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....
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:
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?
Please submit your comments to get this discussion going.