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Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity.
Udyavar AR, Wooten DJ, Hoeksema M, Bansal M, Califano A, Estrada L, Schnell S, Irish JM, Massion PP, Quaranta V
(2017) Cancer Res 77: 1063-1074
MeSH Terms: Cell Differentiation, Cell Line, Tumor, Gene Expression, Genetic Heterogeneity, Humans, Lung Neoplasms, Phenotype, Small Cell Lung Carcinoma, Transcription Factors
Show Abstract · Added December 10, 2016
Small cell lung cancer (SCLC) is a devastating disease due to its propensity for early invasion and refractory relapse after initial treatment response. Although these aggressive traits have been associated with phenotypic heterogeneity, our understanding of this association remains incomplete. To fill this knowledge gap, we inferred a set of 33 transcription factors (TF) associated with gene signatures of the known neuroendocrine/epithelial (NE) and non-neuroendocrine/mesenchymal-like (ML) SCLC phenotypes. The topology of this SCLC TF network was derived from prior knowledge and was simulated using Boolean modeling. These simulations predicted that the network settles into attractors, or TF expression patterns, that correlate with NE or ML phenotypes, suggesting that TF network dynamics underlie the emergence of heterogeneous SCLC phenotypes. However, several cell lines and patient tumor specimens failed to correlate with either the NE or ML attractors. By flow cytometry, single cells within these cell lines simultaneously expressed surface markers of both NE and ML differentiation, confirming the existence of a "hybrid" phenotype. Upon exposure to standard-of-care cytotoxic drugs or epigenetic modifiers, NE and ML cell populations converged toward the hybrid state, suggesting possible escape from treatment. Our findings indicate that SCLC phenotypic heterogeneity can be specified dynamically by attractor states of a master regulatory TF network. Thus, SCLC heterogeneity may be best understood as states within an epigenetic landscape. Understanding phenotypic transitions within this landscape may provide insights to clinical applications. .
©2016 American Association for Cancer Research.
2 Communities
3 Members
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9 MeSH Terms
Designing a broad-spectrum integrative approach for cancer prevention and treatment.
Block KI, Gyllenhaal C, Lowe L, Amedei A, Amin ARMR, Amin A, Aquilano K, Arbiser J, Arreola A, Arzumanyan A, Ashraf SS, Azmi AS, Benencia F, Bhakta D, Bilsland A, Bishayee A, Blain SW, Block PB, Boosani CS, Carey TE, Carnero A, Carotenuto M, Casey SC, Chakrabarti M, Chaturvedi R, Chen GZ, Chen H, Chen S, Chen YC, Choi BK, Ciriolo MR, Coley HM, Collins AR, Connell M, Crawford S, Curran CS, Dabrosin C, Damia G, Dasgupta S, DeBerardinis RJ, Decker WK, Dhawan P, Diehl AME, Dong JT, Dou QP, Drew JE, Elkord E, El-Rayes B, Feitelson MA, Felsher DW, Ferguson LR, Fimognari C, Firestone GL, Frezza C, Fujii H, Fuster MM, Generali D, Georgakilas AG, Gieseler F, Gilbertson M, Green MF, Grue B, Guha G, Halicka D, Helferich WG, Heneberg P, Hentosh P, Hirschey MD, Hofseth LJ, Holcombe RF, Honoki K, Hsu HY, Huang GS, Jensen LD, Jiang WG, Jones LW, Karpowicz PA, Keith WN, Kerkar SP, Khan GN, Khatami M, Ko YH, Kucuk O, Kulathinal RJ, Kumar NB, Kwon BS, Le A, Lea MA, Lee HY, Lichtor T, Lin LT, Locasale JW, Lokeshwar BL, Longo VD, Lyssiotis CA, MacKenzie KL, Malhotra M, Marino M, Martinez-Chantar ML, Matheu A, Maxwell C, McDonnell E, Meeker AK, Mehrmohamadi M, Mehta K, Michelotti GA, Mohammad RM, Mohammed SI, Morre DJ, Muralidhar V, Muqbil I, Murphy MP, Nagaraju GP, Nahta R, Niccolai E, Nowsheen S, Panis C, Pantano F, Parslow VR, Pawelec G, Pedersen PL, Poore B, Poudyal D, Prakash S, Prince M, Raffaghello L, Rathmell JC, Rathmell WK, Ray SK, Reichrath J, Rezazadeh S, Ribatti D, Ricciardiello L, Robey RB, Rodier F, Rupasinghe HPV, Russo GL, Ryan EP, Samadi AK, Sanchez-Garcia I, Sanders AJ, Santini D, Sarkar M, Sasada T, Saxena NK, Shackelford RE, Shantha Kumara HMC, Sharma D, Shin DM, Sidransky D, Siegelin MD, Signori E, Singh N, Sivanand S, Sliva D, Smythe C, Spagnuolo C, Stafforini DM, Stagg J, Subbarayan PR, Sundin T, Talib WH, Thompson SK, Tran PT, Ungefroren H, Vander Heiden MG, Venkateswaran V, Vinay DS, Vlachostergios PJ, Wang Z, Wellen KE, Whelan RL, Yang ES, Yang H, Yang X, Yaswen P, Yedjou C, Yin X, Zhu J, Zollo M
