Direct inference and control of genetic population structure from RNA sequencing data

Abstract

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

RNA sequencing variants are enriched for eQTL in cattle tissues

“…High-throughput RNA sequencing (RNA-seq) has been frequently applied for measuring gene expression levels (1), assembling de novo transcriptomes (2), detecting copy number alterations (3), and identifying genomic variants that influence gene expression (4). Genotypes called from RNA-seq have also been used to determine population structure (5,6). Historically, RNA-seq has been viewed as unreliable input for performing genetic variation calling for several reasons: RNA-seq typically targets fewer fragments than DNA sequencing, messenger RNA (mRNA) only produces transcripts in coding regions (~1% of a mammalian genome), and a comparative lack of algorithmic development relative to DNA variant callers.…”