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Prefrontal Inefficiency Is Associated With Polygenic Risk for Schizophrenia

Led by Hakon Heimer Posted on 6 Feb 2014

In our Forum discussion "journal club" series, the editors of Schizophrenia Bulletin provide access to the full text of a recent article. A short introduction by a journal editor gets us started, and then it's up to our readers to share their ideas and insights, questions, and reactions to the selected paper. So read on....

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An introduction by Associate Editor Vishwajit Nimgaonkar gets us started, and then it's up to our readers to share their ideas and insights, questions, and reactions to these papers.

Send in your comments now! The paper under discussion:

Prefrontal Inefficiency Is Associated With Polygenic Risk for Schizophrenia.
Walton E, Geisler D, Lee PH, Hass J, Turner JA, Liu J, Sponheim SR, White T, Wassink TH, Roessner V, Gollub RL, Calhoun VD, Ehrlich S. Schizophr Bull. 2013 Dec 10.


Background Text
By Vishwajit L. Nimgaonkar, Professor of Psychiatry and Human Genetics at the University of Pittsburgh and Associate Editor, Schizophrenia Bulletin

This manuscript is remarkable because it shows that highly accurate, individualized estimates of genetic risks for schizophrenia can be related to specific functional changes in the brain during magnetic resonance imaging (MRI) scans.

Schizophrenia is a common, severe disorder for which the causes are largely unknown. Based on extensive prior analyses, it is well known that much of its causation is heritable. Abnormalities in brain structure and function have been identified among patients with schizophrenia and have been widely replicated using MRI studies, but it has been challenging to relate such abnormalities precisely to the well documented genetic risk for schizophrenia. The problems occur primarily because it is difficult to quantify a given individual's genetic risk; this could previously be estimated only by using unreliable family history information. Recent genomewide association studies have pinpointed genetic risk to multiple DNA markers called single nucleotide polymorphisms (SNPs). Each SNP explains a very small fraction of the genetic risk for schizophrenia, but computing a summary score based on risk conferred by a combination of SNPs explains a much larger fraction of the genetic risk. This score, also called the polygenic risk score (PGRS), can be computed for individual patients if genotype assays are completed for a large number of SNPs across the genome by using highly accurate SNP arrays.

In the current study, brain imaging, genetic, and behavioral data from participants of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia from four participating sites were analyzed. Among 92 schizophrenia patients and 114 healthy controls for whom structural and brain imaging data were available after imaging quality control steps, individual PGRS estimates were obtained. Functional MRI was used to evaluate a conventional working memory task, previously shown to consistently activate the dorsolateral prefrontal cortex (DLPFC) in healthy controls and patients with schizophrenia. A positive association between PGRS and neural activity was evident in an area including the left DLPFC and left ventrolateral prefrontal cortex (VLPFC) by using a model that accounted for the effects of acquisition site, diagnosis, population stratification, and number of non-missing genotypes per individual. Very similar results were obtained when the choice of SNPs to estimate PGRS was based on two recently reported databases. Further, network analyses were conducted by using these SNPs.

This study is likely a harbinger of other studies that will use PGRS in more sophisticated brain imaging studies. Other remarkable aspects of the study include its use of MRI scans generated from four independent sites and the careful attention to detailed statistical analyses.

These are the questions we pose to our readers:

  • How might the analyses be extended to other brain imaging studies?
  • How should the PGRS risk estimates be refined?

Last comment on 29 Apr 2014 by Matcheri Keshavan


Submitted by Matcheri Keshavan on

The study by Walton et al. uniquely combines investigation of polygenic risk scores in schizophrenia (PGRS) with a well-known brain-based phenotype in this illness, i.e., prefrontal inefficiency while performing a working memory task. The strengths of this study include a rigorous multi-site imaging methodology, and replication of their finding of an association between PGRS and prefrontal inefficiency using data from two independent GWAS discovery samples. This study adds to a growing body of literature which suggests that the genetic basis of a complex disease such as schizophrenia may be more tractable than previously thought. The researchers' observation of a significant relationship between an imaging biomarker and PGRS, even with a modest sample size (by the standards of modern genetic studies), points to the power of investigating endo- (or intermediate) phenotypes, which are closer to the genetic etiology than phenotypic characteristics and symptom-based diagnoses. However, only 4.3 percent of the variance in the prefrontal biomarker was explained by PGRS. It is worth considering the potential reasons for this sobering observation, and potential directions for the future.

First, the etiological and pathophysiological heterogeneity of schizophrenia is important to keep in mind while examining the degree to which a given genotype-phenotype relationship is generalizable to the disease as a whole. Thus, only a part of the etiology of this complex illness may be related to polygenic risk factors, and as the authors point out, environmental risk and gene-gene and gene-environmental interactions must be considered. Further, prefrontal inefficiency may not be seen in all patients with the schizophrenia phenotype, and having a larger set of independent, and heritable, biomarkers might have strengthened the explanatory power of PGRS. It is also possible that examining a subgroup of schizophrenia subjects enriched for prefrontal inefficiency (such as patients with impaired executive function) might have led to stronger findings.

Second, there is increasing evidence that other major psychiatric illnesses such as bipolar disorder and major depression may also manifest prefrontal inefficiency (Jogia et al., 2012; Wagner et al., 2006). Clearly, an approach such as that used in this study needs to be agnostic to diagnoses (Keshavan et al., 2013); perhaps using expanded polygene scores from GWAS datasets of schizophrenia as well as bipolar and depressive disorder patients may be worthwhile.

Finally, inefficient recruitment of frontostriatal activity during working memory has been observed in unaffected young relatives of schizophrenia, pointing to its potential value as a premorbid risk marker (Diwadkar et al., 2012). This points to the promise of examining the genetic underpinnings of preclinical disease, which may one day assist in early diagnosis and preventive intervention.