite cattle breed, is well known for its milk production, high slaughter rate, carcass traits, and meat quality. Nowadays, it is widely bred in Jilin and Hebei Province and the Inner Mongolia Autonomous region. However, the population structure and the genetic basis of prominent characteristics of CRS are still unknown. In this study, we systematically describe their population structure, genetic diversity, and selection signature based on genotyping data from 61 CRS individuals with GGP Bovine 100 K chip. The results showed that CRS cattle had low inbreeding levels and had formed a unique genetic structure feature. Using two complementary methods (including comprehensive haplotype score and complex likelihood ratio), we identified 1291 and 1285 potentially selected genes, respectively. There were 141 genes annotated in common 106 overlapping genomic regions covered 5.62 Mb, including PLAG1, PRKG2, DGAT1, PARP10, TONSL, ADCK5, and BMP3, most of which were enriched in pathways related to muscle growth and differentiation, milk production, and lipid metabolism. This study will contribute to understanding the genetic mechanism behind artificial selection and give an extensive reference for subsequent breeding.

The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle

BACKGROUND

A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle.

RESULTS

We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits.

CONCLUSIONS

The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.

Association of Single Nucleotide Polymorphism in the DGAT1 Gene with the Fatty Acid Composition of Cows Milked Once and Twice a Day

A single nucleotide polymorphism (SNP) rs109421300 of the diacylglycerol acyltransferase 1 (DGAT1) on bovine chromosome 14 is associated with fat yield, fat percentage, and protein percentage. This study aimed to investigate the effect of SNP rs109421300 on production traits and the fatty acid composition of milk from cows milked once a day (OAD) and twice a day (TAD) under New Zealand grazing conditions. Between September 2020 and March 2021, 232 cows from a OAD herd and 182 cows from a TAD herd were genotyped. The CC genotype of SNP rs109421300 was associated with significantly (p < 0.05) higher fat yield, fat percentage, and protein percentage, and lower milk and protein yields in both milking frequencies. The CC genotype was also associated with significantly (p < 0.05) higher proportions of C16:0 and C18:0, higher predicted solid fat content at 10 °C (SFC₁₀), and lower proportions of C4:0 and C18:1 cis-9 in both milking frequencies. The association of SNP with fatty acids was similar in both milking frequencies, with differences in magnitudes. The SFC₁₀ of cows milked OAD was lower than cows milked TAD for all three SNP genotypes suggesting the suitability of OAD milk for producing easily spreadable butter. These results demonstrate that selecting cows with the CC genotype is beneficial for New Zealand dairy farmers with the current payment system, however, this would likely result in less spreadable butter.

Strong evidence for association between K232A polymorphism of the DGAT1 gene and milk fat and protein contents: A meta-analysis

The relationship between K232A polymorphism of the DGAT1 gene and milk yield and composition was evaluated by meta-analysis of pooled data of more than 10,000 genotyped cattle. Four genetic models, including dominant (AA+KA vs. KK), recessive (AA vs. KA+KK), additive (AA vs. KK), and co-dominant (AA+KK vs. KA) were used to analyze the data. The standardized mean difference (SMD) was used to measure the size of the effects of the A and K alleles of K232A polymorphism on milk-related traits. The results showed that additive model was the best model for describing the effects of K232A polymorphism on studied traits. Under additive model, milk fat content was strongly decreased in cows having the AA genotype (SMD = -1.320). Furthermore, the AA genotype reduced the protein content of milk (SMD = -0.400). A significant difference in daily milk yield (SMD = 0.225) and lactation yield (SMD = 0.697) was found between cows carrying AA and KK genotypes, suggesting the positive effects of the K allele on these traits. Cook's distance measurement suggested some studies as outliers and sensitivity analyses by removing influential studies revealed that the results of meta-analyses for daily milk yield, fat content and protein content were not sensitive to outliers. However, the outcome of the meta-analysis for lactation yield was strongly influenced by outlier studies. Egger's test and Begg's funnel plots showed no evidence of publication bias in included studies. In conclusion, the K allele of K232A polymorphism showed a tremendous effect on increasing fat and protein contents in the milk of cattle, especially when 2 copies of this allele are inherited together, whereas the A allele of K232A polymorphism had negative effects on these traits.

Genetic analysis of production traits and body size measurements and their relationships with metabolic diseases in German Holstein cattle

This study sheds light on the genetic complexity and interplay of production, body size, and metabolic health in dairy cattle. Phenotypes for body size-related traits from conformation classification (130,166 animals) and production (101,562 animals) of primiparous German Holstein cows were available. Additionally, 21,992, 16,641, and 7,096 animals were from herds with recordings of the metabolic diseases ketosis, displaced abomasum, and milk fever in first, second, and third lactation. Moreover, all animals were genotyped. Heritabilities of traits and genetic correlations between all traits were estimated and GWAS were performed. Heritability was between 0.240 and 0.333 for production and between 0.149 and 0.368 for body size traits. Metabolic diseases were lowly heritable, with estimates ranging from 0.011 to 0.029 in primiparous cows, from 0.008 to 0.031 in second lactation, and from 0.037 to 0.052 in third lactation. Production was found to have negative genetic correlations with body condition score (BCS; -0.279 to -0.343) and udder depth (-0.348 to -0.419). Positive correlations were observed for production and body depth (0.138-0.228), dairy character (DCH) (0.334-0.422), and stature (STAT) (0.084-0.158). In first parity cows, metabolic disease traits were unfavorably correlated with production, with genetic correlations varying from 0.111 to 0.224, implying that higher yielding cows have more metabolic problems. Genetic correlations of disease traits in second and third lactation with production in primiparous cows were low to moderate and in most cases unfavorable. While BCS was negatively correlated with metabolic diseases (-0.255 to -0.470), positive correlations were found between disease traits and DCH (0.269-0.469) as well as STAT (0.172-0.242). Thus, the results indicate that larger and sharper animals with low BCS are more susceptible to metabolic disorders. Genome-wide association studies revealed several significantly associated SNPs for production and conformation traits, confirming previous findings from literature. Moreover, for production and conformation traits, shared significant signals on Bos taurus autosome (BTA) 5 (88.36 Mb) and BTA 6 (86.40 to 87.27 Mb) were found, implying pleiotropy. Additionally, significant SNPs were observed for metabolic diseases on BTA 3, 10, 14, 17, and 26 in first lactation and on BTA 2, 6, 8, 17, and 23 in third lactation. Overall, this study provides important insights into the genetic basis and interrelations of relevant traits in today's Holstein cattle breeding programs, and findings may help to improve selection decisions.

Identification and annotation of milk associated genes from milk somatic cells using expression and RNA-seq data

It is of interest to identify and annotate milk associated genes using expression profiling and RNA-Seq data from milk somatic cells. RNA-Seq data was pre-processed and mapping was done to identify differentially expressed genes (DEG). The functional insights about the up and down regulated genes were gleaned using the protein-protein interaction Network in the STRING database followed by CytoHubba analysis in Cytoscope. Gene ontology, annotation and pathway enrichment was completed using ShinyGO, David tool and QTL analysis. These analysis shows that 21 genes are linked with the secretion of milk.

