Wenzhong Liu

Natural or artificial selection could shape genetic architecture, e.g., the relationship between minor allele frequency (MAF) and the effect sizes of causal variants (CVs). This study aimed to investigate the impact of the MAF–effect size relationship (as a selection signature, S) on genomic prediction and heritability estimation in livestock, using both simulated data (Holstein) and real datasets (Holstein and pigs). We evaluated the performance of two models: (1) selection-adjusted genomic best linear unbiased prediction (GBLUP-S), and (2) MAF-stratified selection-adjusted genomic best linear unbiased prediction (GBLUP-SMS). Simulation results demonstrated that for traits under strong negative selection (S < −1), both GBLUP-S and GBLUP-SMS outperformed classic GBLUP. The prediction accuracy of GBLUP-S improved by 0.011–0.031, while GBLUP-SMS achieved a gain of 0.005–0.025. Furthermore, GBLUP-SMS exhibited lower sensitivity to variations in S-values, whereas GBLUP-S heavily relied on accurate S specification. When the true S was matched, GBLUP-SMS generated more unbiased (or comparable) heritability estimates and higher prediction accuracy relative to GBLUP-S. Critically, mismatched S in GBLUP-S led to increased bias in heritability estimates and reduced prediction accuracy. Cross-validation with real phenotypic data from Holsteins and pigs demonstrated that implementing selection-adjust methods improved prediction accuracy by 0.015 for FP in Holsteins and 0.01 for T1 in pigs, while enhancing the unbiasedness of heritability estimates across all traits. Negative selection signatures were identified for cattle (S = −0.5) and pig T1, T2, and T3 (S = −1.5, −1, and −2, respectively). These findings advance the theoretical framework of GBLUP-based genomic prediction and heritability estimation.

Lüliang black goat (LBG) is a unique livestock genetic resource of great significance for the local agriculture and economic development of Shanxi, China. However, the kidding rate of LBG is relatively low, which limits efforts to improve the reproductive performance and economic benefits of this breed. Therefore, improving the kidding characteristics of LBG is crucial for increasing its economic benefits. In this study, 20 LBG does were selected for whole-genome resequencing and divided into two groups: 10 in the T group (does with three consecutive kiddings of twin kids) and 10 in the S group (does with three consecutive kiddings of single kids). Based on whole-genome resequencing data, this study comprehensively assessed the population structure and genetic diversity of LBG and explored the related genes that affect reproductive performance. Three selection signal analysis methods—Fst (Fixation Index), π (nucleotide diversity), and XP-CLR (Cross Population Composite Likelihood Ratio)—were applied to screen a total of 838 genes, and enrichment analysis was performed to identify genes closely related to the reproductive performance of LBG, including ENPP3, APC, and GLI2. A generalized linear model was used to conduct a correlation analysis between non-synonymous mutations in the three genes and the number of kids produced. Two loci that were significantly correlated with kidding number were identified (p < 0.05): GLI2 g.63400363 C>T and GLI2 g.63417538 C>T. In general, the LBG population has high genetic diversity and good prospects for genetic improvement. The findings revealed that mining high-fecundity selection characteristics provides a basis for research on goat reproductive mechanisms.

Lvliang black goat (LBG) is an excellent local breed resource in China that is known for its black fur, excellent meat quality, and strong adaptability. Studying the genetic mechanism and germplasm characteristics of LBG can provide theoretical and practical basis for the protection of the genetic resources of this breed and help implement conservation and breeding. In this study, the genetic diversity of the LBG population was evaluated using whole-genome SNP data. It was found that the LBG population had a high genetic diversity and a low degree of inbreeding. According to the clustering results of male goats and the relationship between individuals, the LBG population was divided into 13 families. Then, through population structure analysis, it was found that LBG had a close genetic relationship with the Nanjiang goat and Qinggoda goat populations, and they may have the same ancestors. The LBG population has retained some ancient genetic characteristics and is a special population that integrates local genetic characteristics and foreign gene flow. Through four selection signal analyses, we detected multiple candidate genes related to economic traits (CFL2, SCD, NLRP14, etc.) and adaptability (C4BPA, FUT8, PRNP, etc.) in the LBG population. In addition, in a comparative analysis with three commercial breeds (Saanen goat, Boer goat and Angora goat) we also found multiple genes related to physical characteristics (ERG, NRG3, EDN3, etc.). Finally, we performed functional enrichment analysis on these genes and explored their genetic mechanisms. This study provides important data support for the protection and breeding of LBG and provides a new perspective for enriching the genetic diversity of goat populations.

