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

Simultaneous Effects of Single-Nucleotide Polymorphisms on the Estimated Breeding Value of Milk, Fat, and Protein Yield of Holstein Friesian Cows in Hungary

This study aimed to find SNPs that have an effect on the estimated breeding values (EBVs) of milk (MY), fat (FY), and protein yield (PY) of Holstein Friesian cows in Hungary. Holstein Friesian cows (n = 2963) were genotyped on a Eurogenomics (EuroG_MDv4) chip. The EBVs for MY, FY, and PY were obtained from the Association of Hungarian Holstein Breeders (AHHB). The loci associated with the EBVs were identified via three approaches: the calculation of genetic distance of the SNPs (Fst_marker), linear regression, and haplotype association tests. Nine SNPs were significantly associated with MY, FY, and PY located on BTA 2, 5, 28, and X. Among the nine SNPs identified, BTB-00219372 on BTA 5 had a positive β coefficient for MY and a negative β coefficient for FY and PY. In addition, BovineHD3000027615 on BTA X had a positive β coefficient for both MY and PY, as well as a negative β coefficient for FY. The identified SNPs were located near several genes that remain unstudied in cattle, which are potential targets for closer scrutiny in relation to milk properties. The markers associated with two or three EBVs could be used in selection with high efficiency to accelerate genetic development and help AHHB experts achieve their breeding. Most marker effects point in the same direction on EBVs; however, we found that BTB-00219372 and BovineHD3000027615 could be used with caution to increase one EBV while decreasing the other EBV or EBVs.

Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

BACKGROUND

Mastitis is a disease that incurs significant costs in the dairy industry. A promising approach to mitigate its negative effects is to genetically improve the resistance of dairy cattle to mastitis. A meta-analysis of genome-wide association studies (GWAS) across multiple breeds for clinical mastitis (CM) and its indicator trait, somatic cell score (SCS), is a powerful method to identify functional genetic variants that impact mastitis resistance.

RESULTS

We conducted meta-analyses of eight and fourteen GWAS on CM and SCS, respectively, using 30,689 and 119,438 animals from six dairy cattle breeds. Methods for the meta-analyses were selected to properly account for the multi-breed structure of the GWAS data. Our study revealed 58 lead markers that were associated with mastitis incidence, including 16 loci that did not overlap with previously identified quantitative trait loci (QTL), as curated at the Animal QTLdb. Post-GWAS analysis techniques such as gene-based analysis and genomic feature enrichment analysis enabled prioritization of 31 candidate genes and 14 credible candidate causal variants that affect mastitis.

CONCLUSIONS

Our list of candidate genes can help to elucidate the genetic architecture underlying mastitis resistance and provide better tools for the prevention or treatment of mastitis, ultimately contributing to more sustainable animal production.

Mendelian randomization analysis of 34,497 German Holstein cows to infer causal associations between milk production and health traits

BACKGROUND

Claw diseases and mastitis represent the most important health issues in dairy cattle with a frequently mentioned connection to milk production. Although many studies have aimed at investigating this connection in more detail by estimating genetic correlations, they do not provide information about causality. An alternative is to carry out Mendelian randomization (MR) studies using genetic variants to investigate the effect of an exposure on an outcome trait mediated by genetic variants. No study has yet investigated the causal association of milk yield (MY) with health traits in dairy cattle. Hence, we performed a MR analysis of MY and seven health traits using imputed whole-genome sequence data from 34,497 German Holstein cows. We applied a method that uses summary statistics and removes horizontal pleiotropic variants (having an effect on both traits), which improves the power and unbiasedness of MR studies. In addition, genetic correlations between MY and each health trait were estimated to compare them with the estimates of causal effects that we expected.

RESULTS

All genetic correlations between MY and each health trait were negative, ranging from - 0.303 (mastitis) to - 0.019 (digital dermatitis), which indicates a reduced health status as MY increases. The only non-significant correlation was between MY and digital dermatitis. In addition, each causal association was negative, ranging from - 0.131 (mastitis) to - 0.034 (laminitis), but the number of significant associations was reduced to five nominal and two experiment-wide significant results. The latter were between MY and mastitis and between MY and digital phlegmon. Horizontal pleiotropic variants were identified for mastitis, digital dermatitis and digital phlegmon. They were located within or nearby variants that were previously reported to have a horizontal pleiotropic effect, e.g., on milk production and somatic cell count.

CONCLUSIONS

Our results confirm the known negative genetic connection between health traits and MY in dairy cattle. In addition, they provide new information about causality, which for example points to the negative energy balance mediating the connection between these traits. This knowledge helps to better understand whether the negative genetic correlation is based on pleiotropy, linkage between causal variants for both trait complexes, or indeed on a causal association.