Genetic diversity of DGAT1 gene linked to milk production in cattle populations of Ethiopia

Background

Diacylglycerol acyl-CoA acyltransferase 1 (DGAT1) has become a promising candidate gene for milk production traits because of its important role as a key enzyme in catalyzing the final step of triglyceride synthesis. Thus use of bovine DGAT1 gene as milk production markers in cattle is well established. However, there is no report on polymorphism of the DGAT1 gene in Ethiopian cattle breeds. The present study is the first comprehensive report on diversity, evolution, neutrality evaluation and genetic differentiation of DGAT1 gene in Ethiopian cattle population. The aim of this study was to characterize the genetic variability of exon 8 region of DGAT1 gene in Ethiopian cattle breeds.

Results

Analysis of the level of genetic variability at the population and sequence levels with genetic distance in the breeds considered revealed that studied breeds had 11, 0.615 and 0.010 haplotypes, haplotype diversity and nucleotide diversity respectively. Boran-Holstein showed low minor allele frequency and heterozygosity, while Horro showed low nucleotide and haplotype diversities. The studied cattle DGAT1 genes were under purifying selection. The neutrality test statistics in most populations were negative and statistically non-significant (p > 0.10) and consistent with a populations in genetic equilibrium or in expansion. Analysis for heterozygosity, polymorphic information content and inbreeding coefficient revealed sufficient genetic variation in DGAT1 gene. The pairwise F_ST values indicated significant differentiation among all the breeds (F_ST = 0.13; p ≤ 0.05), besides the rooting from the evolutionary or domestication history of the cattle inferred from the phylogenetic tree based on the neighbourhood joining method. There was four separated cluster among the studied cattle breeds, and they shared a common node from the constructed tree.

Conclusion

The cattle populations studied were polymorphic for DGAT1 locus. The DGAT1 gene locus is extremely crucial and may provide baseline information for in-depth understanding, exploitation of milk gene variation and could be used as a marker in selection programmes to enhance the production potential and to accelerate the rate of genetic gain in Ethiopian cattle populations exposed to different agro ecology condition.

Acyl-CoA:diacylglycerol acyltransferase: Properties, physiological roles, metabolic engineering and intentional control

Acyl-CoA:diacylglycerol acyltransferase (DGAT, EC 2.3.1.20) catalyzes the last reaction in the acyl-CoA-dependent biosynthesis of triacylglycerol (TAG). DGAT activity resides mainly in membrane-bound DGAT1 and DGAT2 in eukaryotes and bifunctional wax ester synthase-diacylglycerol acyltransferase (WSD) in bacteria, which are all membrane-bound proteins but exhibit no sequence homology to each other. Recent studies also identified other DGAT enzymes such as the soluble DGAT3 and diacylglycerol acetyltransferase (EaDAcT), as well as enzymes with DGAT activities including defective in cuticular ridges (DCR) and steryl and phytyl ester synthases (PESs). This review comprehensively discusses research advances on DGATs in prokaryotes and eukaryotes with a focus on their biochemical properties, physiological roles, and biotechnological and therapeutic applications. The review begins with a discussion of DGAT assay methods, followed by a systematic discussion of TAG biosynthesis and the properties and physiological role of DGATs. Thereafter, the review discusses the three-dimensional structure and insights into mechanism of action of human DGAT1, and the modeled DGAT1 from Brassica napus. The review then examines metabolic engineering strategies involving manipulation of DGAT, followed by a discussion of its therapeutic applications. DGAT in relation to improvement of livestock traits is also discussed along with DGATs in various other eukaryotic organisms.

Rare and population-specific functional variation across pig lines

BACKGROUND

It is expected that functional, mainly missense and loss-of-function (LOF), and regulatory variants are responsible for most phenotypic differences between breeds and genetic lines of livestock species that have undergone diverse selection histories. However, there is still limited knowledge about the existing missense and LOF variation in commercial livestock populations, in particular regarding population-specific variation and how it can affect applications such as across-breed genomic prediction.

METHODS

We re-sequenced the whole genome of 7848 individuals from nine commercial pig lines (average sequencing coverage: 4.1×) and imputed whole-genome genotypes for 440,610 pedigree-related individuals. The called variants were categorized according to predicted functional annotation (from LOF to intergenic) and prevalence level (number of lines in which the variant segregated; from private to widespread). Variants in each category were examined in terms of their distribution along the genome, alternative allele frequency, per-site Wright's fixation index (F_ST), individual load, and association to production traits.

RESULTS

Of the 46 million called variants, 28% were private (called in only one line) and 21% were widespread (called in all nine lines). Genomic regions with a low recombination rate were enriched with private variants. Low-prevalence variants (called in one or a few lines only) were enriched for lower allele frequencies, lower F_ST, and putatively functional and regulatory roles (including LOF and deleterious missense variants). On average, individuals carried fewer private deleterious missense alleles than expected compared to alleles with other predicted consequences. Only a small subset of the low-prevalence variants had intermediate allele frequencies and explained small fractions of phenotypic variance (up to 3.2%) of production traits. The significant low-prevalence variants had higher per-site F_ST than the non-significant ones. These associated low-prevalence variants were tagged by other more widespread variants in high linkage disequilibrium, including intergenic variants.

CONCLUSIONS

Most low-prevalence variants have low minor allele frequencies and only a small subset of low-prevalence variants contributed detectable fractions of phenotypic variance of production traits. Accounting for low-prevalence variants is therefore unlikely to noticeably benefit across-breed analyses, such as the prediction of genomic breeding values in a population using reference populations of a different genetic background.

Association Studies and Genomic Prediction for Genetic Improvements in Agriculture

To feed the fast growing global population with sufficient food using limited global resources, it is urgent to develop and utilize cutting-edge technologies and improve efficiency of agricultural production. In this review, we specifically introduce the concepts, theories, methods, applications and future implications of association studies and predicting unknown genetic value or future phenotypic events using genomics in the area of breeding in agriculture. Genome wide association studies can identify the quantitative genetic loci associated with phenotypes of importance in agriculture, while genomic prediction utilizes individual genetic value to rank selection candidates to improve the next generation of plants or animals. These technologies and methods have improved the efficiency of genetic improvement programs for agricultural production via elite animal breeds and plant varieties. With the development of new data acquisition technologies, there will be more and more data collected from high-through-put technologies to assist agricultural breeding. It will be crucial to extract useful information among these large amounts of data and to face this challenge, more efficient algorithms need to be developed and utilized for analyzing these data. Such development will require knowledge from multiple disciplines of research.

