@article {894, title = {Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium.}, journal = {Genome Med}, volume = {13}, year = {2021}, month = {2021 04 21}, pages = {66}, abstract = {

BACKGROUND: The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression.

METHODS: We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants.

RESULTS: We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections.

CONCLUSIONS: These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.

}, keywords = {Adult, Aged, Aged, 80 and over, Angiotensin-Converting Enzyme 2, Asthma, Bronchi, Cardiovascular Diseases, COVID-19, Gene Expression, Genetic Variation, Humans, Middle Aged, Obesity, Pulmonary Disease, Chronic Obstructive, Quantitative Trait Loci, Respiratory Mucosa, Risk Factors, SARS-CoV-2, Smoking}, issn = {1756-994X}, doi = {10.1186/s13073-021-00866-2}, author = {Kasela, Silva and Ortega, Victor E and Martorella, Molly and Garudadri, Suresh and Nguyen, Jenna and Ampleford, Elizabeth and Pasanen, Anu and Nerella, Srilaxmi and Buschur, Kristina L and Barjaktarevic, Igor Z and Barr, R Graham and Bleecker, Eugene R and Bowler, Russell P and Comellas, Alejandro P and Cooper, Christopher B and Couper, David J and Criner, Gerard J and Curtis, Jeffrey L and Han, MeiLan K and Hansel, Nadia N and Hoffman, Eric A and Kaner, Robert J and Krishnan, Jerry A and Martinez, Fernando J and McDonald, Merry-Lynn N and Meyers, Deborah A and Paine, Robert and Peters, Stephen P and Castro, Mario and Denlinger, Loren C and Erzurum, Serpil C and Fahy, John V and Israel, Elliot and Jarjour, Nizar N and Levy, Bruce D and Li, Xingnan and Moore, Wendy C and Wenzel, Sally E and Zein, Joe and Langelier, Charles and Woodruff, Prescott G and Lappalainen, Tuuli and Christenson, Stephanie A} } @article {831, title = {Identification of Required Host Factors for SARS-CoV-2 Infection in Human Cells.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 01 07}, pages = {92-105.e16}, abstract = {

To better understand host-virus genetic dependencies and find potential therapeutic targets for COVID-19, we performed a genome-scale CRISPR loss-of-function screen to identify host factors required for SARS-CoV-2 viral infection of human alveolar epithelial cells. Top-ranked genes cluster into distinct pathways, including the vacuolar ATPase proton pump, Retromer, and Commander complexes. We validate these gene targets using several orthogonal methods such as CRISPR knockout, RNA interference knockdown, and small-molecule inhibitors. Using single-cell RNA-sequencing, we identify shared transcriptional changes in cholesterol biosynthesis upon loss of top-ranked genes. In addition, given the key role of the ACE2 receptor in the early stages of viral entry, we show that loss of RAB7A reduces viral entry by sequestering the ACE2 receptor inside cells. Overall, this work provides a genome-scale, quantitative resource of the impact of the loss of each host gene on fitness/response to viral infection.

}, keywords = {A549 Cells, Alveolar Epithelial Cells, Angiotensin-Converting Enzyme 2, Biosynthetic Pathways, Cholesterol, Clustered Regularly Interspaced Short Palindromic Repeats, COVID-19, Endosomes, Gene Expression Profiling, Gene Knockdown Techniques, Gene Knockout Techniques, Genome-Wide Association Study, Host-Pathogen Interactions, Humans, rab GTP-Binding Proteins, RNA Interference, SARS-CoV-2, Single-Cell Analysis, Viral Load}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.10.030}, author = {Daniloski, Zharko and Jordan, Tristan X and Wessels, Hans-Hermann and Hoagland, Daisy A and Kasela, Silva and Legut, Mateusz and Maniatis, Silas and Mimitou, Eleni P and Lu, Lu and Geller, Evan and Danziger, Oded and Rosenberg, Brad R and Phatnani, Hemali and Smibert, Peter and Lappalainen, Tuuli and tenOever, Benjamin R and Sanjana, Neville E} } @article {842, title = {Cell type-specific genetic regulation of gene expression across human tissues.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

