Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

The Genotype-Tissue Expression (GTEx) project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals. A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project. Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

Context.— Despite widespread use of formalin-fixed, paraffin-embedded (FFPE) tissue in clinical and research settings, potential effects of variable tissue processing remain largely unknown. Objective.— To elucidate molecular effects associated with clinically relevant preanalytical variability, the National Cancer Institute initiated the Biospecimen Preanalytical Variables (BPV) program. Design.— The BPV program, a well-controlled series of systematic, blind and randomized studies, investigated whether a delay to fixation (DTF) or time in fixative (TIF) affects the quantity and quality of DNA and RNA isolated from FFPE colon, kidney, and ovarian tumors in comparison to case-matched snap-frozen controls. Results.— DNA and RNA yields were comparable among FFPE biospecimens subjected to different DTF and TIF time points. DNA and RNA quality metrics revealed assay- and time point–specific effects of DTF and TIF. A quantitative reverse transcription–polymerase chain reaction (qRT-PCR) assay was superior when assessing RNA quality, consistently detecting differences between FFPE and snap-frozen biospecimens and among DTF and TIF time points. RNA Integrity Number and DV200 (representing the percentage of RNA fragments longer than 200 nucleotides) displayed more limited sensitivity. Differences in DNA quality (Q-ratio) between FFPE and snap-frozen biospecimens and among DTF and TIF time points were detected with a qPCR-based assay. Conclusions.— DNA and RNA quality may be adversely affected in some tumor types by a 12-hour DTF or a TIF of 72 hours. Results presented here as well as those of additional BPV molecular analyses underway will aid in the identification of acceptable delays and optimal fixation times, and quality assays that are suitable predictors of an FFPE biospecimen's fit-for-purpose.