RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species - Jehl, Frédéric; Degalez, Fabien; Bernard, Maria; Lecerf, Frédéric; Lagoutte, Laetitia; Désert, Colette; Coulée, Manon; Bouchez, Olivier; Leroux, Sophie; Abasht, Behnam; Tixier-Boichard, Michèle; Bed’hom, Bertrand; et al. (Frontiers in Genetics, 2021)
In addition to their common usages to study gene expression, RNA seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA seq and DNA seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA seq and shared by DNA seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call rate and read number per GT) on the concordance of GT with DNA seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA seq of 382 birds of 11 chicken populations, we found 9.5M SNPs in total, of which ~550,000 SNPs per tissue and population with a reliable GT (call rate≥50%) and among them, ~340,000 with a MAF≥10%. We showed that such RNA seq data from one tissue can be used to i) detect SNPs with a strong predicted impact on proteins, ii) study, cis regulations of gene expression with here ~29% of genes that were cis-regulated, and iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.