Search

Your search keyword '"Kasper D. Hansen"' showing total 7 results
Start Over Author "Kasper D. Hansen" Publisher elsevier bv
7 results on '"Kasper D. Hansen"'

1. A metabolomics approach identified toxins associated with uremic symptoms in advanced chronic kidney disease

Author

Andrew S. Levey, Eliseo Guallar, Jiun-Ruey Hu, Kasper D. Hansen, Leslie Myint, Lesley A. Inker, Tariq Shafi, Eugene P. Rhee, Josef Coresh, and Morgan E. Grams

Subjects
medicine.medical_specialty, Weakness, Nausea, business.industry, Renal function, Bayes Theorem, Disease, medicine.disease, Article, Uremia, Cross-Sectional Studies, Nephrology, Internal medicine, medicine, Vomiting, Humans, Metabolomics, Prospective Studies, Renal Insufficiency, Chronic, medicine.symptom, Prospective cohort study, business, Glomerular Filtration Rate, Kidney disease
Abstract

Uremic symptoms are common in patients with advanced chronic kidney disease, but the toxins that cause these symptoms are unknown. To evaluate this, we performed a cross-sectional study of the 12 month post-randomization follow-up visit of Modification of Diet in Renal Disease (MDRD) participants reporting uremic symptoms who also had available stored serum. We quantified 1,163 metabolites by liquid chromatography-tandem mass spectrometry. For each uremic symptom, we calculated a score as the severity multiplied by the number of days the symptom was experienced. We analyzed the associations of the individual symptom scores with metabolites using linear models with empirical Bayesian inference, adjusted for multiple comparisons. Among 695 participants, the mean measured glomerular filtration rate (mGFR) was 28 mL/min/1.73 m(2). Uremic symptoms were more common in the subgroup of 214 patients with an mGFR under 20 mL/min/1.73 m(2) (mGFR under 20 subgroup) than in the full group. For all metabolites with significant associations, the direction of the association was concordant in the full group and the subgroup. For gastrointestinal symptoms (bad taste, loss of appetite, nausea, and vomiting), eleven metabolites were associated with symptoms. For neurologic symptoms (decreased alertness, falling asleep during the day, forgetfulness, lack of pep and energy, tiring easily/weakness), seven metabolites were associated with symptoms. Associations were consistent across sensitivity analyses. Thus, our proof of principle study demonstrates the potential for metabolomics to understand metabolic pathways associated with uremic symptoms. Larger, prospective studies with external validation are needed.

Published
2022

2. Promoter CpG Density Predicts Downstream Gene Loss-of-Function Intolerance

Author

Hans T. Bjornsson, Kasper D. Hansen, and Leandros Boukas

Subjects
Computational biology, Joint analysis, Biology, Article, 03 medical and health sciences, 0302 clinical medicine, Loss of Function Mutation, Genetics, Humans, Epigenetics, Promoter Regions, Genetic, Exome, Gene, Genetics (clinical), Loss function, 030304 developmental biology, 0303 health sciences, Genome, Human, Promoter, Exons, DNA Methylation, CpG site, CpG Islands, Human genome, RNA Polymerase II, Transcription Initiation Site, 030217 neurology & neurosurgery, GC-content
Abstract

The aggregation and joint analysis of large numbers of exome sequences has recently made it possible to de-rive estimates of intolerance to loss-of-function (LoF) variation for human genes. Here, we demonstrate strong and widespread coupling between genic LoF-intolerance and promoter CpG density across the human genome. Genes downstream of the most CpG-rich pro-moters (top 10% CpG density) have a 67.2% probability of being highly LoF-intolerant, using the LOEUF metric from gnomAD. This is in contrast to 7.4% of genes downstream of the most CpG-poor (bottom 10% CpG density) promoters. Combining promoter CpG density with exonic and promoter conservation explains 33.4% of the variation in LOEUF, and the contribution of CpG density exceeds the individual contributions of exonic and promoter conservation. We leverage this to train a simple and easily interpretable predictive model that out-performs other existing predictors and allows us to classify 1,760 genes – which currently lack reliable LOEUF estimates – as highly LoF-intolerant or not. These predictions have the potential to aid in the interpretation of novel patient variants. Moreover, our results reveal that high CpG density is not merely a generic feature of human promoters, but is preferentially encountered at the promoters of the most selectively constrained genes, calling into question the prevailing view that CpG islands are not subject to selection.

