TRRAP, PIK3CB), which are known to play a key role in the regulation of biological processes that have high metabolic demand and are related to cell growth and regeneration, metabolism, and immune function. The genes identified and their associated functional variants may serve as candidate genetic markers and can be implemented into breeding programs to help improve the selection for feed efficiency in dairy cattle.

Background Optimization of an RNA-Sequencing (RNA-Seq) pipeline is critical to maximize power and accuracy to identify genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study used RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n = 6 low-RFI, n = 6 high-RFI). Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by RFI group, iii) merged samples by RFI and tissue group. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR aligner. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. Results On average, total reads detected for Approach i) non-merged samples for liver and muscle, were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), merging samples by RFI group, total reads detected for each merged group was 162,030,705, and for Approach iii), merging samples by RFI group and tissues, was 324,061,410, revealing the highest read depth for Approach iii). Additionally, Approach iii) merging samples by RFI group and tissues, revealed the highest read depth per variant coverage (572.59 ± 3993.11) and encompassed the majority of localized positional genes detected by each approach. This suggests Approach iii) had optimized detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive detection. Approach iii) was then used to detect unique SNPs fixed within low- (12,145) and high-RFI (14,663) groups. Functional annotation of SNPs revealed positional candidate genes, for each RFI group (2886 for low-RFI, 3075 for high-RFI), which were significantly (P < 0.05) associated with immune and metabolic pathways. Conclusion The most optimized RNA-Seq pipeline allowed for more accurate identification of SNPs, associated positional candidate genes, and significantly associated metabolic pathways in muscle and liver tissues, providing insight on the underlying genetic architecture of feed efficiency in beef cattle.

Optimization of an RNA-Sequencing (RNA-Seq) pipeline can maximize power and accuracy for identifying genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study determined an optimized pipeline for variant detection using a dataset with multiple samples and tissues. The RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n=6 low-RFI, n=6 high-RFI) were used. Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by group for low-RFI and for high-RFI for each tissue, iii) merged samples by group and tissue for low-and high-RFI for both tissues. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR.Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. Approaches were compared by comparing read depth, overlap of SNP detection results, and following SNP annotation for positional candidate genes. On average, total reads detected for Approach i) individual liver and muscle samples were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), total reads detected for each merged group of samples was 162,030,705, and for Approach iii) was 324,061,410, revealing the highest read depth.Additionally, Approach iii) encompassed the majority of localized positional genes detected by each approach, suggesting Approach iii) be applied to maximize detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive rate.Approach iii) was used to detect unique SNPs fixed within low-(12,145) and high-RFI (14,663) groups.Annotation of moderate to high functional impact SNPs revealed co-localized positional candidate genes for each RFI group (2,886 for low-RFI, 3,075 for high-RFI), which were significantly (P<0.05) associated with immune and metabolism pathways. The most optimized RNA-Seq pipeline allowed for more accurate identification of SNP, associated positional candidate genes, and associated metabolic pathways in muscle and liver tissues, providing insight on the genetic architecture of feed efficiency in beef cattle.