<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kasela, Silva</style></author><author><style face="normal" font="default" size="100%">Ortega, Victor E</style></author><author><style face="normal" font="default" size="100%">Martorella, Molly</style></author><author><style face="normal" font="default" size="100%">Garudadri, Suresh</style></author><author><style face="normal" font="default" size="100%">Nguyen, Jenna</style></author><author><style face="normal" font="default" size="100%">Ampleford, Elizabeth</style></author><author><style face="normal" font="default" size="100%">Pasanen, Anu</style></author><author><style face="normal" font="default" size="100%">Nerella, Srilaxmi</style></author><author><style face="normal" font="default" size="100%">Buschur, Kristina L</style></author><author><style face="normal" font="default" size="100%">Barjaktarevic, Igor Z</style></author><author><style face="normal" font="default" size="100%">Barr, R Graham</style></author><author><style face="normal" font="default" size="100%">Bleecker, Eugene R</style></author><author><style face="normal" font="default" size="100%">Bowler, Russell P</style></author><author><style face="normal" font="default" size="100%">Comellas, Alejandro P</style></author><author><style face="normal" font="default" size="100%">Cooper, Christopher B</style></author><author><style face="normal" font="default" size="100%">Couper, David J</style></author><author><style face="normal" font="default" size="100%">Criner, Gerard J</style></author><author><style face="normal" font="default" size="100%">Curtis, Jeffrey L</style></author><author><style face="normal" font="default" size="100%">Han, MeiLan K</style></author><author><style face="normal" font="default" size="100%">Hansel, Nadia N</style></author><author><style face="normal" font="default" size="100%">Hoffman, Eric A</style></author><author><style face="normal" font="default" size="100%">Kaner, Robert J</style></author><author><style face="normal" font="default" size="100%">Krishnan, Jerry A</style></author><author><style face="normal" font="default" size="100%">Martinez, Fernando J</style></author><author><style face="normal" font="default" size="100%">McDonald, Merry-Lynn N</style></author><author><style face="normal" font="default" size="100%">Meyers, Deborah A</style></author><author><style face="normal" font="default" size="100%">Paine, Robert</style></author><author><style face="normal" font="default" size="100%">Peters, Stephen P</style></author><author><style face="normal" font="default" size="100%">Castro, Mario</style></author><author><style face="normal" font="default" size="100%">Denlinger, Loren C</style></author><author><style face="normal" font="default" size="100%">Erzurum, Serpil C</style></author><author><style face="normal" font="default" size="100%">Fahy, John V</style></author><author><style face="normal" font="default" size="100%">Israel, Elliot</style></author><author><style face="normal" font="default" size="100%">Jarjour, Nizar N</style></author><author><style face="normal" font="default" size="100%">Levy, Bruce D</style></author><author><style face="normal" font="default" size="100%">Li, Xingnan</style></author><author><style face="normal" font="default" size="100%">Moore, Wendy C</style></author><author><style face="normal" font="default" size="100%">Wenzel, Sally E</style></author><author><style face="normal" font="default" size="100%">Zein, Joe</style></author><author><style face="normal" font="default" size="100%">Langelier, Charles</style></author><author><style face="normal" font="default" size="100%">Woodruff, Prescott G</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author><author><style face="normal" font="default" size="100%">Christenson, Stephanie A</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">NHLBI SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS)</style></author><author><style face="normal" font="default" size="100%">NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Angiotensin-Converting Enzyme 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Asthma</style></keyword><keyword><style  face="normal" font="default" size="100%">Bronchi</style></keyword><keyword><style  face="normal" font="default" size="100%">Cardiovascular Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Obesity</style></keyword><keyword><style  face="normal" font="default" size="100%">Pulmonary Disease, Chronic Obstructive</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Respiratory Mucosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Smoking</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 04 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">66</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33883027?