Genetic factors influencing milk and fat yields in tropically adapted dairy cattle: insights from quantitative trait loci analysis and gene associations.

OBJECTIVE: The objective of this study was to identify genes associated with 305-day milk yield (MY) and fat yield (FY) that also influence the adaptability of the Thai multibreed dairy cattle population to tropical conditions. METHODS: A total of 75,776 imputed and actual single nucleotide polymorphisms (SNPs) from 2,661 animals were used to identify genomic regions associated with MY and FY using the single-step genomic best linear unbiased predictions. Fixed effects included herd-yearseason, breed regression, heterosis regression and calving age regression effects. Random effects were animal additive genetic and residual. Individual SNPs with a p-value smaller than 0.05 were selected for gene mapping, function analysis, and quantitative trait loci (QTL) annotation analysis. RESULTS: A substantial number of QTLs associated with MY (9,334) and FY (8,977) were identified by integrating SNP genotypes and QTL annotations. Notably, we discovered 17 annotated QTLs within the health and exterior QTL classes, corresponding to nine unique genes. Among these genes, Rho GTPase activating protein 15 (ARHGAP15) and catenin alpha 2 (CTNNA2) have previously been linked to physiological traits associated with tropical adaptation in various cattle breeds. Interestingly, these two genes also showed signs of positive selection, indicating their potential role in conferring tolerance to trypanosomiasis, a prevalent tropical disease. CONCLUSION: Our findings provide valuable insights into the genetic basis of MY and FY in the Thai multibreed dairy cattle population, shedding light on the underlying mechanisms of tropical adaptation. The identified genes represent promising targets for future breeding strategies aimed at improving milk and fat production while ensuring resilience to tropical challenges. This study significantly contributes to our understanding of the genetic factors influencing milk production and adaptability in dairy cattle, facilitating the development of sustainable genetic selection strategies and breeding programs in tropical environments.

A comparison of five sets of overlapping and non-overlapping sliding windows for semen production traits in the Thai multibreed dairy population.

OBJECTIVE: This study compared five distinct sets of biological pathways and associated genes related to semen volume (VOL), number of sperm (NS), and sperm motility (MOT) in the Thai multibreed dairy population. METHODS: The phenotypic data included 13,533 VOL records, 12,773 NS records, and 12,660 MOT records from 131 bulls. The genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNPs) from 72 animals. The SNP additive genetic variances for VOL, NS, and MOT were estimated for SNP windows of one SNP (SW1), ten SNP (SW10), 30 SNP (SW30), 50 SNP (SW50), and 100 SNP (SW100) using a single-step genomic best linear unbiased prediction approach. The fixed effects in the model were contemporary group, ejaculate order, bull age, ambient temperature, and heterosis. The random effects accounted for animal additive genetic effects, permanent environment effects, and residual. The SNPs explaining at least 0.001% of the additive genetic variance in SW1, 0.01% in SW10, 0.03% in SW30, 0.05% in SW50, and 0.1% in SW100 were selected for gene identification through the NCBI database. The pathway analysis utilized genes associated with the identified SNP windows. RESULTS: Comparison of overlapping and non-overlapping SNP windows revealed notable differences among the identified pathways and genes associated with the studied traits. Overlapping windows consistently yielded a larger number of shared biological pathways and genes than non-overlapping windows. In particular, overlapping SW30 and SW50 identified the largest number of shared pathways and genes in the Thai multibreed dairy population. CONCLUSION: This study yielded valuable insights into the genetic architecture of VOL, NS, and MOT. It also highlighted the importance of assessing overlapping and non-overlapping SNP windows of various sizes for their effectiveness to identify shared pathways and genes influencing multiple traits.

Comparative studies of structure and dynamics of caprine, leporine, ovine, and equine serum albumins.

Serum albumin (SA) is the most prevalent protein found in blood. Human albumin was used as an albumin substitute in hypoalbuminemia pets due to high sequence similarity. SAs from furry animals were also reported to be the major indoor allergens. Sensitizing to one of SAs coupled with high sequence identity can lead to cross-reactive antibodies in allergic individuals. Thus, understanding the structural and dynamic characters of SAs is crucial for not only albumin substitution but also allergen therapy. Herein, Molecular dynamics (MD) simulations were performed to elucidate the structural and dynamic dissimilarity and similarity of economic animals [equine (ESA), caprine (CASA), ovine (OSA), and leporine (LSA)] to albumins from human (HSA), bovine (BSA), porcine (PSA), and pets [cat (FSA) and dog (CSA)]. The aim is to explore the feasibility of HSA substitution and understand how albumins cause the cross-reactivity. Generally, all albumins studied here show the scissoring motion like other mammalian albumins. The uniqueness of each albumin is defined by different sequence identity of domain I. Also, the drug binding affinity of studied albumins differs from HSA, CSA, FSA, BSA, and PSA. Especially, LSA displays the most deviated behavior from the group. So, such albumin may not be suitable for albumin therapy for pets and humans. CASA, OSA, and ESA share similar characteristics, therefore it is possible to use them to monitor the osmotic pressure among their species, but the allergenic response must be seriously considered. An insight obtained here can be useful to develop albumin therapy and understand clinical allergy.Communicated by Ramaswamy H. Sarma.