(2015) Semin Cancer Biol 35 Suppl: S276-S304
MeSH Terms: Antineoplastic Agents, Phytogenic, Drug Resistance, Neoplasm, Genetic Heterogeneity, Humans, Molecular Targeted Therapy, Neoplasms, Precision Medicine, Signal Transduction, Tumor Microenvironment
Show Abstract · Added August 8, 2016
Targeted therapies and the consequent adoption of "personalized" oncology have achieved notable successes in some cancers; however, significant problems remain with this approach. Many targeted therapies are highly toxic, costs are extremely high, and most patients experience relapse after a few disease-free months. Relapses arise from genetic heterogeneity in tumors, which harbor therapy-resistant immortalized cells that have adopted alternate and compensatory pathways (i.e., pathways that are not reliant upon the same mechanisms as those which have been targeted). To address these limitations, an international task force of 180 scientists was assembled to explore the concept of a low-toxicity "broad-spectrum" therapeutic approach that could simultaneously target many key pathways and mechanisms. Using cancer hallmark phenotypes and the tumor microenvironment to account for the various aspects of relevant cancer biology, interdisciplinary teams reviewed each hallmark area and nominated a wide range of high-priority targets (74 in total) that could be modified to improve patient outcomes. For these targets, corresponding low-toxicity therapeutic approaches were then suggested, many of which were phytochemicals. Proposed actions on each target and all of the approaches were further reviewed for known effects on other hallmark areas and the tumor microenvironment. Potential contrary or procarcinogenic effects were found for 3.9% of the relationships between targets and hallmarks, and mixed evidence of complementary and contrary relationships was found for 7.1%. Approximately 67% of the relationships revealed potentially complementary effects, and the remainder had no known relationship. Among the approaches, 1.1% had contrary, 2.8% had mixed and 62.1% had complementary relationships. These results suggest that a broad-spectrum approach should be feasible from a safety standpoint. This novel approach has potential to be relatively inexpensive, it should help us address stages and types of cancer that lack conventional treatment, and it may reduce relapse risks. A proposed agenda for future research is offered.
Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
0 Communities
1 Members
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9 MeSH Terms
Clinical implications of molecular heterogeneity in triple negative breast cancer.
Lehmann BD, Pietenpol JA
(2015) Breast 24 Suppl 2: S36-40
MeSH Terms: Antineoplastic Agents, Female, Genetic Heterogeneity, Humans, Molecular Targeted Therapy, Mutation, Patient Selection, Phenotype, Transcription, Genetic, Triple Negative Breast Neoplasms
Show Abstract · Added February 15, 2016
Triple negative breast cancer (TNBC) is a molecularly heterogeneous disease lacking recurrent targetable alterations and thus therapeutic advances have been challenging. The absence of ER, PR and HER2 amplifications, leaves combination chemotherapy as the standard of care treatment option in the adjuvant, neoadjuvant and metastatic settings. Recently, multiple studies have shed some light on the heterogeneity of TNBC and identified distinct transcriptional subtypes with unique biologies. Herein we review the molecular heterogeneity and the impact on previous and future clinical trials.
