The impact of sex on gene expression across human tissues. - Oliva, Meritxell; Muñoz-Aguirre, Manuel; Kim-Hellmuth, Sarah; Wucher, Valentin; Gewirtz, Ariel D H; Cotter, Daniel J; Parsana, Princy; Kasela, Silva; Balliu, Brunilda; Viñuela, Ana; Castel, Stephane E; Mohammadi, Pejman; et al. (Science, 2020)
Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation.
Keywords: Chromosomes, Human, X; Disease; Epigenesis, Genetic; Female; Gene Expression; Gene Expression Regulation; Genetic Variation; Genome-Wide Association Study; Humans; Male; Organ Specificity; Promoter Regions, Genetic; Quantitative Trait Loci; Sex Characteristics; Sex Factors

Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. - Kim-Hellmuth, Sarah; Bechheim, Matthias; Pütz, Benno; Mohammadi, Pejman; Nédélec, Yohann; Giangreco, Nicholas; Becker, Jessica; Kaiser, Vera; Fricker, Nadine; Beier, Esther; Boor, Peter; Castel, Stephane E; et al. (Nat Commun, 2017)
The immune system plays a major role in human health and disease, and understanding genetic causes of interindividual variability of immune responses is vital. Here, we isolate monocytes from 134 genotyped individuals, stimulate these cells with three defined microbe-associated molecular patterns (LPS, MDP, and 5'-ppp-dsRNA), and profile the transcriptomes at three time points. Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions. We characterize the dynamics of genetic regulation on early and late immune response and observe an enrichment of reQTLs in distal cis-regulatory elements. In addition, reQTLs are enriched for recent positive selection with an evolutionary trend towards enhanced immune response. Finally, we uncover reQTL effects in multiple GWAS loci and show a stronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases. This demonstrates the importance of infectious stimuli in modifying genetic predisposition to disease.Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.
Keywords: Acetylmuramyl-Alanyl-Isoglutamine; Adjuvants, Immunologic; Adolescent; Adult; Autoimmune Diseases; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Genetic Predisposition to Disease; Healthy Volunteers; Humans; Indicators and Reagents; Lipids; Lipopolysaccharides; Male; Monocytes; Quantitative Trait Loci; Regulatory Sequences, Nucleic Acid; RNA, Double-Stranded; RNA, Messenger; Young Adult

Quantifying the regulatory effect size of -acting genetic variation using allelic fold change. - Mohammadi, Pejman; Castel, Stephane E; Brown, Andrew A; Lappalainen, Tuuli (Genome Res, 2017)
Mapping -acting expression quantitative trait loci (-eQTL) has become a popular approach for characterizing proximal genetic regulatory variants. In this paper, we describe and characterize log allelic fold change (aFC), the magnitude of expression change associated with a given genetic variant, as a biologically interpretable unit for quantifying the effect size of -eQTLs and a mathematically convenient approach for systematic modeling of -regulation. This measure is mathematically independent from expression level and allele frequency, additive, applicable to multiallelic variants, and generalizable to multiple independent variants. We provide efficient tools and guidelines for estimating aFC from both eQTL and allelic expression data sets and apply it to Genotype Tissue Expression (GTEx) data. We show that aFC estimates independently derived from eQTL and allelic expression data are highly consistent, and identify technical and biological correlates of eQTL effect size. We generalize aFC to analyze genes with two eQTLs in GTEx and show that in nearly all cases the two eQTLs act independently in regulating gene expression. In summary, aFC is a solid measure of -regulatory effect size that allows quantitative interpretation of cellular regulatory events from population data, and it is a valuable approach for investigating novel aspects of eQTL data sets.
Keywords: Alleles; Databases, Genetic; Gene Expression; Gene Expression Profiling; Gene Regulatory Networks; Genetic Variation; Humans; Models, Theoretical; Quantitative Trait Loci