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Population scale studies combining genetic information with molecular phenotypes (for example, gene expression) have become a standard to dissect the effects of genetic variants onto organismal phenotypes. These kinds of data sets require powerful, fast and versatile methods able to discover molecular Quantitative Trait Loci (molQTL). Here we propose such a solution, QTLtools, a modular framework that contains multiple new and well-established methods to prepare the data, to discover proximal and distal molQTLs and, finally, to integrate them with GWAS variants and functional annotations of the genome. We demonstrate its utility by performing a complete expression QTL study in a few easy-to-perform steps. QTLtools is open source and available at https://qtltools.github.io/qtltools/ . Analysis of molecular quantitative trait loci (molQTL) can help interpret genome-wide association studies and requires efficient approaches to correct for multiple testing. This study describes a bioinformatics toolkit called QTLtool that can handle large data sets and quickly perform multiple types of molQTL analyses.

DNA sequence variation has been associated with quantitative changes in molecular phenotypes such as gene expression, but its impact on chromatin states is poorly characterized. To understand the interplay between chromatin and genetic control of gene regulation, we quantified allelic variability in transcription factor binding, histone modifications, and gene expression within humans. We found abundant allelic specificity in chromatin and extensive local, short-range, and long-range allelic coordination among the studied molecular phenotypes. We observed genetic influence on most of these phenotypes, with histone modifications exhibiting strong context-dependent behavior. Our results implicate transcription factors as primary mediators of sequence-specific regulation of gene expression programs, with histone modifications frequently reflecting the primary regulatory event.

This study presents a new approach to estimate the tissues contributing to the genetic causality for complex traits and diseases. The method assesses tissue sharing of eQTLs among 44 tissues and then uses these tissue-sharing estimates to infer the tissues where trait-associated variants likely exert their function. How to interpret the biological causes underlying the predisposing markers identified through genome-wide association studies (GWAS) remains an open question. One direct and powerful way to assess the genetic causality behind GWAS is through analysis of expression quantitative trait loci (eQTLs). Here we describe a new approach to estimate the tissues behind the genetic causality of a variety of GWAS traits, using the cis -eQTLs in 44 tissues from the Genotype-Tissue Expression (GTEx) Consortium. We have adapted the regulatory trait concordance (RTC) score to measure the probability of eQTLs being active in multiple tissues and to calculate the probability that a GWAS-associated variant and an eQTL tag the same functional effect. By normalizing the GWAS–eQTL probabilities by the tissue-sharing estimates for eQTLs, we generate relative tissue-causality profiles for GWAS traits. Our approach not only implicates the gene likely mediating individual GWAS signals, but also highlights tissues where the genetic causality for an individual trait is likely manifested.

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution. GASPACHO is a statistical method that identifies nonlinear dynamic genetic effects using single-cell RNA-seq data. Analysis of an antiviral response in human fibroblasts identifies 1,275 expression QTLs, many of which colocalize with risk loci for autoimmune and infectious diseases.

ObjectivesSystemic lupus erythematosus (SLE) diagnosis and treatment remain empirical and the molecular basis for its heterogeneity elusive. We explored the genomic basis for disease susceptibility and severity.MethodsmRNA sequencing and genotyping in blood from 142 patients with SLE and 58 healthy volunteers. Abundances of cell types were assessed by CIBERSORT and cell-specific effects by interaction terms in linear models. Differentially expressed genes (DEGs) were used to train classifiers (linear discriminant analysis) of SLE versus healthy individuals in 80% of the dataset and were validated in the remaining 20% running 1000 iterations. Transcriptome/genotypes were integrated by expression-quantitative trail loci (eQTL) analysis; tissue-specific genetic causality was assessed by regulatory trait concordance (RTC).ResultsSLE has a ‘susceptibility signature’ present in patients in clinical remission, an ‘activity signature’ linked to genes that regulate immune cell metabolism, protein synthesis and proliferation, and a ‘severity signature’ best illustrated in active nephritis, enriched in druggable granulocyte and plasmablast/plasma–cell pathways. Patients with SLE have also perturbed mRNA splicing enriched in immune system and interferon signalling genes. A novel transcriptome index distinguished active versus inactive disease—but not low disease activity—and correlated with disease severity. DEGs discriminate SLE versus healthy individuals with median sensitivity 86% and specificity 92% suggesting a potential use in diagnostics. Combined eQTL analysis from the Genotype Tissue Expression (GTEx) project and SLE-associated genetic polymorphisms demonstrates that susceptibility variants may regulate gene expression in the blood but also in other tissues.ConclusionSpecific gene networks confer susceptibility to SLE, activity and severity, and may facilitate personalised care.

Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% − 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles. The authors study the widespread transcriptomic response of macrophages to a variety of environmental stimuli. They show that genetic determinants of this response are overrepresented among those linked to immune-mediated diseases.

The majority of immune-mediated disease (IMD) risk loci are located in non-coding regions of the genome, making it difficult to decipher their functional effects in relevant physiological contexts. To assess the extent to which alternative splicing contributes to IMD risk, we mapped genetic variants associated with alternative splicing (splicing quantitative trait loci or sQTL) in macrophages exposed to a wide range of environmental stimuli. We found that genes involved in innate immune response pathways undergo extensive differential splicing in response to stimulation and detected significant sQTL effects for over 5734 genes across all stimulation conditions. We colocalised sQTL signals for over 700 genes with IMD-associated risk loci from 22 IMDs with high confidence (PP4 ≥ 0.75). Approximately half of the colocalisations implicate lowly-used splice junctions (mean usage ratio <0.1). Finally, we demonstrate how an inflammatory bowel disease (IBD) risk allele increases the usage of a lowly-used isoform of PTPN2 , a negative regulator of inflammation. Together, our findings highlight the role alternative splicing plays in IMD risk, and suggest that lowly-used splicing events significantly contribute to complex disease risk. The authors show that alternative splicing is an important layer of macrophage response to environmental stimuli. Genetic determinants of this response, often targeting low-usage splicing events, are linked to several immune-mediated diseases.

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis -eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis- eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis- eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis- eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2 . Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues. Mechanistic inference following GWAS is hampered by the lack of tissue-specific transcriptomic resources. Here the authors combine genetic variants predisposing to type 2 diabetes with human pancreatic islet RNA-seq data. They identify 7741 islet expression quantitative trait loci (eQTLs), providing a resource for functional interpretation of association signals mapping to non-coding sequence.

Systemic Lupus Erythematosus (SLE) is the prototype of autoimmune diseases, characterized by extensive gene expression perturbations in peripheral blood immune cells. Circumstantial evidence suggests that these perturbations may be due to altered epigenetic profiles and chromatin accessibility but the relationship between transcriptional deregulation and genome organization remains largely unstudied. In this work we propose a genomic approach that leverages patterns of gene coexpression from genome-wide transcriptome profiles in order to identify statistically robust Domains of Co-ordinated gene Expression (DCEs). Application of this method on a large transcriptome profiling dataset of 148 SLE patients and 52 healthy individuals enabled the identification of significant disease-associated alterations in gene co-regulation patterns, which also correlate with SLE activity status. Low disease activity patient genomes are characterized by extensive fragmentation leading to overall fewer DCEs of smaller size. High disease activity genomes display extensive redistribution of co-expression domains with expanded and newly-appearing (emerged) DCEs. The dynamics of domain fragmentation and redistribution are associated with SLE clinical endophenotypes, with genes of the interferon pathway being highly enriched in DCEs that become disrupted and with functions associated to more generalized symptoms, being located in domains that emerge anew in high disease activity genomes. Our results suggest strong links between the SLE phenotype and the underlying genome structure and underline an important role for genome organization in shaping gene expression in SLE.

Aneuploidy is a major source of gene dosage imbalance due to copy number alterations (CNA), and viable human trisomies are model disorders of altered gene expression. We study gene and allele-specific expression (ASE) of 9668 single-cell fibroblasts from trisomy 21 (T21) discordant twins and from mosaic T21, T18, T13 and T8. We examine 928 single cells with deep scRNAseq. Expected and observed overexpression of trisomic genes in trisomic vs. diploid bulk RNAseq is not detectable in trisomic vs. diploid single cells. Instead, for trisomic genes with low-to-average expression, their altered gene dosage is mainly due to the higher fraction of trisomic cells simultaneously expressing these genes, in agreement with a stochastic 2-state burst-like model of transcription. These results, confirmed in a further analysis of 8740 single fibroblasts with shallow scRNAseq, suggest that the specific transcriptional profile of each gene contributes to the phenotypic variability of trisomies. We propose an improved model to understand the effects of CNA and, generally, of gene regulation on gene dosage imbalance. Gene dosage anomalies such as those caused by aneuploidy underlie diseases including Down syndrome. Here, the authors perform allele-specific single cell transcriptome analysis to investigate the mechanisms of gene dosage imbalance in fibroblasts with trisomies T21, T18, T13 and T8.