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/.
Chromatin state variation at gene regulatory elements is abundant across individuals, yet we understand little about the genetic basis of this variability. Here, we profiled several histone ...modifications, the transcription factor (TF) PU.1, RNA polymerase II, and gene expression in lymphoblastoid cell lines from 47 whole-genome sequenced individuals. We observed that distinct cis-regulatory elements exhibit coordinated chromatin variation across individuals in the form of variable chromatin modules (VCMs) at sub-Mb scale. VCMs were associated with thousands of genes and preferentially cluster within chromosomal contact domains. We mapped strong proximal and weak, yet more ubiquitous, distal-acting chromatin quantitative trait loci (cQTL) that frequently explain this variation. cQTLs were associated with molecular activity at clusters of cis-regulatory elements and mapped preferentially within TF-bound regions. We propose that local, sequence-independent chromatin variation emerges as a result of genetic perturbations in cooperative interactions between cis-regulatory elements that are located within the same genomic domain.
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•Modules of correlated molecular phenotypes represent inter-individual chromatin variation•Variable chromatin modules (VCMs) are embedded within chromosomal contact domains•VCMs are orchestrated by cis-acting genetic variation•VCMs rationalize chromatin state changes that are independent of local DNA sequence
Spatially defined chromosome regions, termed variable chromatin modules, exhibit coordinated chromatin state changes across cis-regulatory elements. Within these modules, genetic changes distal from the regulatory element itself can induce variation in chromatin patterns between individuals.
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.
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.
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.
RNA sequencing (RNA-seq) is the current gold-standard method to quantify gene expression for expression quantitative trait locus (eQTL) studies. However, a potential caveat in these studies is that ...RNA-seq reads carrying the non-reference allele of variant loci can have lower probability to map correctly to the reference genome, which could bias gene quantifications and cause false positive eQTL associations. In this study, we analyze the effect of this allelic mapping bias in eQTL discovery.
We simulate RNA-seq read mapping over 9.5 M common SNPs and indels, with 15.6% of variants showing biased mapping rate for reference versus non-reference reads. However, removing potentially biased RNA-seq reads from an eQTL dataset of 185 individuals has a very small effect on gene and exon quantifications and eQTL discovery. We detect only a handful of likely false positive eQTLs, and overall eQTL SNPs show no significant enrichment for high mapping bias.
Our results suggest that RNA-seq quantifications are generally robust against allelic mapping bias, and that this does not have a severe effect on eQTL discovery. Nevertheless, we provide our catalog of putatively biased loci to allow better controlling for mapping bias to obtain more accurate results in future RNA-seq studies.
Large genomic datasets combining genotype and sequence data, such as for expression quantitative trait loci (eQTL) detection, require perfect matching between both data types.
We described here MBV ...(Match BAM to VCF); a method to quickly solve sample mislabeling and detect cross-sample contamination and PCR amplification bias.
MBV is implemented in C ++ as an independent component of the QTLtools software package, the binary and source codes are freely available at https://qtltools.github.io/qtltools/ .
olivier.delaneau@unige.ch or emmanouil.dermitzakis@unige.ch.
Supplementary data are available at Bioinformatics online.
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.
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.
Abstract
Plasma cell-free DNA (cfDNA) is a surrogate marker of neutrophil extracellular traps (NETs) that contribute to immunothrombosis. There is growing interest about the mechanisms underlying NET ...formation and elevated cfDNA, but little is known about the factors involved. We aimed to identify genes involved in the regulation of cfDNA levels using data from the Genetic Analysis of Idiopathic Thrombophilia (GAIT-2) Project.
Imputed genotypes, whole blood RNA-Seq data, and plasma cfDNA quantification were available for 935 of the GAIT-2 participants from 35 families with idiopathic thrombophilia. We performed heritability and GWAS analysis for cfDNA. The heritability of cfDNA was 0.26 (
p
= 3.7 × 10
−6
), while the GWAS identified a significant association (rs1687391,
p
= 3.55 × 10
−10
) near the
ORM1
gene, on chromosome 9. An eQTL (expression quantitative trait loci) analysis revealed a significant association between the lead GWAS variant and the expression of
ORM1
in whole blood (
p
= 6.14 × 10
−9
). Additionally,
ORM1
expression correlated with levels of cfDNA (
p
= 4.38 × 10
−4
). Finally, genetic correlation analysis between cfDNA and thrombosis identified a suggestive association (
ρ
g
= 0.43,
p
= 0.089).
All in all, we show evidence of the role of
ORM1
in regulating cfDNA levels in plasma, which might contribute to the susceptibility to thrombosis through mechanisms of immunothrombosis.
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