Published
2020

3. Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

Author

Michael C. Schatz, Anthony A. Philippakis, Enis Afgan, Eric Banks, Vincent J. Carey, Robert J. Carroll, Alessandro Culotti, Kyle Ellrott, Jeremy Goecks, Robert L. Grossman, Ira M. Hall, Kasper D. Hansen, Jonathan Lawson, Jeffrey T. Leek, Anne O’Donnell Luria, Stephen Mosher, Martin Morgan, Anton Nekrutenko, Brian D. O’Connor, Kevin Osborn, Benedict Paten, Candace Patterson, Frederick J. Tan, Casey Overby Taylor, Jennifer Vessio, Levi Waldron, Ting Wang, Kristin Wuichet, Alexander Baumann, Andrew Rula, Anton Kovalsy, Clare Bernard, Derek Caetano-Anollés, Geraldine A. Van der Auwera, Justin Canas, Kaan Yuksel, Kate Herman, M. Morgan Taylor, Marianie Simeon, Michael Baumann, Qi Wang, Robert Title, Ruchi Munshi, Sushma Chaluvadi, Valerie Reeves, William Disman, Salin Thomas, Allie Hajian, Elizabeth Kiernan, Namrata Gupta, Trish Vosburg, Ludwig Geistlinger, Marcel Ramos, Sehyun Oh, Dave Rogers, Frances McDade, Mim Hastie, Nitesh Turaga, Alexander Ostrovsky, Alexandru Mahmoud, Dannon Baker, Dave Clements, Katherine E.L. Cox, Keith Suderman, Nataliya Kucher, Sergey Golitsynskiy, Samantha Zarate, Sarah J. Wheelan, Kai Kammers, Ana Stevens, Carolyn Hutter, Christopher Wellington, Elena M. Ghanaim, Ken L. Wiley, Shurjo K. Sen, Valentina Di Francesco, Deni s Yuen, Brian Walsh, Luke Sargent, Vahid Jalili, John Chilton, Lori Shepherd, B.J. Stubbs, Ash O’Farrell, Benton A. Vizzier, Charles Overbeck, Charles Reid, David Charles Steinberg, Elizabeth A. Sheets, Julian Lucas, Lon Blauvelt, Louise Cabansay, Noah Warren, Brian Hannafious, Tim Harris, Radhika Reddy, Eric Torstenson, M. Katie Banasiewicz, Haley J. Abel, and Jason Walker

Subjects
Genetics, Biochemistry, Genetics and Molecular Biology (miscellaneous)
Abstract

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types.

