Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. - Wikidata - wikimedia - TriplyDB

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder.

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder.

http://www.wikidata.org/entity/Q35059381

about

Long-term outcomes of antipsychotic treatment in patients with first-episode schizophrenia: a systematic review Genetics and genomics of psychiatric disease Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine Multi-breed and multi-trait co-association analysis of meat tenderness and other meat quality traits in three French beef cattle breeds.Advancing the understanding of autism disease mechanisms through genetics Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data Developing and evaluating polygenic risk prediction models for stratified disease prevention.Post-GWAS Prioritization Through Data Integration Provides Novel Insights on Chronic Obstructive Pulmonary Disease.An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments.Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm.Bipolar disorder risk gene FOXO6 modulates negative symptoms in schizophrenia: a neuroimaging genetics study Prediction of gene expression with cis-SNPs using mixed models and regularization methods Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster.Leveraging functional annotations in genetic risk prediction for human complex diseases.Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction Genotype-environment interaction on human cognitive function conditioned on the status of breastfeeding and maternal smoking around birth.Do Molecular Markers Inform About Pleiotropy?Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores.Epidemiological support for genetic variability at hypothalamic-pituitary-adrenal axis and serotonergic system as risk factors for major depression.Two-Variance-Component Model Improves Genetic Prediction in Family Datasets.Chronic gastroesophageal reflux disease shares genetic background with esophageal adenocarcinoma and Barrett's esophagus.Phenome-wide analysis of genome-wide polygenic scores.Genetic and environmental determinants of violence risk in psychotic disorders: a multivariate quantitative genetic study of 1.8 million Swedish twins and siblings MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information Connectomic markers of disease expression, genetic risk and resilience in bipolar disorder Molecular genetic approaches to understanding the comorbidity of psychiatric disorders.Analytical Models For Genetics of Human Traits Influenced By Sex.Using information of relatives in genomic prediction to apply effective stratified medicine.Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method A polygenic risk score analysis of psychosis endophenotypes across brain functional, structural, and cognitive domains.Embracing polygenicity: a review of methods and tools for psychiatric genetics research.Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.).Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.Genetic and environmental influences on the codevelopment among borderline personality disorder traits, major depression symptoms, and substance use disorder symptoms from adolescence to young adulthood.Evidence for Genetic Overlap Between Schizophrenia and Age at First Birth in Women.Partitioning the heritability of coronary artery disease highlights the importance of immune-mediated processes and epigenetic sites associated with transcriptional activity.A hierarchical causal taxonomy of psychopathology across the life span.Integrative genetic risk prediction using non-parametric empirical Bayes classification.A Fast Method that Uses Polygenic Scores to Estimate the Variance Explained by Genome-wide Marker Panels and the Proportion of Variants Affecting a Trait.Statistical Methods for Testing Genetic Pleiotropy.