Genomic dissection of the correlation between milk yield and various health traits using functional and evolutionary information about imputed sequence variants of 34,497 German Holstein cows

BACKGROUND

Over the last decades, it was subject of many studies to investigate the genomic connection of milk production and health traits in dairy cattle. Thereby, incorporating functional information in genomic analyses has been shown to improve the understanding of biological and molecular mechanisms shaping complex traits and the accuracies of genomic prediction, especially in small populations and across-breed settings. Still, little is known about the contribution of different functional and evolutionary genome partitioning subsets to milk production and dairy health. Thus, we performed a uni- and a bivariate analysis of milk yield (MY) and eight health traits using a set of ~34,497 German Holstein cows with 50K chip genotypes and ~17 million imputed sequence variants divided into 27 subsets depending on their functional and evolutionary annotation. In the bivariate analysis, eight trait-combinations were observed that contrasted MY with each health trait. Two genomic relationship matrices (GRM) were included, one consisting of the 50K chip variants and one consisting of each set of subset variants, to obtain subset heritabilities and genetic correlations. In addition, 50K chip heritabilities and genetic correlations were estimated applying merely the 50K GRM.

RESULTS

In general, 50K chip heritabilities were larger than the subset heritabilities. The largest heritabilities were found for MY, which was 0.4358 for the 50K and 0.2757 for the subset heritabilities. Whereas all 50K genetic correlations were negative, subset genetic correlations were both, positive and negative (ranging from -0.9324 between MY and mastitis to 0.6662 between MY and digital dermatitis). The subsets containing variants which were annotated as noncoding related, splice sites, untranslated regions, metabolic quantitative trait loci, and young variants ranked highest in terms of their contribution to the traits` genetic variance. We were able to show that linkage disequilibrium between subset variants and adjacent variants did not cause these subsets` high effect.

CONCLUSION

Our results confirm the connection of milk production and health traits in dairy cattle via the animals` metabolic state. In addition, they highlight the potential of including functional information in genomic analyses, which helps to dissect the extent and direction of the observed traits` connection in more detail.

Unraveling the genomic background of resilience based on variability in milk yield and milk production levels in North American Holstein cattle through genome-wide association study and Mendelian randomization analyses

Breeding more resilient animals will benefit the dairy cattle industry in the long term, especially as global climate changes become more severe. Previous studies have reported genetic parameters for various milk yield-based resilience indicators, but the underlying genomic background of these traits remain unknown. In this study, we conducted GWAS of 62,029 SNPs with 4 milk yield-based resilience indicators, including the weighted occurrence frequency (wfPert) and accumulated milk losses (dPert) of milk yield perturbations, and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. These variables were previously derived from 5.6 million daily milk yield records from 21,350 lactations (parities 1-3) of 11,787 North American Holstein cows. The average daily milk yield (ADMY) throughout lactation was also included to compare the shared genetic background of resilience indicators with milk yield. The differential genetic background of these indicators was first revealed by the significant genomic regions identified and significantly enriched biological pathways of positional candidate genes, which confirmed the genetic difference among resilience indicators. Interestingly, the functional analyses of candidate genes suggested that the regulation of intestinal homeostasis is most likely affecting resilience derived based on variability in milk yield. Based on Mendelian randomization analyses of multiple instrumental SNPs, we further found an unfavorable causal association of ADMY with LnVar. In conclusion, the resilience indicators evaluated are genetically different traits, and there are causal associations of milk yield with some of the resilience indicators evaluated. In addition to providing biological insights into the molecular regulation mechanisms of resilience derived based on variability in milk yield, this study also indicates the need for developing selection indexes combining multiple indicator traits and taking into account their genetic relationship for breeding more resilient dairy cattle.

Development of TaqMan PCR assay for genotyping SNP rs211250281 of the bovine agpat6 gene

Milk fat percentage is an important production trait of dairy cattle and is one of the goals of breeding programs. Over 95% of the milk fat accounts for triacylglycerols. AGPAT6 (1-acylglycerol-3-phosphate O-acyltransferase 6) catalyzes an intermediary step of triglyceride synthesis in the mammary cells. Genome-wide association studies identified SNP rs211250281 (g27: 36520069 G/T) in the agpat6 gene associated with milk fat content and fat-to-protein ratio in dairy cattle. The article presents data on the development of TaqMan PCR assay for genotyping SNP rs211250281 of the bovine agpat6 gene. In this method, a primer pair, initiating amplification of 75-bp fragments of the agpat6 gene, and two allele-specific TaqMan probes are used. Identification of the G and T alleles is based on a comparison of the final fluorescence intensity of FAM and VIC dyes, respectively. The developed TaqMan PCR assay was validated by Sanger sequencing method. The results of both methods fully coincided, that confirmed high accuracy of the developed TaqMan PCR assay. This reliable, simple, rapid, and high-throughput method could be a suitable tool for studying the distribution of the SNP rs211250281 among different cattle breeds and its association with milk fat content.

Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle

Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation.

Candidate genes for mastitis resistance in dairy cattle: a data integration approach

BACKGROUND

Inflammation of the mammary tissue (mastitis) is one of the most detrimental health conditions in dairy ruminants and is considered the most economically important infectious disease of the dairy sector. Improving mastitis resistance is becoming an important goal in dairy ruminant breeding programmes. However, mastitis resistance is a complex trait and identification of mastitis-associated alleles in livestock is difficult. Currently, the only applicable approach to identify candidate loci for complex traits in large farm animals is to combine different information that supports the functionality of the identified genomic regions with respect to a complex trait.

METHODS

To identify the most promising candidate loci for mastitis resistance we integrated heterogeneous data from multiple sources and compiled the information into a comprehensive database of mastitis-associated candidate loci. Mastitis-associated candidate genes reported in association, expression, and mouse model studies were collected by searching the relevant literature and databases. The collected data were integrated into a single database, screened for overlaps, and used for gene set enrichment analysis.

RESULTS

The database contains candidate genes from association and expression studies and relevant transgenic mouse models. The 2448 collected candidate loci are evenly distributed across bovine chromosomes. Data integration and analysis revealed overlaps between different studies and/or with mastitis-associated QTL, revealing promising candidate genes for mastitis resistance.

CONCLUSION

Mastitis resistance is a complex trait influenced by numerous alleles. Based on the number of independent studies, we were able to prioritise candidate genes and propose a list of the 22 most promising. To our knowledge this is the most comprehensive database of mastitis associated candidate genes and could be helpful in selecting genes for functional validation studies.

A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle

Claw diseases and mastitis represent the most important disease traits in dairy cattle with increasing incidences and a frequently mentioned connection to milk yield. Yet, many studies aimed to detect the genetic background of both trait complexes via fine-mapping of quantitative trait loci. However, little is known about genomic regions that simultaneously affect milk production and disease traits. For this purpose, several tools to detect local genetic correlations have been developed. In this study, we attempted a detailed analysis of milk production and disease traits as well as their interrelationship using a sample of 34,497 50K genotyped German Holstein cows with milk production and claw and udder disease traits records. We performed a pedigree-based quantitative genetic analysis to estimate heritabilities and genetic correlations. Additionally, we generated GWAS summary statistics, paying special attention to genomic inflation, and used these data to identify shared genomic regions, which affect various trait combinations. The heritability on the liability scale of the disease traits was low, between 0.02 for laminitis and 0.19 for interdigital hyperplasia. The heritabilities for milk production traits were higher (between 0.27 for milk energy yield and 0.48 for fat-protein ratio). Global genetic correlations indicate the shared genetic effect between milk production and disease traits on a whole genome level. Most of these estimates were not significantly different from zero, only mastitis showed a positive one to milk (0.18) and milk energy yield (0.13), as well as a negative one to fat-protein ratio (-0.07). The genomic analysis revealed significant SNPs for milk production traits that were enriched on Bos taurus autosome 5, 6, and 14. For digital dermatitis, we found significant hits, predominantly on Bos taurus autosome 5, 10, 22, and 23, whereas we did not find significantly trait-associated SNPs for the other disease traits. Our results confirm the known genetic background of disease and milk production traits. We further detected 13 regions that harbor strong concordant effects on a trait combination of milk production and disease traits. This detailed investigation of genetic correlations reveals additional knowledge about the localization of regions with shared genetic effects on these trait complexes, which in turn enables a better understanding of the underlying biological pathways and putatively the utilization for a more precise design of breeding schemes.

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

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

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

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

Cross-population selection signatures in Canchim composite beef cattle

Analyses of livestock genomes have been used to detect selection signatures, which are genomic regions associated with traits under selection leading to a change in allele frequency. The objective of the present study was to characterize selection signatures in Canchim composite beef cattle using cross-population analyses with the founder Nelore and Charolais breeds. High-density single nucleotide polymorphism genotypes were available on 395 Canchim representing the target population, along with genotypes from 809 Nelore and 897 Charolais animals representing the reference populations. Most of the selection signatures were co-located with genes whose functions agree with the expectations of the breeding programs; these genes have previously been reported to associate with meat quality, as well as reproductive traits. Identified genes were related to immunity, adaptation, morphology, as well as behavior, could give new perspectives for understanding the genetic architecture of Canchim. Some selection signatures identified genes that were recently introduced in Canchim, such as the loci related to the polled trait.