Inputs for optimizing selection platform for milk production traits of dairy Sahiwal cattle

The premises for the potential success of molecular breeding is the ability to identify major genes associated with important dairy related traits. The present study was taken up with the objectives to identify single nucleotide polymorphism (SNP) of bovine MASP2 and SIRT1 genes and its effect on estimated breeding values (EBVs) and to estimate genetic parameters for lactation milk yield (LMY), 305-day milk yield (305dMY), 305-day fat yield (305dFY), 305-day solid not fat yield (305dSNFY) and lactation length (LL) in Sahiwal dairy cattle to devise a promising improvement strategy. Genetic parameters and breeding values of milk production traits were estimated from 935 Sahiwal cattle population (1979–2019) reared at National Dairy Research Institute at Karnal, India. A total of 7 SNPs, where one SNP (g.499C>T) in exon 2 and four SNPs (g.576G>A, g.609T>C, g.684G>T and g.845A>G) in exon 3 region of MASP2 gene and 2 SNPs (g.-306T>C and g.-274G>C) in the promoter region of SIRT1 gene were identified in Sahiwal cattle population. Five of these identified SNPs were chosen for further genotyping by PCR-RFLP and association analysis. Association analysis was performed using estimated breeding values (n = 150) to test the effect of SNPs on LMY, 305dMY, 305dFY, 305dSNFY and LL. Association analysis revealed that, three SNP markers (g.499C>T, g.609T>C and g.-306T>C) were significantly associated with all milk yield traits. The estimates for heritability using repeatability model for LMY, 305dMY, 305dFY, 305dSNFY and LL were low, however the corresponding estimates from first parity were 0.20±0.08, 0.17±0.08, 0.13±0.09, 0.13±0.09 and 0.24, respectively. The repeatability estimates were moderate to high indicating consistency of performance over the parities and hence reliability of first lactation traits. Genetic correlations among the traits of first parity were high (0.55 to 0.99). From the results we could conclude that optimum strategy to improve the Sahiwal cattle further would be selecting the animals based on their first lactation 305dMY. Option top include the significant SNP in selection criteria can be explored. Taken together, a 2-stage selection approach, select Sahiwal animals early for the SNP and then on the basis of first lactation 305dMY will help to save resources.

Using mid-infrared spectroscopy to increase GWAS power to detect QTL associated with blood urea nitrogen

Blood urea nitrogen (BUN) is an indicator trait for urinary nitrogen excretion. Measuring BUN level requires a blood sample, which limits the number of records that can be obtained. Alternatively, BUN can be predicted using mid-infrared (MIR) spectroscopy of a milk sample and thus records become available on many more cows through routine milk recording processes. The genetic correlation between MIR predicted BUN (MBUN) and BUN is 0.90. Hence, genetically, BUN and MBUN can be considered as the same trait. The objective of our study was to perform genome-wide association studies (GWAS) for BUN and MBUN, compare these two GWAS and detect quantitative trait loci (QTL) for both traits, and compare the detected QTL with previously reported QTL for milk urea nitrogen (MUN). The dataset used for our analyses included 2098 and 18,120 phenotypes for BUN and MBUN, respectively, and imputed whole-genome sequence data. The GWAS for MBUN was carried out using either the full dataset, the 2098 cows with records for BUN, or 2000 randomly selected cows, so that the dataset size is comparable to that for BUN. The GWAS results for BUN and MBUN were very different, in spite of the strong genetic correlation between the two traits. We detected 12 QTL for MBUN, on bovine chromosomes 2, 3, 9, 11, 12, 14 and X, and one QTL for BUN on chromosome 13. The QTL detected on chromosomes 11, 14 and X overlapped with QTL detected for MUN. The GWAS results were highly sensitive to the subset of records used. Hence, caution is warranted when interpreting GWAS based on small datasets, such as for BUN. MBUN may provide an attractive alternative to perform a more powerful GWAS to detect QTL for BUN.

Effects of DGAT1 on milk performance in Sudanese Butana × Holstein crossbred cattle

The improvement of milk production of indigenous Sudanese cattle such as Bos indicus Butana and its cross with Holstein is a major goal of the Sudanese government to ensure sufficient healthy nutrition in the country. In this study, we investigated the K232A polymorphism of diacylglycerol acyltransferase (DGAT1), a well-known modulator of milk production in other breeds. We determined allele frequencies and the allele effects on milk production. Therefore, 93 purebred Butana and 203 Butana × Holstein crossbred cattle were genotyped using competitive allele-specific PCR assays. Association analysis was performed using a linear mixed model in R. In purebred Butana cattle, the lysine DGAT1 protein variant K232, which is found to be associated with higher fat and protein contents, as well as higher fat yield was highly frequent at 0.929, while its frequency in Butana × Holstein crossbred cattle was 0.394. Significant effects were found on milk yield (P = 7.6 × 10^-20), fat yield (P = 2.2 × 10^-17), protein yield (P = 2.0 × 10^-19) and lactose yield (P = 4.0 × 10^-18) in crossbred cattle. As expected, the protein variant K232 was disadvantageous since it was decreasing milk, protein, and lactose yields by 1.741 kg, 0.063 kg and 0.084 kg, respectively. No significant effects were found for milk fat, protein, and lactose contents. The high frequency of the lysine DGAT1 protein variant K232 in Butana cattle could contribute to their high milk fat content in combination with low milk yield. In Butana × Holstein crossbred cattle, the DGAT1 marker can be used for effective selection and thus genetic improvement of milk production.

GWAS and genomic prediction of milk urea nitrogen in Australian and New Zealand dairy cattle

Background

Urinary nitrogen leakage is an environmental concern in dairy cattle. Selection for reduced urinary nitrogen leakage may be done using indicator traits such as milk urea nitrogen (MUN). The result of a previous study indicated that the genetic correlation between MUN in Australia (AUS) and MUN in New Zealand (NZL) was only low to moderate (between 0.14 and 0.58). In this context, an alternative is to select sequence variants based on genome-wide association studies (GWAS) with a view to improve genomic prediction accuracies. A GWAS can also be used to detect quantitative trait loci (QTL) associated with MUN. Therefore, our objectives were to perform within-country GWAS and a meta-GWAS for MUN using records from up to 33,873 dairy cows and imputed whole-genome sequence data, to compare QTL detected in the GWAS for MUN in AUS and NZL, and to use sequence variants selected from the meta-GWAS to improve the prediction accuracy for MUN based on a joint AUS-NZL reference set.

Results

Using the meta-GWAS, we detected 14 QTL for MUN, located on chromosomes 1, 6, 11, 14, 19, 22, 26 and the X chromosome. The three most significant QTL encompassed the casein genes on chromosome 6, PAEP on chromosome 11 and DGAT1 on chromosome 14. We selected 50,000 sequence variants that had the same direction of effect for MUN in AUS and MUN in NZL and that were most significant in the meta-analysis for the GWAS. The selected sequence variants yielded a genetic correlation between MUN in AUS and MUN in NZL of 0.95 and substantially increased prediction accuracy in both countries.

Conclusions

Our results demonstrate how the sharing of data between two countries can increase the power of a GWAS and increase the accuracy of genomic prediction using a multi-country reference population and sequence variants selected based on a meta-GWAS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00707-9.

Polymorphism detection of <i>DGAT1</i> and <i>Lep</i> genes in Anatolian water buffalo (<i>Bubalus bubalis</i>) populations in Turkey

Acyl-CoA: diacylglycerol–acyltransferase 1 (DGAT1) enzyme plays a key role in controlling the synthesis rate triglyceride from diacylglycerol. Leptin (LP, OB, obese) is an important hormone that synthesizes mostly from adipose tissue and regulates glucose metabolism and homeostasis. DGAT1 and Lep genes are closely related to reproduction, growth, milk yield and composition in water buffalo breeds. This study aimed to identify genetic variation in the DGAT1 and Lep gene regions in 150 water buffalo individuals from five different provinces of Turkey using DNA sequencing. A total of 38 nucleotide variations and indels have identified 761 bp long partial intron 2 and exon 3 and 5′ UTR regions of the Lep gene in Anatolian water buffalo populations; 422 bp long partial exon 7–9 and exon 8 regions of DGAT1 gene were amplified and two mutations were defined in the point of 155 and 275 nucleotide that is three genotypes for S allele and Y allele of DGAT1 gene in intron 7 in Anatolian buffalo populations, respectively. These SNPs may have an effect on reproduction, growth, milk yield and composition in water buffalo populations and may prove to be useful for water buffalo breeding.

Evidence for and localization of proposed causative variants in cattle and pig genomes

BACKGROUND

This paper reviews the localization of published potential causative variants in contemporary pig and cattle reference genomes, and the evidence for their causality. In spite of the difficulties inherent to the identification of causative variants from genetic mapping and genome-wide association studies, researchers in animal genetics have proposed putative causative variants for several traits relevant to livestock breeding.

RESULTS

For this review, we read the literature that supports potential causative variants in 13 genes (ABCG2, DGAT1, GHR, IGF2, MC4R, MSTN, NR6A1, PHGK1, PRKAG3, PLRL, RYR1, SYNGR2 and VRTN) in cattle and pigs, and localized them in contemporary reference genomes. We review the evidence for their causality, by aiming to separate the evidence for the locus, the proposed causative gene and the proposed causative variant, and report the bioinformatic searches and tactics needed to localize the sequence variants in the cattle or pig genome.

CONCLUSIONS

Taken together, there is usually good evidence for the association at the locus level, some evidence for a specific causative gene at eight of the loci, and some experimental evidence for a specific causative variant at six of the loci. We recommend that researchers who report new potential causative variants use referenced coordinate systems, show local sequence context, and submit variants to repositories.

New loci and neuronal pathways for resilience to heat stress in cattle

While understanding the genetic basis of heat tolerance is crucial in the context of global warming's effect on humans, livestock, and wildlife, the specific genetic variants and biological features that confer thermotolerance in animals are still not well characterized. We used dairy cows as a model to study heat tolerance because they are lactating, and therefore often prone to thermal stress. The data comprised almost 0.5 million milk records (milk, fat, and proteins) of 29,107 Australian Holsteins, each having around 15 million imputed sequence variants. Dairy animals often reduce their milk production when temperature and humidity rise; thus, the phenotypes used to measure an individual's heat tolerance were defined as the rate of milk production decline (slope traits) with a rising temperature-humidity index. With these slope traits, we performed a genome-wide association study (GWAS) using different approaches, including conditional analyses, to correct for the relationship between heat tolerance and level of milk production. The results revealed multiple novel loci for heat tolerance, including 61 potential functional variants at sites highly conserved across 100 vertebrate species. Moreover, it was interesting that specific candidate variants and genes are related to the neuronal system (ITPR1, ITPR2, and GRIA4) and neuroactive ligand-receptor interaction functions for heat tolerance (NPFFR2, CALCR, and GHR), providing a novel insight that can help to develop genetic and management approaches to combat heat stress.

Association of DGAT1 With Cattle, Buffalo, Goat, and Sheep Milk and Meat Production Traits

Milk fatty acids are essential for many dairy product productions, while intramuscular fat (IMF) is associated with the quality of meat. The triacylglycerols (TAGs) are the major components of IMF and milk fat. Therefore, understanding the polymorphisms and genes linked to fat synthesis is important for animal production. Identifying quantitative trait loci (QTLs) and genes associated with milk and meat production traits has been the objective of various mapping studies in the last decade. Consistently, the QTLs on chromosomes 14, 15, and 9 have been found to be associated with milk and meat production traits in cattle, goat, and buffalo and sheep, respectively. Diacylglycerol O-acyltransferase 1 (DGAT1) gene has been reported on chromosomes 14, 15, and 9 in cattle, goat, and buffalo and sheep, respectively. Being a key role in fat metabolism and TAG synthesis, the DGAT1 has obtained considerable attention especially in animal milk production. In addition to milk production, DGAT1 has also been a subject of interest in animal meat production. Several polymorphisms have been documented in DGAT1 in various animal species including cattle, buffalo, goat, and sheep for their association with milk production traits. In addition, the DGAT1 has also been studied for their role in meat production traits in cattle, sheep, and goat. However, very limited studies have been conducted in cattle for association of DGAT1 with meat production traits in cattle. Moreover, not a single study reported the association of DGAT1 with meat production traits in buffalo; thus, further studies are warranted to fulfill this huge gap. Keeping in view the important role of DGAT1 in animal production, the current review article was designed to highlight the major development and new insights on DGAT1 effect on milk and meat production traits in cattle, buffalo, sheep, and goat. Moreover, we have also highlighted the possible future contributions of DGAT1 for the studied species.

EEF1D facilitates milk lipid synthesis by regulation of PI3K-Akt signaling in mammals

Mammal's milk is an abundantly foremost source of proteins, lipids, and micronutrients for human nutrition and health. Understanding the molecular mechanisms underlying synthesis of milk components provides practical benefits to improve the milk quality via systematic breeding program in mammals. Through RNAi with EEF1D in primary bovine mammary epithelial cells, we phenotypically observed aberrant formation of cytoplasmic lipid droplets and significantly decreased milk triglyceride level by 37.7%, and exploited the mechanisms by which EEF1D regulated milk lipid synthesis via insulin (PI3K-Akt), AMPK, and PPAR pathways. In the EEF1D CRISPR/Cas9 knockout mice, incompletely developed mammary glands at 9th day postpartum with small or unformed lumens, and significantly decreased triglyceride concentration in milk by 23.4% were observed, as well as the same gene expression alterations in the three pathways. For dairy cattle, we identified a critical regulatory mutation modifying EEF1D transcription activity, which interpreted 7% of the genetic variances of milk lipid yield and percentage. Our findings highlight the significance of EEF1D in mammary gland development and milk lipid synthesis in mammals.

Feeding System Resizes the Effects of DGAT1 Polymorphism on Milk Traits and Fatty Acids Composition in Modicana Cows

Simple Summary

Genetic selection for single-locus polymorphisms could offer suitable opportunities to rapidly improve milk traits in local unselected cattle breeds characterized by low production levels. Since these hardy breeds are generally raised in traditional extensive and semi-intensive systems, which make wide use of grazing resources, the interactive effect between genotype and feeding system is worthy of investigation. In Modicana cattle breed, milk composition and fatty acid profile were influenced by both genetic polymorphisms at the DGAT1 K232A locus and feeding systems. The milk from homozygous AA cows was associated with a more favorable fatty acid composition due to a lower percentage of total saturated fatty acids, saturated to unsaturated ratio, atherogenic index, and a greater presence of oleic acid and total unsaturated fatty acids. Our finding confirmed the important role of pasture feeding on milk composition: the high nutritional and healthy value of milk obtained in extensive systems by pasture-fed cows. The interaction between the two experimental factors also appears to play a role: in our experimental condition, it seems that high pasture feeding can resize the effect of the DGAT1 genotype on milk traits and fatty acid composition in Modicana cows.