P2860

Q26770497-77B96548-AD1E-45B7-BF37-CCF9E387061B Q26781213-2664E68A-E961-4E02-9082-00D74345CB5D Q26801618-DDB4FD96-E4CC-42EA-85A8-E1961CA45A3F Q27319909-25ECD890-876B-491B-A74F-D72BEB6EE776 Q28069757-4F630DFF-7AA5-4514-9FBA-F1D65DA5EC2C Q28917124-DF597CE1-224F-4928-BF3B-78CC7CD98578 Q30251440-47232A18-B4A6-4E24-9EB8-ED942E95F719 Q30826972-D4D9ABCF-B173-43BA-BFB5-5B6B148EDA3D Q31030312-56286AFE-FECA-4B05-9191-F7D22D794DC3 Q31152380-FEE3DE3B-FA85-4FF4-B409-B98B565DE489 Q33599296-2D91A41A-26A8-45E1-B57D-B0F995C038A8 Q33660160-5246A23F-3DFA-4CED-A00B-4FD7EDBAAA2B Q33729273-8994898D-8B23-4EFE-8AC0-C96FAE4B7649 Q33825680-C5161B88-A49B-4025-89D4-E852BB7E0DE3 Q33829049-256A1124-B3EF-4C1D-956E-D7DE4690E5F3 Q33920967-E0E79C16-9D7E-44A5-B14C-A4F89F0F18BE Q36050108-E698424F-1705-47BD-A4E9-0623AD092A41 Q36133352-559C2ECD-9A4E-4D61-910A-95B5AEA31062 Q36213137-F8F68176-A8DE-49D0-B3E5-732D2429A543 Q36339854-E8112297-33A5-49F3-BF2F-C8D5F2DDA301 Q36551285-DB5BD4B3-6255-4318-AC1D-C43C9765BC9E Q36619995-E5B111EF-DFD0-4045-AACE-E0F889540E8E Q36828155-FDD5015B-66DE-4972-AB48-76547C168631 Q36845451-2B359D57-1FAA-4047-A6A0-64BB1E9C94A0 Q37347814-95A9BD74-D0E2-4523-8747-943CBCB74167 Q37365390-6194D04D-EAA8-4F6E-9A5F-B7FB93F440EE Q37607874-54BF86FE-99BA-43F3-A93E-FAA84DCE57A6 Q37633573-0196E3B7-AC86-4E18-8BC1-E3EFD3BF3B67 Q38603450-D423B929-C6A0-48A1-8E3D-35470F670240 Q38603544-02B23D79-E9F6-449D-8ED9-7F4DE5CF30C8 Q38605415-9AF487F0-B059-4705-8B72-EE26BC5E5319 Q38715012-7BC86834-C43B-41E2-BE4F-21DB0B8F763E Q38779856-2BF7DE92-9A7D-4954-8B87-958328D67035 Q38831637-E256BA3F-ADEA-4167-9CED-341F1B205BC5 Q38891223-FBEF54CB-A8C4-474D-A8C3-72D1BC581B93 Q38980695-37555680-01A2-4560-8ADF-E974E0D96880 Q39048783-58032E2C-91D8-42AC-9331-EC2653F195DC Q39238156-E02CDB73-B271-4AE7-B2EB-D7AD4539E722 Q39334653-7D849E99-397D-4CE3-9240-F3724B99AFF7 Q39488140-153DC3A2-0511-446E-903C-2E47F82503C4

P2860

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder.

http://www.wikidata.org/entity/Q35059381

description

2015 nî lūn-bûn

@nan

2015 թուականի Յունուարին հրատարակուած գիտական յօդուած

@hyw

2015 թվականի հունվարին հրատարակված գիտական հոդված

@hy

2015年の論文

@ja

2015年論文

@yue

2015年論文

@zh-hant

2015年論文

@zh-hk

2015年論文

@zh-mo

2015年論文

@zh-tw

2015年论文

@wuu

name

Joint analysis of psychiatric ...... and major depressive disorder.

@ast

Joint analysis of psychiatric ...... and major depressive disorder.

@en

type

label

Joint analysis of psychiatric ...... and major depressive disorder.

@ast

Joint analysis of psychiatric ...... and major depressive disorder.

@en

prefLabel

Joint analysis of psychiatric ...... and major depressive disorder.

@ast

Joint analysis of psychiatric ...... and major depressive disorder.

