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

Area of Science:

• Genomics
• Molecular Biology
• Animal Science

Background:

• RNA sequencing (RNA-Seq) is a powerful tool for gene expression analysis.
• Beyond expression, RNA-Seq can efficiently detect genetic polymorphisms and RNA editing.
• Understanding genetic underpinnings of production traits is crucial in livestock breeding.

Purpose of the Study:

• To identify genes and mutations influencing production traits in pigs using RNA-Seq.
• To leverage single nucleotide variant (SNV) data for genetic association studies.
• To explore RNA editing events in relation to phenotypic variation.

Main Methods:

• Analysis of hypothalamic and hepatic transcriptomes from extreme pigs using RNA-Seq.
• Single nucleotide variant (SNV) calling and identification of potential single nucleotide polymorphisms (SNPs).
• Association analysis between informative SNPs and production traits (growth, fatness, cut yields).

Main Results:

• Over 125,000 SNVs identified per tissue, with 78% classified as potential SNPs.
• 4396 and 1862 informative SNPs detected in hypothalamus and liver, respectively.
• Association analysis identified four potential causal genes: RETSAT, COPA, RNMT, and PALMD.
• New RNA editing sites were detected and validated, including modifications in NR3C1, ACSM2B, and COG3.

Conclusions:

• RNA-Seq is effective for detecting SNPs and RNA editing in pigs.
• Identified genes and mutations provide insights into the genetic basis of production traits.
• Detected RNA editing events warrant further investigation for their role in phenotypic variation.