Abstract

The interaction between genetic polymorphism and feeding system on milk traits and fatty acid composition was investigated in Modicana cows. Two DGAT1 K232A genotypes (AK and AA) and two feeding regimes, extensive system (EX) with 8 h of grazing without concentrate (EX) and semi-intensive systems (SI) with 2 h of grazing with concentrate, were investigated. DGAT1 genotype did not influence milk yield and composition. The feeding system affected milk composition: protein was significantly higher in SI and lactose in the EX system. A significant genotype × feeding system interaction was observed: the protein and casein levels of AK cows were higher in the SI compared to the EX system. Milk fatty acids profile, total saturated to total unsaturated fatty acids, n-6 to n-3 ratios, and atherogenic index were affected by the feeding system, improving the healthy properties of milk from animals reared in the extensive system. DGAT1 genotype influenced the fatty acid composition: milk from AA cows had a more favorable fatty acid composition due to lower total saturated fatty acids, saturated to unsaturated ratio, atherogenic index, and higher levels of oleic acid and total unsaturated fatty acids. Furthermore, an interaction genotype x feeding system was observed: the AK milk was richer in short-chain FAs (C4:0–C8:0) and C10:0 only in the EX but not in the SI system. Our data suggest that a high amount of green forage in the diet of Modicana cows can resize the effect of the DGAT1 genotype on milk traits and fatty acids composition.

Genome-Wide Identification of Candidate Genes for Milk Production Traits in Korean Holstein Cattle

Simple Summary

Milk production traits that are economically important in the dairy industry have been considered the main selection criteria for breeding. The present genome-wide association study was performed to identify chromosomal loci and candidate genes with potential effects on milk production phenotypes in a Korean Holstein population. A total of eight significant quantitative trait locus regions were identified for milk yield (Bos taurus autosome (BTA) 7 and 14), adjusted 305-d fat yield (BTA 3, 5, and 14), adjusted 305-d protein yield (BTA 8), and somatic cell score (BTA 8 and 23) of milk production traits. Furthermore, we discovered three main candidate genes (diacylglycerol O-acyltransferase 1 (DGAT1), phosphodiesterase 4B (PDE4B), and anoctamin 2 (ANO2)) through bioinformatics analysis. These genes may help to understand better the underlying genetic and molecular mechanisms for milk production phenotypes in the Korean Holstein population.

Abstract

We performed a genome-wide association study and fine mapping using two methods (single marker regression: frequentist approach and Bayesian C (BayesC): fitting selected single nucleotide polymorphisms (SNPs) in a Bayesian framework) through three high-density SNP chip platforms to analyze milk production phenotypes in Korean Holstein cattle (n = 2780). We identified four significant SNPs for each phenotype in the single marker regression model: AX-311625843 and AX-115099068 on Bos taurus autosome (BTA) 14 for milk yield (MY) and adjusted 305-d fat yield (FY), respectively, AX-428357234 on BTA 18 for adjusted 305-d protein yield (PY), and AX-185120896 on BTA 5 for somatic cell score (SCS). Using the BayesC model, we discovered significant 1-Mb window regions that harbored over 0.5% of the additive genetic variance effects for four milk production phenotypes. The concordant significant SNPs and 1-Mb window regions were characterized into quantitative trait loci (QTL). Among the QTL regions, we focused on a well-known gene (diacylglycerol O-acyltransferase 1 (DGAT1)) and newly identified genes (phosphodiesterase 4B (PDE4B), and anoctamin 2 (ANO2)) for MY and FY, and observed that DGAT1 is involved in glycerolipid metabolism, fat digestion and absorption, metabolic pathways, and retinol metabolism, and PDE4B is involved in cAMP signaling. Our findings suggest that the candidate genes in QTL are strongly related to physiological mechanisms related to the fat production and consequent total MY in Korean Holstein cattle.

Evaluation of non-synonym mutation in DGAT1 K232A as a marker for milk production traits in Ongole cattle and Murrah buffalo from Southern India

Various candidate genes have been reported to affect milk yield and composition in dairy cattle. A non-synonymous mutation in the DGAT1 gene, i.e., K232A was reported to have a strong association with milk yield and milk composition of Bos taurus. A study has been undertaken on 502 unrelated individuals belonging to indigenous Ongole cattle, crossbred cattle, and Murrah buffaloes from the Indian sub-continent with the objective to determine the polymorphism of the K232A locus and their association with milk yield and composition. Typing DGAT1 K232A allelic variation by PCR-RFLP using CfrI restriction enzyme revealed three genotypes in crossbred cattle. Genotype KK was more prevalent (0.60) in Jersey crossbred, whereas in Holstein Friesian crossbred it was KA genotype (0.48). In Ongole cattle and Murrah buffaloes, the locus did not exhibit polymorphism. The least-square mean of milk yields pooled over lactations across the DGAT1 variants was significantly (P < 0.05) higher among the homozygous (AA) genotypes, both in Jersey crossbred and HF crossbred cattle after adjusting for the effects of farm, parity, and season. The fat, SNF, and protein content values of AA genotypes were less than the KK genotypes in both the genetic groups (P > 0.05). The fixation of the DGAT1^K allele at the locus in Bos indicus cattle and Bubalus bubalis in the present study did not support its use as a reliable universal marker for milk production and composition traits.

Comparative accuracies of genetic values predicted for economically important milk traits, genome-wide association, and linkage disequilibrium patterns of Canadian Holstein cows

Genomic selection methodologies and genome-wide association studies use powerful statistical procedures that correlate large amounts of high-density SNP genotypes and phenotypic data. Actual 305-d milk (MY), fat (FY), and protein (PY) yield data on 695 cows and 76,355 genotyping-by-sequencing-generated SNP marker genotypes from Canadian Holstein dairy cows were used to characterize linkage disequilibrium (LD) structure of Canadian Holstein cows. Also, the comparison of pedigree-based BLUP, genomic BLUP (GBLUP), and Bayesian (BayesB) statistical methods in the genomic selection methodologies and the comparison of Bayesian ridge regression and BayesB statistical methods in the genome-wide association studies were carried out for MY, FY, and PY. Results from LD analysis revealed that as marker distance decreases, LD increases through chromosomes. However, unexpected high peaks in LD were observed between marker pairs with larger marker distances on all chromosomes. The GBLUP and BayesB models resulted in similar heritability estimates through 10-fold cross-validation for MY and PY; however, the GBLUP model resulted in higher heritability estimates than BayesB model for FY. The predictive ability of GBLUP model was significantly lower than that of BayesB for MY, FY, and PY. Association analyses indicated that 28 high-effect markers and markers on Bos taurus autosome 14 located within 6 genes (DOP1B, TONSL, CPSF1, ADCK5, PARP10, and GRINA) associated significantly with FY.