@en

P1433

Q35059381-C095009B-2856-4470-BCE2-29F7E1BC7E25

P1476

Q35059381-B1367E5E-320A-470F-8255-FDF562F4191A

P2093

Q35059381-217B26C2-D541-49AB-8199-D2057F84DB82

Q35059381-33423087-F3FF-4B8D-BDE5-0673F3849673

Q35059381-3768255A-3B32-4855-BF57-6DA4E8034778

Q35059381-538669A6-4DB9-4D60-BA2D-86CE31125C84

Q35059381-555B4A47-F711-4866-B6C0-75ECBD2C2540

Q35059381-99856D8F-3A95-48C0-AAA4-8914B4249D86

Q35059381-AA767AE1-FEF1-40FB-B1D8-08D75C42BF62

Q35059381-C9E4C89D-03D3-4B1F-AA70-9B6503D99BE3

Q35059381-CE779057-DCC0-44E0-A399-6BA8779F15E3

Q35059381-D1084404-3EB9-44AB-8014-64DF63F1444A

P2860

Q35059381-04B9DDC3-FF51-4F65-90CB-82903BC61B24

Q35059381-0C487CB0-854C-488E-8F9F-68CABA921699

Q35059381-15D29D22-ED74-4E3E-9C07-3593AF66CF5B

Q35059381-2CCA00F8-3D4E-434F-BA1A-71ED69E2BA98

Q35059381-3352F54E-0806-4BEE-A407-0EBF6B2AD443

Q35059381-38F83C38-9505-427E-936C-BD3AA5EA5A49

Q35059381-3972D7AA-79F3-40A6-B7F7-6869E59B301D

Q35059381-493D828B-FF2A-40C8-B097-93ADF19BA714

Q35059381-4B79540D-BAF5-4DFA-A1C1-82A89E9F314F

Q35059381-4CD4BE41-0DE8-4EE7-9F8C-D53B28743562

P304

Q35059381-E05B72A3-84B3-4A80-B724-82F489D46444

P31

Q35059381-B7AE74DE-DD96-4EEE-8F80-647F5FBC67E8

P356

Q35059381-7933C13A-5F4D-40DE-B19B-4B7C178205A3

P407

Q35059381-D3B30F14-CE3C-4BF1-8860-5759ADF68075

P433

Q35059381-6745B436-F060-4CD1-A92F-A045D0861513

P478

Q35059381-7086F81A-9A63-4E0E-86EA-7F4B01A74524

P50

Q35059381-1DE5AAC3-EE6F-48BD-959F-D0537DF6C0C4

Q35059381-3AF1D162-B84A-45F4-9AD0-8ECC28A27557

Q35059381-5E7B09F3-87E1-460C-B583-AA07A6A3CEB0

Q35059381-680C9FA4-C123-48F4-A22D-F5128E3A0E46

Q35059381-70BEF7F5-F699-4138-949D-C6F8A6D92F2D

Q35059381-70E5432C-0DDE-4C9B-825C-12C6D702C802

Q35059381-F19BD3E8-C2D2-4875-B8BF-C4D4970EAF9A

P577

Q35059381-11CA9F54-C9DC-45AC-9332-551E8E4CC291

P5875

Q35059381-E31E50AE-4073-4EC4-A4D2-FA7F71063799

P698

Q35059381-EE2B8A1A-6BB4-4220-BCE7-DC47684D5594

P921

Q35059381-12BE87F0-AAC0-4E07-943C-A9D3F767CB37

Q35059381-E55518BA-D95B-44ED-ADDC-09A16FADAFB6

P932

Q35059381-73528075-A04C-4B3B-92A5-8BAFDE0C7515

P356

J.AJHG.2014.12.006

P1433

American Journal of Human Genetics

P1476

Joint analysis of psychiatric ...... and major depressive disorder.

@en

P2093

Cross-Disorder Working Group of the Psychiatric Genomics Consortium

http://www.w3.org/2001/XMLSchema#string

Douglas F Levinson

http://www.w3.org/2001/XMLSchema#string

James B Potash

http://www.w3.org/2001/XMLSchema#string

Jianxin Shi

http://www.w3.org/2001/XMLSchema#string

Jordan W Smoller

http://www.w3.org/2001/XMLSchema#string

Naomi R Wray

http://www.w3.org/2001/XMLSchema#string

Robert Maier

http://www.w3.org/2001/XMLSchema#string

S Hong Lee

http://www.w3.org/2001/XMLSchema#string

Stephan Ripke

http://www.w3.org/2001/XMLSchema#string

William A Scheftner

http://www.w3.org/2001/XMLSchema#string

P2860

Potential etiologic and functional implications of genome-wide association loci for human diseases and traits

An integrated map of genetic variation from 1,092 human genomes

Biological insights from 108 schizophrenia-associated genetic loci

Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis

Five years of GWAS discovery

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Genome-wide association analysis identifies 13 new risk loci for schizophrenia

Genome-wide association study in a Swedish population yields support for greater CNV and MHC involvement in schizophrenia compared with bipolar disorder

Genome-wide association study of recurrent early-onset major depressive disorder

GCTA: a tool for genome-wide complex trait analysis

P304

283-294

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1016/J.AJHG.2014.12.006

http://www.w3.org/2001/XMLSchema#string

P407

P433

2

http://www.w3.org/2001/XMLSchema#string

P478

96

http://www.w3.org/2001/XMLSchema#string

P50

Myrna M. Weissman

William Coryell

Kenneth S. Kendler

Christina M. Hultman

P577

2015-01-29T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

271709898

http://www.w3.org/2001/XMLSchema#string

P698

25640677

http://www.w3.org/2001/XMLSchema#string

P921

bipolar disorder

P932

4320268

http://www.w3.org/2001/XMLSchema#string