Copyright © 2015 Elsevier Ltd. All rights reserved.
1 Communities
1 Members
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10 MeSH Terms
Three Molecular Subtypes of Gastric Adenocarcinoma Have Distinct Histochemical Features Reflecting Epstein-Barr Virus Infection Status and Neuroendocrine Differentiation.
Speck O, Tang W, Morgan DR, Kuan PF, Meyers MO, Dominguez RL, Martinez E, Gulley ML
(2015) Appl Immunohistochem Mol Morphol 23: 633-45
MeSH Terms: Adenocarcinoma, Carcinoma, Neuroendocrine, Cell Differentiation, Chromogranin A, Epithelial Cells, Epstein-Barr Virus Infections, Gastric Mucosa, Gastrins, Gene Expression, Genetic Heterogeneity, Herpesvirus 4, Human, Humans, Immunohistochemistry, In Situ Hybridization, Fluorescence, Lectins, C-Type, Pancreatitis-Associated Proteins, Prognosis, Stomach, Stomach Neoplasms
Show Abstract · Added May 18, 2016
Current histopathologic classification schemes for gastric adenocarcinoma have limited clinical utility and are difficult to apply due to tumor heterogeneity. Elucidation of molecular subtypes of gastric cancer may contribute to our understanding of gastric cancer biology and to the development of new molecular markers that may lead to improved diagnosis, therapy, or prognosis. We previously demonstrated that Epstein-Barr virus (EBV)-infected gastric cancers have a distinct human gene expression profile compared with uninfected cancers. We now examine the histopathologic features characterizing infected (n=14) and uninfected (n=89) cancers; the latter of which are now further divided into 2 major molecular subtypes based on expression patterns of 93 RNAs. One uninfected gastric cancer subtype was distinguished by upregulation of 3 genes with neuroendocrine (NE) function (CHGA, GAST, and REG4 encoding chromogranin, gastrin, and the secreted peptide REG4 involved in epithelial cell regeneration), implicating hormonal factors in the pathogenesis of a major class of gastric adenocarcinomas. Evidence of NE differentiation (molecular, immunohistochemical, or morphologic) was mutually exclusive of EBV infection. EBV-infected tumors tended to have solid-type morphology with lymphoid stroma. This study reveals novel molecular subtypes of gastric cancer and their associated morphologies that demonstrate divergent NE features.
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19 MeSH Terms
Multiple nonglycemic genomic loci are newly associated with blood level of glycated hemoglobin in East Asians.
Chen P, Takeuchi F, Lee JY, Li H, Wu JY, Liang J, Long J, Tabara Y, Goodarzi MO, Pereira MA, Kim YJ, Go MJ, Stram DO, Vithana E, Khor CC, Liu J, Liao J, Ye X, Wang Y, Lu L, Young TL, Lee J, Thai AC, Cheng CY, van Dam RM, Friedlander Y, Heng CK, Koh WP, Chen CH, Chang LC, Pan WH, Qi Q, Isono M, Zheng W, Cai Q, Gao Y, Yamamoto K, Ohnaka K, Takayanagi R, Kita Y, Ueshima H, Hsiung CA, Cui J, Sheu WH, Rotter JI, Chen YD, Hsu C, Okada Y, Kubo M, Takahashi A, Tanaka T, van Rooij FJ, Ganesh SK, Huang J, Huang T, Yuan J, Hwang JY, CHARGE Hematology Working Group, Gross MD, Assimes TL, Miki T, Shu XO, Qi L, Chen YT, Lin X, Aung T, Wong TY, Teo YY, Kim BJ, Kato N, Tai ES
(2014) Diabetes 63: 2551-62
MeSH Terms: Adult, Aged, Asian Continental Ancestry Group, Blood Glucose, Cohort Studies, Far East, Female, Genetic Heterogeneity, Genetic Loci, Genome-Wide Association Study, Glycated Hemoglobin A, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide
Show Abstract · Added March 27, 2014