Improving Genomic Prediction of Crossbred and Purebred Dairy Cattle

This study assessed the accuracy and bias of genomic prediction (GP) in purebred Holstein (H) and Jersey (J) as well as crossbred (H and J) validation cows using different reference sets and prediction strategies. The reference sets were made up of different combinations of 36,695 H and J purebreds and crossbreds. Additionally, the effect of using different sets of marker genotypes on GP was studied (conventional panel: 50k, custom panel enriched with, or close to, causal mutations: XT_50k, and conventional high-density with a limited custom set: pruned HDnGBS). We also compared the use of genomic best linear unbiased prediction (GBLUP) and Bayesian (emBayesR) models, and the traits tested were milk, fat, and protein yields. On average, by including crossbred cows in the reference population, the prediction accuracies increased by 0.01–0.08 and were less biased (regression coefficient closer to 1 by 0.02–0.16), and the benefit was greater for crossbreds compared to purebreds. The accuracy of prediction increased by 0.02 using XT_50k compared to 50k genotypes without affecting the bias. Although using pruned HDnGBS instead of 50k also increased the prediction accuracy by about 0.02, it increased the bias for purebred predictions in emBayesR models. Generally, emBayesR outperformed GBLUP for prediction accuracy when using 50k or pruned HDnGBS genotypes, but the benefits diminished with XT_50k genotypes. Crossbred predictions derived from a joint pure H and J reference were similar in accuracy to crossbred predictions derived from the two separate purebred reference sets and combined proportional to breed composition. However, the latter approach was less biased by 0.13. Most interestingly, using an equalized breed reference instead of an H-dominated reference, on average, reduced the bias of prediction by 0.16–0.19 and increased the accuracy by 0.04 for crossbred and J cows, with a little change in the H accuracy. In conclusion, we observed improved genomic predictions for both crossbreds and purebreds by equalizing breed contributions in a mixed breed reference that included crossbred cows. Furthermore, we demonstrate, that compared to the conventional 50k or high-density panels, our customized set of 50k sequence markers improved or matched the prediction accuracy and reduced bias with both GBLUP and Bayesian models.

Responses of triacylglycerol synthesis in Skeletonema marinoi to nitrogen and phosphate starvations

Skeletonema marinoi is one of the most widespread marine planktonic diatoms in temperate coastal regions and sometimes can form massive blooms. Yet, the molecular mechanisms of triacylglycerol (TAG) synthesis in nutrient-deficient conditions for this species are still unknown. This study aimed to investigate how the TAG biosynthetic pathway of S. marinoi reacts to the culture age and nitrogen (N) or phosphorus (P) deficiency at molecular levels. Meanwhile, we also described the physiological and biochemical changes of S. marinoi in response to N or P starvation over time. To obtain reliable qRT-PCR data, six putative reference genes were identified for assessing expression stability using geNorm and BestKeeper software, and Actin exhibited the most stable expression across 45 tested S. marinoi samples. We found that the expression of TAG biosynthesis-related genes and ACCase enzyme activity varied in response to the different nutrient conditions and culture age. Taken together, we speculated that the capacity of TAG biosynthesis in S. marinoi is induced by N or P stress, and increases with culture age. Furthermore, TAG biosynthesis appears to respond more strongly to P deficiency than to N deficiency. Our study provides important insights into how diatoms regulate the TAG biosynthetic pathway when stressed by nutrient limitation. Besides, the data obtained from this study also provide useful clues for further exploring genes that can be used for metabolic engineering to enhance lipid production.

Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis

Native cattle breeds can carry specific signatures of selection reflecting their adaptation to the local environmental conditions and response to the breeding strategy used. In this study, we comprehensively analysed high-density single nucleotide polymorphism (SNP) genotypes to characterise the population structure and detect the selection signatures in Russian native Yaroslavl and Kholmogor dairy cattle breeds, which have been little influenced by introgression with transboundary breeds. Fifty-six samples of pedigree-recorded purebred animals, originating from different breeding farms and representing different sire lines, of the two studied breeds were genotyped using a genome-wide bovine genotyping array (Bovine HD BeadChip). Three statistical analyses—calculation of fixation index (F_ST) for each SNP for the comparison of the pairs of breeds, hapFLK analysis, and estimation of the runs of homozygosity (ROH) islands shared in more than 50% of animals—were combined for detecting the selection signatures in the genome of the studied cattle breeds. We confirmed nine and six known regions under putative selection in the genomes of Yaroslavl and Kholmogor cattle, respectively; the flanking positions of most of these regions were elucidated. Only two of the selected regions (localised on BTA 14 at 24.4–25.1 Mbp and on BTA 16 at 42.5–43.5 Mb) overlapped in Yaroslavl, Kholmogor and Holstein breeds. In addition, we detected three novel selection sweeps in the genome of Yaroslavl (BTA 4 at 4.74–5.36 Mbp, BTA 15 at 17.80–18.77 Mbp, and BTA 17 at 45.59–45.61 Mbp) and Kholmogor breeds (BTA 12 at 82.40–81.69 Mbp, BTA 15 at 16.04–16.62 Mbp, and BTA 18 at 0.19–1.46 Mbp) by using at least two of the above-mentioned methods. We expanded the list of candidate genes associated with the selected genomic regions and performed their functional annotation. We discussed the possible involvement of the identified candidate genes in artificial selection in connection with the origin and development of the breeds. Our findings on the Yaroslavl and Kholmogor breeds obtained using high-density SNP genotyping and three different statistical methods allowed the detection of novel putative genomic regions and candidate genes that might be under selection. These results might be useful for the sustainable development and conservation of these two oldest Russian native cattle breeds.

GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle

Simple Summary

Understanding the genetic architecture underlying milk production traits in cattle is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we performed a genome-wide association study for milk production and quality traits in Holstein cattle. In the total of ten significant single-nucleotide polymorphisms (SNPs) associated with milk fat and protein, six are located in previously reported quantitative traits locus (QTL) regions. The study not only identified the effect of DGAT1 gene on milk fat and protein but also found several novel candidate genes. In addition, some pleiotropic SNPs and QTLs were identified that associated with more than two traits, these results could provide some basis for molecular breeding in dairy cattle.

Abstract

High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10⁻⁷), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.

