It is a unique feature that fat can be deposited in sheep tails and rumps. To elucidate the genetic mechanism underlying this trait, we collected 120 individuals from three Chinese indigenous sheep breeds with extreme tail types, namely large fat-tailed sheep (n = 40), Altay sheep (n = 40), and Tibetan sheep (n = 40), and genotyped them using the Ovine Infinium HD SNP BeadChip. Then genomic scan for selection signatures was performed using the hapFLK. In total, we identified 25 genomic regions exhibiting evidence of having been under selection. Bioinformatic analysis of the genomic regions showed that selection signatures related to multiple candidate genes had a demonstrated role in phenotypic variation. Nine genes have documented association with sheep tail types, including WDR92, TBX12, WARS2, BMP2, VEGFA, PDGFD, HOXA10, ALX4, and ETAA1. Moreover, a number of genes were of particular interest, including RXFP2 associated with the presence/absence and morphology of horns; MITF involved in coat color; LIN52 and SYNDIG1L related to the number of teats; MSRB3 gene associated with ear sizes; LTBP2 considered as a positional candidate genes for number of ribs; JAZF1 regulating lipid metabolism; PGRMC2, SPAG17, TSHR, GTF2A1, and LARP1B implicated with reproductive traits. Our findings provide insights into fat tail formation and a reference for carrying out molecular breeding and conservation in sheep.

Low-coverage sequencing (LCS) followed by imputation has been proposed as a cost-effective genotyping approach for obtaining genotypes of whole-genome variants. Imputation performance is essential for the effectiveness of this approach. Several imputation methods have been proposed and successfully applied in genomic studies in human and other species. However, there are few reports on the performance of these methods in livestock. Here, we evaluated a variety of imputation methods, including Beagle v4.1, GeneImp v1.3, GLIMPSE v1.1.0, QUILT v1.0.0, Reveel, and STITCH v1.6.5, with varying sequencing depth, sample size, and reference panel size using LCS data of Holstein cattle. We found that all of these methods, except Reveel, performed well in most cases with an imputation accuracy over 0.9; on the whole, GLIMPSE, QUILT, and STITCH performed better than the other methods. For species with no reference panel available, STITCH followed by Beagle would be an optimal strategy, whereas for species with reference panel available, QUILT would be the method of choice. Overall, this study illustrated the promising potential of LCS for genomic analysis in livestock.

Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.

Understanding the molecular and cellular mechanisms underlying complex traits in pigs is crucial for enhancing genetic gain via artificial selection and utilizing pigs as models for human disease and biology. Here, we conducted comprehensive genome-wide association studies (GWAS) followed by a cross-breed meta-analysis for 232 complex traits and a within-breed meta-analysis for 12 traits, using 28.3 million imputed sequence variants in 70 328 animals across 14 pig breeds. We identified 6878 quantitative trait loci (QTL) for 139 complex traits. Leveraging the Pig Genotype-Tissue Expression resource, we systematically investigated the biological context and regulatory mechanisms behind these trait-QTLs, ultimately prioritizing 14 829 variant-gene-tissue-trait regulatory circuits. For instance, rs344053754 regulates UGT2B31 expression in the liver and intestines, potentially by modulating enhancer activity, ultimately influencing litter weight at weaning in pigs. Furthermore, we observed conservation of certain genetic and regulatory mechanisms underlying complex traits between humans and pigs. Overall, our cross-breed meta-GWAS in pigs provides invaluable resources and novel insights into the genetic regulatory and evolutionary mechanisms of complex traits in mammals.

Objective: Ovarian follicular development, which dependent on the proliferation and differentiation of granulosa cells (GCs), is a complex biological process in which miRNA plays an important role. Our previous study showed that miR-458b-5p is associated with ovarian follicular development in chicken. The detailed function and molecular mechanism of miR-458b-5p in GCs is unclear.Methods: The luciferase reporter assay was used to verify the targeting relationship between miR-458b-5p and catenin beta-1 (CTNNB1), which is an important transcriptional regulatory factor of the Wnt/β-catenin pathway. The cell counting kit-8 (CCK-8) assay, flow cytometry with propidium iodide (PI) and annexin V-fluorescein isothiocyanate (FITC) labeling were applied to explore the effect of miR-458b-5p on proliferation, cell cycle and apoptosis of chicken GCs. Quantitative real-time polymerase chain reaction and Western blot were used to detect the mRNA and protein expression levels.Results: We demonstrated that the expression of miR-458b-5p and CTNNB1 showed the opposite relationship in GCs and theca cells of hierarchical follicles. The luciferase reporter assay confirmed that CTNNB1 is the direct target of miR-458b-5p. Using CCK-8 assay and flow cytometry with PI and Annexin V-FITC labeling, we observed that transfection with the miR-458b-5p mimics significantly reduced proliferation and has no effects on apoptosis of chicken GCs. In addition, miR-458b-5p decreased the mRNA and protein expression of CD44 molecule and matrix metallopeptidase 7, which are the downstream effectors of CTNNB1 in Wnt/β-Catenin pathway and play functional roles in cell proliferation.Conclusion: Taken together, the data indicate that miR-458b-5p regulates ovarian GCs proliferation through Wnt/β-catenin signaling pathway by targeting CTNNB1, suggesting that miR-458b-5p and its target gene CTNNB1 may potentially play a role in chicken ovarian follicular development.

Background: Breed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, F_ST, and I_n) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities. Results: We found that all combinations performed quite well with identification accuracies over 95% in all scenarios. However, there was no combination which performed the best and robust across all scenarios. We proposed to integrate the three breed-informative detection methods, named DFI, and integrate the three machine learning methods, KNN, SVM, and RF, named KSR. We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99% in most cases and was very robust in all scenarios. The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases. Conclusions: The current study showed that the combination of DFI and KSR was the optimal strategy. Using sequence data resulted in higher accuracies than using chip data in most cases. However, the differences were generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification.