OTTO: a new strategy to extract mental disease-relevant combinations of GWAS hits from individuals

Authored by H Ehrenreich, M Mitjans, der Auwera S Van, T P Centeno, M Begemann, H J Grabe, S Bonn, K-A Nave

Date Published: 2018

DOI: 10.1038/mp.2016.208

Sponsors: European Union German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

Despite high heritability of schizophrenia, genome-wide association studies (GWAS) have not yet revealed distinct combinations of single-nucleotide polymorphisms (SNPs), relevant for mental disease-related, quantifiable behavioral phenotypes. Here we propose an individual-based model to use genome-wide significant markers for extracting first genetic signatures of such behavioral continua. `OTTO' (old Germanic = heritage) marks an individual characterized by a prominent phenotype and a particular load of phenotype-associated risk SNPs derived from GWAS that likely contributed to the development of his personal mental illness. This load of risk SNPs is shared by a small squad of `similars' scattered under the genetically and phenotypically extremely heterogeneous umbrella of a schizophrenia end point diagnosis and to a variable degree also by healthy subjects. In a discovery sample of 41000 deeply phenotyped schizophrenia patients and several independent replication samples, including the general population, a gradual increase in the severity of `OTTO's phenotype' expression is observed with an increasing share of `OTTO's risk SNPs', as exemplified here by autistic and affective phenotypes. These data suggest a model in which the genetic contribution to dimensional behavioral traits can be extracted from combinations of GWAS SNPs derived from individuals with prominent phenotypes. Even though still in the `model phase' owing to a world-wide lack of sufficiently powered, deeply phenotyped replication samples, the OTTO approach constitutes a conceptually novel strategy to delineate biological subcategories of mental diseases starting from GWAS findings and individual subjects.

Tags

health population Inventory Association Brain Volume Autistic phenotypes Schizophrenia risk Variants