Fatty acid desaturase 2 affects the milk-production traits in Chinese Holsteins

As a member of the fatty acid desaturase family, fatty acid desaturase 2 (FADS2) gene is a rate-limiting enzyme in the synthesis of unsaturated fatty acids and within/near to the reported QTL regions for milk-production traits. We previously found that FADS2 is differentially expressed during different lactations of Chinese Holstein cows, and participates in lipid metabolic processes by influencing the insulin, PI3K-Akt, MAPK, AMPK, mTOR and PPAR signaling pathways. Therefore, we considered this gene as a candidate gene for milk-production traits. In this study, we identified 12 SNPs in FADS2 by re-sequencing, including two SNPs in the 5' flanking region, one in the seventh exon, five in introns, two in the 3' untranslated region and two in the 3' flanking region. The 29:g.40378819C>T is a missense mutation that causes alanine (GCG) to be replaced with valine (GTG). Through single marker association analysis, we found that all of the 12 SNPs were significantly associated with 305 day milk yield, fat yield, fat percentage, protein yield or protein percentage (p < 0.0493). The results of the subsequent haplotype association analysis also confirmed the associations between the gene and milk-production traits. In summary, this study suggests that there is a significant genetic association between FADS2 and milk-production traits, and that the SNPs with significant genetic effects can provide important molecular information for the development of a genomic selection chip in dairy cattle.

Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans

Background

Imputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species.

Results

In this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed MRAP2, LEPROT, PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs.

Conclusion

Our post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08373-3.

The construction of a haplotype reference panel using extremely low coverage whole genome sequences and its application in genome-wide association studies and genomic prediction in Duroc pigs

Extremely low coverage whole genome sequencing (lcWGS) is an economical technique to obtain high-density single nucleotide polymorphisms (SNPs). Here, we explored the feasibility of constructing a haplotype reference panel (lcHRP) using lcWGS and evaluated the effects of lcHRP through a genome-wide association study (GWAS) and genomic prediction in pigs. A total of 297 and 974 Duroc pigs were genotyped using lcWGS and a 50 K SNP array, respectively. We obtained 19,306,498 SNPs using lcWGS with an accuracy of 0.984. With the help of lcHRP, the accuracy of imputation from the SNP array to lcWGS was 0.922. Compared to the SNP array findings, those from the imputation-based GWAS identified more signals across four traits. With the integration of the top 1% imputation-based GWAS findings as genomic features, the accuracies of genomic prediction was improved by 6.0% to 13.2%. This study showed the great potential of lcWGS in pigs' molecular breeding.

Genome-wide Association Study for Carcass Primal Cut Yields Using Single-step Bayesian Approach in Hanwoo Cattle

The importance of meat and carcass quality is growing in beef cattle production to meet both producer and consumer demands. Primal cut yields, which reflect the body compositions of carcass, could determine the carcass grade and, consequently, command premium prices. Despite its importance, there have been few genome-wide association studies on these traits. This study aimed to identify genomic regions and putative candidate genes related to 10 primal cut traits, including tenderloin, sirloin, striploin, chuck, brisket, top round, bottom round, shank, flank, and rib in Hanwoo cattle using a single-step Bayesian regression (ssBR) approach. After genomic data quality control, 43,987 SNPs from 3,745 genotyped animals were available, of which 3,467 had phenotypic records for the analyzed traits. A total of 16 significant genomic regions (1-Mb window) were identified, of which five large-effect quantitative trait loci (QTLs) located on chromosomes 6 at 38–39 Mb, 11 at 21–22 Mb, 14 at 6–7 Mb and 26–27 Mb, and 19 at 26–27 Mb were associated with more than one trait, while the remaining 11 QTLs were trait-specific. These significant regions were harbored by 154 genes, among which TOX, FAM184B, SPP1, IBSP, PKD2, SDCBP, PIGY, LCORL, NCAPG, and ABCG2 were noteworthy. Enrichment analysis revealed biological processes and functional terms involved in growth and lipid metabolism, such as growth (GO:0040007), muscle structure development (GO:0061061), skeletal system development (GO:0001501), animal organ development (GO:0048513), lipid metabolic process (GO:0006629), response to lipid (GO:0033993), metabolic pathways (bta01100), focal adhesion (bta04510), ECM–receptor interaction (bta04512), fat digestion and absorption (bta04975), and Rap1 signaling pathway (bta04015) being the most significant for the carcass primal cut traits. Thus, identification of quantitative trait loci regions and plausible candidate genes will aid in a better understanding of the genetic and biological mechanisms regulating carcass primal cut yields.

Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multitrait GWA studies

Genetic correlations between traits may cause correlated responses to selection. Previous models described the conditions under which genetic correlations are expected to be maintained. Selection, mutation, and migration are all proposed to affect genetic correlations, regardless of whether the underlying genetic architecture consists of pleiotropic or tightly linked loci affecting the traits. Here, we investigate the conditions under which pleiotropy and linkage have different effects on the genetic correlations between traits by explicitly modeling multiple genetic architectures to look at the effects of selection strength, degree of correlational selection, mutation rate, mutational variance, recombination rate, and migration rate. We show that at mutation-selection(-migration) balance, mutation rates differentially affect the equilibrium levels of genetic correlation when architectures are composed of pairs of physically linked loci compared to architectures of pleiotropic loci. Even when there is perfect linkage (no recombination within pairs of linked loci), a lower genetic correlation is maintained than with pleiotropy, with a lower mutation rate leading to a larger decrease. These results imply that the detection of causal loci in multitrait association studies will be affected by the type of underlying architectures, whereby pleiotropic variants are more likely to be underlying multiple detected associations. We also confirm that tighter linkage between nonpleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.

The Contribution of Copy Number Variants and Single Nucleotide Polymorphisms to the Additive Genetic Variance of Carcass Traits in Cattle

The relative contributions of both copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) to the additive genetic variance of carcass traits in cattle is not well understood. A detailed understanding of the relative importance of CNVs in cattle may have implications for study design of both genomic predictions and genome-wide association studies. The first objective of the present study was to quantify the relative contributions of CNV data and SNP genotype data to the additive genetic variance of carcass weight, fat, and conformation for 945 Charolais, 923 Holstein-Friesian, and 974 Limousin sires. The second objective was to jointly consider SNP and CNV data in a least absolute selection and shrinkage operator (LASSO) regression model to identify genomic regions associated with carcass weight, fat, and conformation within each of the three breeds separately. A genomic relationship matrix (GRM) based on just CNV data did not capture any variance in the three carcass traits when jointly evaluated with a SNP-derived GRM. In the LASSO regression analysis, a total of 987 SNPs and 18 CNVs were associated with at least one of the three carcass traits in at least one of the three breeds. The quantitative trait loci (QTLs) corresponding to the associated SNPs and CNVs overlapped with several candidate genes including previously reported candidate genes such as MSTN and RSAD2, and several potential novel candidate genes such as ACTN2 and THOC1. The results of the LASSO regression analysis demonstrated that CNVs can be used to detect associations with carcass traits which were not detected using the set of SNPs available in the present study. Therefore, the CNVs and SNPs available in the present study were not redundant forms of genomic data.

Genomewide Association Analyses of Lactation Persistency and Milk Production Traits in Holstein Cattle Based on Imputed Whole-Genome Sequence Data

Lactation persistency and milk production are among the most economically important traits in the dairy industry. In this study, we explored the association of over 6.1 million imputed whole-genome sequence variants with lactation persistency (LP), milk yield (MILK), fat yield (FAT), fat percentage (FAT%), protein yield (PROT), and protein percentage (PROT%) in North American Holstein cattle. We identified 49, 3991, 2607, 4459, 805, and 5519 SNPs significantly associated with LP, MILK, FAT, FAT%, PROT, and PROT%, respectively. Various known associations were confirmed while several novel candidate genes were also revealed, including ARHGAP35, NPAS1, TMEM160, ZC3H4, SAE1, ZMIZ1, PPIF, LDB2, ABI3, SERPINB6, and SERPINB9 for LP; NIM1K, ZNF131, GABRG1, GABRA2, DCHS1, and SPIDR for MILK; NR6A1, OLFML2A, EXT2, POLD1, GOT1, and ETV6 for FAT; DPP6, LRRC26, and the KCN gene family for FAT%; CDC14A, RTCA, HSTN, and ODAM for PROT; and HERC3, HERC5, LALBA, CCL28, and NEURL1 for PROT%. Most of these genes are involved in relevant gene ontology (GO) terms such as fatty acid homeostasis, transporter regulator activity, response to progesterone and estradiol, response to steroid hormones, and lactation. The significant genomic regions found contribute to a better understanding of the molecular mechanisms related to LP and milk production in North American Holstein cattle.

