@article {102, title = {The impact of sex on gene expression across human tissues.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

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}, issn = {1095-9203}, doi = {10.1126/science.aba3066}, author = {Oliva, Meritxell and Mu{\~n}oz-Aguirre, Manuel and Kim-Hellmuth, Sarah and Wucher, Valentin and Gewirtz, Ariel D H and Cotter, Daniel J and Parsana, Princy and Kasela, Silva and Balliu, Brunilda and Vi{\~n}uela, Ana and Castel, Stephane E and Mohammadi, Pejman and Aguet, Fran{\c c}ois and Zou, Yuxin and Khramtsova, Ekaterina A and Skol, Andrew D and Garrido-Mart{\'\i}n, Diego and Reverter, Ferran and Brown, Andrew and Evans, Patrick and Gamazon, Eric R and Payne, Anthony and Bonazzola, Rodrigo and Barbeira, Alvaro N and Hamel, Andrew R and Martinez-Perez, Angel and Soria, Jos{\'e} Manuel and Pierce, Brandon L and Stephens, Matthew and Eskin, Eleazar and Dermitzakis, Emmanouil T and Segr{\`e}, Ayellet V and Im, Hae Kyung and Engelhardt, Barbara E and Ardlie, Kristin G and Montgomery, Stephen B and Battle, Alexis J and Lappalainen, Tuuli and Guigo, Roderic and Stranger, Barbara E} } @article {101, title = {Transcriptomic signatures across human tissues identify functional rare genetic variation.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

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}, issn = {1095-9203}, doi = {10.1126/science.aaz5900}, author = {Ferraro, Nicole M and Strober, Benjamin J and Einson, Jonah and Abell, Nathan S and Aguet, Fran{\c c}ois and Barbeira, Alvaro N and Brandt, Margot and Bucan, Maja and Castel, Stephane E and Davis, Joe R and Greenwald, Emily and Hess, Gaelen T and Hilliard, Austin T and Kember, Rachel L and Kotis, Bence and Park, YoSon and Peloso, Gina and Ramdas, Shweta and Scott, Alexandra J and Smail, Craig and Tsang, Emily K and Zekavat, Seyedeh M and Ziosi, Marcello and Ardlie, Kristin G and Assimes, Themistocles L and Bassik, Michael C and Brown, Christopher D and Correa, Adolfo and Hall, Ira and Im, Hae Kyung and Li, Xin and Natarajan, Pradeep and Lappalainen, Tuuli and Mohammadi, Pejman and Montgomery, Stephen B and Battle, Alexis} } @article {25, title = {Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.}, journal = {Nat Commun}, volume = {8}, year = {2017}, month = {2017 Aug 16}, pages = {266}, abstract = {

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{\textquoteright}-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}, issn = {2041-1723}, doi = {10.1038/s41467-017-00366-1}, author = {Kim-Hellmuth, Sarah and Bechheim, Matthias and P{\"u}tz, Benno and Mohammadi, Pejman and N{\'e}d{\'e}lec, Yohann and Giangreco, Nicholas and Becker, Jessica and Kaiser, Vera and Fricker, Nadine and Beier, Esther and Boor, Peter and Castel, Stephane E and N{\"o}then, Markus M and Barreiro, Luis B and Pickrell, Joseph K and M{\"u}ller-Myhsok, Bertram and Lappalainen, Tuuli and Schumacher, Johannes and Hornung, Veit} } @article {30, title = {Quantifying the regulatory effect size of -acting genetic variation using allelic fold change.}, journal = {Genome Res}, volume = {27}, year = {2017}, month = {2017 Nov}, pages = {1872-1884}, abstract = {

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}, issn = {1549-5469}, doi = {10.1101/gr.216747.116}, author = {Mohammadi, Pejman and Castel, Stephane E and Brown, Andrew A and Lappalainen, Tuuli} }