Development and comparison of RNA-Sequencing pipelines for more accurate SNP identification: Practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle.

Abstract 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.

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