SwePub
Sök i LIBRIS databas

  Extended search

onr:"swepub:oai:gup.ub.gu.se/213844"
 

Search: onr:"swepub:oai:gup.ub.gu.se/213844" > Joint Analysis of P...

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Maier, R. (author)
Moser, G. (author)
Chen, G. B. (author)
show more...
Ripke, S. (author)
Coryell, W. (author)
Potash, J. B. (author)
Scheftner, W. A. (author)
Shi, J. X. (author)
Weissman, M. M. (author)
Hultman, C. M. (author)
Karolinska Institutet
Landén, Mikael, 1966 (author)
Karolinska Institutet,Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Institute of Neuroscience and Physiology
Levinson, D. F. (author)
Kendler, K. S. (author)
Smoller, J. W. (author)
Wray, N. R. (author)
Lee, S. H. (author)
show less...
 (creator_code:org_t)
Elsevier BV, 2015
2015
English.
In: American Journal of Human Genetics. - : Elsevier BV. - 0002-9297 .- 1537-6605. ; 96:2, s. 283-294
  • Journal article (peer-reviewed)
Abstract Subject headings
Close  
  • Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine (hsv//eng)

Keyword

GENOME-WIDE ASSOCIATION
RESTRICTED MAXIMUM-LIKELIHOOD
AVERAGE
INFORMATION
GENETIC RISK
LOCI
SELECTION
TRAITS
MODELS
IDENTIFICATION
POPULATION
Genetics & Heredity

Publication and Content Type

ref (subject category)
art (subject category)

Find in a library

To the university's database

  • 1 of 1
  • Previous record
  • Next record
  •    To hitlist

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view