Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Genomic profiling of human vascular cells identifies TWIST1 as a causal gene for common vascular diseases.

Genome-wide association studies have identified multiple novel genomic loci associated with vascular diseases. Many of these loci are common non-coding variants that affect the expression of disease-relevant genes within coronary vascular cells. To identify such genes on a genome-wide level, we performed deep transcriptomic analysis of genotyped primary human coronary artery smooth muscle cells (HCASMCs) and coronary endothelial cells (HCAECs) from the same subjects, including splicing Quantitative Trait Loci (sQTL), allele-specific expression (ASE), and colocalization analyses. We identified sQTLs for TARS2, YAP1, CFDP1, and STAT6 in HCASMCs and HCAECs, and 233 ASE genes, a subset of which are also GTEx eGenes in arterial tissues. Colocalization of GWAS association signals for coronary artery disease (CAD), migraine, stroke and abdominal aortic aneurysm with GTEx eGenes in aorta, coronary artery and tibial artery discovered novel candidate risk genes for these diseases. At the CAD and stroke locus tagged by rs2107595 we demonstrate colocalization with expression of the proximal gene TWIST1. We show that disrupting the rs2107595 locus alters TWIST1 expression and that the risk allele has increased binding of the NOTCH signaling protein RBPJ. Finally, we provide data that TWIST1 expression influences vascular SMC phenotypes, including proliferation and calcification, as a potential mechanism supporting a role for TWIST1 in CAD.

Genetic and Epigenetic Fine Mapping of Complex Trait Associated Loci in the Human Liver.

Deciphering the impact of genetic variation on gene regulation is fundamental to understanding common, complex human diseases. Although histone modifications are important markers of gene regulatory elements of the genome, any specific histone modification has not been assayed in more than a few individuals in the human liver. As a result, the effects of genetic variation on histone modification states in the liver are poorly understood. Here, we generate the most comprehensive genome-wide dataset of two epigenetic marks, H3K4me3 and H3K27ac, and annotate thousands of putative regulatory elements in the human liver. We integrate these findings with genome-wide gene expression data collected from the same human liver tissues and high-resolution promoter-focused chromatin interaction maps collected from human liver-derived HepG2 cells. We demonstrate widespread functional consequences of natural genetic variation on putative regulatory element activity and gene expression levels. Leveraging these extensive datasets, we fine-map a total of 74 GWAS loci that have been associated with at least one complex phenotype. Our results reveal a repertoire of genes and regulatory mechanisms governing complex disease development and further the basic understanding of genetic and epigenetic regulation of gene expression in the human liver tissue.

Transposable elements are the primary source of novelty in primate gene regulation.

Gene regulation shapes the evolution of phenotypic diversity. We investigated the evolution of liver promoters and enhancers in six primate species using ChIP-seq (H3K27ac and H3K4me1) to profile cis-regulatory elements (CREs) and using RNA-seq to characterize gene expression in the same individuals. To quantify regulatory divergence, we compared CRE activity across species by testing differential ChIP-seq read depths directly measured for orthologous sequences. We show that the primate regulatory landscape is largely conserved across the lineage, with 63% of the tested human liver CREs showing similar activity across species. Conserved CRE function is associated with sequence conservation, proximity to coding genes, cell-type specificity, and transcription factor binding. Newly evolved CREs are enriched in immune response and neurodevelopmental functions. We further demonstrate that conserved CREs bind master regulators, suggesting that while CREs contribute to species adaptation to the environment, core functions remain intact. Newly evolved CREs are enriched in young transposable elements (TEs), including Long-Terminal-Repeats (LTRs) and SINE-VNTR-Alus (SVAs), that significantly affect gene expression. Conversely, only 16% of conserved CREs overlap TEs. We tested the cis-regulatory activity of 69 TE subfamilies by luciferase reporter assays, spanning all major TE classes, and showed that 95.6% of tested TEs can function as either transcriptional activators or repressors. In conclusion, we demonstrated the critical role of TEs in primate gene regulation and illustrated potential mechanisms underlying evolutionary divergence among the primate species through the noncoding genome.

The YscE/YscG chaperone and YscF N-terminal sequences target YscF to the Yersinia pestis type III secretion apparatus.

The needle structures of type III secretion (T3S) systems are formed by the secretion and polymerization of a needle subunit protein, YscF in Yersinia pestis. A subset of T3S systems employ unique heterodimeric chaperones, YscE and YscG in Y. pestis, to prevent the polymerization of needle subunits within the bacterial cell. We demonstrate that the YscE/YscG chaperone is also required for stable YscF expression and for secretion of YscF. Overexpression of a functional maltose-binding protein (MBP)-YscG hybrid protein stabilized cytoplasmic YscF but YscF was not secreted in the absence of YscE. Furthermore, a YscE mutant protein was identified that functioned with YscG to stabilize cytosolic YscF; however, YscF was not secreted. These findings confirm a role for the YscE/YscG chaperone in YscF secretion and suggest that YscE may have a specific role in this process. Recent studies have shown that YscF deleted of its N-terminal 15 residues is still secreted and functional, suggesting that YscF may not require an N-terminal secretion signal. However, we demonstrate that YscF contains an N-terminal secretion signal and that a functional N-terminal signal is required for YscF secretion.

Session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction.

This PSB 2023 session discusses challenges in clinical implication and application of risk prediction models, which includes but is not limited to: implementation of risk models, responsible use of polygenic risk scores (PGS), and other risk prediction strategies. We focus on the development and use of new, scalable methods for harmonizing and refining risk prediction models by incorporating genetic and non-genetic risk factors, applying new phenotyping strategies, and integrating clinical factors and biomarkers. Lastly, we will discuss innovation in expanding the utility of these prediction models to underrepresented populations. This session focuses on the overarching theme of enabling early diagnosis, and treatment and preventive measures related to complex diseases and comorbidities.

