Rare and Common Regulatory Variation in Population-Scale Sequenced Human Genomes

Population-scale genome sequencing allows the characterization of functional effects of a broad spectrum of genetic variants underlying human phenotypic variation. Here, we investigate the influence of rare and common genetic variants on gene expression patterns, using variants identified from sequencing data from the 1000 genomes project in an African and European population sample and gene expression data from lymphoblastoid cell lines. We detect comparable numbers of expression quantitative trait loci (eQTLs) when compared to genotypes obtained from HapMap 3, but as many as 80% of the top expression quantitative trait variants (eQTVs) discovered from 1000 genomes data are novel. The properties of the newly discovered variants suggest that mapping common causal regulatory variants is challenging even with full resequencing data; however, we observe significant enrichment of regulatory effects in splice-site and nonsense variants. Using RNA sequencing data, we show that 46.2% of nonsynonymous variants are differentially expressed in at least one individual in our sample, creating widespread potential for interactions between functional protein-coding and regulatory variants. We also use allele-specific expression to identify putative rare causal regulatory variants. Furthermore, we demonstrate that outlier expression values can be due to rare variant effects, and we approximate the number of such effects harboured in an individual by effect size. Our results demonstrate that integration of genomic and RNA sequencing analyses allows for the joint assessment of genome sequence and genome function

Genetic and Epigenetic Regulation of Human lincRNA Gene Expression

Large intergenic noncoding RNAs (lincRNAs) are still poorly functionally characterized. We analyzed the genetic and epigenetic regulation of human lincRNA expression in the GenCord collection by using three cell types from 195 unrelated European individuals. We detected a considerable number of cis expression quantitative trait loci (cis-eQTLs) and demonstrated that the genetic regulation of lincRNA expression is independent of the regulation of neighboring protein-coding genes. lincRNAs have relatively more cis-eQTLs than do equally expressed protein-coding genes with the same exon number. lincRNA cis-eQTLs are located closer to transcription start sites (TSSs) and their effect sizes are higher than cis-eQTLs found for protein-coding genes, suggesting that lincRNA expression levels are less constrained than that of protein-coding genes. Additionally, lincRNA cis-eQTLs can influence the expression level of nearby protein-coding genes and thus could be considered as QTLs for enhancer activity. Enrichment of expressed lincRNA promoters in enhancer marks provides an additional argument for the involvement of lincRNAs in the regulation of transcription in cis. By investigating the epigenetic regulation of lincRNAs, we observed both positive and negative correlations between DNA methylation and gene expression (expression quantitative trait methylation [eQTMs]), as expected, and found that the landscapes of passive and active roles of DNA methylation in gene regulation are similar to protein-coding genes. However, lincRNA eQTMs are located closer to TSSs than are protein-coding gene eQTMs. These similarities and differences in genetic and epigenetic regulation between lincRNAs and protein-coding genes contribute to the elucidation of potential functions of lincRNAs

Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies

Summary: Genevar (GENe Expression VARiation) is a database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression. Genevar allows researchers to investigate expression quantitative trait loci (eQTL) associations within a gene locus of interest in real time. The database and application can be installed on a standard computer in database mode and, in addition, on a server to share discoveries among affiliations or the broader community over the Internet via web services protocols. Availability: http://www.sanger.ac.uk/resources/software/genevar Contact: [email protected]

Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci

© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. Genetic studies have revealed that autoimmune susceptibility variants are over-represented in memory CD4+ T cell regulatory elements1–3. Understanding how genetic variation affects gene expression in different T cell physiological states is essential for deciphering genetic mechanisms of autoimmunity4,5. Here, we characterized the dynamics of genetic regulatory effects at eight time points during memory CD4+ T cell activation with high-depth RNA-seq in healthy individuals. We discovered widespread, dynamic allele-specific expression across the genome, where the balance of alleles changes over time. These genes were enriched fourfold within autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes. HLA-DQB1 alleles had one of three distinct transcriptional regulatory programs. Using CRISPR–Cas9 genomic editing we demonstrated that a promoter variant is causal for T cell–specific control of HLA-DQB1 expression. Our study shows that genetic variation in cis-regulatory elements affects gene expression in a manner dependent on lymphocyte activation status, contributing to the interindividual complexity of immune responses

Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies

Summary: Genevar (GENe Expression VARiation) is a database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression. Genevar allows researchers to investigate expression quantitative trait loci (eQTL) associations within a gene locus of interest in real time. The database and application can be installed on a standard computer in database mode and, in addition, on a server to share discoveries among affiliations or the broader community over the Internet via web services protocols

Early progression to active tuberculosis is a highly heritable trait driven by 3q23 in Peruvians

Abstract: Of the 1.8 billion people worldwide infected with Mycobacterium tuberculosis, 5–15% will develop active tuberculosis (TB). Approximately half will progress to active TB within the first 18 months after infection, presumably because they fail to mount an effective initial immune response. Here, in a genome-wide genetic study of early TB progression, we genotype 4002 active TB cases and their household contacts in Peru. We quantify genetic heritability (hg2) of early TB progression to be 21.2% (standard error 0.08). This suggests TB progression has a strong genetic basis, and is comparable to traits with well-established genetic bases. We identify a novel association between early TB progression and variants located in a putative enhancer region on chromosome 3q23 (rs73226617, OR = 1.18; P = 3.93 × 10−8). With in silico and in vitro analyses we identify rs73226617 or rs148722713 as the likely functional variant and ATP1B3 as a potential causal target gene with monocyte specific function

Cis and Trans Effects of Human Genomic Variants on Gene Expression

This work was funded by the Louis-Jeantet Foundation (http://www.jeantet.ch/), the European Research Council (Grant ID: 260927 http://erc.europa.eu/), the Swiss National Foundation (Grant ID: 130342 http://www.snf.ch), NCCR Frontiers In Genetics (http://www.frontiers-in-genetics.org), the UK Medical Research Council (http://www.mrc.ac.uk) and the Wellcome Trust (Grant ID: 092731).