Short communication: Locus-specific interrelations between gene expression and DNA methylation patterns in bovine mammary gland infected by coagulase-positive and coagulase-negative staphylococci

Pathogens are able to alter the cell cycle program and immune response of the host by changing the transcription and epigenetics of genes responsible for cell cycle control and inflammation. In this regard, we evaluated interrelations between DNA methylation and expression of autophagy, apoptosis, and lipid metabolism-related genes in a sample set of mammary gland secretory tissue sections derived from bovine mammary glands infected with coagulase-negative and coagulase-positive staphylococci. We assessed relative transcript abundance and DNA bisulfite sequencing in loci of the ATG5, IGF1R, TERT, and DGAT1 genes. Lack of DNA methylation in ATG5 and DGAT1 loci might be associated with maintenance of ATG5 and DGAT1 expression regardless of the health status of bovine mammary gland. Complete methylation of intragenic CpG regions in the IGF1R locus was apparently not related to the presence of its transcript in the investigated udder parenchyma samples. Detected hypermethylation of the TERT upstream element was associated with a small amount of TERT mRNA in bovine mammary gland, regardless of the presence, or absence, of the pathogen. A significant decrease in TERT gene expression in tissue sections of mammary gland free of bacteria and in those infected with coagulase-positive staphylococci was observed in parenchyma samples infected with coagulase-negative staphylococci. Two possible explanations are the direct involvement of the TERT gene in the etiology of bovine mastitis or the increase of TERT mRNA due to activation of the MAPK signaling pathway in response to release of exotoxins by coagulase-negative bacteria in the bovine mammary gland.

A new mechanism for a familiar mutation - bovine DGAT1 K232A modulates gene expression through multi-junction exon splice enhancement

BACKGROUND

The DGAT1 gene encodes an enzyme responsible for catalysing the terminal reaction in mammary triglyceride synthesis, and underpins a well-known pleiotropic quantitative trait locus (QTL) with a large influence on milk composition phenotypes. Since first described over 15 years ago, a protein-coding variant K232A has been assumed as the causative variant underlying these effects, following in-vitro studies that demonstrated differing levels of triglyceride synthesis between the two protein isoforms.

RESULTS

We used a large RNAseq dataset to re-examine the underlying mechanisms of this large milk production QTL, and hereby report novel expression-based functions of the chr14 g.1802265AA > GC variant that encodes the DGAT1 K232A substitution. Using expression QTL (eQTL) mapping, we demonstrate a highly-significant mammary eQTL for DGAT1, where the K232A mutation appears as one of the top associated variants for this effect. By conducting in vitro expression and splicing experiments in bovine mammary cell culture, we further show modulation of splicing efficiency by this mutation, likely through disruption of an exon splice enhancer as a consequence of the allele encoding the 232A variant.

CONCLUSIONS

The relative contributions of the enzymatic and transcription-based mechanisms now attributed to K232A remain unclear; however, these results suggest that transcriptional impacts contribute to the diversity of lactation effects observed at the DGAT1 locus.

Food intake-related genes in chicken determined through combinatorial genome-wide association study and transcriptome analysis

The chicken gizzard is the primary digestive and absorptive organ regulating food intake and metabolism. Body weight is a typical complex trait regulated by an interactive polygene network which is under the control of an interacting network of polygenes. To simplify these genotype-phenotype associations, the gizzard is a suitable target organ to preliminarily explore the mechanism underlying the regulation of chicken growth through controlled food intake. This study aimed to identify key food intake-related genes through combinatorial GWAS and transcriptome analysis. We performed GWAS of body weight in an F₂ intercrossed population and transcriptional profiling analysis of gizzards from chickens with different body weight. We identified a major 10 Mb quantitative trait locus (QTL) on chromosome 1 and numerous minor QTL distributed among 24 chromosomes. Combining data regarding QTL and gizzard gene expression, two hub genes, MLNR and HTR2A, and a list of core genes with small effect were found to be associated with food intake. Furthermore, the neuroactive ligand-receptor interaction pathway was found to play a key role in regulating the appetite of chickens. The present results show the major-minor gene interactions in metabolic pathways and provide insights into the genetic architecture and gene regulation during food intake in chickens.

Milk fat globule size development in the mammary epithelial cell: a potential role for ether phosphatidylethanolamine

Milk fat globule (MFG) size is a milk production trait characteristic to the individual animal and has important effects on the functional and nutritional properties of milk. Although the regulation of MFG size in the mammary epithelial cell is not fully understood, lipid droplet (LD) fusion prior to secretion is believed to play a role. We selected cows that consistently produced milk with predominantly small or large MFGs to compare their lipidomic profiles, with focus on the polar lipid fraction. The polar lipid composition of the monolayer surrounding the LD is believed to either promote or prevent LD fusion. Using a targeted LC–MS/MS approach we studied the relative abundance of 301 detected species and found significant differences between the studied groups. Here we show that the lipidomic profile of milk from small MFG cows is characterised by higher phosphatidylcholine to phosphatidylethanolamine ratios. In contrast, the milk from large MFG cows contained more ether-phosphatidylethanolamine species. This is the first time that a potential role for ether-phosphatidylethanolamine in MFG size development has been suggested.

Identification of candidate genes for milk production traits by RNA sequencing on bovine liver at different lactation stages

Background

RNA-sequencing was performed to explore the bovine liver transcriptomes of Holstein cows to detect potential functional genes related to lactation and milk composition traits in dairy cattle. The bovine transcriptomes of the nine liver samples from three Holstein cows during dry period (50-d prepartum), early lactation (10-d postpartum), and peak of lactation (60-d postpartum) were sequenced using the Illumina HiSeq 2500 platform.

Results

A total of 204, 147 and 81 differentially expressed genes (DEGs, p < 0.05, false discovery rate q < 0.05) were detected in early lactation vs. dry period, peak of lactation vs. dry period, and peak of lactation vs. early lactation comparison groups, respectively. Gene ontology and KEGG pathway analysis showed that these DEGs were significantly enriched in specific biological processes related to metabolic and biosynthetic and signaling pathways of PPAR, AMPK and p53 (p < 0.05). Ten genes were identified as promising candidates affecting milk yield, milk protein and fat traits in dairy cattle by using an integrated analysis of differential gene expression, previously reported quantitative trait loci (QTL), data from genome-wide association studies (GWAS), and biological function information. These genes were APOC2, PPP1R3B, PKLR, ODC1, DUSP1, LMNA, GALE, ANGPTL4, LPIN1 and CDKN1A.

Conclusion

This study explored the complexity of the liver transcriptome across three lactation periods in dairy cattle by performing RNA sequencing. Integrated analysis of DEGs and reported QTL and GWAS data allowed us to find ten key candidate genes influencing milk production traits.

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.

Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle

BACKGROUND

Production and health traits are central in cattle breeding. Advances in next-generation sequencing technologies and genotype imputation have increased the resolution of gene mapping based on genome-wide association studies (GWAS). Thus, numerous candidate genes that affect milk yield, milk composition, and mastitis resistance in dairy cattle are reported in the literature. Effect-bearing variants often affect multiple traits. Because the detection of overlapping quantitative trait loci (QTL) regions from single-trait GWAS is too inaccurate and subjective, multi-trait analysis is a better approach to detect pleiotropic effects of variants in candidate genes. However, large sample sizes are required to achieve sufficient power. Multi-trait meta-analysis is one approach to deal with this problem. Thus, we performed two multi-trait meta-analyses, one for three milk production traits (milk yield, protein yield and fat yield), and one for milk yield and mastitis resistance.

RESULTS

For highly correlated traits, the power to detect pleiotropy was increased by multi-trait meta-analysis compared with the subjective assessment of overlapping of single-trait QTL confidence intervals. Pleiotropic effects of lead single nucleotide polymorphisms (SNPs) that were detected from the multi-trait meta-analysis were confirmed by bivariate association analysis. The previously reported pleiotropic effects of variants within the DGAT1 and MGST1 genes on three milk production traits, and pleiotropic effects of variants in GHR on milk yield and fat yield were confirmed. Furthermore, our results suggested that variants in KCTD16, KCNK18 and ENSBTAG00000023629 had pleiotropic effects on milk production traits. For milk yield and mastitis resistance, we identified possible pleiotropic effects of variants in two genes, GC and DGAT1.

CONCLUSIONS

Multi-trait meta-analysis improves our ability to detect pleiotropic interactions between milk production traits and identifies variants with pleiotropic effects on milk production traits and mastitis resistance. In particular, this should contribute to better understand the biological mechanisms that underlie the unfavorable genetic correlation between milk yield and mastitis.

Genome-wide identification of Diacylglycerol Acyltransferases (DGAT) family genes influencing Milk production in Buffalo

Background

The diacylglycerol acyltransferases (DGAT) are a vital group of enzymes in catalyzing triacylglycerol biosynthesis. DGAT genes like DGAT1 and DGAT2, have been identified as two functional candidate genes affecting milk production traits, especially for fat content in milk. Buffalo milk is famous for its excellent quality, which is rich in fat and protein content. Therefore, this study aimed to characterize DGAT family genes in buffalo and to find candidate markers or DGAT genes influencing lactation performance.

Results

We performed a genome-wide study and identified eight DGAT genes in buffalo. All the DGAT genes classified into two distinct clades (DGAT1 and DGAT2 subfamily) based on their phylogenetic relationships and structural features. Chromosome localization displayed eight buffalo DGAT genes distributed on five chromosomes. Collinearity analysis revealed that the DGAT family genes were extensive homologous between buffalo and cattle. Afterward, we discovered genetic variants loci within the genomic regions that DGAT genes located in buffalo. Seven haplotype blocks were constructed and were associated with buffalo milk production traits. Single marker association analyses revealed four most significant single nucleotide polymorphisms (SNPs) mainly affecting milk protein percentage or milk fat yield in buffalo. Genes functional analysis indicated that these DGAT family genes could influence lactation performance in the mammal through regulating lipid metabolism.

Conclusion

In the present study, we performed a comprehensive analysis for the DGAT family genes in buffalo, which including identification, structural characterization, phylogenetic classification, chromosomal distribution, collinearity analysis, association analysis, and functional analysis. These findings provide useful information for an in-depth study to determine the role of DGAT family gens play in the regulation of milk production and milk quality improvement in buffalo.

Effect of DNA markers on the fertility traits of Japanese Black cattle for improving beef quantity and quality

Carcass traits have been efficiently improved by recent selection using DNA markers in beef cattle. Additionally, DNA markers might have an effect on other traits such as fertility traits; therefore attention should also be paid to such pleiotropic effects. However, the effects of the markers on both carcass and fertility traits have never been evaluated in the same population, since they are generally measured in different populations. The objective in the current study was to discuss effectiveness of DNA markers developed for carcass traits through investigation of their effects on carcass and fertility traits in a population. We genotyped six markers SCD V293A, FASN g.841G>C, PLAG1 g.49066C>G, NCAPG I442M, DGAT1 K232A, and EDG1 g.1471620G>T in a Japanese Black cattle population (n=515). To investigate their effects on carcass and fertility traits, we performed statistical analysis (ANOVA and the Tukey–Kramer honestly significant difference (HSD) test). In the results, three of six markers, SCD V293A, NCAPG I442M, and EGD1 g.1471620G>T, were significantly associated with both carcass and fertility traits. Remarkably, the same allele for each marker had positive effects on both traits, suggesting that we would be able to simultaneously improve them using these markers in this population. However, previous studies reported that the effects of DNA markers could differ among populations. Therefore, it is necessary to confirm the effect of the marker in each population before it is used for improvement.

Targeted genotyping to identify potential functional variants associated with cholesterol content in bovine milk

High blood cholesterol concentration, mainly caused by high dietary cholesterol, is a potential risk factor for human health. Dairy products are important sources of human dietary cholesterol intake. Therefore, monitoring bovine milk cholesterol concentration is important for human health benefit. Genetic selection for improvement of cow milk cholesterol content requires understanding of the genetics of milk cholesterol. For this purpose, we performed analyses of additive and dominance effects of 126 potentially functional SNPs within 43 candidate genes with milk cholesterol content [expressed as mg of cholesterol in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk)]. The additive and dominance effects of SNPs rs380643365 in AGPAT1 (P = 0.04) and rs134357240 in SOAT1 (P = 0.035) genes associated significantly with CHL_fat. Moreover, five (rs109326954 and rs523413537 in DGAT1, rs109376747 in LDLR, rs42781651 in FAM198B and rs109967779 in ACAT2) and four (rs137347384 in RBM19, rs109376747 in LDLR, rs42016945 in PPARG and rs110862179 in SCAP) SNPs were significantly associated with CHL_milk (P < 0.05) based on additive and dominance effect analyses respectively. Rs109326954 and rs523413537 in DGAT1 explained a considerable portion of the phenotypic variance of CHL_milk (7.54 and 6.84% respectively), and might be useful in selection programs for reduced milk cholesterol content. Several significantly associated SNPs were in genes (such as ACAT2 and LDLR) involved in cholesterol metabolism in the liver or cholesterol transport, suggesting multiple mechanisms regulating milk cholesterol content. Nine and seven SNPs identified by additive or dominance effect analyses associated significantly with milk yield and fat yield respectively. Further analyses are required to better understand the consequences of these variants and their potential use in genomic selection of the studied traits.

A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

Background

During the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~ 850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis.

Results

We found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K.

Conclusions

Using high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level.

Significant genetic effects of JAK2 and DGAT1 mutations on milk fat content and mastitis resistance in Holsteins

Improving the production traits and resistance against mastitis in dairy cattle is a challenge for animal scientists across the globe. The present study was designed to investigate the genetic effects of single nucleotide polymorphisms (SNPs) in Janus kinase 2 (JAK2) and diacylglycerol acyltransferase (DGAT1) genes with production and mastitis-related traits. Four SNPs in JAK2 and one in DGAT1 were analyzed through Chinese Cow's SNPs Chip-I (CCSC-I) and genotyped in a population of 312 Chinese Holsteins. Our findings demonstrated that milk fat percentage, somatic cell count (SCC), somatic cell score (SCS), serum cytokines interleukin 6 (IL-6) and interferon gamma (IFN-γ) showed significant associations (P < 0.05) with at least one or more identified SNPs. Consequently, the analysis based on haplotypes amongst the SNPs in JAK2 revealed noteworthy (P < 0.05) association with SCC and IL-6. Collectively, our results verified the pleiotropic ability of detected SNPs in bovine JAK2 and DGAT1 for milk fat percentage as well as mastitis-related traits. The significant SNPs in both the genes could serve as powerful genetic markers to minimize mastitis risk. In addition, besides SCC and SCS, the IFN-γ and IL-6 could also be used as indicators of improved genetic resistance against mastitis.