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Cummings, Beryl B</style></author><author><style face="normal" font="default" size="100%">Einson, Jonah</style></author><author><style face="normal" font="default" size="100%">Sousa, Christina</style></author><author><style face="normal" font="default" size="100%">Hoffman, Paul</style></author><author><style face="normal" font="default" size="100%">Donkervoort, Sandra</style></author><author><style face="normal" font="default" size="100%">Jiang, Zhuoxun</style></author><author><style face="normal" font="default" size="100%">Mohassel, Payam</style></author><author><style face="normal" font="default" size="100%">Foley, A Reghan</style></author><author><style face="normal" font="default" size="100%">Wheeler, Heather E</style></author><author><style face="normal" font="default" size="100%">Im, Hae Kyung</style></author><author><style face="normal" font="default" size="100%">Bonnemann, Carsten G</style></author><author><style face="normal" font="default" size="100%">MacArthur, Daniel G</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic regulatory variation in populations informs transcriptome analysis in rare disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Science</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Science</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 10 18</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">366</style></volume><pages><style face="normal" font="default" size="100%">351-356</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6463</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31601707?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author><author><style face="normal" font="default" size="100%">Scott, Alexandra J</style></author><author><style face="normal" font="default" size="100%">Brandt, Margot</style></author><author><style face="normal" font="default" size="100%">Hall, Ira M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genomic Analysis in the Age of Human Genome Sequencing.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Mar 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">177</style></volume><pages><style face="normal" font="default" size="100%">70-84</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Affordable genome sequencing technologies promise to revolutionize the field of human genetics by enabling comprehensive studies that interrogate all classes of genome variation, genome-wide, across the entire allele frequency spectrum. Ongoing projects worldwide are sequencing many thousands-and soon millions-of human genomes as part of various gene mapping studies, biobanking efforts, and clinical programs. However, while genome sequencing data production has become routine, genome analysis and interpretation remain challenging endeavors with many limitations and caveats. Here, we review the current state of technologies for genetic variant discovery, genotyping, and functional interpretation and discuss the prospects for future advances. We focus on germline variants discovered by whole-genome sequencing, genome-wide functional genomic approaches for predicting and measuring variant functional effects, and implications for studies of common and rare human disease.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30901550?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kim-Hellmuth, Sarah</style></author><author><style face="normal" font="default" size="100%">Bechheim, Matthias</style></author><author><style face="normal" font="default" size="100%">Pütz, Benno</style></author><author><style face="normal" font="default" size="100%">Mohammadi, Pejman</style></author><author><style face="normal" font="default" size="100%">Nédélec, Yohann</style></author><author><style face="normal" font="default" size="100%">Giangreco, Nicholas</style></author><author><style face="normal" font="default" size="100%">Becker, Jessica</style></author><author><style face="normal" font="default" size="100%">Kaiser, Vera</style></author><author><style face="normal" font="default" size="100%">Fricker, Nadine</style></author><author><style face="normal" font="default" size="100%">Beier, Esther</style></author><author><style face="normal" font="default" size="100%">Boor, Peter</style></author><author><style face="normal" font="default" size="100%">Castel, Stephane E</style></author><author><style face="normal" font="default" size="100%">Nöthen, Markus M</style></author><author><style face="normal" font="default" size="100%">Barreiro, Luis B</style></author><author><style face="normal" font="default" size="100%">Pickrell, Joseph K</style></author><author><style face="normal" font="default" size="100%">Müller-Myhsok, Bertram</style></author><author><style face="normal" font="default" size="100%">Lappalainen, Tuuli</style></author><author><style face="normal" font="default" size="100%">Schumacher, Johannes</style></author><author><style face="normal" font="default" size="100%">Hornung, Veit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acetylmuramyl-Alanyl-Isoglutamine</style></keyword><keyword><style  face="normal" font="default" size="100%">Adjuvants, Immunologic</style></keyword><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Autoimmune Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Healthy Volunteers</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Indicators and Reagents</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipids</style></keyword><keyword><style  face="normal" font="default" size="100%">Lipopolysaccharides</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Monocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantitative Trait Loci</style></keyword><keyword><style  face="normal" font="default" size="100%">Regulatory Sequences, Nucleic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Double-Stranded</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Aug 16</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">266</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/28814792?dopt=Abstract</style></custom1></record></records></xml>