About the group

The group examines data from national and international 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

  • JCVI – 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 Denmark, Fang Fang Yu Li; Sweden, Unnur Valdimarsdottir, Iceland

Selected publications

  1. O’Connell, KS. et al. Genomics yields biological and phenotypic insights into bipolar disorder. Nature 639 :968-975 (2025). https://doi.org/10.1038/s41586-024-08468-9.

    This recent discovery of genetic factors in complex mental disorders, applied our novel tools, the MiXeR suite of tools, and increased the explained variance and improved prediction of causal mechanisms of complex diseases.

     

  2. Reas, ET. et al. Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures. Alzheimers Dement 19, 5511-5158 (2023). https://doi.org/10.1002/alz.13112 
     

    We developed at multimodal prediction tool, the multimodal hazard score combined age, genetics, brain atrophy, and memory to time to conversion from mild cognitive impairment to dementia with clinically relevant accuracy.

     

  3. Hindley, G. et al. Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy. Nature Human Behaviour 7, 1584-1600 (2023). https://doi.org:10.1038/s41562-023-01630-9 
     

    This paper identified shared genetic mechanisms across mental traits and behavioural phenotypes futher supporting a distributed polygenetic signal identified with multivariate methods (MOSTest) which boost genetic discovery to distributed genetic effects across phenotypes.

     

  4. Hindley, G. et al. Charting the Landscape of Genetic Overlap Between Mental Disorders and Related Traits Beyond Genetic Correlation. American Journal of Psychiatry 179, 833-843 (2022). https://doi.org:10.1176/appi.ajp.21101051 
     

    This paper revealed extensive genetic overlap between major mental disorders, quantified the number of causal genetic variants shared and unique for mental traits and disorders, applying novel mathematical modelling (MiXeR).

     

  5. Wightman, DP. et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nature Genetics 53, 1276–1282 (2021). https://doi.org/10.1038/s41588-021-00921-z 
     

    This paper revealed a series of new genetic loci associated Alzheimer’s disease, increasing the explained variance of the genetic variants associated with Alzheimer’s disease, forming the foundations for new prediction and stratification approaches.

     

  6. Mullins, N. et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nature Genetics 53, 817-829 (2021). https://doi.org:10.1038/s41588-021-00857-4 . 
     

    This paper discovered genetic variants associated with bipolar disorders, revealed new molecular mechanisms and identified genetic architecture. It also showed how large international samples can discover mechanisms of mental disorders.

     

  7. Jansen IE. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk. Nature Genetics 53, 1276–1282 (2021). https://doi.org/10.1038/s41588-021-00921-z 
     

    This paper discovered genetic variants associated with Alzheimer’s disease, applying novel phenotyping approaches, and revealed new functional pathways

     

  8. van der Meer, D. et al. Understanding the genetic determinants of the brain with MOSTest. Nature Communications 11, 3512 (2020). https://doi.org:10.1038/s41467-020-17368-1 
     

    This paper introduced the MOSTest methodology to boost statistical power which allow for discovery of novel genetic variants and revealed a distributed polygenetic pleiotropy across brain regions

     

  9. Frei, O. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nature Communications 10, 2417 (2019). https://doi.org:10.1038/s41467-019-10310-0 
     

    This paper presents a novel statistical tool that quantifies the polygenic overlap between two traits, irrespective of genetic correlation, thereby allowing novel discoveries without requiring access to raw genotyping information. A key novelty of this method is the discovery of genetic overlap even when genetic correlation is zero (mixed effect directions).

     

  10. Desikan, RS. et al. Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score. PLOS Medicine, 14(3):e1002258. (2017) https://doi.org/10.1371/journal.pmed.1002258 
     

    The study presents a genetic risk prediction tool, and the concept of estimating age of onset of Alzheimer’s disease from genetic data.

     

  11. Frei O. et al. Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets. Nat Genet. 2024 Jun;56(6):1310-1318. doi: 10.1038/s41588-024-01771-1 
     

    The study presents a functional annotation tools for more targeted follow up analysis of genetic associations allowing identification of more specific c pathway risk prediction tool, and the concept of estimating age of onset of Alzheimer’s disease from genetic data.