BackgroundIn this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response variables. Three scenarios with and without missing data were simulated; no missing data, 90% missing data in a trait with high heritability, and 90% missing data in a trait with low heritability. The simulated genome had a length of 500 cM with 5000 equally spaced single nucleotide polymorphism markers and 300 randomly distributed quantitative trait loci (QTL). The true breeding values of each trait were determined using 200 of the QTLs, and the remaining 100 QTLs were assumed to affect both the high (trait I with heritability of 0.3) and the low (trait II with heritability of 0.05) heritability traits. The genetic correlation between traits I and II was 0.5, and the residual correlation was zero.ResultsThe results showed that when there were no missing records, MTGM and STGM gave the same reliability for the genomic predictions for trait I while, for trait II, MTGM performed better that STGM. When there were missing records for one of the two traits, MTGM performed much better than STGM. In general, the difference in reliability of genomic EBVs predicted using the EBV response variables estimated from either the multiple-trait or single-trait models was relatively small for the trait without missing data. However, for the trait with missing data, the EBV response variable obtained from the multiple-trait model gave a more reliable genomic prediction than the EBV response variable from the single-trait model.ConclusionsThese results indicate that MTGM performed better than STGM for the trait with low heritability and for the trait with a limited number of records. Even when the EBV response variable was obtained using the multiple-trait model, the genomic prediction using MTGM was more reliable than the prediction using the STGM.

The swamp type of the Asian water buffalo is assumed to have been domesticated by about 4000 years BP, following the introduction of rice cultivation. Previous localizations of the domestication site were based on mitochondrial DNA (mtDNA) variation within China, accounting only for the maternal lineage. We carried out a comprehensive sampling of China, Taiwan, Vietnam, Laos, Thailand, Nepal and Bangladesh and sequenced the mtDNA Cytochrome b gene and control region and the Y-chromosomal ZFY, SRY and DBY sequences. Swamp buffalo has a higher diversity of both maternal and paternal lineages than river buffalo, with also a remarkable contrast between a weak phylogeographic structure of river buffalo and a strong geographic differentiation of swamp buffalo. The highest diversity of the swamp buffalo maternal lineages was found in south China and north Indochina on both banks of the Mekong River, while the highest diversity in paternal lineages was in the China/Indochina border region. We propose that domestication in this region was later followed by introgressive capture of wild cows west of the Mekong. Migration to the north followed the Yangtze valley as well as a more eastern route, but also involved translocations of both cows and bulls over large distances with a minor influence of river buffaloes in recent decades. Bayesian analyses of various migration models also supported domestication in the China/Indochina border region. Coalescence analysis yielded consistent estimates for the expansion of the major swamp buffalo haplogroups with a credibility interval of 900 to 3900 years BP. The spatial differentiation of mtDNA and Y-chromosomal haplotype distributions indicates a lack of gene flow between established populations that is unprecedented in livestock.

Background: Heat stress is known to affect follicular dynamics, oocyte maturation, and fertilization by impairing steroidogenic ability and viability of bovine granulosa cell (bGCs). The present study explored the physiological and molecular response of bGCs to different heat stress intensities in-vitro. We exposed the primary bGCs to heat stress (HS) at 39°C, 40°C and 41°C along with control samples (38°C) for 2 h. To evaluate the impact of heat stress on bGCs, several in vitro cellular parameters including cell apoptosis, intracellular reactive oxygen species (ROS) accumulation and HSP70 kinetics were assessed by flow cytometry, florescence microscopy and western blot, respectively. Furthermore, the ELISA was performed to confirm the 17β-estradiol (E 2 ) and progesterone (P 4 ) levels. In addition, the RNA sequencing (RNA-Seq) method was used to get the molecular based response of bGCs to different heat treatments. Results: Our findings revealed that the HS significantly decreased the cell viability, E 2 and P 4 levels in bGCs, whereas, increased the cellular apoptosis and ROS. Moreover, the RNA-Seq experiments showed that all the treatments (39°C, 40°C and 41°C) significantly regulated many differentially expressed genes (DEGs) i.e. BCL2L1, STAR, CYP11A1, CASP3, SOD2, HSPA13, and MAPK8IP1 and pathways associated with heat stress, apoptosis, steroidogenesis, and oxidative stress. Conclusively, our data demonstrated that the impact of 40°C treatment was comparatively detrimental for cell viability, apoptosis and ROS accumulation. Notably, a similar trend of gene expression was reported by RT-qPCR for RNA-seq data. Conclusions:Our study presented a worthy strategy for the first time to characterize the cellular and transcriptomic adaptation of bGCs to heat stress (39, 40 and 41°C) in-vitro. The results infer that these genes and pathways reported in present study could be useful candidates/indicators for heat stress research in dairy cattle. Moreover, the established model of bGCs to heat stress in the current study provides an appropriate platform to understand the mechanism of how heat-stressed bGCs can affect the quality of oocytes and developing embryo.

The availability of whole genome sequencing (WGS) data enables the discovery of causative single nucleotide polymorphisms (SNPs) or SNPs in high linkage disequilibrium with causative SNPs. This study investigated effects of integrating SNPs selected from imputed WGS data into the data of 54K chip on genomic prediction in Danish Jersey. The WGS SNPs, mainly including peaks of quantitative trait loci, structure variants, regulatory regions of genes, and SNPs within genes with strong effects predicted with variant effect predictor, were selected in previous analyses for dairy breeds in Denmark–Finland–Sweden (DFS) and France (FRA). Animals genotyped with 54K chip, standard LD chip, and customized LD chip which covered selected WGS SNPs and SNPs in the standard LD chip, were imputed to 54K together with DFS and FRA SNPs. Genomic best linear unbiased prediction (GBLUP) and Bayesian four-distribution mixture models considering 54K and selected WGS SNPs as one (a one-component model) or two separate genetic components (a two-component model) were used to predict breeding values. For milk production traits and mastitis, both DFS (0.025) and FRA (0.029) sets of additional WGS SNPs improved reliabilities, and inclusions of all selected WGS SNPs generally achieved highest improvements of reliabilities (0.034). A Bayesian four-distribution model yielded higher reliabilities than a GBLUP model for milk and protein, but extra gains in reliabilities from using selected WGS SNPs were smaller for a Bayesian four-distribution model than a GBLUP model. Generally, no significant difference was observed between one-component and two-component models, except for using GBLUP models for milk.

BackgroundCanine hip dysplasia (HD) is a common polygenic trait characterized by hip malformation that results in osteoarthritis (OA). The condition in dogs is very similar to developmental dysplasia of the human hip which also leads to OA.Methodology/Principal FindingsA total of 721 dogs, including both an association and linkage population, were genotyped. The association population included 8 pure breeds (Labrador retriever, Greyhounds, German Shepherd, Newfoundland, Golden retriever, Rottweiler, Border Collie and Bernese Mountain Dog). The linkage population included Labrador retrievers, Greyhounds, and their crosses. Of these, 366 dogs were genotyped at ∼22,000 single nucleotide polymorphism (SNP) loci and a targeted screen across 8 chromosomes with ∼3,300 SNPs was performed on 551 dogs (196 dogs were common to both sets). A mixed linear model approach was used to perform an association study on this combined association and linkage population. The study identified 4 susceptibility SNPs associated with HD and 2 SNPs associated with hip OA.Conclusion/SignificanceThe identified SNPs included those near known genes (PTPRD, PARD3B, and COL15A1) reported to be associated with, or expressed in, OA in humans. This suggested that the canine model could provide a unique opportunity to identify genes underlying natural HD and hip OA, which are common and debilitating conditions in both dogs and humans.