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Precision psychiatry

The group uses big data and new analytical methods to clarify causes and risk factors in severe mental disorders to improve prevention, diagnosis and treatment.

Photo: Adriano Winterton, NORMENT

About the group

The group examines data from NORMENT and large databases that include up to several million individuals.

We develop mathematical models to understand variation in the human genome, in order to identify genetic and environmental factors contributing to disease development.

This research is done in close collaboration with international researchers and consortia, as well as Nordic partners.

The group's long-term goal it to get in the position of future precision medicine – which has great potential in psychiatry. 

Aims

  • Develop novel tools for big data analysis of mental illness causes 
  • Develop and validate prediction and stratification tools
  • Identify genetic factors involved in the development of mental illness, and comorbidities
  • Identify gene-environment interplay in the development of severe mental illness 

Projects

  • Conditional and conjunctional FDR
  • Univariate and bivariate mixture models of GWAS
  • Psychiatric Genomics Consortium
  • Nordic collaboration on psychiatric genetics
  • Comorbidity in mental illness – overlapping genetic risk factors
  • Novel tools for data collection and recruitment

Collaboration

  • UCSD – Anders M. Dale, San Diego, USA
  • PGC – Pat Sullivan et al, Univ North Carolina USA/Karolinska Inst., Sweden
  • ENIGMA – Paul Thompson et al., Univ Southern California, USA
  • Nordic Psychiatric Genetics – Thomas Werge et al., Univ Copenhagen, Denmark

Selected publications

  1. Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, …, , Goldstein DB, Stefansson K & Collier DA. Common variants conferring risk of schizophrenia. Nature, 2009  460(7256):744-7. 
  2. Andreassen OA, Djurovic S, Thompson WK, Schork AJ, Kendler KS, …. Desikan RS, Dale AM. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am J Hum Genet. 2013;92(2):197-209. 
  3. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014 Jul 24;511(7510):421-7. 
  4. Andreassen OA, Harbo HF, Wang Y, .. Psychiatric Genomics Consortium (PGC) Bipolar Disorder and Schizophrenia Work Groups; International Multiple Sclerosis Genetics Consortium (IMSGC), McEvoy LK, Desikan RS, Lie BA, Djurovic S, Dale AM. Mol Psychiatry. 2015 Feb;20(2):207-14 
  5. Hibar DP, Westlye LT, .. Andreassen OA. Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry. 2016 Feb 9  21 (12), 1710. 
  6. Lo MT, Hinds DA, …..Andreassen OA, CH Chen. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat Genet. 2017;49(1):152-6. 
  7. Desikan RS, Fan CC, . Andreassen OA Dale AM. Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score. PLoS Med. 2017;14(3):e1002258. 
  8. Smeland OB, Frei O, .. Andreassen OA. Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function. JAMA Psychiatry. 2017 Oct 1;74(10):1065-1075. 
  9. Seibert T .., Andreassen OA, Dale AM. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts. BMJ 2018 Jan 10;360:j5757.
  10. Pardinas AF et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet. 2018 Mar;50(3):381-389. doi: 10.1038/s41588-018-0059-2. Epub 2018 Feb 26.
     
Published Nov. 12, 2018 10:35 AM - Last modified June 19, 2019 9:55 AM

Contact

Group leader