Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle

Re-sequencing of the casein genes in Swedish Red cattle giving milk with diverse protein profiles and extreme rennet coagulation properties

Impaired rennet coagulation properties in milk could lead to prolonged processing times and production losses. Heritability for milk coagulation has previously been estimated to be 0.28 to 0.45, indicating that genetic selection can be used to manipulate this trait. The CN proteins are expressed by the genes CSN1S1, CSN2, CSN1S2, and CSN3 and are located on bovine chromosome 6. To better understand the effect of genetic variation in the CN genes on milk coagulation, blood and milk samples from 30 Swedish Red Dairy Cattle (RDC) with divergent coagulation properties were investigated. DNA from the 30 cows was sequenced for the CN genes to determine the theoretical AA sequence and to look for genetic variation in the untranslated regions. The aim is to confirm the protein genetic variants previously reported, while searching for additional genetic variation in the CN genes of 30 RDC. We observed genetic variation in 116 SNPs in the known CN genes where 10% of the SNPs are exon variants and the remaining 90% are intron variants. A total of 2.5% of the SNPs are found in the 5'- or 3'-untranslated region (UTR) regions of the exons; 2% are synonymous variants and 6% are missense variants that concurred with the known protein variants for CSN1S1, CSN2, and CSN3. Furthermore, 6% of the SNPs are splice polypyrimidine tract intron variants. The 2 genetic variants in the 5'- and 3'-UTR in CSN1S1 and CSN3 are found with protein variants CSN1S1C and CSN3B. Because both UTR variants are associated with gain and loss of micro RNA and transcription factors, this could explain differences in expression of the genetic protein variants. Preliminary chi-squared analysis and comparison with previous GWAS studies showed potential connections between the identified SNPs and coagulation properties of milk. By advancing the knowledge of the connection between the DNA sequence and the functional properties of the CN proteins, we hope to learn more about the cheese coagulation properties of milk from RDC.

Microbial Evolution in Artisanal Pecorino-like Cheeses Produced from Two Farms Managing Two Different Breeds of Sheep (Comisana and Lacaune)

"Pecorino" is a typical semi-hard cheese obtained with raw or heat-treated sheep milk using procedures to valorize the raw material's chemical and microbiological properties. In the present study, using a high-throughput method of 16S rRNA gene sequencing, we assessed the evolution of the microbiome composition from milk to Pecorino-like cheese in artisanal processes using milk from Comisana and Lacaune sheep breeds. The comparative analysis of the bacterial community composition revealed significant differences in the presence and abundance of specific taxa in the milk microbiomes of the Comisana and Lacaune breeds. Next-Generation Sequencing (NGS) analysis also revealed differences in the curd microbiomes related to dairy farming practices, which have a relevant effect on the final structure of the Pecorino cheese microbiome.

Genome-Wide Analysis of Milk Production Traits and Selection Signatures in Serbian Holstein-Friesian Cattle

To improve the genomic evaluation of milk-related traits in Holstein-Friesian (HF) cattle it is essential to identify the associated candidate genes. Novel SNP-based analyses, such as the genetic mapping of inherited diseases, GWAS, and genomic selection, have led to a new era of research. The aim of this study was to analyze the association of each individual SNP in Serbian HF cattle with milk production traits and inbreeding levels. The SNP 60 K chip Axiom Bovine BovMDv3 was deployed for the genotyping of 334 HF cows. The obtained genomic results, together with the collected phenotypic data, were used for a GWAS. Moreover, the identification of ROH segments was performed and served for inbreeding coefficient evaluation and ROH island detection. Using a GWAS, a polymorphism, rs110619097 (located in the intron of the CTNNA3 gene), was detected to be significantly (p < 0.01) associated with the milk protein concentration in the first lactation (adjusted to 305 days). The average genomic inbreeding value (FROH) was 0.079. ROH islands were discovered in proximity to genes associated with milk production traits and genomic regions under selection pressure for other economically important traits of dairy cattle. The findings of this pilot study provide useful information for a better understanding of the genetic architecture of milk production traits in Serbian HF dairy cows and can be used to improve lactation performances in Serbian HF cattle breeding programs.

Optimization of lactic acid bacterial starter culture to improve the quality and flavor characteristics of traditional Hurood

Hurood is a traditional fermented milk product prepared by traditional Mongolian techniques of fermenting raw milk, partial degreasing, heating, whey drainage, emulsification of curd, and molding. Currently, Hurood available in the market is generally prepared by small-scale enterprises at home or in open air. Therefore, lack of standardization of bacterial starter culture leads to variation in the flavor and sensory properties of Hurood from batch to batch. In this study, we aimed to assess the best starter culture combination obtained from 37 lactic acid bacterial strains isolated from traditional Hurood. The solidification state and sensory quality were used as indexes for determining the fermentation efficiency of the bacterial starter culture combinations. The yield and texture characteristics were used to determine the optimal ratio of bacterial strains in a combination and the processing conditions for traditional Hurood production. The most optimal bacterial culture combination was observed to be NF 9-3:NF 10-4:CH 3-1 in 5:4:1 ratio and in 3% amount. The most optimal whey temperature and heating-stirring temperature were observed to be 55°C to 60°C and 85°C to 90°C, respectively. Hurood prepared with the optimal combination of bacterial strains exhibited significantly enhanced sensory quality, flavor, and contents of AA and fatty acids. Therefore, the use of optimal starter culture of lactic acid bacteria could produce Hurood with significantly superior sensory qualities, making the product more acceptable to consumers.

