Regulation of transcript structure generates transcript diversity and plays an important role in human disease. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure. In this paper, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from GTEx tissues and cell lines, complementing the GTEx resource. We identified just under 100,000 new transcripts for annotated genes, and validated the protein expression of a similar proportion of novel and annotated transcripts. We developed a new computational package, LORALS, to analyze genetic effects of rare and common variants on the transcriptome via allele-specific analysis of long reads. We called allele-specific expression and transcript structure events, providing novel insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data.We were able to perturb transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we use this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.
Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.
Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21 392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.
SummaryRegulation of transcript structure generates transcript diversity and plays an important role in human disease. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure. In this paper, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from GTEx tissues and cell lines, complementing the GTEx resource. We identified just under 100,000 new transcripts for annotated genes, and validated the protein expression of a similar proportion of novel and annotated transcripts. We developed a new computational package, LORALS, to analyze genetic effects of rare and common variants on the transcriptome via allele-specific analysis of long reads. We called allele-specific expression and transcript structure events, providing novel insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we use this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.
Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrated a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n=18,502). We identified 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterized the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (BioVU; n=85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicated these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in BioVU, pan-UK Biobank, and Biobank Japan. Our study highlights putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.
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