Glycated hemoglobin A1c (HbA1c) is used as a measure of glycemic control and also as a diagnostic criterion for diabetes. To discover novel loci harboring common variants associated with HbA1c in East Asians, we conducted a meta-analysis of 13 genome-wide association studies (GWAS; N = 21,026). We replicated our findings in three additional studies comprising 11,576 individuals of East Asian ancestry. Ten variants showed associations that reached genome-wide significance in the discovery data set, of which nine (four novel variants at TMEM79 [P value = 1.3 × 10(-23)], HBS1L/MYB [8.5 × 10(-15)], MYO9B [9.0 × 10(-12)], and CYBA [1.1 × 10(-8)] as well as five variants at loci that had been previously identified [CDKAL1, G6PC2/ABCB11, GCK, ANK1, and FN3KI]) showed consistent evidence of association in replication data sets. These variants explained 1.76% of the variance in HbA1c. Several of these variants (TMEM79, HBS1L/MYB, CYBA, MYO9B, ANK1, and FN3K) showed no association with either blood glucose or type 2 diabetes. Among individuals with nondiabetic levels of fasting glucose (<7.0 mmol/L) but elevated HbA1c (≥6.5%), 36.1% had HbA1c <6.5% after adjustment for these six variants. Our East Asian GWAS meta-analysis has identified novel variants associated with HbA1c as well as demonstrated that the effects of known variants are largely transferable across ethnic groups. Variants affecting erythrocyte parameters rather than glucose metabolism may be relevant to the use of HbA1c for diagnosing diabetes in these populations.
© 2014 by the American Diabetes Association.
0 Communities
2 Members
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15 MeSH Terms
Patterns and processes of somatic mutations in nine major cancers.
Jia P, Pao W, Zhao Z
(2014) BMC Med Genomics 7: 11
MeSH Terms: CpG Islands, Cytidine Deaminase, Databases, Genetic, Gene Expression Regulation, Neoplastic, Genetic Heterogeneity, Genome, Human, Humans, Mutation, Neoplasms, Smoking, Sunlight
Show Abstract · Added March 10, 2014
BACKGROUND - Cancer genomes harbor hundreds to thousands of somatic nonsynonymous mutations. DNA damage and deficiency of DNA repair systems are two major forces to cause somatic mutations, marking cancer genomes with specific somatic mutation patterns. Recently, several pan-cancer genome studies revealed more than 20 mutation signatures across multiple cancer types. However, detailed cancer-type specific mutation signatures and their different features within (intra-) and between (inter-) cancer types remain largely unexplored.
METHODS - We employed a matrix decomposition algorithm, namely Non-negative Matrix Factorization, to survey the somatic mutations in nine major human cancers, involving a total of ~2100 genomes.
RESULTS - Our results revealed 3-5 independent mutational signatures in each cancer, implying that a range of 3-5 predominant mutational processes likely underlie each cancer genome. Both mutagen exposure (tobacco and sun) and changes in DNA repair systems (APOBEC family, POLE, and MLH1) were found as mutagenesis forces, each of which marks the genome with an evident mutational signature. We studied the features of several signatures and their combinatory patterns within and across cancers. On one hand, we found each signature may influence a cancer genome with different influential magnitudes even in the same cancer type and the signature-specific load reflects intra-cancer heterogeneity (e.g., the smoking-related signature in lung cancer smokers and never smokers). On the other hand, inter-cancer heterogeneity is characterized by combinatory patterns of mutational signatures, where no cancers share the same signature profile, even between two lung cancer subtypes (lung adenocarcinoma and squamous cell lung cancer).
CONCLUSIONS - Our work provides a detailed overview of the mutational characteristics in each of nine major cancers and highlights that the mutational signature profile is representative of each cancer.
0 Communities
2 Members
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11 MeSH Terms
New strategies for triple-negative breast cancer--deciphering the heterogeneity.