Intra- and Interspecies RNA-Seq Based Variants in the Lactation Process of Ruminants

The RNA-Seq data provides new opportunities for the detection of transcriptome variants' single nucleotide polymorphisms (SNPs) in various species and tissues. Herein, milk samples from two sheep breeds and two cow breeds were utilized to characterize the genetic variation in the coding regions in three stages (before-peak (BP), peak (P), and after-peak (AP)) of the lactation process. In sheep breeds Assaf and Churra, 100,462 and 97,768, 65,996 and 62,161, and 78,656 and 39,245 variants were observed for BP, P, and AP lactation stages, respectively. The number of specific variants was 59,798 and 76,419, 11,483 and 49,210, and 104,033 and 320,817 in cow breeds Jersy and Kashmiri, respectively, for BP, P, and AP stages. Via the transcriptome analysis of variation in regions containing QTL for fat, protein percentages, and milk yield, we detected a number of pathways and genes harboring mutations that could influence milk production attributes. Many SNPs detected here can be regarded as appropriate markers for custom SNP arrays or genotyping platforms to conduct association analyses among commercial populations. The results of this study offer new insights into milk production genetic mechanisms in cow and sheep breeds, which can contribute to designing suitable breeding systems for optimal milk production.

Predictive formulas for different measures of cheese yield using milk composition from individual goat samples

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CY_CURD), total solids (%CY_SOLIDS), and water retained (%CY_WATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R²_VAL) and the root mean square error of validation (RMSE_VAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R²_VAL = 0.65, 0.96, and 0.23 for %CY_CURD, %CY_SOLIDS, and %CY_WATER, respectively). The WAV procedure showed R²_VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CY_CURD and %CY_WATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production.

First genome-wide association study investigating blood pressure and renal traits in domestic cats

Hypertension (HTN) and chronic kidney disease (CKD) are common in ageing cats. In humans, blood pressure (BP) and renal function are complex heritable traits. We performed the first feline genome-wide association study (GWAS) of quantitative traits systolic BP and creatinine and binary outcomes HTN and CKD, testing 1022 domestic cats with a discovery, replication and meta-analysis design. No variants reached experimental significance level in the discovery stage for any phenotype. Follow up of the top 9 variants for creatinine and 5 for systolic BP, one SNP reached experimental-wide significance for association with creatinine in the combined meta-analysis (chrD1.10258177; P = 1.34 × 10^–6). Exploratory genetic risk score (GRS) analyses were performed. Within the discovery sample, GRS of top SNPs from the BP and creatinine GWAS show strong association with HTN and CKD but did not validate in independent replication samples. A GRS including SNPs corresponding to human CKD genes was not significant in an independent subset of cats. Gene-set enrichment and pathway-based analysis (GSEA) was performed for both quantitative phenotypes, with 30 enriched pathways with creatinine. Our results support the utility of GWASs and GSEA for genetic discovery of complex traits in cats, with the caveat of our findings requiring validation.

Genetic Parameter Estimation and Genome-Wide Association Study-Based Loci Identification of Milk-Related Traits in Chinese Holstein

Accurately estimating the genetic parameters and revealing more genetic variants underlying milk production and quality are conducive to the genetic improvement of dairy cows. In this study, we estimate the genetic parameters of five milk-related traits of cows-namely, milk yield (MY), milk fat percentage (MFP), milk fat yield (MFY), milk protein percentage (MPP), and milk protein yield (MPY)-based on a random regression test-day model. A total of 95,375 test-day records of 9,834 cows in the lower reaches of the Yangtze River were used for the estimation. In addition, genome-wide association studies (GWASs) for these traits were conducted, based on adjusted phenotypes. The heritability, as well as the standard errors, of MY, MFP, MFY, MPP, and MPY during lactation ranged from 0.22 ± 0.02 to 0.31 ± 0.04, 0.06 ± 0.02 to 0.15 ± 0.03, 0.09 ± 0.02 to 0.28 ± 0.04, 0.07 ± 0.01 to 0.16 ± 0.03, and 0.14 ± 0.02 to 0.27 ± 0.03, respectively, and the genetic correlations between different days in milk (DIM) within lactations decreased as the time interval increased. Two, six, four, six, and three single nucleotide polymorphisms (SNPs) were detected, which explained 5.44, 12.39, 8.89, 10.65, and 7.09% of the phenotypic variation in MY, MFP, MFY, MPP, and MPY, respectively. Ten Kyoto Encyclopedia of Genes and Genomes pathways and 25 Gene Ontology terms were enriched by analyzing the nearest genes and genes within 200 kb of the detected SNPs. Moreover, 17 genes in the enrichment results that may play roles in milk production and quality were selected as candidates, including CAMK2G, WNT3A, WNT9A, PLCB4, SMAD9, PLA2G4A, ARF1, OPLAH, MGST1, CLIP1, DGAT1, PRMT6, VPS28, HSF1, MAF1, TMEM98, and F7. We hope that this study will provide useful information for in-depth understanding of the genetic architecture of milk production and quality traits, as well as contribute to the genomic selection work of dairy cows in the lower reaches of the Yangtze River.

