Association of CXCR1 gene polymorphism with clinical mastitis, reproductive disorders and performance traits in Hardhenu (Bos taurus × Bos indicus) cattle

The present study investigated the presence of CXCR1 gene polymorphisms and their association with clinical mastitis, reproductive disorders and performance traits in Hardhenu cattle. Genotyping of the targeted SNP rs211042414 (C>T) at the g.106216468 loci of the CXCR1 gene was performed through PCR amplification and Bsa1 restriction enzyme digestion. The genotypic frequencies revealed three genotypes: CC, CT and TT, with the C allele being the most prevalent. Significant associations were found between the targeted SNP and clinical mastitis occurrence using chi-square and logistic regression analyses. The CC genotype showed higher susceptibility to clinical mastitis with a higher odds ratio of 3.47 compared to TT (1.00) and CT (2.90) genotypes (p < .05). Furthermore, least squares analysis revealed significant associations between genotypes and performance traits such as total milk yield, 305-day milk yield and peak yield (p < .05). The CC genotype exhibited higher milk yields than CT and TT genotypes, indicating a positive association between the C allele and increased milk production. These findings have practical implications for the genetic improvement of Hardhenu cattle. Incorporating the identified CXCR1 gene polymorphisms into existing selection criteria can help enhance disease resistance and milk production traits. However, further validation with a larger sample size is necessary to strengthen the observed associations and ensure their practical applicability.

A genome-wide association study reveals additive and dominance effects on growth and fatness traits in large white pigs

The dominance effect is a kind of non-additive effect due to the interaction between alleles at the same locus. Quantitative traits such as growth traits in farm animals have been found to be influenced by dominance effects. However, dominance effects are usually ignored in the genome-wide association study (GWAS) of complex traits for farm animals. In this study, we performed GWAS and genetic parameters estimation for the two traits age at 100 kg (AGE) and backfat thickness at 100 kg (BF) of 3572 Large White pigs. The pigs were from three breeding farms of China and were genotyped by an in-house designed 50k SNP chip. Our results showed significant non-zero variance for the dominance effect of AGE, while the dominance effect of BF was not significant. Using a GWAS model accounting for both additive and dominance effects, we identified three additive and two dominance significant SNPs for the trait AGE. For the trait BF, three genome-wide significant additive SNPs were detected, but no significant SNP was found for the dominance effect. In total, six important functional genes (NPAS3, USP16, PARN, ARL15, GPC3, ABHD4) near significant SNPs were identified as candidate genes associated with AGE or BF. Notably, ARL15 and PARN were associated with AGE near the dominance association signals. Overall, the newly detected SNPs and newly identified candidate genes in our study added new information about the genetic architectures of growth and fatness traits in pigs, and have the potential to be applied to the pig breeding program in the future.

Shared Ancestry and Signatures of Recent Selection in Gotland Sheep

Gotland sheep, a breed native to Gotland, Sweden (an island in the Baltic Sea), split from the Gute sheep breed approximately 100 years ago, and since, has probably been crossed with other breeds. This breed has recently gained popularity, due to its pelt quality. This study estimates the shared ancestors and identifies recent selection signatures in Gotland sheep using 600 K single nucleotide polymorphism (SNP) genotype data. Admixture analysis shows that the Gotland sheep is a distinct breed, but also has shared ancestral genomic components with Gute (~50%), Karakul (~30%), Romanov (~20%), and Fjällnäs (~10%) sheep breeds. Two complementary methods were applied to detect selection signatures: A Bayesian population differentiation F_ST and an integrated haplotype homozygosity score (iHS). Our results find that seven significant SNPs (q-value < 0.05) using the F_ST analysis and 55 significant SNPs (p-value < 0.0001) using the iHS analysis. Of the candidate genes that contain significant markers, or are in proximity to them, we identify several belongings to the keratin genes, RXFP2, ADCY1, ENOX1, USF2, COX7A1, ARHGAP28, CRYBB2, CAPNS1, FMO3, and GREB1. These genes are involved in wool quality, polled and horned phenotypes, fertility, twining rate, meat quality, and growth traits. In summary, our results provide shared founders of Gotland sheep and insight into genomic regions maintained under selection after the breed was formed. These results contribute to the detection of candidate genes and QTLs underlying economic traits in sheep.

Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle

The objective of the present study was to perform a genome-wide association study (GWAS) for growth curve parameters using nonlinear models that fit original weight-age records. In this study, data from 808 Chinese Simmental beef cattle that were weighed at 0, 6, 12, and 18 months of age were used to fit the growth curve. The Gompertz model showed the highest coefficient of determination (R² = 0.954). The parameters' mature body weight (A), time-scale parameter (b), and maturity rate (K) were treated as phenotypes for single-trait GWAS and multi-trait GWAS. In total, 9, 49, and 7 significant SNPs associated with A, b, and K were identified by single-trait GWAS; 22 significant single nucleotide polymorphisms (SNPs) were identified by multi-trait GWAS. Among them, we observed several candidate genes, including PLIN3, KCNS3, TMCO1, PRKAG3, ANGPTL2, IGF-1, SHISA9, and STK3, which were previously reported to associate with growth and development. Further research for these candidate genes may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.