Binding Modes of Carnostatine, Homocarnosine, and Ophidine to Human Carnosinase 1.

Carnosine (CAR), anserine (ANS), homocarnosine (H-CAR), and ophidine (OPH) are histidine-containing dipeptides that show a wide range of therapeutic properties. With their potential physiological effects, these bioactive dipeptides are considered as bioactive food components. However, such dipeptides display low stability due to their rapid degradation by human serum carnosinase 1 (CN1). A dimeric CN1 hydrolyzes such histidine-containing compounds with different degrees of reactivities. A selective CN inhibitor, carnostatine (CARN), was reported to effectively inhibit CN's activity. To date, the binding mechanisms of CAR and ANS have been recently reported, while no clear information about H-CAR, OPH, and CARN binding is available. Thus, in this work, molecular dynamics simulations were employed to elucidate the binding mechanism of H-CAR, OPH, and CARN. Among all, the amine end and imidazole ring are the main players for trapping all of the ligands in a pocket. OPH shows the poorest binding affinity, while CARN displays the tightest binding. Such firm binding is due to the longer amine chain and the additional hydroxyl (-OH) group of CARN. H-CAR and CARN are analogous, but the absence of the -OH moiety in H-CAR significantly enhances its mobility, resulting in the reduction in binding affinity. For OPH which is an ANS analogue, the methylated imidazole ring destroys the OPH-CN1 interaction network at this region, consequentially leading to the poor binding ability. An insight into how CN recognizes and binds its substrates obtained here will be useful for designing an effective strategy to prolong the lifetime of CAR and its analogues after ingestion.

Muscle fiber characteristics and expression level of Troponin T3, Toll-like receptor 2, and Toll-like receptor 4 genes in chicken meat with white striping.

Background and Aim: The poultry industry faces an emerging muscular defect in chicken meat called white striping (WS). The biological processes associated with WS myopathy are immune system activation, angiogenesis, hypoxia, cell death, and striated muscle contraction. We examined the Troponin T3 (TNNT3), Toll-like receptor 2 (TLR2), and Toll-like receptor 4 (TLR4) genes based on their functions related to muscle contraction and the innate immune system. This study aimed to determine the muscle fiber characteristics (MFCs) and expression level of TNNT3, TLR2, and TLR4 genes in white striping chicken meat (WSCM). Materials and Methods: A total of 428 breast samples were randomly collected from a commercial poultry processing plant. The samples were classified into four levels: 0 (normal), 1 (moderate WS), 2 (severe WS), and 3 (extreme WS). Five samples per group were selected to evaluate MFCs, including total number of muscle fibers, muscle fiber diameter, cross-sectional area, endomysium thickness, and perimysium thickness. Five samples per group were selected for ribonucleic acid (RNA) isolation to evaluate the messenger RNA (mRNA) expression levels of TNNT3, TLR2, and TLR4 genes related to WS. Results: Statistical analysis revealed that the total number of fibers, endomysium thickness, and perimysium thickness significantly differed between groups (p < 0.05). Muscle fiber diameter and cross-sectional area did not significantly differ (p > 0.05). The expression of the TNNT3 gene did not significantly differ among groups (p > 0.05). Toll-like receptor 2 and TLR4 mRNA expression significantly differed among groups (p < 0.05). Conclusion: These detailed MFCs will provide baseline information to observe WS in chicken meat. Toll-like receptor 2 and TLR4 genes may play a role in the occurrence of WS in chicken meat through non-specific immune reactions.

High-Level Gene Flow Restricts Genetic Differentiation in Dairy Cattle Populations in Thailand: Insights from Large-Scale Mt D-Loop Sequencing.

Domestication and artificial selection lead to the development of genetically divergent cattle breeds or hybrids that exhibit specific patterns of genetic diversity and population structure. Recently developed mitochondrial markers have allowed investigation of cattle diversity worldwide; however, an extensive study on the population-level genetic diversity and demography of dairy cattle in Thailand is still needed. Mitochondrial D-loop sequences were obtained from 179 individuals (hybrids of Bos taurus and B. indicus) sampled from nine different provinces. Fifty-one haplotypes, of which most were classified in haplogroup "I", were found across all nine populations. All sampled populations showed severely reduced degrees of genetic differentiation, and low nucleotide diversity was observed in populations from central Thailand. Populations that originated from adjacent geographical areas tended to show high gene flow, as revealed by patterns of weak network structuring. Mismatch distribution analysis was suggestive of a stable population, with the recent occurrence of a slight expansion event. The results provide insights into the origins and the genetic relationships among local Thai cattle breeds and will be useful for guiding management of cattle breeding in Thailand.