Mayer IA, Abramson VG, Lehmann BD, Pietenpol JA
(2014) Clin Cancer Res 20: 782-90
MeSH Terms: Antineoplastic Agents, Female, Genetic Heterogeneity, Humans, Molecular Targeted Therapy, Transcriptome, Triple Negative Breast Neoplasms
Show Abstract · Added March 23, 2014
Triple-negative breast cancer (TNBC) is a heterogeneous disease; gene expression analyses recently identified six distinct TNBC subtypes, each displaying a unique biology. Exploring novel approaches to treatment of these subtypes is critical because less than 30% of women with metastatic breast cancer survive five years and virtually all women with metastatic TNBC will ultimately die of their disease despite systemic therapy. To date, not a single targeted therapy has been approved for the treatment of TNBC and cytotoxic chemotherapy remains the standard treatment. We discuss the current and upcoming therapeutic strategies being explored in an attempt to "target" TNBC.
©2014 AACR
1 Communities
4 Members
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7 MeSH Terms
Differential response to neoadjuvant chemotherapy among 7 triple-negative breast cancer molecular subtypes.
Masuda H, Baggerly KA, Wang Y, Zhang Y, Gonzalez-Angulo AM, Meric-Bernstam F, Valero V, Lehmann BD, Pietenpol JA, Hortobagyi GN, Symmans WF, Ueno NT
(2013) Clin Cancer Res 19: 5533-40
MeSH Terms: Adult, Aged, Antineoplastic Combined Chemotherapy Protocols, Cluster Analysis, Female, Gene Expression Profiling, Genetic Heterogeneity, Humans, Middle Aged, Neoplasm Grading, Neoplasm Staging, Recurrence, Reproducibility of Results, Treatment Outcome, Triple Negative Breast Neoplasms
Show Abstract · Added September 3, 2013
PURPOSE - The clinical relevancy of the 7-subtype classification of triple-negative breast cancer (TNBC) reported by Lehmann and colleagues is unknown. We investigated the clinical relevancy of TNBC heterogeneity by determining pathologic complete response (pCR) rates after neoadjuvant chemotherapy, based on TNBC subtypes.
EXPERIMENTAL DESIGN - We revalidated the Lehmann and colleagues experiments using Affymetrix CEL files from public datasets. We applied these methods to 146 patients with TNBC with gene expression microarrays obtained from June 2000 to March 2010 at our institution. Of those, 130 had received standard neoadjuvant chemotherapy and had evaluable pathologic response data. We classified the TNBC samples by subtype and then correlated subtype and pCR status using Fisher exact test and a logistic regression model. We also assessed survival and compared the subtypes with PAM50 intrinsic subtypes and residual cancer burden (RCB) index.
RESULTS - TNBC subtype and pCR status were significantly associated (P = 0.04379). The basal-like 1 (BL1) subtype had the highest pCR rate (52%); basal-like 2 (BL2) and luminal androgen receptor had the lowest (0% and 10%, respectively). TNBC subtype was an independent predictor of pCR status (P = 0.022) by a likelihood ratio test. The subtypes better predicted pCR status than did the PAM50 intrinsic subtypes (basal-like vs. non basal-like).
CONCLUSIONS - Classifying TNBC by 7 subtypes predicts high versus low pCR rate. We confirm the clinical relevancy of the 7 subtypes of TNBC. We need to prospectively validate whether the pCR rate differences translate into long-term outcome differences. The 7-subtype classification may spur innovative personalized medicine strategies for patients with TNBC.
©2013 AACR.
1 Communities
3 Members
0 Resources
15 MeSH Terms
Autotaxin signaling governs phenotypic heterogeneity in visceral and parietal mesothelia.