Published
2022

4. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Author

Olivia M. de Goede, Daniel C. Nachun, Nicole M. Ferraro, Michael J. Gloudemans, Abhiram S. Rao, Craig Smail, Tiffany Y. Eulalio, François Aguet, Bernard Ng, Jishu Xu, Alvaro N. Barbeira, Stephane E. Castel, Sarah Kim-Hellmuth, YoSon Park, Alexandra J. Scott, Benjamin J. Strober, Christopher D. Brown, Xiaoquan Wen, Ira M. Hall, Alexis Battle, Tuuli Lappalainen, Hae Kyung Im, Kristin G. Ardlie, Sara Mostafavi, Thomas Quertermous, Karla Kirkegaard, Stephen B. Montgomery, Shankara Anand, Stacey Gabriel, Gad A. Getz, Aaron Graubert, Kane Hadley, Robert E. Handsaker, Katherine H. Huang, Xiao Li, Daniel G. MacArthur, Samuel R. Meier, Jared L. Nedzel, Duyen T. Nguyen, Ayellet V. Segrè, Ellen Todres, Brunilda Balliu, Rodrigo Bonazzola, Andrew Brown, Donald F. Conrad, Daniel J. Cotter, Nancy Cox, Sayantan Das, Emmanouil T. Dermitzakis, Jonah Einson, Barbara E. Engelhardt, Eleazar Eskin, Elise D. Flynn, Laure Fresard, Eric R. Gamazon, Diego Garrido-Martín, Nicole R. Gay, Roderic Guigó, Andrew R. Hamel, Yuan He, Paul J. Hoffman, Farhad Hormozdiari, Lei Hou, Brian Jo, Silva Kasela, Seva Kashin, Manolis Kellis, Alan Kwong, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Manuel Muñoz-Aguirre, Andrew B. Nobel, Meritxell Oliva, Yongjin Park, Princy Parsana, Ferran Reverter, John M. Rouhana, Chiara Sabatti, Ashis Saha, Matthew Stephens, Barbara E. Stranger, Nicole A. Teran, Ana Viñuela, Gao Wang, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G. Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Debra Bradbury, Tanya Krubit, Jeffrey A. McLean, Liqun Qi, Karna Robinson, Nancy V. Roche, Anna M. Smith, David E. Tabor, Anita Undale, Jason Bridge, Lori E. Brigham, Barbara A. Foster, Bryan M. Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F. Leinweber, Alisa McDonald, Michael T. Moser, Kevin Myer, Kimberley D. Ramsey, Brian Roe, Saboor Shad, Jeffrey A. Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D. Jewell, Daniel C. Rohrer, Dana R. Valley, David A. Davis, Deborah C. Mash, Mary E. Barcus, Philip A. Branton, Leslie Sobin, Laura K. Barker, Heather M. Gardiner, Maghboeba Mosavel, Laura A. Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W. James Kent, Christopher M. Lee, Conner C. Powell, Kate R. Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J. Trevanion, Daniel R. Zerbino, Nathan S. Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A. Doherty, Andrew P. Feinberg, Kasper D. Hansen, Peter F. Hickey, Farzana Jasmine, Lihua Jiang, Rajinder Kaul, Muhammad G. Kibriya, Jin Billy Li, Qin Li, Shin Lin, Sandra E. Linder, Brandon L. Pierce, Lindsay F. Rizzardi, Andrew D. Skol, Kevin S. Smith, Michael Snyder, John Stamatoyannopoulos, Hua Tang, Meng Wang, Latarsha J. Carithers, Ping Guan, Susan E. Koester, A. Roger Little, Helen M. Moore, Concepcion R. Nierras, Abhi K. Rao, Jimmie B. Vaught, and Simona Volpi

Subjects
Multifactorial Inheritance, Population, Quantitative Trait Loci, Coronary Artery Disease, Disease, Computational biology, Quantitative trait locus, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Humans, education, Gene, 030304 developmental biology, 0303 health sciences, education.field_of_study, Gene Expression Profiling, Genetic Variation, Inflammatory Bowel Diseases, Long non-coding RNA, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Organ Specificity, Expression quantitative trait loci, Trait, RNA, Long Noncoding, 030217 neurology & neurosurgery
Abstract