Accuracy of genomic-polygenic estimated breeding value for milk yield and fat yield in the Thai multibreed dairy population with five single nucleotide polymorphism sets

Objective: The objectives were to compare variance components, genetic parameters, prediction accuracies, and genomic-polygenic EBV rankings for milk yield (MY) and fat yield (FY) in the Thai multibreed dairy population computed using five SNP sets from GeneSeek GGP80K chip. Methods: The dataset contained monthly MY and FY of 8,361 first-lactation cows from 810 farms. Variance components, genetic parameters, and EBV for five SNP sets from the GeneSeek GGP80K chip were obtained using a 2-trait single-step average-information REML procedure. The SNP sets were the complete SNP set (all available SNP; SNP100), top 75% set (SNP75), top 50% set (SNP50), top 25% set (SNP25) and top 5% set (SNP5). The 2-trait models included herd-year-season, heterozygosity and age at first calving as fixed effects, and animal additive genetic and residual as random effects. Results: The estimates of additive genetic variances for MY and FY from SNP subsets were mostly higher than those of the complete set. The SNP25 MY and FY heritability estimates (0.276 and 0.183) were higher than those from SNP75 (0.265 and 0.168), SNP50 (0.275 and 0.179), SNP5 (0.231 and 0.169) and SNP100 (0.251and 0.159). The SNP25 EBV accuracies for MY and FY (39.76% and 33.82%) were higher than for SNP75 (35.01% and 32.60%), SNP50 (39.64% and 33.38%), SNP5 (38.61% and 29.70%) and SNP100 (34.43% and 31.61%). All rank correlations between SNP100 and SNP subsets were above 0.98 for both traits, except for SNP100 and SNP5 (0.93 for MY; 0.92 for FY). Conclusion: The high SNP25 estimates of genetic variances, heritabilities, EBV accuracies, and rank correlations between SNP100 and SNP25 for MY and FY indicated that genotyping animals with SNP25 dedicated chip would be a suitable alternative to maintain genotyping costs low while speeding up genetic progress for MY and FY in the Thai dairy population.

Pathway enrichment and protein interaction network analysis for milk yield, fat yield and age at first calving in a Thai multibreed dairy population.

OBJECTIVE: This research aimed to determine biological pathways and protein-protein interaction (PPI) networks for 305-d milk yield (MY), 305-d fat yield (FY), and age at first calving (AFC) in the Thai multibreed dairy population. METHODS: Genotypic information contained 75,776 imputed and actual single nucleotide polymorphisms (SNP) from 2,661 animals. Single-step genomic best linear unbiased predictions were utilized to estimate SNP genetic variances for MY, FY, and AFC. Fixed effects included herd-year-season, breed regression and heterosis regression effects. Random effects were animal additive genetic and residual. Individual SNP explaining at least 0.001% of the genetic variance for each trait were used to identify nearby genes in the National Center for Biotechnology Information database. Pathway enrichment analysis was performed. The PPI of genes were identified and visualized of the PPI network. RESULTS: Identified genes were involved in 16 enriched pathways related to MY, FY, and AFC. Most genes had two or more connections with other genes in the PPI network. Genes associated with MY, FY, and AFC based on the biological pathways and PPI were primarily involved in cellular processes. The percent of the genetic variance explained by genes in enriched pathways (303) was 2.63% for MY, 2.59% for FY, and 2.49% for AFC. Genes in the PPI network (265) explained 2.28% of the genetic variance for MY, 2.26% for FY, and 2.12% for AFC. CONCLUSION: These sets of SNP associated with genes in the set enriched pathways and the PPI network could be used as genomic selection targets in the Thai multibreed dairy population. This study should be continued both in this and other populations subject to a variety of environmental conditions because predicted SNP values will likely differ across populations subject to different environmental conditions and changes over time.

Characterization of biological pathways associated with semen traits in the Thai multibreed dairy population.