Insight into the Candidate Genes and Enriched Pathways Associated with Height, Length, Length to Height Ratio and Body-Weight of Korean Indigenous Breed, Jindo Dog Using Gene Set Enrichment-Based GWAS Analysis

As a companion and hunting dog, height, length, length to height ratio (LHR) and body-weight are the vital economic traits for Jindo dog. Human selection and targeted breeding have produced an extraordinary diversity in these traits. Therefore, the identification of causative markers, genes and pathways that help us to understand the genetic basis of this variability is essential for their selection purposes. Here, we performed a genome-wide association study (GWAS) combined with enrichment analysis on 757 dogs using 118,879 SNPs. The genomic heritability (h²) was 0.33 for height and 0.28 for weight trait in Jindo. At p-value < 5 × 10^-5, ten, six, thirteen and eleven SNPs on different chromosomes were significantly associated with height, length, LHR and body-weight traits, respectively. Based on our results, HHIP, LCORL and NCAPG for height, IGFI and FGFR3 for length, DLK1 and EFEMP1 for LHR and PTPN2, IGFI and RASAL2 for weight can be the potential candidate genes because of the significant SNPs located in their intronic or upstream regions. The gene-set enrichment analysis highlighted here nine and seven overlapping significant (p < 0.05) gene ontology (GO) terms and pathways among traits. Interestingly, the highlighted pathways were related to hormone synthesis, secretion and signalling were generally involved in the metabolism, growth and development process. Our data provide an insight into the significant genes and pathways if verified further, which will have a significant effect on the breeding of the Jindo dog's population.

Genome-Wide Association Study Identifies 12 Loci Associated with Body Weight at Age 8 Weeks in Korean Native Chickens

Meat from Korean native chickens (KNCs) has high consumer demand; however, slow growth performance and high variation in body weight (BW) of KNCs remain an issue. Genome-wide association study (GWAS) is a powerful method to identify quantitative trait-associated genomic loci. A GWAS, based on a large-scale KNC population, is needed to identify underlying genetic mechanisms related to its growth traits. To identify BW-associated genomic regions, we performed a GWAS using the chicken 60K single nucleotide polymorphism (SNP) panel for 1328 KNCs. BW was measured at 8 weeks of age, from 2018 to 2020. Twelve SNPs were associated with BW at the suggestive significance level (p < 2.95 × 10⁻⁵) and located near or within 11 candidate genes, including WDR37, KCNIP4, SLIT2, PPARGC1A, MYOCD and ADGRA3. Gene set enrichment analysis based on the GWAS results at p < 0.05 (1680 SNPs) showed that 32 Gene Ontology terms and two Kyoto Encyclopedia of Genes and Genomes pathways, including regulation of transcription, motor activity, the mitogen-activated protein kinase signaling pathway, and tight junction, were significantly enriched (p < 0.05) for BW-associated genes. These pathways are involved in cell growth and development, related to BW gain. The identified SNPs are potential biomarkers in KNC breeding.

Genes and Pathways Affecting Sheep Productivity Traits: Genetic Parameters, Genome-Wide Association Mapping, and Pathway Enrichment Analysis

Ewe productivity is a composite and maternal trait that is considered the most important economic trait in sheep meat production. The objective of this study was the application of alternative genome-wide association study (GWAS) approaches followed by gene set enrichment analysis (GSEA) on the ewes’ genome to identify genes affecting pregnancy outcomes and lamb growth after parturition in Iranian Baluchi sheep. Three maternal composite traits at birth and weaning were considered. The traits were progeny birth weight, litter mean weight at birth, total litter weight at birth, progeny weaning weight, litter mean weight at weaning, and total litter weight at weaning. GWASs were performed on original phenotypes as well as on estimated breeding values. The significant SNPs associated with composite traits at birth were located within or near genes RDX, FDX1, ARHGAP20, ZC3H12C, THBS1, and EPG5. Identified genes and pathways have functions related to pregnancy, such as autophagy in the placenta, progesterone production by the placenta, placental formation, calcium ion transport, and maternal immune response. For composite traits at weaning, genes (NR2C1, VEZT, HSD17B4, RSU1, CUBN, VIM, PRLR, and FTH1) and pathways affecting feed intake and food conservation, development of mammary glands cytoskeleton structure, and production of milk components like fatty acids, proteins, and vitamin B-12, were identified. The results show that calcium ion transport during pregnancy and feeding lambs by milk after parturition can have the greatest impact on weight gain as compared to other effects of maternal origin.

Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle

The aims of this study were to investigate potential functional relationships among milk protein fractions in dairy cattle and to carry out a structural equation model (SEM) GWAS to provide a decomposition of total SNP effects into direct effects and effects mediated by traits that are upstream in a phenotypic network. To achieve these aims, we first fitted a mixed Bayesian multitrait genomic model to infer the genomic correlations among 6 milk nitrogen fractions [4 caseins (CN), namely κ-, β-, α_S1-, and α_S2-CN, and 2 whey proteins, namely β-lactoglobulin (β-LG) and α-lactalbumin (α-LA)], in a population of 989 Italian Brown Swiss cows. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc.). A Bayesian network approach using the max-min hill-climbing (MMHC) algorithm was implemented to model the dependencies or independence among traits. Strong and negative genomic correlations were found between β-CN and α_S1-CN (-0.706) and between β-CN and κ-CN (-0.735). The application of the MMHC algorithm revealed that κ-CN and β-CN seemed to directly or indirectly influence all other milk protein fractions. By integrating multitrait model GWAS and SEM-GWAS, we identified a total of 127 significant SNP for κ-CN, 89 SNP for β-CN, 30 SNP for α_S1-CN, and 14 SNP for α_S2-CN (mostly shared among CN and located on Bos taurus autosome 6) and 15 SNP for β-LG (mostly located on Bos taurus autosome 11), whereas no SNP passed the significance threshold for α-LA. For the significant SNP, we assessed and quantified the contribution of direct and indirect paths to total marker effect. Pathway analyses confirmed that common regulatory mechanisms (e.g., energy metabolism and hormonal and neural signals) are involved in the control of milk protein synthesis and metabolism. The information acquired might be leveraged for setting up optimal management and selection strategies aimed at improving milk quality and technological characteristics in dairy cattle.

Breed of goat affects the prediction accuracy of milk coagulation properties using Fourier-transform infrared spectroscopy

The prediction of traditional goat milk coagulation properties (MCP) and curd firmness over time (CF_t) parameters via Fourier-transform infrared (FTIR) spectroscopy can be of significant economic interest to the dairy industry and can contribute to the breeding objectives for the genetic improvement of dairy goat breeds. Therefore, the aims of this study were to (1) explore the variability of milk FTIR spectra from 4 goat breeds (Camosciata delle Alpi, Murciano-Granadina, Maltese, and Sarda), and to assess the possible discriminant power of milk FTIR spectra among breeds, (2) assess the viability to predict coagulation traits by using milk FTIR spectra, and (3) quantify the effect of the breed on the prediction accuracy of MCP and CF_t parameters. In total, 611 individual goat milk samples were used. Analysis of variance of measured MCP and CF_t parameters was carried out using a mixed model including the farm and pendulum as random factors, and breed, parity, and days in milk as fixed factors. Milk spectra for each goat were collected over the spectral range from wavenumber 5,011 to 925 × cm^-1. Discriminant analysis of principal components was used to assess the ability of FTIR spectra to identify breed of origin. A Bayesian model was used to calibrate equations for each coagulation trait. The accuracy of the model and the prediction equation was assessed by cross-validation (CRV; 80% training and 20% testing set) and stratified CRV (SCV; 3 breeds in the training set, one breed in the testing set) procedures. Prediction accuracy was assessed by using coefficient of determination of validation (R²_VAL), the root mean square error of validation (RMSE_VAL), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R²_VAL values obtained with the CRV procedure were moderate to high for the majority of coagulation traits, with RMSE_VAL and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits.

Genome-wide association study and pathway analysis identify NTRK2 as a novel candidate gene for litter size in sheep

Litter size is one of the most important economic traits in sheep. Identification of gene variants that are associated with the prolificacy rate is an important step in breeding program success and profitability of the farm. So, to identify genetic mechanisms underlying the variation in litter size in Iranian Baluchi sheep, a two-step genome-wide association study (GWAS) was performed. GWAS was conducted using genotype data from 91 Baluchi sheep. Estimated breeding values (EBVs) for litter size calculated for 3848 ewes and then used as the response variable. Besides, a pathway analysis using GO and KEGG databases were applied as a complementary approach. A total of three single nucleotide polymorphisms (SNPs) associated with litter size were identified, one each on OAR2, OAR10, and OAR25. The SNP on OAR2 is located within a novel putative candidate gene, Neurotrophic receptor tyrosine kinase 2. This gene product works as a receptor which is essential for follicular assembly, early follicular growth, and oocyte survival. The SNP on OAR25 is located within RAB4A which is involved in blood vessel formation and proliferation through angiogenesis. The SNP on OAR10 was not associated with any gene in the 1Mb span. Moreover, gene-set analysis using the KEGG database identified several pathways, such as Ovarian steroidogenesis, Steroid hormone biosynthesis, Calcium signaling pathway, and Chemokine signaling. Also, pathway analysis using the GO database revealed several functional terms, such as cellular carbohydrate metabolic, biological adhesion, cell adhesion, cell junction, and cell-cell adherens junction, among others. This is the first study that reports the NTRK2 gene affecting litter size in sheep and our study of this gene functions showed that this gene could be a good candidate for further analysis.

