Search

Dairy cattle production systems are mostly based on using purebreds, but recently crossbreeding has received increased interest. For genetic evaluation including crossbreds, ideally, models accounting for breed specific effects should perform better compared with simple models. A novel single-step best linear unbiased prediction method provides an opportunity to combine purebred and crossbred information when breed specific effects are accounted for. In this study, we applied this single-step method to evaluate two-way crossbredperformance for average daily gain in crossbred beef on dairy. Results showed that the singlestep method, which accounted for breed specific effects, had a higher predictive ability. We conclude that this method is useful in the genetic evaluation for crossbred performance in cattle production system.

Background The feed conversion ratio (FCR) is an important productive trait that greatly affects profits in the pig industry. Elucidating the genetic mechanisms underpinning FCR may promote more efficient improvement of FCR through artificial selection. In this study, we integrated a genome-wide association study (GWAS) with transcriptome analyses of different tissues in Yorkshire pigs (YY) with the aim of identifying key genes and signalling pathways associated with FCR. Results A total of 61 significant single nucleotide polymorphisms (SNPs) were detected by GWAS in YY. All of these SNPs were located on porcine chromosome (SSC) 5, and the covered region was considered a quantitative trait locus (QTL) region for FCR. Some genes distributed around these significant SNPs were considered as candidates for regulating FCR, including TPH2, FAR2, IRAK3, YARS2, GRIP1, FRS2, CNOT2 and TRHDE. According to transcriptome analyses in the hypothalamus, TPH2 exhibits the potential to regulate intestinal motility through serotonergic synapse and oxytocin signalling pathways. In addition, GRIP1 may be involved in glutamatergic and GABAergic signalling pathways, which regulate FCR by affecting appetite in pigs. Moreover, GRIP1, FRS2, CNOT2, and TRHDE may regulate metabolism in various tissues through a thyroid hormone signalling pathway. Conclusions Based on the results from GWAS and transcriptome analyses, the TPH2, GRIP1, FRS2, TRHDE, and CNOT2 genes were considered candidate genes for regulating FCR in Yorkshire pigs. These findings improve the understanding of the genetic mechanisms of FCR and may help optimize the design of breeding schemes.

Additional file 2: Figure S1. Distribution of the minor allele frequencies of the Illumina array-specific SNPs in Scenario 1 and the Affymetrix array-specific SNPs in Scenario 2. Figure S2. Effect of allele frequency on $${2{p}_{j}{q}_{j}\left({q}_{j}-{p}_{j}\right)}^{2}$$ 2 p j q j q j - p j 2 .

Additional file 1: Table S1. Estimates of variance components, standard error (SE) of parameters, − 2 log likelihood (− 2LogL), AIC (Akaike’s Information Criterion) from models MA, MAD, MAD*, MAI, MAID and MAID*. Table S2. Contributed additive genetic variance from the inbreeding depression effect. Table S3. P-value of likelihood ratio test based on model MA. Table S4. P-value of likelihood ratio test based on models MAI, MAD and MAD*. Table S5. P-value of inbreeding depression effect based on the Wald test. Table S6. Average genomic relationships between animals in the imputed and reference sets. Table S7. Converted classical variance components based on the genotypic variance component in models MAD and MAID.

SummaryGenetic analysis is a systematic and complex procedure in animal and plant breeding. With fast development of high-throughput genotyping techniques and algorithms, animal and plant breeding has entered into a genomic era. However, there is a lack of software, which can be used to process comprehensive genetic analyses, in the routine animal and plant breeding program. To make the whole genetic analysis in animal and plant breeding straightforward, we developed a powerful, robust and fast R package that includes genomic data format conversion, genomic data quality control and genotype imputation, breed composition analysis, pedigree tracing, analysis and visualization, pedigree-based and genomic-based relationship matrix construction, and genomic evaluation. In addition, to simplify the application of this package, we also developed a shiny toolkit for users.Availability and implementationblupADC is developed primarily in R with core functions written in C++. The development version is maintained at https://github.com/TXiang-lab/blupADC.Supplementary informationSupplementary data are available online

Semen traits are crucial in commercial pig production since semen from boars is widely used in artificial insemination for both purebred and crossbred pig production. Revealing the genetic architecture of semen traits potentially promotes the efficiencies of improving semen traits through artificial selection. This study is aimed to identify candidate genes related to the semen traits in Duroc boars. First, we identified the genes that were significantly associated with three semen traits, including sperm motility (MO), sperm concentration (CON), and semen volume (VOL) in a Duroc boar population through a genome-wide association study (GWAS). Second, we performed a weighted gene co-expression network analysis (WGCNA). A total of 2, 3, and 20 single-nucleotide polymorphisms were found to be significantly associated with MO, CON, and VOL, respectively. Based on the haplotype block analysis, we identified one genetic region associated with MO, which explained 6.15% of the genetic trait variance. ENSSSCG00000018823 located within this region was considered as the candidate gene for regulating MO. Another genetic region explaining 1.95% of CON genetic variance was identified, and, in this region, B9D2, PAFAH1B3, TMEM145, and CIC were detected as the CON-related candidate genes. Two genetic regions that accounted for 2.23% and 2.48% of VOL genetic variance were identified, and, in these two regions, WWC2, CDKN2AIP, ING2, TRAPPC11, STOX2, and PELO were identified as VOL-related candidate genes. WGCNA analysis showed that, among these candidate genes, B9D2, TMEM145, WWC2, CDKN2AIP, TRAPPC11, and PELO were located within the most significant module eigengenes, confirming these candidate genes’ role in regulating semen traits in Duroc boars. The identification of these candidate genes can help to better understand the genetic architecture of semen traits in boars. Our findings can be applied for semen traits improvement in Duroc boars.