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.

Background: Intramuscular fat (IMF) content is an important factor in porcine meat quality. Previously, we showed that miR-34a was less abundant in liver tissue from pigs with higher backfat thickness, compared to pigs with lower backfat thickness. The purpose of this present study was to explore the role of miR-34a in adipogenesis.Result: Bioinformatics analysis identified Acyl-CoA synthetase long chain family member 4 (ACSL4) as a putative target of miR-34a. Using a luciferase reporter assay, we verified that miR-34a binds theACSL4mRNA at the 3’UTR. To examine the role of the miR-34a-ACSL4interaction in IMF deposition in the pig, mRNA and protein expression of theACSL4gene was measured in primary intramuscular preadipocytes transfected with miR-34a mimic and inhibitor. Our results showed thatACSL4is expressed throughout the entire differentiation process in pig preadipocytes, similar to the lipogenesis-associated genesPPARγandaP2.Transfection with miR-34a mimic reduced lipid droplet formation during adipogenesis, while miR-34a inhibitor increased lipid droplet accumulation. Transfection with miR-34a mimic also reduced the mRNA and protein expression ofACSL4and lipogenesis genes, includingPPARγ, aP2,andSREBP-1C, but increased the expression of steatolysis genes such asATGLandSirt1. In contrast, the miR-34a inhibitor had the opposite effect on gene expression. Further, knockdown ofACSL4decreased lipid droplet accumulation.Conclusions: Our results support the hypothesis that miR-34a regulates intramuscular fat deposition in porcine adipocytes by targetingACSL4.

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.

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.

In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).

Cattle body size measurements constitute the conformation traits that facilitate their production, fertility, and longevity status. Prioritizing functional variants and causal genes of conformation traits is essential for understanding their genetic basis. In this study, we conducted single-trait and multitrait GWAS for 20 body conformation traits using imputed sequence data in 7,674 Chinese Holstein individuals and identified 27 QTL regions. Leveraging these QTL regions, we performed multitrait Bayesian fine-mapping to identify 30 independent credible sets of putative causal variants. Incorporating GWAS and cis-acting expression QTL data, Mendelian randomization was used to infer 153 putative causal gene-trait relationships. The previously reported genes, such as CCND2, TMTC2, and NRG3, were confirmed in our study. Of note, several novel candidate causal genes were also identified, such as C1R, RIMS1, SERPINB8, NETO2, TTYH3, TTC3, ANAPC4, and PSMD13. Our results provide new insights into the regulatory mechanisms of body conformation traits in cattle.