Mechanistic insight into the progressive retinal atrophy disease in dogs via pathway-based genome-wide association analysis

The retinal degenerative disease, progressive retinal atrophy (PRA) is a major reason of vision impairment in canine population. Canine PRA signifies an inherently dissimilar category of retinal dystrophies which has solid resemblances to human retinis pigmentosa. Even though much is known about the biology of PRA, the knowledge about the intricate connection among genetic loci, genes and pathways associated to this disease in dogs are still remain unknown. Therefore, we have performed a genome wide association study (GWAS) to identify susceptibility single nucleotide polymorphisms (SNPs) of PRA. The GWAS was performed using a case–control based association analysis method on PRA dataset of 129 dogs and 135,553 markers. Further, the gene-set and pathway analysis were conducted in this study. A total of 1,114 markers associations with PRA trait at p < 0.01 were extracted and mapped to 640 unique genes, and then selected significant (p < 0.05) enriched 35 gene ontology (GO) terms and 5 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways contain these genes. In particular, apoptosis process, homophilic cell adhesion, calcium ion binding, and endoplasmic reticulum GO terms as well as pathways related to focal adhesion, cyclic guanosine monophosphate)-protein kinase G signaling, and axon guidance were more likely associated to the PRA disease in dogs. These data could provide new insight for further research on identification of potential genes and causative pathways for PRA in dogs.

Identification of Candidate Genes and Pathways Associated with Obesity-Related Traits in Canines via Gene-Set Enrichment and Pathway-Based GWAS Analysis

The present study aimed to identify causative loci and genes enriched in pathways associated with canine obesity using a genome-wide association study (GWAS). The GWAS was first performed to identify candidate single-nucleotide polymorphisms (SNPs) associated with obesity and obesity-related traits including body weight and blood sugar in 18 different breeds of 153 dogs. A total of 10 and 2 SNPs were found to be significantly (p < 3.74 × 10^-7) associated with body weight and blood sugar, respectively. None of the SNPs were identified to be significantly associated with obesity trait. We subsequently followed up the GWAS analysis with gene-set enrichment and pathway analyses. A gene-set with 1057, 1409, and 1243 SNPs annotated to 449, 933 and 820 genes for obesity, body weight, and blood sugar, respectively was created by sub-setting the GWAS result at a threshold of p < 0.01 for the gene-set enrichment analysis. In total, 84 GO and 21 KEGG pathways for obesity, 114 GO and 44 KEGG pathways for blood sugar, 120 GO and 24 KEGG pathways for body weight were found to be enriched. Among the pathways and GO terms, we highlighted five enriched pathways (Wnt signaling pathway, adherens junction, pathways in cancer, axon guidance, and insulin secretion) and seven GO terms (fat cell differentiation, calcium ion binding, cytoplasm, nucleus, phospholipid transport, central nervous system development, and cell surface) that were found to be shared among all the traits. Our data provide insights into the genes and pathways associated with obesity and obesity-related traits.

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

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.

Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Simple Summary

Hanwoo is an indigenous cattle breed in Korea and popular for meat production owing to its rapid growth and high-quality meat. Its yearling weight and carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score) are economically important for the selection of young and proven bulls. In recent decades, the advent of high throughput genotyping technologies has made it possible to perform genome-wide association studies (GWAS) for the detection of genomic regions associated with traits of economic interest in different species. In this study, we conducted a weighted single-step genome-wide association study which combines all genotypes, phenotypes and pedigree data in one step (ssGBLUP). It allows for the use of all SNPs simultaneously along with all phenotypes from genotyped and ungenotyped animals. Our results revealed 33 relevant genomic regions related to the traits of interest. Gene set enrichment analysis indicated that the identified candidate genes were related to biological processes and functional terms that were involved in growth and lipid metabolism. In conclusion, these results suggest that the incorporation of GWAS results and network analysis can help us to better understand the genetic bases underlying growth and carcass traits.

Abstract

In recent years, studies on the biological mechanisms underlying complex traits have been facilitated by innovations in high-throughput genotyping technology. We conducted a weighted single-step genome-wide association study (WssGWAS) to evaluate backfat thickness, carcass weight, eye muscle area, marbling score, and yearling weight in a cohort of 1540 Hanwoo beef cattle using BovineSNP50 BeadChip. The WssGWAS uncovered thirty-three genomic regions that explained more than 1% of the additive genetic variance, mostly located on chromosomes 6 and 14. Among the identified window regions, seven quantitative trait loci (QTL) had pleiotropic effects and twenty-six QTL were trait-specific. Significant pathways implicated in the measured traits through Gene Ontology (GO) term enrichment analysis included the following: lipid biosynthetic process, regulation of lipid metabolic process, transport or localization of lipid, regulation of growth, developmental growth, and multicellular organism growth. Integration of GWAS results of the studied traits with pathway and network analyses facilitated the exploration of the respective candidate genes involved in several biological functions, particularly lipid and growth metabolism. This study provides novel insight into the genetic bases underlying complex traits and could be useful in developing breeding schemes aimed at improving growth and carcass traits in Hanwoo beef cattle.

Association Analysis between SPP1, POFUT1 and PRLR Gene Variation and Milk Yield, Composition and Coagulation Traits in Sarda Sheep

Many studies focus on the identification of genomic regions that undergo selective processes, where evidence of selection is revealed and positional candidate genes are identified. The aim of the research was to evaluate the association between positional candidate genes, namely secreted phosphoprotein 1 (SPP1, sheep chromosome Ovis aries OAR6, 36.651-36.658 Mb), protein O-fucosyltransferase 1 (POFUT1, OAR13, 61.006-61.027 Mb) and prolactin receptor (PRLR, OAR16, 38.969-39.028 Mb) with milk yield, composition and coagulation traits. Eight single nucleotide polymorphisms (SNPs) mapping to the three genes were genotyped in 380 Sarda dairy sheep. Statistical analysis revealed an association between SNP rs161844011 at SPP1 (chromosome position Oar_v3 OAR6:36651870, gene region exon 7) and somatic cell score, while POFUT1 SNP rs424501869 (OAR13:61007495, intron 1) was associated with curd firmness both 45 and 60 min after rennet addition (p = 0.015 and p = 0.007, respectively). SNP rs400874750 at PRLR gene (OAR16:39004070, intron 2) had a significant association with lactose content (p = 0.020), somatic cell score (p = 0.038), rennet coagulation time (p = 0.018) and curd firming time (p = 0.047). The outcome of this research confirmed predictions based on genomic studies, producing new information regarding the SPP1, POFUT1 and PRLR genes, which may be useful for future breeding schemes.

Haplotype-Based Genome-Wide Association Study and Identification of Candidate Genes Associated with Carcass Traits in Hanwoo Cattle

Hanwoo, is the most popular native beef cattle in South Korea. Due to its extensive popularity, research is ongoing to enhance its carcass quality and marbling traits. In this study we conducted a haplotype-based genome-wide association study (GWAS) by constructing haplotype blocks by three methods: number of single nucleotide polymorphisms (SNPs) in a haplotype block (nsnp), length of genomic region in kb (Len) and linkage disequilibrium (LD). Significant haplotype blocks and genes associated with them were identified for carcass traits such as BFT (back fat thickness), EMA (eye Muscle area), CWT (carcass weight) and MS (marbling score). Gene-set enrichment analysis and functional annotation of genes in the significantly-associated loci revealed candidate genes, including PLCB1 and PLCB4 present on BTA13, coding for phospholipases, which might be important candidates for increasing fat deposition due to their role in lipid metabolism and adipogenesis. CEL (carboxyl ester lipase), a bile-salt activated lipase, responsible for lipid catabolic process was also identified within the significantly-associated haplotype block on BTA11. The results were validated in a different Hanwoo population. The genes and pathways identified in this study may serve as good candidates for improving carcass traits in Hanwoo cattle.

A Gene-Set Enrichment and Protein-Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle

Non-synonymous SNPs and protein coding SNPs within the promoter region of genes (regulatory SNPs) might have a significant effect on carcass traits. Imputed sequence level data of 10,215 Hanwoo bulls, annotated and filtered to include only regulatory SNPs (450,062 SNPs), were used in a genome-wide association study (GWAS) to identify loci associated with backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). A total of 15, 176, and 1 SNPs were found to be significantly associated (p < 1.11 × 10-7) with BFT, CWT, and EMA, respectively. The significant loci were BTA4 (CWT), BTA6 (CWT), BTA14 (CWT and EMA), and BTA19 (BFT). BayesR estimated that 1.1%~1.9% of the SNPs contributed to more than 0.01% of the phenotypic variance. So, the GWAS was complemented by a gene-set enrichment (GSEA) and protein-protein interaction network (PPIN) analysis in identifying the pathways affecting carcass traits. At p < 0.005 (~2,261 SNPs), 25 GO and 18 KEGG categories, including calcium signaling, cell proliferation, and folate biosynthesis, were found to be enriched through GSEA. The PPIN analysis showed enrichment for 81 candidate genes involved in various pathways, including the PI3K-AKT, calcium, and FoxO signaling pathways. Our finding provides insight into the effects of regulatory SNPs on carcass traits.

Goat cheese yield and recovery of fat, protein, and total solids in curd are affected by milk coagulation properties

The aims of the present research were to quantify the effects of each coagulation trait, traditional milk coagulation properties [MCP: rennet coagulation time (RCT), curd-firming time (k₂₀), and curd firmness at 30 min (a₃₀)], and modeled curd-firming over time (CF_t) parameters [estimated rennet coagulation time (RCT_eq), curd-firming instant rate constant (k_CF), and potential curd firmness (CF_P)] directly on the following: (1) recovery of 3 milk components in the curd (%REC), (2) 3 measures of cheese yield (%CY), and (3) 3 daily cheese yield traits (dCY) from goat milk. Cheese-making traits were analyzed using 2 mixed different models, the first to test MCP and the second to test CF_t parameters. Pearson correlations were also calculated. Significant and favorable relationships (negative for time intervals and positive for CF measures) were found between the traditional MCP and the CF_t parameters and %REC and %CY traits. The effects of milk fat and protein contents were particularly important on all cheese-making traits, with the only exception being the effect of fat content on water retention in cheese (%CY_WATER). We found an optimum value of milk k₂₀, associated with the highest recovery of components and cheese yield in solids (%CY_SOLIDS). In addition, a lower level of curd water retention and an increased fresh curd yield (%CY_CURD) were associated with greater recovery of fat. The collection of all available information during the process of milk coagulation and curd-firming allowed us to discover the effect of RCT_eq on %REC traits and %CY_SOLIDS, which had not previously been revealed for traditional RCT. Moreover, higher k_CF values were associated with increased %CY_CURD and %CY_SOLIDS. Given that CF_t parameters showed a high level of independence from one another, these can also be easily used and characterized in future applications at the industry level. Information provided by traditional and modeled coagulation properties could efficiently support the goat dairy industry and lay the foundations for a quality payment scheme for goat milk.

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

BACKGROUND

Milk quality in dairy cattle is routinely assessed via analysis of mid-infrared (MIR) spectra; this approach can also be used to predict the milk's cheese-making properties (CMP) and composition. When this method of high-throughput phenotyping is combined with efficient imputations of whole-genome sequence data from cows' genotyping data, it provides a unique and powerful framework with which to carry out genomic analyses. The goal of this study was to use this approach to identify genes and gene networks associated with milk CMP and composition in the Montbéliarde breed.

RESULTS

Milk cheese yields, coagulation traits, milk pH and contents of proteins, fatty acids, minerals, citrate, and lactose were predicted from MIR spectra. Thirty-six phenotypes from primiparous Montbéliarde cows (1,442,371 test-day records from 189,817 cows) were adjusted for non-genetic effects and averaged per cow. 50 K genotypes, which were available for a subset of 19,586 cows, were imputed at the sequence level using Run6 of the 1000 Bull Genomes Project (comprising 2333 animals). The individual effects of 8.5 million variants were evaluated in a genome-wide association study (GWAS) which led to the detection of 59 QTL regions, most of which had highly significant effects on CMP and milk composition. The results of the GWAS were further subjected to an association weight matrix and the partial correlation and information theory approach and we identified a set of 736 co-associated genes. Among these, the well-known caseins, PAEP and DGAT1, together with dozens of other genes such as SLC37A1, ALPL, MGST1, SEL1L3, GPT, BRI3BP, SCD, GPAT4, FASN, and ANKH, explained from 12 to 30% of the phenotypic variance of CMP traits. We were further able to identify metabolic pathways (e.g., phosphate and phospholipid metabolism and inorganic anion transport) and key regulator genes, such as PPARA, ASXL3, and bta-mir-200c that are functionally linked to milk composition.

CONCLUSIONS

By using an approach that integrated GWAS with network and pathway analyses at the whole-genome sequence level, we propose candidate variants that explain a substantial proportion of the phenotypic variance of CMP traits and could thus be included in genomic evaluation models to improve milk CMP in Montbéliarde cows.

Integrated PTR-ToF-MS, GWAS and biological pathway analyses reveal the contribution of cow's genome to cheese volatilome

Volatile organic compounds (VOCs) are small molecules that contribute to the distinctive flavour of cheese which is an important attribute for consumer acceptability. To investigate whether cow's genetic background might contribute to cheese volatilome, we carried out genome-wide association studies (GWAS) and pathway-based analyses for 173 spectrometric peaks tentatively associated with several VOCs obtained from proton-transfer-reaction mass spectrometry (PTR-ToF-MS) analyses of 1,075 model cheeses produced using raw whole-milk from Brown Swiss cows. Overall, we detected 186 SNPs associated with 120 traits, several of which mapped close to genes involved in protein (e.g. CSN3, GNRHR and FAM169A), fat (e.g. AGPAT3, SCD5, and GPAM) and carbohydrate (e.g. B3GNT2, B4GALT1, and PHKB) metabolism. Gene set enrichment analysis showed that pathways connected with proteolysis/amino acid metabolism (purine and nitrogen metabolism) as well as fat metabolism (long-term potentiation) and mammary gland function (tight junction) were overrepresented. Our results provide the first evidence of a putative link between cow's genes and cheese flavour and offer new insights into the role of potential candidate loci and the biological functions contributing to the cheese volatilome.

Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information

Gir is one of the main cattle breeds raised in tropical South American countries. Strong artificial selection through its domestication resulted in increased genetic differentiation among the countries in recent years. Over the years, genomic studies in Gir have become more common. However, studies of population structure and signatures of selection in divergent Gir populations are scarce and need more attention to better understand genetic differentiation, gene flow, and genetic distance. Genotypes of 173 animals selected for growth traits and 273 animals selected for milk production were used in this study. Clear genetic differentiation between beef and dairy populations was observed. Different criteria led to genetic divergence and genetic differences in allele frequencies between the two populations. Gene segregation in each population was forced by artificial selection, promoting isolation, and increasing genetic variation between them. Results showed evidence of selective forces in different regions of the genome. A total of 282 genes were detected under selection in the test population based on the fixation index (Fst), integrated haplotype score (iHS), and cross-population extend haplotype homozygosity (XP-EHH) approaches. The QTL mapping identified 35 genes associated with reproduction, milk composition, growth, meat and carcass, health, or body conformation traits. The investigation of genes and pathways showed that quantitative traits associated to fertility, milk production, beef quality, and growth were involved in the process of differentiation of these populations. These results would support further investigations of population structure and differentiation in the Gir breed.

