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.

Autor: Lam S; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road E, Guelph, Ontario, N1G2W1, Canada., Zeidan J; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road E, Guelph, Ontario, N1G2W1, Canada., Miglior F; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road E, Guelph, Ontario, N1G2W1, Canada., Suárez-Vega A; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road E, Guelph, Ontario, N1G2W1, Canada., Gómez-Redondo I; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road E, Guelph, Ontario, N1G2W1, Canada.; Spanish National Institute for Agriculture and Food Research and Technology, Carretera de La Coruña, 28040, Madrid, Spain., Fonseca PAS; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road E, Guelph, Ontario, N1G2W1, Canada., Guan LL; Department of Agriculture, Food & Nutritional Science, University of Alberta, Edmonton, Alberta, T6H 2P5, Canada., Waters S; Teagasc, Animal & Grassland Research and Innovation Centre, Grange, Dunsany, Co. Meath, C15 PW93, Ireland., Cánovas A; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, 50 Stone Road E, Guelph, Ontario, N1G2W1, Canada. acanovas@uoguelph.ca.
Jazyk: angličtina
Zdroj: BMC genomics [BMC Genomics] 2020 Oct 08; Vol. 21 (1), pp. 703. Date of Electronic Publication: 2020 Oct 08.
DOI: 10.1186/s12864-020-07107-7
Abstrakt: 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.
Databáze: MEDLINE
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