Intrauterine growth restriction (IUGR) is a common perinatal complication in animal reproduction, with long-lasting negative effects on neonates and postnatal animals, which seriously negatively affects livestock production. In this study, we aimed to identify potential genes associated with the diagnosis of IUGR through bioinformatics analysis. Based on the 73 differentially expressed related genes obtained by differential analysis and weighted gene co-expression network analysis, we used three machine learning algorithms to identify 4 IUGR-related hub genes (IUGR-HGs), namely, ADAM9, CRYL1, NDP52, and SERPINA7, whose ROC curves showed that they are a good diagnostic target for IUGR. Next, we identified two molecular subtypes of IUGR through consensus clustering analysis and constructed a gene scoring system based on the IUGR-HGs. The results showed that the IUGR score was positively correlated with the risk of IUGR. The AUC value of IUGR scoring accuracy was 0.970. Finally, we constructed a new artificial neural network model based on the four IUGR-HGs to diagnose sheep IUGR, and its accuracy reached 0.956. In conclusion, the IUGR-HGs we identified provide new potential molecular markers and models for the diagnosis of IUGR in sheep; they can better diagnose whether sheep have IUGR. The present findings provide new perspectives on the diagnosis of IUGR.

The QKI genes encode RNA-binding proteins regulating cell proliferation, differentiation, and apoptosis. The Goat QKI has six isoforms, but their roles in myogenesis are unclear. In this study, the six isoforms of the QKI gene were overexpressed in goat myoblast. Immunofluorescence, qPCR and Western blot were used to evaluate the effect of QKI on the differentiation of goat myoblast. An RNA-Seq was performed on the cells with the gain of the function from the major isoforms to screen differentially expressed genes (DEGs). The results show that six isoforms had different degrees of deletion in exons 6 and 7, and caused the appearance of different types of encoded amino acids. The expression levels of the QKI-1 and QKI-5 groups were upregulated in the biceps femoris and latissimus dorsi muscle tissues compared with those of the QKI-4, QKI-7, QKI-3 and QKI-6 groups. After 6 d of myoblast differentiation, QKI-5 and the myogenic differentiators MyoG, MyoD, and MyHC were upregulated. Compared to the negative control group, QKI promoted myotube differentiation and the myoblasts overexpressing QKI-5 formed large, abundant myotubes. In summary, we identified that the overexpression of the QKI gene promotes goat-myoblast differentiation and that QKI-5 is the major isoform, with a key role. The RNA-Seq screened 76 upregulated and 123 downregulated DEGs between the negative control and the QKI-5-overexpressing goat myoblasts after d 6 of differentiation. The GO and KEGG analyses associated the downregulated DEGs with muscle-related biological functions. Only the pathways related to muscle growth and development were enriched. This study provides a theoretical basis for further exploring the regulatory mechanism of QKI in skeletal-muscle development in goats.

Domestic goats are commonly reared for meat and milk production in several regions of the world. However, the genetic mechanism underlying muscle development and meat quality of goats is limited. Therefore, the aim of this study was to identify known and novel genes regulating muscle development and meat quality of goats using second- and third-generation sequencing technologies. To achieve this, the meat quality and transcriptomes of longissimus dorsi (LD) and biceps femoris (BF) muscle tissues of Lingqiu Greyback goats were examined and compared. Differentially expressed genes (DEGs) and isoforms (DEIs) were functionally annotated. Results showed that 45,574 full-length transcripts covering 18,491 loci were characterized, and 12,566 genes were co-expressed in all samples. Differential expression analysis identified 231 DEGs, including 45 novel genes in the LD and BF muscles of the goats. Additionally, 1173 DEIs were found, in which 642 novel isoforms were identified in this study. Functional annotation and pathway analysis of the DEGs and DEIs revealed that some of them were associated with muscle growth and lipid metabolism. Overall, the findings of this study contribute to the understanding of the transcriptomic diversity underlying meat quality and muscle development of goat.