Integration of GWAS, pathway and network analyses reveals novel mechanistic insights into the synthesis of milk proteins in dairy cows

The quantities and proportions of protein fractions have notable effects on the nutritional and technological value of milk. Although much is known about the effects of genetic variants on milk proteins, the complex relationships among the set of genes and pathways regulating the different protein fractions synthesis and secretion into milk in dairy cows are still not completely understood. We conducted genome-wide association studies (GWAS) for milk nitrogen fractions in a cohort of 1,011 Brown Swiss cows, which uncovered 170 significant single nucleotide polymorphism (SNPs), mostly located on BTA6 and BTA11. Gene-set analysis and the network-based Associated Weight Matrix approach revealed that the milk proteins associated genes were involved in several biological functions, particularly ion and cation transmembrane transporter activity and neuronal and hormone signalling, according to the structure and function of casein micelles. Deeper analysis of the transcription factors and their predicted target genes within the network revealed that GFI1B, ZNF407 and NR5A1 might act as master regulators of milk protein synthesis and secretion. The information acquired provides novel insight into the regulatory mechanisms controlling milk protein synthesis and secretion in bovine mammary gland and may be useful in breeding programmes aimed at improving milk nutritional and/or technological properties.

SNP co-association and network analyses identify E2F3, KDM5A and BACH2 as key regulators of the bovine milk fatty acid profile

The fatty acid (FA) profile has a considerable impact on the nutritional and technological quality of milk and dairy products. The molecular mechanism underlying the regulation of fat metabolism in bovine mammary gland have been not completely elucidated. We conducted genome-wide association studies (GWAS) across 65 milk FAs and fat percentage in 1,152 Brown Swiss cows. In total, we identified 175 significant single nucleotide polymorphism (SNPs) spanning all chromosomes. Pathway analyses revealed that 12:0 was associated with the greatest number of overrepresented categories/pathways (e.g. mitogen-activated protein kinase (MAPK) activity and protein phosphorylation), suggesting that it might play an important biological role in controlling milk fat composition. An Associated Weight Matrix approach based on SNP co-associations predicted a network of 791 genes related to the milk FA profile, which were involved in several connected molecular pathways (e.g., MAPK, lipid metabolism and hormone signalling) and undetectable through standard GWAS. Analysis of transcription factors and their putative target genes within the network identified BACH2, E2F3 and KDM5A as key regulators of milk FA metabolism. These findings contribute to increasing knowledge of FA metabolism and mammary gland functionality in dairy cows and may be useful in developing targeted breeding practices to improve milk quality.

Genome-wide association and pathway-based analysis using latent variables related to milk protein composition and cheesemaking traits in dairy cattle

The aim of this study was to perform genome-wide associations (GWAS) and gene-set enrichment analyses with protein composition and cheesemaking-related latent variables (factors; F) in a cohort of 1,011 Italian Brown Swiss cows. Factor analysis was applied to identify latent structures of 26 phenotypes related to bovine milk quantity and quality, protein fractions [α_S1-, α_S2-, β-, and κ-casein (CN), β-lactoglobulin, and α-lactalbumin (α-LA)], coagulation and curd firming at time t (CF_t) measures, and cheese properties [cheese yield (%CY) and nutrients recovery in the curd] of individual cows. Ten orthogonal F were extracted, explaining 74% of the original variability. Factor 1_%CY underlined the %CY characteristics, F2_CFt was related to the CF_t process parameters, F3_Yield was considered as descriptor of milk and solids yield, whereas F4_{Cheese N} underscored the presence of nitrogenous compounds (N) into the cheese. Four more F were related to the milk caseins (F5_αS1-β-CN, F7_β-κ-CN, F8_αS2-CN, and F9_αS1-CN-Ph) and 1 F was linked to the whey protein (F10_α-LA); 1 F underlined the udder health status (F6_{Udder health}). All cows were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc., San Diego, CA). Single marker regression GWAS were fitted. Gene-set enrichment analysis was run on GWAS results, using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases, to reveal ontologies or pathways associated with the F. All F but F3_Yield showed significance in GWAS. Signals in 10 Bos taurus autosomes (BTA) were detected. High peaks on BTA6 (∼87 Mbp) were found for F6_β-κ-CN, F5_αS1-β-CN, and at the tail of BTA11 (∼104 Mbp) for F4_{Cheese N}. Gene-set enrichment analyses showed significant results (false discovery rate at 5%) for F8_αS2-CN, F1_%CY, F4_{Cheese N}, and F10_α-LA. For F8_αS2-CN, 33 Gene Ontology terms and 3 Kyoto Encyclopedia of Genes and Genomes categories were enriched, including terms related to ion transport and homeostasis, neuron function or part, and GnRH signaling pathway. Our results support the feasibility of factor analysis as a dimension reduction technique in genomic studies and evidenced a potential key role of α_S2-CN in milk quality and composition.