The objective of this research was to characterize biological pathways associated with semen volume (VOL), number of sperm (NS), and sperm motility (MOT) of dairy bulls in the Thai multibreed dairy population. Phenotypes for VOL (n = 13,535), NS (n = 12,773), and MOT (n = 12,660) came from 131 bulls of the Dairy Farming Promotion Organization of Thailand. Genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNP) from 72 animals. The SNP variances for VOL, NS, and MOT were estimated using a three-trait genomic-polygenic repeatability model. Fixed effects were contemporary group, ejaculate order, age of bull, ambient temperature, and heterosis. Random effects were animal additive genetic, permanent environmental, and residual. Individual SNP explaining at least 0.001% of the total genetic variance for each trait were selected to identify associated genes in the NCBI database (UMD Bos taurus 3.1 assembly) using the R package Map2NCBI. A set of 1,999 NCBI genes associated with all three semen traits was utilized for the pathway analysis conducted with the ClueGO plugin of Cytoscape using information from the Kyoto Encyclopedia of Genes and Genomes database. The pathway analysis revealed seven significant biological pathways involving 127 genes that explained 1.04% of the genetic variance for VOL, NS, and MOT. These genes were known to affect cell structure, motility, migration, proliferation, differentiation, survival, apoptosis, signal transduction, oxytocin release, calcium channel, neural development, and immune system functions related to sperm morphology and physiology during spermatogenesis.

Genetic parameters, predictions, and rankings for semen production traits in a Thailand multi-breed dairy population using genomic-polygenic and polygenic models.

The objectives were to compare estimates of variance components, genetic parameters, prediction accuracies, and rankings of bulls for semen volume (VOL), number of sperm (NS), and motility (MOT) using genomic-polygenic (GPRM) and polygenic repeatability models (PRM). The dataset comprised 13,535 VOL, 12,773 NS, and 12,660 MOT from 131 bulls collected from 2001 to 2017 in the Semen Production and Dairy Genetic Evaluation Center of the Dairy Farming Promotion Organization of Thailand. Genotypic data encompassed 76,519 actual and imputed SNP from 72 animals. The three-trait GPRM and PRM included the fixed effects of contemporary group, ejaculate order, age of bull, ambient temperature, and heterosis. Random effects were animal additive genetic, permanent environmental, and residual. Variance components and genetic parameters were estimated using AIREMLF90. GPRM heritabilities were slightly greater than PRM for MOT (0.27 compared with 0.24), and slightly less for VOL (0.11 compared with 0.12), and NS (0.17 compared with 0.19). Repeatabilities were slightly less for GPRM than PRM (0.44 compared with 0.45 for MOT, 0.26 compared with 0.28 for NS, and 0.20 compared with 0.21 for VOL). Additive genetic correlations were high between NS and MOT (GPRM: 0.76, PRM: 0.78), moderate between VOL and NS (GPRM: 0.43, PRM: 0.55), and near zero between VOL and MOT (GPRM: -0.13, PRM: 0.04). Rank correlations between GPRM and PRM estimated breeding values (EBV) were high for all traits. The similarity between GPRM and PRM results suggested that SNP data from the small number of genotyped animals had a minimal impact on genetic predictions in this population.

Imputation Accuracy from Low to Moderate Density Single Nucleotide Polymorphism Chips in a Thai Multibreed Dairy Cattle Population.

The objective of this study was to investigate the accuracy of imputation from low density (LDC) to moderate density SNP chips (MDC) in a Thai Holstein-Other multibreed dairy cattle population. Dairy cattle with complete pedigree information (n = 1,244) from 145 dairy farms were genotyped with GeneSeek GGP20K (n = 570), GGP26K (n = 540) and GGP80K (n = 134) chips. After checking for single nucleotide polymorphism (SNP) quality, 17,779 SNP markers in common between the GGP20K, GGP26K, and GGP80K were used to represent MDC. Animals were divided into two groups, a reference group (n = 912) and a test group (n = 332). The SNP markers chosen for the test group were those located in positions corresponding to GeneSeek GGP9K (n = 7,652). The LDC to MDC genotype imputation was carried out using three different software packages, namely Beagle 3.3 (population-based algorithm), FImpute 2.2 (combined family- and population-based algorithms) and Findhap 4 (combined family- and population-based algorithms). Imputation accuracies within and across chromosomes were calculated as ratios of correctly imputed SNP markers to overall imputed SNP markers. Imputation accuracy for the three software packages ranged from 76.79% to 93.94%. FImpute had higher imputation accuracy (93.94%) than Findhap (84.64%) and Beagle (76.79%). Imputation accuracies were similar and consistent across chromosomes for FImpute, but not for Findhap and Beagle. Most chromosomes that showed either high (73%) or low (80%) imputation accuracies were the same chromosomes that had above and below average linkage disequilibrium (LD; defined here as the correlation between pairs of adjacent SNP within chromosomes less than or equal to 1 Mb apart). Results indicated that FImpute was more suitable than Findhap and Beagle for genotype imputation in this Thai multibreed population. Perhaps additional increments in imputation accuracy could be achieved by increasing the completeness of pedigree information.