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Combined Analysis of Large Genetic Samples: New Statistical Approaches Improve Gene Discovery

Published online by Cambridge University Press:  23 March 2020

O. Smeland ,
Y. Wang ,
K. Kauppi ,
O. Frei ,
A.M. Dale  and
O.A. Andreassen
O. Smeland
Affiliation:
Oslo university hospital, norment- kg jebsen centre for psychosis research- institute of clinical medicine, Oslo, Norway
Y. Wang
Affiliation:
University of Oslo, NORMENT- KG Jebsen centre for psychosis research- institute of clinical medicine, Oslo, Norway
K. Kauppi
Affiliation:
University of California- San Diego, department of radiology, San Diego, USA
O. Frei
Affiliation:
University of Oslo, NORMENT- KG Jebsen centre for psychosis research- institute of clinical medicine, Oslo, Norway
A.M. Dale
Affiliation:
University of California- San Diego, department of radiology, San Diego, USA
O.A. Andreassen
Affiliation:
Oslo university hospital, norment- kg jebsen centre for psychosis research- institute of clinical medicine, Oslo, Norway

Abstract

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Introduction

Cognitive dysfunction is recognized as a core feature of schizophrenia and is considered an important predictor of functional outcomes. Despite this, current treatment strategies largely fail to ameliorate these cognitive impairments. In order to develop more efficient treatment strategies, a better understanding of the pathogenesis of cognitive dysfunction is needed. Accumulating evidence indicates that genetic risk of schizophrenia contributes to cognitive dysfunction. However, the precise genetic variants jointly influencing schizophrenia and cognitive function remain to be determined.

Aims

Here, we aimed to identify gene loci shared between schizophrenia and general cognitive function, a phenotype that captures the shared variation in performance across several cognitive domains.

Methods

Using a Bayesian statistical framework, we compared genome-wide association study (GWAS) data on schizophrenia from the Psychiatric Genomics Consortium cohort (n = 79,757) with GWAS data on general cognitive function from the CHARGE Consortium (n = 53,949). By conditioning the false discovery rate (FDR) on shared associations, this statistical approach increases power to detect gene loci.

Results

We observed substantial polygenetic overlap between schizophrenia and general cognitive function, which replicated across independent schizophrenia sub-studies. Using the conditional FDR approach we increased discovery of gene loci and identified 13 loci shared between schizophrenia and general cognitive function. The majority of these loci (11/13) shows opposite directions of allelic effects in the phenotypes, in line with previous genetic studies and the observed cognitive dysfunction in schizophrenia.

Conclusions

Our study extends the current understanding of the genetic etiology influencing schizophrenia and general cognitive function by identifying shared gene loci between the phenotypes.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Type
Workshop: big data in psychiatry. unprecedented opportunities, new strategies
Information
European Psychiatry , Volume 41 , Issue S1: Abstract of the 25th European Congress of Psychiatry , April 2017 , pp. S56
Copyright
Copyright © European Psychiatric Association 2017
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