Understanding the genomic architecture of clinical mastitis in Bos indicus

This study elucidated potential genetic variants and QTLs associated with clinical mastitis incidence traits in Bos indicus breed, Sahiwal. Estimated breeding values for the traits (calculated using Bayesian inference) were used as pseudo-phenotypes for association with genome-wide SNPs and further QTL regions underlying the traits were identified. In all, 25 SNPs were found to be associated with the traits at the genome-wide suggestive threshold (p ≤ 5 × 10^-4) and these SNPs were used to define QTL boundaries based on the linkage disequilibrium structure. A total of 16 QTLs were associated with the trait EBVs including seven each for clinical mastitis incidence (CMI) in first and second lactations and two for CMI in third lactation. Nine out of sixteen QTLs overlapped with the already reported QTLs for mastitis traits, whereas seven were adjudged as novel ones. Important candidates for clinical mastitis in the identified QTL regions included DNAJB9, ELMO1, ARHGAP26, NR3C1, CACNB2, RAB4A, GRB2, NUP85, SUMO2, RBPJ, and RAB33B genes. These findings shed light on the genetic architecture of the disease in Bos indicus, and present potential regions for fine mapping and downstream analysis in future.

Whole Genome DNA Methylation Variations in Mammary Gland Tissues from Holstein Cattle Producing Milk with Various Fat and Protein Contents

Milk fat and protein contents are among key elements of milk quality, and they are attracting more attention in response to consumers' demand for high-quality dairy products. To investigate the potential regulatory roles of DNA methylation underlying milk component yield, whole genome bisulfite sequencing was employed to profile the global DNA methylation patterns of mammary gland tissues from 17 Canada Holstein cows with various milk fat and protein contents. A total of 706, 2420 and 1645 differentially methylated CpG sites (DMCs) were found between high vs. low milk fat (HMF vs. LMF), high vs. low milk protein (HMP vs. LMP), and high vs. low milk fat and protein (HMFP vs. LMFP) groups, respectively (q value < 0.1). Twenty-seven, 56 and 67 genes harboring DMCs in gene regions (denoted DMC genes) were identified for HMF vs. LMF, HMP vs. LMP and HMFP vs. LMFP, respectively. DMC genes from HMP vs. LMP and HMFP vs. LMFP comparisons were significantly overrepresented in GO terms related to aerobic electron transport chain and/or mitochondrial ATP (adenosine triphosphate) synthesis coupled electron transport. A total of 83 (HMF vs. LMF), 708 (HMP vs. LMP) and 408 (HMFP vs. LMFP) DMCs were co-located with 87, 147 and 158 quantitative trait loci (QTL) for milk component and yield traits, respectively. In conclusion, the identified methylation changes are potentially involved in the regulation of milk fat and protein yields, as well as the variation in reported co-located QTLs.

Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model

Genome-wide association studies are a powerful tool to identify genomic regions and variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. The objectives of this study were to identify genomic regions as well as genes and pathways associated with the first-lactation milk, fat, protein, and total solid yields; fat, protein, and total solid percentage; and somatic cell score (SCS) in a Thai dairy cattle population. Effects of SNPs were estimated by a weighted single-step GWAS, which back-solved the genomic breeding values predicted using single-step genomic BLUP (ssGBLUP) fitting a single-trait random regression test-day model. Genomic regions that explained at least 0.5% of the total genetic variance were selected for further analyses of candidate genes. Despite the small number of genotyped animals, genomic predictions led to an improvement in the accuracy over the traditional BLUP. Genomic predictions using weighted ssGBLUP were slightly better than the ssGBLUP. The genomic regions associated with milk production traits contained 210 candidate genes on 19 chromosomes [Bos taurus autosome (BTA) 1 to 7, 9, 11 to 16, 20 to 21, 26 to 27 and 29], whereas 21 candidate genes on 3 chromosomes (BTA 11, 16, and 21) were associated with SCS. Many genomic regions explained a small fraction of the genetic variance, indicating polygenic inheritance of the studied traits. Several candidate genes coincided with previous reports for milk production traits in Holstein cattle, especially a large region of genes on BTA14. We identified 141 and 5 novel genes related to milk production and SCS, respectively. These novel genes were also found to be functionally related to heat tolerance (e.g., SLC45A2, IRAG1, and LOC101902172), longevity (e.g., SYT10 and LOC101903327), and fertility (e.g., PAG1). These findings may be attributed to indirect selection in our population. Identified biological networks including intracellular cell transportation and protein catabolism implicate milk production, whereas the immunological pathways such as lymphocyte activation are closely related to SCS. Further studies are required to validate our findings before exploiting them in genomic selection.