}, keywords = {Cells, Gene Expression Regulation, Humans, Organ Specificity, Quantitative Trait Loci, RNA, Long Noncoding, Transcriptome}, issn = {1095-9203}, doi = {10.1126/science.aaz8528}, author = {Kim-Hellmuth, Sarah and Aguet, Francois and Oliva, Meritxell and Mu{\~n}oz-Aguirre, Manuel and Kasela, Silva and Wucher, Valentin and Castel, Stephane E and Hamel, Andrew R and Vi{\~n}uela, Ana and Roberts, Amy L and Mangul, Serghei and Wen, Xiaoquan and Wang, Gao and Barbeira, Alvaro N and Garrido-Mart{\'\i}n, Diego and Nadel, Brian B and Zou, Yuxin and Bonazzola, Rodrigo and Quan, Jie and Brown, Andrew and Martinez-Perez, Angel and Soria, Jos{\'e} Manuel and Getz, Gad and Dermitzakis, Emmanouil T and Small, Kerrin S and Stephens, Matthew and Xi, Hualin S and Im, Hae Kyung and Guig{\'o}, Roderic and Segr{\`e}, Ayellet V and Stranger, Barbara E and Ardlie, Kristin G and Lappalainen, Tuuli} } @article {837, 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, Francois 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 Guig{\'o}, Roderic and Stranger, Barbara E} } @article {845, 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, Francois 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 {871, title = {A vast resource of allelic expression data spanning human tissues.}, journal = {Genome Biol}, volume = {21}, year = {2020}, month = {2020 09 11}, pages = {234}, abstract = {

Allele expression (AE) analysis robustly measures cis-regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 samples spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis-regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.

}, issn = {1474-760X}, doi = {10.1186/s13059-020-02122-z}, author = {Castel, Stephane E and Aguet, Francois and Mohammadi, Pejman and Ardlie, Kristin G and Lappalainen, Tuuli} } @article {706, title = {Genetic regulatory variation in populations informs transcriptome analysis in rare disease.}, journal = {Science}, volume = {366}, year = {2019}, month = {2019 10 18}, pages = {351-356}, abstract = {

Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.

}, issn = {1095-9203}, doi = {10.1126/science.aay0256}, author = {Mohammadi, Pejman and Castel, Stephane E and Cummings, Beryl B and Einson, Jonah and Sousa, Christina and Hoffman, Paul and Donkervoort, Sandra and Jiang, Zhuoxun and Mohassel, Payam and Foley, A Reghan and Wheeler, Heather E and Im, Hae Kyung and B{\"o}nnemann, Carsten G and MacArthur, Daniel G and Lappalainen, Tuuli} } @article {524, title = {Identifying Genes Whose Mutant Transcripts Cause Dominant Disease Traits by Potential Gain-of-Function Alleles.}, journal = {Am J Hum Genet}, volume = {103}, year = {2018}, month = {2018 08 02}, pages = {171-187}, abstract = {

Premature termination codon (PTC)-bearing transcripts are often degraded by nonsense-mediated decay (NMD) resulting in loss-of-function (LoF) alleles. However, not all PTCs result in LoF mutations, i.e., some such transcripts escape NMD and are translated to truncated peptide products that result in disease due to gain-of-function (GoF) effects. Since the location of the PTC is a major factor determining transcript fate, we hypothesized that depletion of protein-truncating variants (PTVs) within the gene region predicted to escape NMD in control databases could provide a rank for genic susceptibility for disease through GoF versus LoF. We developed an NMD escape intolerance score to rank genes based on the depletion of PTVs that would render them able to escape NMD using the Atherosclerosis Risk in Communities Study (ARIC) and the Exome Aggregation Consortium (ExAC) control databases, which was further used to screen the Baylor-Center for Mendelian Genomics disease database. This analysis revealed 1,996 genes significantly depleted for PTVs that are predicted to escape from NMD, i.e., PTVesc; further studies provided evidence that revealed a subset as candidate genes underlying Mendelian phenotypes. Importantly, these genes have characteristically low pLI scores, which can cause them to be overlooked as candidates for dominant diseases. Collectively, we demonstrate that this NMD escape intolerance score is an effective and efficient tool for gene discovery in Mendelian diseases due to production of truncated or altered proteins. More importantly, we provide a complementary analytical tool to aid identification of genes associated with dominant traits through a mechanism distinct from LoF.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2018.06.009}, author = {Coban-Akdemir, Zeynep and White, Janson J and Song, Xiaofei and Jhangiani, Shalini N and Fatih, Jawid M and Gambin, Tomasz and Bayram, Yavuz and Chinn, Ivan K and Karaca, Ender and Punetha, Jaya and Poli, Cecilia and Boerwinkle, Eric and Shaw, Chad A and Orange, Jordan S and Gibbs, Richard A and Lappalainen, Tuuli and Lupski, James R and Carvalho, Claudia M B} }