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

Published
2021

5. A Quantitative Proteome Map of the Human Body

Author

Lihua Jiang, Meng Wang, Shin Lin, Ruiqi Jian, Xiao Li, Joanne Chan, Guanlan Dong, Huaying Fang, Aaron E. Robinson, Michael P. Snyder, François Aguet, Shankara Anand, Kristin G. Ardlie, Stacey Gabriel, Gad Getz, Aaron Graubert, Kane Hadley, Robert E. Handsaker, Katherine H. Huang, Seva Kashin, Daniel G. MacArthur, Samuel R. Meier, Jared L. Nedzel, Duyen Y. Nguyen, Ayellet V. Segrè, Ellen Todres, Brunilda Balliu, Alvaro N. Barbeira, Alexis Battle, Rodrigo Bonazzola, Andrew Brown, Christopher D. Brown, Stephane E. Castel, Don Conrad, Daniel J. Cotter, Nancy Cox, Sayantan Das, Olivia M. de Goede, Emmanouil T. Dermitzakis, Barbara E. Engelhardt, Eleazar Eskin, Tiffany Y. Eulalio, Nicole M. Ferraro, Elise Flynn, Laure Fresard, Eric R. Gamazon, Diego Garrido-Martín, Nicole R. Gay, Roderic Guigó, Andrew R. Hamel, Yuan He, Paul J. Hoffman, Farhad Hormozdiari, Lei Hou, Hae Kyung Im, Brian Jo, Silva Kasela, Manolis Kellis, Sarah Kim-Hellmuth, Alan Kwong, Tuuli Lappalainen, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Stephen B. Montgomery, Manuel Muñoz-Aguirre, Daniel C. Nachun, Andrew B. Nobel, Meritxell Oliva, YoSon Park, Yongjin Park, Princy Parsana, Ferran Reverter, John M. Rouhana, Chiara Sabatti, Ashis Saha, Andrew D. Skol, Matthew Stephens, Barbara E. Stranger, Benjamin J. Strober, Nicole A. Teran, Ana Viñuela, Gao Wang, Xiaoquan Wen, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G. Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Mary E. Barcus, Debra Bradbury, Tanya Krubit, Jeffrey A. McLean, Liqun Qi, Karna Robinson, Nancy V. Roche, Anna M. Smith, Leslie Sobin, David E. Tabor, Anita Undale, Jason Bridge, Lori E. Brigham, Barbara A. Foster, Bryan M. Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F. Leinweber, Alisa McDonald, Michael T. Moser, Kevin Myer, Kimberley D. Ramsey, Brian Roe, Saboor Shad, Jeffrey A. Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D. Jewell, Daniel C. Rohrer, Dana R. Valley, David A. Davis, Deborah C. Mash, Philip A. Branton, Laura K. Barker, Heather M. Gardiner, Maghboeba Mosavel, Laura A. Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W. James Kent, Christopher M. Lee, Conner C. Powell, Kate R. Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J. Trevanion, Daniel R. Zerbino, Nathan S. Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A. Doherty, Andrew P. Feinberg, Kasper D. Hansen, Peter F. Hickey, Farzana Jasmine, Rajinder Kaul, Muhammad G. Kibriya, Jin Billy Li, Qin Li, Sandra E. Linder, Brandon L. Pierce, Lindsay F. Rizzardi, Kevin S. Smith, John Stamatoyannopoulos, Hua Tang, Latarsha J. Carithers, Ping Guan, Susan E. Koester, A. Roger Little, Helen M. Moore, Concepcion R. Nierras, Abhi K. Rao, Jimmie B. Vaught, and Simona Volpi

Subjects
Proteomics, Cell signaling, Proteome, Quantitative proteomics, Gene Expression, Disease, Computational biology, Biology, ENCODE, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Humans, Secretion, RNA, Messenger, Gene, 030304 developmental biology, 0303 health sciences, Gene Expression Profiling, RNA, Metabolism, Phenotype, Secretory protein, 030217 neurology & neurosurgery
Abstract

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.SummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

Published
2020

6. SA131A MASSIVELY PARALLEL REPORTER ASSAY FOR VARIANTS ASSOCIATED WITH SCHIZOPHRENIA AND ALZHEIMER'S DISEASE

Author

Kasper D. Hansen, Dimitrios Avramopoulos, Loyal A. Goff, Leslie Myint, Ruihua Wang, and Leandros Boukas

Subjects
Pharmacology, Reporter gene, business.industry, Schizophrenia (object-oriented programming), Computational biology, Disease, Psychiatry and Mental health, Neurology, Medicine, Pharmacology (medical), Neurology (clinical), business, Massively parallel, Biological Psychiatry
Published
2019

7. 725 Socio-economic status and breast cancer in Denmark

Author

Marianne Ewertz, Jørgen Holm Petersen, Kasper D. Hansen, Elsebeth Lynge, and Hella Danø

Subjects
Oncology, Cancer Research, medicine.medical_specialty, Breast cancer, business.industry, Internal medicine, Medicine, business, Socioeconomics, medicine.disease, Socioeconomic status
Published
2003

Catalog

Books, media, physical & digital resources
See catalog results