Genomic regions underlying uniformity of yearling weight in Nellore cattle evaluated under different response variables

The growth curve determination and economic trait correlation for Qinchuan bull population

Carcass weight, as a measure of meat yield, and body measurements are directly correlated traits in livestock. However, longitudinally collected phenotype records of local breeds are not comprehensive. The research was performed on Qinchuan bull population to understand their growth and development, and data from Qinchuan bull that was weighed and measured at birth, 6, 12, 18, and 24 months of age was analyzed. Furthermore, Logistic, Brody, Gompertz, and Bertallanffy were used to fit the growth curves for weight and body size traits. The results showed that the four curve models have good fitting degrees for the weight and body size (R2 > 0.99), and the Bertallanffy model exhibited a good fit to the measured data of body weight, and the model estimated the inflection point of body weight as (5.43 months of age, 122.01 kg). Particularly, the limited mature body weight can reach 557.8 kg by the Brody model. Body weight was significantly positively correlated with body height, hip height, body length, chest circumference, abdominal girth, and calf girth (p < 0.0001), and the correlation between body weight and body length was the highest (r = 0.975). The regression equation predicting body weight was Y = -275.691 + 3.28 X3 + 1.311 X4 - 0.397 X5.

Genome and chromosome wide association studies for growth traits in Simmental and Simbrah cattle

OBJECTIVE

The objective of this study was to perform genome (genome wide association studies [GWAS]) and chromosome (CWAS) wide association analyses to identify single nucleotide polymorphisms (SNPs) associated with growth traits in registered Simmental and Simbrah cattle.

METHODS

The phenotypes were deregressed BLUP EBVs for birth weight, weaning weight direct, weaning weight maternal, and yearling weight. The genotyping was performed with the GGP Bovine 150k chip. After the quality control analysis, 105,129 autosomal SNP from 967 animals (473 Simmental and 494 Simbrah) were used to carry out genotype association tests. The two association analyses were performed per breed and using combined information of the two breeds. The SNP associated with growth traits were mapped to their corresponding genes at 100 kb on either side.

RESULTS

A difference in magnitude of posterior probabilities was found across breeds between genome and chromosome wide association analyses. A total of 110, 143, and 302 SNP were associated with GWAS and CWAS for growth traits in the Simmental-, Simbrah-and joint -data analyses, respectively. It stands out from the enrichment analysis of the pathways for RNA polymerase (POLR2G, POLR3E) and GABAergic synapse (GABRR1, GABRR3) for Simmental cattle and p53 signaling pathway (BID, SERPINB5) for Simbrah cattle.

CONCLUSION

Only 6,265% of the markers associated with growth traits were found using CWAS and GWAS. The associated markers using the CWAS analysis, which were not associated using the GWAS, represents information that due to the model and priors was not associated with the traits.

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.

Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

BACKGROUND

The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM.

RESULTS

Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar.

CONCLUSIONS

The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

Combined approaches to reveal genes associated with litter size in Yunshang black goats

Intensive artificial selection has been imposed in Yunshang black goats, the first black specialist mutton goat breed in China, with a breeding object of improving reproductive performance, which has contributed to reshaping of the genome including the characterization of SNP, ROH and haplotype. However, variation in reproductive ability exists in the present population. A WGS was implemented in two subpopulations (polytocous group, PG, and monotocous group, MG) with evident differences of litter size. Following the mapping to reference genome, and SNP calling and pruning, three approaches - GWAS, ROH analysis and detection of signatures of selection - were employed to unveil candidate genes responsible for litter size. Consequently, 12 candidate genes containing OSBPL8 with the minimum P-value were uncovered by GWAS. Differences were observed in the pattern of ROH between two subpopulations that shared similar low inbreeding coefficients. Two ROH hotspots and 12 corresponding genes emerged from ROH pool association analysis. Based on the nS_L statistic, 15 and 61 promising genes were disclosed under selection for MG and PG respectively. Of them, some promising genes participate in ovarian function (PPP2R5C, CDC25A, ESR1, RPS26 and SERPINBs), seasonal reproduction (DIO3, BTG1 and CRYM) and metabolism (OSBPL8, SLC39A5 and SERPINBs). Our study pinpointed some novel promising genes influencing litter size, provided a comprehensive insight into genetic makeup of litter size and might facilitate selective breeding in goats.

Genomic diversity and selection sweeps identified in Indian swamp buffaloes reveals it's uniqueness with riverine buffaloes

The present investigation was focused to study genomic diversity of Indian swamp buffalo populations through reduced representation approach (ddRAD). The heterozygosity (F_ST) among the swamp buffaloes was 0.11 between Assam and Manipuri; 0.20 between swamp (Manipuri) and riverine buffaloes; 0.30 between swamp (Manipuri) and cattle. The average observed and expected heterozygosity in swamp buffalo populations was 0.254 and 0.221 respectively. The Inbreeding coefficient (F_IS) value was 0.02 among the swamp buffaloes. PCA and structure analysis revealed Manipuri swamp buffalo was genetically distinct and closely related to Nagaland swamp buffalo and least to Assam swamp buffalo. Identification of selective sweeps revealed 1087 regions to have undergone selection related to immune response, adaptation and nervous system. A total of 3451 SSRs were identified in the genome of swamp buffaloes. The study evidenced the genomic diversity in the swamp buffalo populations and its uniqueness in comparison with riverine buffalo and cattle.

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0-0.86) and the heritability of residual variance is low (median = 0.01; range 0-0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities.