Background: In quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression results are dependent on optimal amplification of housekeeping genes (HKGs). RNA-seq technology offers a novel approach to detect new HKGs with improved stability. Goat (Capra hircus) is an economically important livestock species and plays an indispensable role in the world animal fiber and meat industry. Unfortunately, uniform and reliable HKGs for skin research have not been identified in goat. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using high-throughput sequencing technology. Results: Based on the transcriptome dataset of 39 goat skin tissue samples, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. Commonly used HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also examined. Four different experimental variables: (1) different development stages, (2) hair follicle cycle stages, (3) breeds, and (4) sampling sites were used for determination and validation. Four algorithms (geNorm, NormFinder, BestKeeper, and ΔCt method) and a comprehensive algorithm (ComprFinder, developed inhouse) were used to assess the stability of each HKG. It was shown that NCBP3 + SDHA + PTPRA were more stably expressed than previously used genes in all conditions analysis, and that this combination was effective at normalizing target gene expression. Moreover, a new algorithm for comprehensive analysis, ComprFinder, was developed and released. Conclusion: This study presents the first list of candidate HKGs for C. hircus skin tissues based on an RNA-seq dataset. We propose that the NCBP3 + SDHA + PTPRA combination could be regarded as a triplet set of HKGs in skin molecular biology experiments in C. hircus and other closely related species. In addition, we also encourage researchers who perform candidate HKG evaluations and who require comprehensive analysis to adopt our new algorithm, ComprFinder.

Identifying associations between genetic markers and economic traits has practical benefits for the meat goat industry. To better understand the genomic regions and biological pathways contributing to body conformation traits of meat goats, a genome-wide association study was performed using Dazu black goats (DBGs), a Chinese indigenous goat breed. In particular, 150 DBGs were genotyped by whole-genome sequencing, and six body conformation traits, including body height (BH), body length (BL), cannon circumference (CC), chest depth (CD), chest width (CW), and heart girth (HG), were examined. In total, 53 potential SNPs were associated with these body conformation traits. A bioinformatics analysis was performed to evaluate the genes located close to the significant SNPs. Finally, 42 candidate genes (e.g., PSTPIP2, C7orf57, CCL19, FGF9, SGCG, FIGN, and SIPA1L) were identified as components of the genetic architecture underlying body conformation traits. Our results provide useful biological information for the improvement of growth performance and have practical applications for genomic selection in goats.

Background: In Quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression data is dependent on optimal amplification of house-keeping genes (HKGs). The RNA-seq technology offers a novel approach to detect new HKGs with improved stability. Goat (Capra hircus) is an economically important livestock species, and plays an indispensable role in the world animal fiber and meat industry. Unfortunately, uniform and reliable HKGs be used in skin research of goats have not been identified. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using the new high-throughput sequencing technology.Results: Based on the transcriptome dataset of 39 goat skin tissues, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. The classical HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also considered. Four different experimental materials: (1) different development stages, (2) hair follicle cycle stages, (3) breeds and (4) sampling sites were provided for determination and validation. Four algorithms (geNorm, NormFinder, BestKeeper, and ΔCt method) and a comprehensive algorithm (ComprFinder, developed in-house) were used to assess the stability of each HKG. It was observed that NCBP3+SDHA+PTPRA was more stably expressed than previously used genes, in all conditions analyzed. This combination was effective at normalizing target gene expression. Moreover, a new algorithm, ComprFinder, was developed and released for comprehensive analysis.Conclusion: This study presents the first data of candidate HKGs selection for skin tissues of C. hircus based on an RNA-seq dataset. We propose the use of the NCBP3+SDHA+PTPRA combination as the triplet HKGs in skin molecular biology in C. hircus and other closely related species in order to standardize analyses across studies. In addition, we also encourage researchers who are performing candidate HKG evaluations and have the needs of a comprehensive analysis to adopt our new algorithm, ComprFinder.

Background: In Quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression data is dependent on optimal amplification of house-keeping genes (HKGs). RNA-seq technology offers a novel approach to detect new HKGs with improved stability. Goat (Capra hircus) is an economically important livestock species and plays an indispensable role in the world animal fiber and meat industry. Unfortunately, uniform and reliable HKGs for use in goat skin research have not been identified. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using high-throughput sequencing technology.Results: Based on the transcriptome dataset of 39 goat skin tissue samples, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. Commonly used HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also examined. Four different experimental variables: (1) different development stages, (2) hair follicle cycle stages, (3) breeds and (4) sampling sites, were used for determination and validation. Four algorithms (geNorm, NormFinder, BestKeeper, and ΔCt method) and a comprehensive algorithm (ComprFinder, developed in-house) were used to assess the stability of each HKG. It was shown that NCBP3+SDHA+PTPRA were more stably expressed than previously used genes in all conditions analysed, and that this combination was effective at normalizing target gene expression. Moreover, a new algorithm for comprehensive analysis, ComprFinder, was developed and released.Conclusion: This study presents the first data of candidate HKGs for C. hircus skin tissues based on an RNA-seq dataset. We propose that the NCBP3+SDHA+PTPRA combination be used as a triplet set of HKGs in skin molecular biology experiments in C. hircus and other closely related species to standardize analyses across studies. In addition, we also encourage researchers who perform candidate HKG evaluations and who have the need for comprehensive analysis to adopt our new algorithm, ComprFinder.

No abstract