Genome-wide association studies reveal novel loci associated with carcass and body measures in beef cattle

Genomic selection has an essential role in the livestock economy by increasing selection productivity. Genomics provides a mechanism to increase the rate of genetic gain using marker-assisted selection. Various quantitative trait loci (QTL) associated with body, carcass and meat quality traits in beef cattle have been found. It is widely accepted that QTL traits in livestock species are regulated by several genes and factors from the environment. Genome-wide association studies (GWAS) are a powerful approach in identifying QTL and to establish genomic regions harboring the genes and polymorphisms associated with specific characteristics in beef cattle. Due to their impact on economic returns, growth, carcass and meat quality traits of cattle are frequently used as essential criteria in selection in breeding programs., GWAS has been used in beef cattle breeding and genetic program and some progress has been made. Furthermore, numerous genes and markers related to productivity traits in beef cattle have been found. This review summarizes the advances in the use of GWAS in beef cattle production and outlines the associations with growth, carcass, and meat quality.

Genomic Regions Associated With Skeletal Type Traits in Beef and Dairy Cattle Are Common to Regions Associated With Carcass Traits, Feed Intake and Calving Difficulty

Linear type traits describing the skeletal characteristics of an animal are moderately to strongly genetically correlated with a range of other performance traits in cattle including feed intake, reproduction traits and carcass merit; thus, type traits could also provide useful insights into the morphological differences among animals underpinning phenotypic differences in these complex traits. The objective of the present study was to identify genomic regions associated with five subjectively scored skeletal linear traits, to determine if these associated regions are common in multiple beef and dairy breeds, and also to determine if these regions overlap with those proposed elsewhere to be associated with correlated performance traits. Analyses were carried out using linear mixed models on imputed whole genome sequence data separately in 1,444 Angus, 1,129 Hereford, 6,433 Charolais, 8,745 Limousin, 1,698 Simmental, and 4,494 Holstein-Friesian cattle, all scored for the linear type traits. There was, on average, 18 months difference in age at assessment of the beef versus the dairy animals. While the majority of the identified quantitative trait loci (QTL), and thus genes, were both trait-specific and breed-specific, a large-effect pleiotropic QTL on BTA6 containing the NCAPG and LCORL genes was associated with all skeletal traits in the Limousin population and with wither height in the Angus. Other than that, little overlap existed in detected QTLs for the skeletal type traits in the other breeds. Only two QTLs overlapped the beef and dairy breeds; both QTLs were located on BTA5 and were associated with height in both the Angus and the Holstein-Friesian, despite the difference in age at assessment. Several detected QTLs in the present study overlapped with QTLs documented elsewhere that are associated with carcass traits, feed intake, and calving difficulty. While most breeding programs select for the macro-traits like carcass weight, carcass conformation, and feed intake, the higher degree of granularity with selection on the individual linear type traits in a multi-trait index underpinning the macro-level goal traits, presents an opportunity to help resolve genetic antagonisms among morphological traits in the pursuit of the animal with optimum performance metrics.

Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Simple Summary

Dominance effects play important roles in determining genetic changes with regard to complex traits. We conducted genomic predictions and genome-wide association studies in order to investigate the effects of dominance on carcass weight, dressing percentage, meat percentage, average daily gain, and chuck roll in 1233 Simmental beef cattle. Using dominance models, we improved the predictive abilities and found several candidate single-nucleotide polymorphisms (SNPs) and genes associated with these traits. Our studies helped us to understand causal mutation mapping and genomic selection models with dominance effects in Chinese Simmental beef cattle.

Abstract

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

GROWTH AND DEVELOPMENT SYMPOSIUM: STEM AND PROGENITOR CELLS IN ANIMAL GROWTH: The regulation of beef quality by resident progenitor cells1

The intramuscular adipose tissue deposition in the skeletal muscle of beef cattle is a highly desired trait essential for high-quality beef. In contrast, the excessive accumulation of crosslinked collagen in intramuscular connective tissue contributes to beef toughness. Recent studies revealed that adipose tissue and connective tissue share an embryonic origin in mice and may be derived from a common immediate bipotent precursor in mice and humans. Having the same linkages in the development of adipose tissue and connective tissue in beef, the lineage commitment and differentiation of progenitor cells giving rise to these tissues may directly affect beef quality. It has been shown that these processes are regulated by some key transcription regulators and are subjective to epigenetic modifications such as DNA methylation, histone modifications, and microRNAs. Continued exploration of relevant regulatory pathways is very important for the identification of mechanisms influencing meat quality and the development of proper management strategies for beef quality improvement.

PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy

Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene, MCHR2, which was associated with presoma muscle development in cattle. In summary, PCA-based multiple-trait GWAS is an efficient model for exploring pleiotropic QTNs in quantitative traits.