Evaluating the Contribution of Cell Type-Specific Alternative Splicing to Variation in Lipid Levels.

BACKGROUND: Genome-wide association studies have identified hundreds of loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contribute to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest. METHODS: We analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell lines (n=166), as well as in an independent collection of primary liver tissues (n=96) to perform discovery of splicing quantitative trait loci (sQTLs). RESULTS: We observe that transcript splicing is highly cell type specific, and the genes that are differentially spliced between iPSCs and hepatocyte-like cells are enriched for metabolism pathway annotations. We identify 1384 hepatocyte-like cell sQTLs and 1455 iPSC sQTLs at a false discovery rate of <5% and find that sQTLs are often shared across cell types. To evaluate the contribution of sQTLs to variation in lipid levels, we conduct colocalization analysis using lipid genome-wide association data. We identify 19 lipid-associated loci that colocalize either with an hepatocyte-like cell expression quantitative trait locus or sQTL. Only 2 loci colocalize with both a sQTL and expression quantitative trait locus, indicating that sQTLs contribute information about genome-wide association studies loci that cannot be obtained by analysis of steady-state gene expression alone. CONCLUSIONS: These results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.

Identification and Development of Therapeutics for COVID-19.

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.

Identification and Development of Therapeutics for COVID-19.

After emerging in China in late 2019, the novel coronavirus SARS-CoV-2 spread worldwide and as of mid-2021 remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a closely related species of SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis to identify many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases, but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease.

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci.

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure.

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).

Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure.

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease.

Correcting for experiment-specific variability in expression compendia can remove underlying signals.

MOTIVATION: In the past two decades, scientists in different laboratories have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technical variability, or "batch effects," may result from combining samples collected and processed at different times and in different settings. Such variability may distort our ability to extract true underlying biological patterns. As more integrative analysis methods arise and data collections get bigger, we must determine how technical variability affects our ability to detect desired patterns when many experiments are combined. OBJECTIVE: We sought to determine the extent to which an underlying signal was masked by technical variability by simulating compendia comprising data aggregated across multiple experiments. METHOD: We developed a generative multi-layer neural network to simulate compendia of gene expression experiments from large-scale microbial and human datasets. We compared simulated compendia before and after introducing varying numbers of sources of undesired variability. RESULTS: The signal from a baseline compendium was obscured when the number of added sources of variability was small. Applying statistical correction methods rescued the underlying signal in these cases. However, as the number of sources of variability increased, it became easier to detect the original signal even without correction. In fact, statistical correction reduced our power to detect the underlying signal. CONCLUSION: When combining a modest number of experiments, it is best to correct for experiment-specific noise. However, when many experiments are combined, statistical correction reduces our ability to extract underlying patterns.

PhenomeXcan: Mapping the genome to the phenome through the transcriptome.

Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx.

BACKGROUND: Population structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization. RESULTS: Here, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up. CONCLUSIONS: We provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

Transcriptomic signatures across human tissues identify functional rare genetic variation.

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

A parasite's perspective on data sharing.

Data generation is expensive in terms of both time and money. Sharing data enables the rapid replication, validation, and application of discoveries, increasing the pace and accuracy of research. As research parasites, or users of other people's data, we recognize that a strong science ecosystem requires that those who share best be recognized. We find that widely accessible benchmark datasets have provided outsized benefits, and we hope that the benefits of sharing will also begin to accrue to individual investigators who share well. Funders can enhance progress by adjusting incentives to better support data sharers, which will make their programmatic investments more effective. We note some funders who are making such efforts.

Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease.

Chronic kidney disease (CKD), a condition in which the kidneys are unable to clear waste products, affects 700 million people globally. Genome-wide association studies (GWASs) have identified sequence variants for CKD; however, the biological basis of these GWAS results remains poorly understood. To address this issue, we created an expression quantitative trait loci (eQTL) atlas for the glomerular and tubular compartments of the human kidney. Through integrating the CKD GWAS with eQTL, single-cell RNA sequencing and regulatory region maps, we identified novel genes for CKD. Putative causal genes were enriched for proximal tubule expression and endolysosomal function, where DAB2, an adaptor protein in the TGF-ß pathway, formed a central node. Functional experiments confirmed that reducing Dab2 expression in renal tubules protected mice from CKD. In conclusion, compartment-specific eQTL analysis is an important avenue for the identification of novel genes and cellular pathways involved in CKD development and thus potential new opportunities for its treatment.

Large, Diverse Population Cohorts of hiPSCs and Derived Hepatocyte-like Cells Reveal Functional Genetic Variation at Blood Lipid-Associated Loci.

Genome-wide association studies have struggled to identify functional genes and variants underlying complex phenotypes. We recruited a multi-ethnic cohort of healthy volunteers (n = 91) and used their tissue to generate induced pluripotent stem cells (iPSCs) and hepatocyte-like cells (HLCs) for genome-wide mapping of expression quantitative trait loci (eQTLs) and allele-specific expression (ASE). We identified many eQTL genes (eGenes) not observed in the comparably sized Genotype-Tissue Expression project's human liver cohort (n = 96). Focusing on blood lipid-associated loci, we performed massively parallel reporter assays to screen candidate functional variants and used genome-edited stem cells, CRISPR interference, and mouse modeling to establish rs2277862-CPNE1, rs10889356-DOCK7, rs10889356-ANGPTL3, and rs10872142-FRK as functional SNP-gene sets. We demonstrated HLC eGenes CPNE1, VKORC1, UBE2L3, and ANGPTL3 and HLC ASE gene ACAA2 to be lipid-functional genes in mouse models. These findings endorse an iPSC-based experimental framework to discover functional variants and genes contributing to complex human traits.