Kristen Dang
Sage Bionetworks
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Kristen Dang.
Nature Neuroscience | 2016
Menachem Fromer; Panos Roussos; Solveig K. Sieberts; Jessica S. Johnson; David H. Kavanagh; Thanneer M. Perumal; Douglas M. Ruderfer; Edwin C. Oh; Aaron Topol; Hardik Shah; Lambertus Klei; Robin Kramer; Dalila Pinto; Zeynep H. Gümüş; A. Ercument Cicek; Kristen Dang; Andrew Browne; Cong Lu; Lu Xie; Ben Readhead; Eli A. Stahl; Jianqiu Xiao; Mahsa Parvizi; Tymor Hamamsy; John F. Fullard; Ying-Chih Wang; Milind Mahajan; Jonathan Derry; Joel T. Dudley; Scott E. Hemby
Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.
Nature Genetics | 2014
Paul C. Boutros; Adam D. Ewing; Kyle Ellrott; Thea Norman; Kristen Dang; Yin Hu; Michael R. Kellen; Christine Suver; J Christopher Bare; Lincoln Stein; Paul T. Spellman; Gustavo Stolovitzky; Stephen H. Friend; Adam A. Margolin; Joshua M. Stuart
Global optimization of somatic variant identification in cancer genomes with a global community challenge
Scientific Data | 2016
Mariet Allen; Minerva M. Carrasquillo; Cory C. Funk; Benjamin D. Heavner; Fanggeng Zou; Curtis S. Younkin; Jeremy D. Burgess; High Seng Chai; Julia E. Crook; James A. Eddy; Hongdong Li; Ben Logsdon; Mette A. Peters; Kristen Dang; Xue Wang; Daniel J. Serie; Chen Wang; Thuy Nguyen; Sarah Lincoln; Kimberly Malphrus; Gina Bisceglio; Ma Li; Todd E. Golde; Lara M. Mangravite; Yan W. Asmann; Nathan D. Price; Ronald C. Petersen; Neill R. Graff-Radford; Dennis W. Dickson; Steven G. Younkin
Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimers disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases. Herein, we describe our collective datasets comprised of GWAS data from 2,099 subjects; microarray gene expression data from 773 brain samples, 186 of which also have RNAseq; and an independent cohort of 556 brain samples with RNAseq. We expect that these datasets, which are available to all qualified researchers, will enable investigators to explore and identify transcriptional mechanisms contributing to neurodegenerative diseases.
American Journal of Human Genetics | 2017
Mads E. Hauberg; Wen Zhang; Claudia Giambartolomei; Oscar Franzén; David L. Morris; Timothy J. Vyse; Arno Ruusalepp; Menachem Fromer; Solveig K. Sieberts; Jessica S. Johnson; Douglas M. Ruderfer; Hardik Shah; Lambertus Klei; Kristen Dang; Thanneer M. Perumal; Benjamin A. Logsdon; Milind Mahajan; Lara M. Mangravite; Laurent Essioux; Hiroyoshi Toyoshiba; Raquel E. Gur; Chang-Gyu Hahn; David A. Lewis; Vahram Haroutunian; Mette A. Peters; Barbara K. Lipska; Joseph D. Buxbaum; Keisuke Hirai; Enrico Domenici; Bernie Devlin
Genome-wide association studies (GWASs) have identified a multitude of genetic loci involved with traits and diseases. However, it is often unclear which genes are affected in such loci and whether the associated genetic variants lead to increased or decreased gene function. To mitigate this, we integrated associations of common genetic variants in 57 GWASs with 24 studies of expression quantitative trait loci (eQTLs) from a broad range of tissues by using a Mendelian randomization approach. We discovered a total of 3,484 instances of gene-trait-associated changes in expression at a false-discovery rate < 0.05. These genes were often not closest to the genetic variant and were primarily identified in eQTLs derived from pathophysiologically relevant tissues. For instance, genes with expression changes associated with lipid traits were mostly identified in the liver, and those associated with cardiovascular disease were identified in arterial tissue. The affected genes additionally point to biological processes implicated in the interrogated traits, such as the interleukin-27 pathway in rheumatoid arthritis. Further, comparing trait-associated gene expression changes across traits suggests that pleiotropy is a widespread phenomenon and points to specific instances of both agonistic and antagonistic pleiotropy. For instance, expression of SNX19 and ABCB9 is positively correlated with both the risk of schizophrenia and educational attainment. To facilitate interpretation, we provide this lexicon of how common trait-associated genetic variants alter gene expression in various tissues as the online database GWAS2Genes.