Shelton EL, Galindo CL, Williams CH, Pfaltzgraff E, Hong CC, Bader DM
(2013) PLoS One 8: e69712
MeSH Terms: Animals, Cell Line, Tumor, Epithelial Cells, Epithelium, Gene Expression, Genetic Heterogeneity, Humans, Intestine, Small, Lung Neoplasms, Mesothelioma, Mice, Mice, Transgenic, Omentum, Peritoneal Neoplasms, Phenotype, Phosphoric Diester Hydrolases, Pleura, Signal Transduction, Viscera
Show Abstract · Added September 20, 2013
Mesothelia, which cover all coelomic organs and body cavities in vertebrates, perform diverse functions in embryonic and adult life. Yet, mesothelia are traditionally viewed as simple, uniform epithelia. Here we demonstrate distinct differences between visceral and parietal mesothelia, the most basic subdivision of this tissue type, in terms of gene expression, adhesion, migration, and invasion. Gene profiling determined that autotaxin, a secreted lysophospholipase D originally discovered as a tumor cell-motility-stimulating factor, was expressed exclusively in the more motile and invasive visceral mesothelia and at abnormally high levels in mesotheliomas. Gain and loss of function studies demonstrate that autotaxin signaling is indeed a critical factor responsible for phenotypic differences within mesothelia. Furthermore, we demonstrate that known and novel small molecule inhibitors of the autotaxin signaling pathway dramatically blunt migratory and invasive behaviors of aggressive mesotheliomas. Taken together, this study reveals distinct phenotypes within the mesothelial cell lineage, demonstrates that differential autotaxin expression is the molecular underpinning for these differences, and provides a novel target and lead compounds to intervene in invasive mesotheliomas.
2 Communities
3 Members
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19 MeSH Terms
Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.
Cross-Disorder Group of the Psychiatric Genomics Consortium, Lee SH, Ripke S, Neale BM, Faraone SV, Purcell SM, Perlis RH, Mowry BJ, Thapar A, Goddard ME, Witte JS, Absher D, Agartz I, Akil H, Amin F, Andreassen OA, Anjorin A, Anney R, Anttila V, Arking DE, Asherson P, Azevedo MH, Backlund L, Badner JA, Bailey AJ, Banaschewski T, Barchas JD, Barnes MR, Barrett TB, Bass N, Battaglia A, Bauer M, Bayés M, Bellivier F, Bergen SE, Berrettini W, Betancur C, Bettecken T, Biederman J, Binder EB, Black DW, Blackwood DH, Bloss CS, Boehnke M, Boomsma DI, Breen G, Breuer R, Bruggeman R, Cormican P, Buccola NG, Buitelaar JK, Bunney WE, Buxbaum JD, Byerley WF, Byrne EM, Caesar S, Cahn W, Cantor RM, Casas M, Chakravarti A, Chambert K, Choudhury K, Cichon S, Cloninger CR, Collier DA, Cook EH, Coon H, Cormand B, Corvin A, Coryell WH, Craig DW, Craig IW, Crosbie J, Cuccaro ML, Curtis D, Czamara D, Datta S, Dawson G, Day R, De Geus EJ, Degenhardt F, Djurovic S, Donohoe GJ, Doyle AE, Duan J, Dudbridge F, Duketis E, Ebstein RP, Edenberg HJ, Elia J, Ennis S, Etain B, Fanous A, Farmer AE, Ferrier IN, Flickinger M, Fombonne E, Foroud T, Frank J, Franke B, Fraser C, Freedman R, Freimer NB, Freitag CM, Friedl M, Frisén L, Gallagher L, Gejman PV, Georgieva L, Gershon ES, Geschwind DH, Giegling I, Gill M, Gordon SD, Gordon-Smith K, Green EK, Greenwood TA, Grice DE, Gross M, Grozeva D, Guan W, Gurling H, De Haan L, Haines JL, Hakonarson H, Hallmayer J, Hamilton SP, Hamshere ML, Hansen