Genome-Wide Association Studies in Indian Buffalo Revealed Genomic Regions for Lactation and Fertility

Murrah breed of buffalo is an excellent dairy germplasm known for its superior milk quality in terms of milk fat and solids-not-fat (SNF); however, it is often reported that Indian buffaloes had lower lactation and fertility potential compared to the non-native cattle of the country. Recent techniques, particularly the genome-wide association studies (GWAS), to identify genomic variations associated with lactation and fertility traits offer prospects for systematic improvement of buffalo. DNA samples were sequenced using the double-digestion restriction-associated DNA (RAD) tag genotyping-by-sequencing. The bioinformatics pipeline was standardized to call the variants, and single-nucleotide polymorphisms (SNPs) qualifying the stringent quality check measures were retained for GWAS. Over 38,000 SNPs were used to perform GWAS on the first two principal components of test-day records of milk yields, fat percentages, and SNF percentages, separately. GWAS was also performed on 305 days’ milk yield; lactation persistency was estimated through the rate of decline after attaining the peak yield method, along with three other standard methods; and breeding efficiency, post-partum breeding interval, and age at sexual maturity were considered fertility traits. Significant association of SNPs was observed for the first principal component, explaining the maximum proportion of variation in milk yield. Furthermore, some potential genomic regions were identified to have a potential role in regulating milk yield and fertility in Murrah. Identification of such genomic regions shall help in carrying out an early selection of high-yielding persistent Murrah buffaloes and, in the long run, would be helpful in shaping their future genetic improvement programs.

Genome-wide interaction study reveals epistatic interactions for beef lipid-related traits in Nellore cattle

Gene-gene interactions cause hidden genetic variation in natural populations and could be responsible for the lack of replication that is typically observed in complex traits studies. This study aimed to identify gene-gene interactions using the empirical Hilbert-Schmidt Independence Criterion method to test for epistasis in beef fatty acid profile traits of Nellore cattle. The dataset contained records from 963 bulls, genotyped using a 777 962k SNP chip. Meat samples of Longissimus muscle, were taken to measure fatty acid composition, which was quantified by gas chromatography. We chose to work with the sums of saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), omega-3 (OM3), omega-6 (OM6), SFA:PUFA and OM3:OM6 fatty acid ratios. The SNPs in the interactions where P < 10 - 8 were mapped individually and used to search for candidate genes. Totals of 602, 3, 13, 23, 13, 215 and 169 candidate genes for SFAs, MUFAs, PUFAs, OM3s, OM6s and SFA:PUFA and OM3:OM6 ratios were identified respectively. The candidate genes found were associated with cholesterol, lipid regulation, low-density lipoprotein receptors, feed efficiency and inflammatory response. Enrichment analysis revealed 57 significant GO and 18 KEGG terms ( P < 0.05), most of them related to meat quality and complementary terms. Our results showed substantial genetic interactions associated with lipid profile, meat quality, carcass and feed efficiency traits for the first time in Nellore cattle. The knowledge of these SNP-SNP interactions could improve understanding of the genetic and physiological mechanisms that contribute to lipid-related traits and improve human health by the selection of healthier meat products.

Genome-wide association study of trypanosome prevalence and morphometric traits in purebred and crossbred Baoulé cattle of Burkina Faso

In this study, single-SNP GWAS analyses were conducted to find regions affecting tolerance against trypanosomosis and morphometrics traits in purebred and crossbred Baoulé cattle of Burkina Faso. The trypanosomosis status (positive and negative) and a wide set of morphological traits were recorded for purebred Baoulé and crossbred Zebu x Baoulé cattle, and genotyped with the Illumina Bovine SNP50 BeadChip. After quality control, 36,203 SNPs and 619 animals including 343 purebred Baoulé and 279 crossbreds were used for the GWAS analyses. Several important genes were found that can influence morphological parameters. Although there were no genes identified with a reported strong connection to size traits, many of them were previously identified in various growth-related studies. A re-occurring theme for the genes residing in the regions identified by the most significant SNPs was pleiotropic effect on growth of the body and the cardiovascular system. Regarding trypanosomosis tolerance, two potentially important regions were identified in purebred Baoulé on chromosomes 16 and 24, containing the CFH, CRBN, TRNT1 and, IL5RA genes, and one additional genomic region in Baoulé, x Zebu crossbreds on chromosome 5, containing MGAT4C and NTS. Almost all of these regions and genes were previously related to the trait of interest, while the CRBN gene was to our knowledge presented in the context of trypanosomiasis tolerance for the first time.

Genome-Wide Scanning for Signatures of Selection Revealed the Putative Genomic Regions and Candidate Genes Controlling Milk Composition and Coat Color Traits in Sahiwal Cattle

Background

In the evolutionary time scale, selection shapes the genetic variation and alters the architecture of genome in the organisms. Selection leaves detectable signatures at the genomic coordinates that provide clues about the protein-coding regions. Sahiwal is a valuable indicine cattle adapted to tropical environments with desirable milk attributes. Insights into the genomic regions under putative selection may reveal the molecular mechanisms affecting the quantitative and other important traits. To understand this, the present investigation was undertaken to explore signatures of selection in the genome of Sahiwal cattle using a medium-density genotyping INDUS chip.

