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.
Highlights
RNA-seq is currently the method of choice to study transcriptome expression in replacement of gene chips (Mortazavi et al, 2008)
Our three goals were to (i) provide an estimation of the number of SNPs and GT that can be detected using RNA-seq data per tissue and population, (ii) present an overview of the predicted consequences of the SNPs located in coding regions, in particular, the number of high-confidence predicted loss-of-function variants, as defined in the work of Genome Aggregation Database (gnomAD), and (iii) give an overview of the potential of RNA-seq for allele-specific expression (ASE) analysis by estimating the number of genes that could be analyzed for ASE with the number of SNPs detected per gene
With RNA-seq, we expected to find most of the SNPs in exonic regions, which represent the majority of expressed regions
Summary
RNA-seq is currently the method of choice to study transcriptome expression in replacement of gene chips (Mortazavi et al, 2008). RNA-seq can detect genomic variations in expressed regions like DNA-seq, as described by Piskol et al (2013) It is interesting in non-model species (wild or domesticated, for example livestock species) in which no exome capturing tools have been developed as an alternative to DNA-seq data, which remains costly to generate and store. In well-known model-species or human, these coding region variants are accessible using whole exome sequencing (WES), as shown by the recent work of the Genome Aggregation Database (gnomAD) (Lek et al, 2016) This consortium analyzed 125,748 human exomes (and much fewer whole genomes: 15,708) from public sources and identified 443,769 high-confidence predicted loss-of-function variants, defined in the work of gnomAD as being either gain of stop (non-sense variants), frameshift or splice site variants. These data could be used to study population genetic diversity from a different point of view compared to the SNP chips, by offering various sets of SNPs with more or less severe functional impacts and not neutral SNPs
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