TF, Hartmann AM, Hautzinger M, Heath AC, Henders AK, Herms S, Hickie IB, Hipolito M, Hoefels S, Holmans PA, Holsboer F, Hoogendijk WJ, Hottenga JJ, Hultman CM, Hus V, Ingason A, Ising M, Jamain S, Jones EG, Jones I, Jones L, Tzeng JY, Kähler AK, Kahn RS, Kandaswamy R, Keller MC, Kennedy JL, Kenny E, Kent L, Kim Y, Kirov GK, Klauck SM, Klei L, Knowles JA, Kohli MA, Koller DL, Konte B, Korszun A, Krabbendam L, Krasucki R, Kuntsi J, Kwan P, Landén M, Långström N, Lathrop M, Lawrence J, Lawson WB, Leboyer M, Ledbetter DH, Lee PH, Lencz T, Lesch KP, Levinson DF, Lewis CM, Li J, Lichtenstein P, Lieberman JA, Lin DY, Linszen DH, Liu C, Lohoff FW, Loo SK, Lord C, Lowe JK, Lucae S, MacIntyre DJ, Madden PA, Maestrini E, Magnusson PK, Mahon PB, Maier W, Malhotra AK, Mane SM, Martin CL, Martin NG, Mattheisen M, Matthews K, Mattingsdal M, McCarroll SA, McGhee KA, McGough JJ, McGrath PJ, McGuffin P, McInnis MG, McIntosh A, McKinney R, McLean AW, McMahon FJ, McMahon WM, McQuillin A, Medeiros H, Medland SE, Meier S, Melle I, Meng F, Meyer J, Middeldorp CM, Middleton L, Milanova V, Miranda A, Monaco AP, Montgomery GW, Moran JL, Moreno-De-Luca D, Morken G, Morris DW, Morrow EM, Moskvina V, Muglia P, Mühleisen TW, Muir WJ, Müller-Myhsok B, Murtha M, Myers RM, Myin-Germeys I, Neale MC, Nelson SF, Nievergelt CM, Nikolov I, Nimgaonkar V, Nolen WA, Nöthen MM, Nurnberger JI, Nwulia EA, Nyholt DR, O'Dushlaine C, Oades RD, Olincy A, Oliveira G, Olsen L, Ophoff RA, Osby U, Owen MJ, Palotie A, Parr JR, Paterson AD, Pato CN, Pato MT, Penninx BW, Pergadia ML, Pericak-Vance MA, Pickard BS, Pimm J, Piven J, Posthuma D, Potash JB, Poustka F, Propping P, Puri V, Quested DJ, Quinn EM, Ramos-Quiroga JA, Rasmussen HB, Raychaudhuri S, Rehnström K, Reif A, Ribasés M, Rice JP, Rietschel M, Roeder K, Roeyers H, Rossin L, Rothenberger A, Rouleau G, Ruderfer D, Rujescu D, Sanders AR, Sanders SJ, Santangelo SL, Sergeant JA, Schachar R, Schalling M, Schatzberg AF, Scheftner WA, Schellenberg GD, Scherer SW, Schork NJ, Schulze TG, Schumacher J, Schwarz M, Scolnick E, Scott LJ, Shi J, Shilling PD, Shyn SI, Silverman JM, Slager SL, Smalley SL, Smit JH, Smith EN, Sonuga-Barke EJ, St Clair D, State M, Steffens M, Steinhausen HC, Strauss JS, Strohmaier J, Stroup TS, Sutcliffe JS, Szatmari P, Szelinger S, Thirumalai S, Thompson RC, Todorov AA, Tozzi F, Treutlein J, Uhr M, van den Oord EJ, Van Grootheest G, Van Os J, Vicente AM, Vieland VJ, Vincent JB, Visscher PM, Walsh CA, Wassink TH, Watson SJ, Weissman MM, Werge T, Wienker TF, Wijsman EM, Willemsen G, Williams N, Willsey AJ, Witt SH, Xu W, Young AH, Yu TW, Zammit S, Zandi PP, Zhang P, Zitman FG, Zöllner S, Devlin B, Kelsoe JR, Sklar P, Daly MJ, O'Donovan MC, Craddock N, Sullivan PF, Smoller JW, Kendler KS, Wray NR, International Inflammatory Bowel Disease Genetics Consortium (IIBDGC)
(2013) Nat Genet 45: 984-94
MeSH Terms: Adult, Attention Deficit Disorder with Hyperactivity, Bipolar Disorder, Child, Child Development Disorders, Pervasive, Crohn Disease, Depressive Disorder, Major, Genetic Heterogeneity, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Inheritance Patterns, Mental Disorders, Polymorphism, Single Nucleotide, Schizophrenia
Show Abstract · Added February 20, 2014
Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
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16 MeSH Terms