Using RNA‐Seq SNP data to reveal potential causal mutations related to pig production traits and RNA editing

Abstract

RNA-Seq technology is widely used in quantitative gene expression studies and identification of non-annotated transcripts. However this technology also can be used for polymorphism detection and RNA editing in transcribed regions in an efficient and cost-effective way. This study used SNP data from an RNA-Seq assay to identify genes and mutations underlying production trait variations in an experimental pig population. The hypothalamic and hepatic transcriptomes of nine extreme animals for growth and fatness from an (Iberian × Landrace) × Landrace backcross were analyzed by RNA-Seq methodology, and SNP calling was conducted. More than 125 000 single nucleotide variants (SNVs) were identified in each tissue, and 78% were considered to be potential SNPs, those SNVs segregating in the context of this study. Potential informative SNPs were detected by considering those showing a homozygous or heterozygous genotype in one extreme group and the alternative genotype in the other group. In this way, 4396 and 1862 informative SNPs were detected in hypothalamus and liver respectively. Out of the 32 SNPs selected for validation, 25 (80%) were confirmed as actual SNPs. Association analyses for growth, fatness and premium cut yields with 19 selected SNPs were carried out, and four potential causal genes (RETSAT, COPA, RNMT and PALMD) were identified. Interestingly, new RNA editing modifications were detected and validated for the NR3C1:g.102797 (ss1985401074) and ACSM2B:g.13374 (ss1985401075) positions and for the COG3:g3.4525 (ss1985401087) modification previously identified across vertebrates, which could lead to phenotypic variation and should be further investigated.

Using genome wide association studies to identify common QTL regions in three different genetic backgrounds based on Iberian pig breed

“…RNA-Seq data obtained from liver, hypothalamus and Longissimus dorsi muscle samples, from pigs with divergent phenotypes for growth and fatness on each backcross [ 43 , 44 ] was used for SNP calling, in order to identify candidate polymorphism within the selected candidate genes. RNA-Seq data filtering, mapping and SNP calling was carried out with CLC Genomics Workbench ( www.clcbio.com ).…”

Genetic Marker Discovery in Complex Traits: A Field Example on Fat Content and Composition in Pigs

“…As costs for whole-genome sequencing become more affordable, sequence variability at the whole-genome level is becoming an alternative to positional or physiological candidate gene approach. As an alternative to whole-genome sequencing, sequence variation information can also be retrieved from RNA-seq experiments and linked to expression levels [85]. The big limitation, and a severe bottleneck, still is how to perform large-scale functional tests on the SNP/INDEL/CNV mutations resulting from these experiments, particularly in non-coding regions.…”

Genome-wide identification and analysis of A-to-I RNA editing events in bovine by transcriptome sequencing

“…Therefore, this phenomenon leads to differences between the final transcript sequence and the DNA region it was transcribed from [ 4 ]. Since its discovery in 1986 in trypanosomes [ 5 ], RNA editing has been reported to occur in a broad range of species ranging from bacteria [ 6 ] to mammals [ 7 , 8 ]. As inosines are read as guanosine by the translation and splicing machineries [ 4 , 9 ], RNA editing can influence alternative splicing [ 10 ], recoding of open reading frames [ 4 ] and can affect miRNA-regulated post-transcriptional gene silencing [ 11 ].…”

“…Novel RNA-editing sites can be discovered by direct comparison between cDNA sequences and their corresponding genomic position. Several recent next generation sequencing (NGS) based studies, have reported RNA editing sites in different vertebrates including human [ 8 , 12 , 14 ], mouse [ 16 ], pig [ 17 ], chicken [ 18 ] and bovine [ 19 ]. There are several challenges for identifying RNA editome using RNA-Seq data including the discrimination of true RNA editing sites from single-nucleotide polymorphisms (SNPs), somatic mutations, systematic sequencing errors and mapping errors [ 20 ].…”