Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing.

BACKGROUND: Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses. RESULTS: Different genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding. CONCLUSIONS: We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.

Marker density and statistical model designs to increase accuracy of genomic selection for wool traits in Angora rabbits.

The Angora rabbit, a well-known breed for fiber production, has been undergoing traditional breeding programs relying mainly on phenotypes. Genomic selection (GS) uses genomic information and promises to accelerate genetic gain. Practically, to implement GS in Angora rabbit breeding, it is necessary to evaluate different marker densities and GS models to develop suitable strategies for an optimized breeding pipeline. Considering a lack in microarray, low-coverage sequencing combined with genotype imputation was used to boost the number of SNPs across the rabbit genome. Here, in a population of 629 Angora rabbits, a total of 18,577,154 high-quality SNPs were imputed (imputation accuracy above 98%) based on low-coverage sequencing of 3.84X genomic coverage, and wool traits and body weight were measured at 70, 140 and 210 days of age. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models, which might because multiple-trait models used information from genetically correlated traits. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for GS in early selection, and optimize breeding programs in rabbits.

Characterization of microRNA Profiles in Pasteurella multocida-Infected Rabbits and Identification of miR-29-5p as a Regulator of Antibacterial Immune Response.

Pasteurella multocida is the pathogenic agent for a variety of severe diseases in livestock, including rabbits. MicroRNAs (miRNAs) participate in the immune response to the pathogen. Distinct miRNA expression patterns were explored in rabbit lung by small-RNA deep sequencing to assess dysregulated miRNAs during P. multocida infection. Totally, 571 miRNAs were screened, of which, 62 were novel, and 32 exhibited differential expression (DE). Of the 32 known DE-miRNAs, 13 and 15 occurred at 1 day and 3 days post-infection (dpi); and ocu-miR-107-3p and ocu-miR-29b-5p were shared between the two time points. Moreover, 7,345 non-redundant target genes were predicted for the 32 DE-miRNAs. Putative target genes were enriched in diverse GO and KEGG pathways and might be crucial for disease resistance. Interestingly, upregulation of ocu-miR-29-5p suppresses P. multocida propagation and downregulates expression of epithelial membrane protein-2 (EMP2) and T-box 4 (TBX4) genes by binding to their 3' untranslated region in RK13 cells. Thus, ocu-miR-29-5p may indirectly inhibit P. multocida invasion by modulating genes related to the host immune response, such as EMP2 and TBX4.