Result

De-correlated composite of multiple selection signals (DCMS), which combines five different univariate statistics, was computed in the dataset to detect the signatures of selection in the Sahiwal genome. Gene annotations, Quantitative Trait Loci (QTL) enrichment, and functional analyses were carried out for the identification of significant genomic regions. A total of 117 genes were identified, which affect a number of important economic traits. The QTL enrichment analysis highlighted 14 significant [False Discovery Rate (FDR)-corrected p-value ≤ 0.05] regions on chromosomes BTA 1, 3, 6, 11, 20, and 21. The top three enriched QTLs were found on BTA 6, 20, and 23, which are associated with exterior, health, milk production, and reproduction traits. The present study on selection signatures revealed some key genes related with coat color (PDGFRA, KIT, and KDR), facial pigmentation (LEF), milk fat percent (MAP3K1, HADH, CYP2U1, and SGMS2), sperm membrane integrity (OSTC), lactation persistency (MRPS30, NNT, CCL28, HMGCS1, NIM1K, ZNF131, and CCDC152), milk yield (GHR and ZNF469), reproduction (NKX2-1 and DENND1A), and bovine tuberculosis susceptibility (RNF144B and PAPSS1). Further analysis of candidate gene prioritization identified four hub genes, viz., KIT, KDR, MAP3K1, and LEF, which play a role in coat color, facial pigmentation, and milk fat percentage in cattle. Gene enrichment analysis revealed significant Gene ontology (GO) terms related to breed-specific coat color and milk fat percent.

Conclusion

The key candidate genes and putative genomic regions associated with economic traits were identified in Sahiwal using single nucleotide polymorphism data and the DCMS method. It revealed selection for milk production, coat color, and adaptability to tropical climate. The knowledge about signatures of selection and candidate genes affecting phenotypes have provided a background information that can be further utilized to understand the underlying mechanism involved in these traits in Sahiwal cattle.

A 12 kb multi-allelic copy number variation encompassing a GC gene enhancer is associated with mastitis resistance in dairy cattle

Clinical mastitis (CM) is an inflammatory disease occurring in the mammary glands of lactating cows. CM is under genetic control, and a prominent CM resistance QTL located on chromosome 6 was reported in various dairy cattle breeds. Nevertheless, the biological mechanism underpinning this QTL has been lacking. Herein, we mapped, fine-mapped, and discovered the putative causal variant underlying this CM resistance QTL in the Dutch dairy cattle population. We identified a ~12 kb multi-allelic copy number variant (CNV), that is in perfect linkage disequilibrium with a lead SNP, as a promising candidate variant. By implementing a fine-mapping and through expression QTL mapping, we showed that the group-specific component gene (GC), a gene encoding a vitamin D binding protein, is an excellent candidate causal gene for the QTL. The multiplicated alleles are associated with increased GC expression and low CM resistance. Ample evidence from functional genomics data supports the presence of an enhancer within this CNV, which would exert cis-regulatory effect on GC. We observed that strong positive selection swept the region near the CNV, and haplotypes associated with the multiplicated allele were strongly selected for. Moreover, the multiplicated allele showed pleiotropic effects for increased milk yield and reduced fertility, hinting that a shared underlying biology for these effects may revolve around the vitamin D pathway. These findings together suggest a putative causal variant of a CM resistance QTL, where a cis-regulatory element located within a CNV can alter gene expression and affect multiple economically important traits.

Estimated genetic parameters for all genetically evaluated traits in Canadian Holsteins

Genetic improvement is a crucial tool to deal with the increasing demand for high quality, sustainably produced dairy. Breeding programs are based on genetic parameters, such as heritability and genetic correlations, for economically important traits in a population. In this study, we estimated population genetic parameters and genetic trends for 67 traits evaluated on heifers and first-lactation Canadian Holstein cows. The data consisted of approximately 500,000 records with pedigree information collected from 1980 to 2019. Genetic parameters were estimated using bivariate linear animal models under a Bayesian approach. Analyses for the 67 traits resulted in 2,211 bivariate combinations, from which the estimated genetic parameters are reported here. The most highly heritable traits were fat percent (0.66) and protein percent (0.69), followed by stature (0.47). Lowest heritabilities (0.01) were observed for disease-related traits, such as lameness and toe ulcer, and calf survival. The genetic correlations between gestation length, calf size, and calving ease measured on both heifer and cows were close to unity. On the other hand, traits such as body condition score and pin width, cystic ovaries and sole ulcer, rear teat placement, and toe ulcer were genetically unrelated. This study reports genetic parameters that have not been previously published for Canadian Holstein cows, and provides updates of those previously estimated. These estimates are useful for building new indexes, updating existing selection indexes, and for predicting correlated responses due to inclusion of novel traits in the breeding programs.