Biological Psychiatry | 2017
Marija Kundakovic; Yan Jiang; David H. Kavanagh; Aslihan Dincer; Leanne Brown; Venu Pothula; Elizabeth Zharovsky; Royce Park; Rivka Jacobov; Isabelle Magro; Bibi S. Kassim; Jennifer Wiseman; Kristen Dang; Solveig K. Sieberts; Panos Roussos; Menachem Fromer; Brent T. Harris; Barbara K. Lipska; Mette A. Peters; Pamela Sklar; Schahram Akbarian
BACKGROUND The nervous system may include more than 100 residue-specific posttranslational modifications of histones forming the nucleosome core that are often regulated in cell-type-specific manner. On a genome-wide scale, some of the histone posttranslational modification landscapes show significant overlap with the genetic risk architecture for several psychiatric disorders, fueling PsychENCODE and other large-scale efforts to comprehensively map neuronal and nonneuronal epigenomes in hundreds of specimens. However, practical guidelines for efficient generation of histone chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) datasets from postmortem brains are needed. METHODS Protocols and quality controls are given for the following: 1) extraction, purification, and NeuN neuronal marker immunotagging of nuclei from adult human cerebral cortex; 2) fluorescence-activated nuclei sorting; 3) preparation of chromatin by micrococcal nuclease digest; 4) ChIP for open chromatin-associated histone methylation and acetylation; and 5) generation and sequencing of ChIP-seq libraries. RESULTS We present a ChIP-seq pipeline for epigenome mapping in the neuronal and nonneuronal nuclei from the postmortem brain. This includes a stepwise system of quality controls and user-friendly data presentation platforms. CONCLUSIONS Our practical guidelines will be useful for projects aimed at histone posttranslational modification mapping in chromatin extracted from hundreds of postmortem brain samples in cell-type-specific manner.
Scientific Data | 2017
Kenneth Daily; Shannan J. Ho Sui; Lynn M. Schriml; Phillip Dexheimer; Nathan Salomonis; Robin Schroll; Stacy Bush; Mehdi Keddache; Christopher N. Mayhew; Samad Lotia; Thanneer M. Perumal; Kristen Dang; Lorena Pantano; Alexander R. Pico; Elke Grassman; Diana Nordling; Winston Hide; Antonis K. Hatzopoulos; Punam Malik; Jose A. Cancelas; Carolyn Lutzko; Bruce J. Aronow; Larsson Omberg
The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples. Cell lines were differentiated and characterized at a central laboratory using standardized cell culture methodologies, protocols, and metadata descriptors. Stem cell and derived differentiated lines were characterized using RNA-seq, miRNA-seq, copy number arrays, DNA methylation arrays, flow cytometry, and molecular histology. All materials, including raw data, metadata, analysis and processing code, and methodological and provenance documentation are publicly available for re-use and interactive exploration at https://www.synapse.org/pcbc. The goal is to provide data that can improve our ability to robustly and reproducibly use human pluripotent stem cells to understand development and disease.
American Journal of Human Genetics | 2018
Amanda Dobbyn; Laura M. Huckins; James Boocock; Laura G. Sloofman; Benjamin S. Glicksberg; Claudia Giambartolomei; Gabriel E. Hoffman; Thanneer M. Perumal; Kiran Girdhar; Yan Jiang; Towfique Raj; Douglas Ruderfer; Robin Kramer; Dalila Pinto; Pamela Sklar; Joseph D. Buxbaum; Bernie Devlin; David A. Lewis; Raquel E. Gur; Chang-Gyu Hahn; Keisuke Hirai; Hiroyoshi Toyoshiba; Enrico Domenici; Laurent Essioux; Lara M. Mangravite; Mette A. Peters; Thomas Lehner; Barbara K. Lipska; A. Ercument Cicek; Cong Lu
Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.
Chemistry & Biology | 2017
Avner Schlessinger; Ruben Abagyan; Heather A. Carlson; Kristen Dang; Justin Guinney; Ross L. Cagan
“Polypharmacology” or “multi-target pharmacology” refers to drugs that modulate the activity of multiple targets at clinically relevant concentrations. The effect of polypharmacology on therapy can be favorable, contributing to drug efficacy, reducing drug resistance, and reducing toxicity and other liabilities. Polypharmacology can be effective in a broad palette of diseases including central nervous system (CNS) disorders, diabetes, and cancer (Keiser et al., 2009). Its strength lies in the ability to “shift networks” into configurations that reduce disease while minimizing whole body toxicity.
Alzheimers & Dementia | 2017
Thanneer M. Perumal; Kristen Dang; James A. Eddy; Benjamin A. Logsdon; Larsson Omberg; Lara M. Mangravite
Alzheimers & Dementia | 2016
Kristen Dang; Thanneer M. Perumal; Mariet Allen; Corey Funk; Minghui Wang; Jishu Xu; Ben Logsdon; Lei Yu; Sarah Schuyler; Stephen H. Friend; David A. Bennett; Bin Zhang; Eric E. Schadt; Philip L. De Jager; Nathan D. Price; Nilufer Ertekin-Taner; Lara M. Mangravite