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

Transcriptomic signatures across human tissues identify functional rare genetic variation. - Ferraro, Nicole M; Strober, Benjamin J; Einson, Jonah; Abell, Nathan S; Aguet, François; Barbeira, Alvaro N; Brandt, Margot; Bucan, Maja; Castel, Stephane E; Davis, Joe R; Greenwald, Emily; Hess, Gaelen T; et al. (Science, 2020)
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.
Keywords: Genetic Variation; Genome, Human; Humans; Multifactorial Inheritance; Organ Specificity; Transcriptome

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