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

Abstract

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 9.5 M 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, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them 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.

GDF15 and ACE2 stratify COVID19 patients according to severity while ACE2 mutations increase infection susceptibility

“…Therefore, we reasoned that patient genotyping from mRNA could better reflect the ACE2 variants expressed in those patients. In fact, previous studies have shown that SNPs could be detected with high precision in transcriptome sequencing approaches as compared to DNA-seq procedures (58, 59). This has led to the emergence of transcriptome or RNA sequencing as a potential alternative approach to variant detection within protein coding regions, since the transcriptome of a given tissue represents a quasi-complete set of transcribed genes (mRNAs) and other noncoding RNAs which also bypasses the need for exome enrichment (60, 61).…”

H3K27ac chromatin acetylation and gene expression analysis reveal sex- and situs-related differences in developing chicken gonads

“…Data generated have been deposited in NCBI database: PRJNA766745. The male and female gonad RNAseq data from previous reports include E4.5 and E6 RNA-seq: PRJNA171809 [ 29 ], E10, E12 and E14 RNA-seq: PRJEB26695 [ 30 ].…”

Watch Out for a Second SNP: Focus on Multi-Nucleotide Variants in Coding Regions and Rescued Stop-Gained

“…The 3,276,615 SNPs analyzed in this study have been detected following the method presented in the companion paper (Jehl et al, 2021) using 767 multi-tissue RNA-seq of 382 birds from 11 chicken populations (see Additional File 1). This SNP set corresponds to the union of the SNPs with reliable genotypes found in each population (list available on http://www.…”

“…Using 3.3M SNPs previously detected from 767 multi-tissue RNA-seq of 382 animals from 11 chicken populations and therefore enriched in coding regions [see the companion paper (Jehl et al, 2021), section "Materials and Methods"], we identified 260,919 unique SNPs in 26,702 transcripts corresponding to 15,835 genes out of 19,545 protein-coding genes (Figure 2, right part-in yellow).…”

“…Considering this aim, we used 9.5M SNPs recently detected in 382 chickens from 767 multi-tissue RNA-seq, enriched by construction in expressed regions and therefore in protein-coding regions. From this 9.5M SNPs, we focused on the 3.3M SNPs with reliable genotypes [see the companion paper (Jehl et al, 2021)]. MNV identification requires properly phased variants, i.e., to be located either on the same haplotype (called therefore MNV) or on two different haplotypes (a case of individual SNPs) (see Figures 1A,B).…”