Schizophrenia (SCZ) is a devastating illness characterized by severe impairments in neurocognition and brain function. Cognitive deficits seriously impair daily functioning and patient outcome, and are not improved by current medications. The fundamental neural mechanisms underlying the cognitive and brain function impairments are essentially unknown, which is a critical barrier to progress in developing effective treatments and early interventions to improve prognosis. This application proposes to address this problem by contributing knowledge of the genetic causes of brain dysfunction in SCZ. Until recently, SCZ genetic research has been obstructed by poorly phenotyped and underpowered patient populations, and limited genotyping technologies. This application proposes to harness the power and enormous effort of recent genome-wide association studies (GWAS) of SCZ that collectively examined over 50,000 SCZ and control subjects, and conclusively identified sequence variants throughout the genome that confer increased risk of SCZ. While providing invaluable data on the genetic architecture of SCZ, the majority of existing GWAS samples has sparse clinical and phenotypic data that limit their utility in delineating the disease relevance of these risk variants. The research proposed here will investigate the established SCZ risk variants and polygenic risk factors identified by prior GWAS to identify associations with neural correlates of intermediate phenotypes. This investigation will be bolstered by a large collection of 6,400 SCZ and control subjects with extensive phenotypic data available only to the Investigators on this application. The sample collection contains extensive data assessing attention, working memory, and other cognitive domains that are impaired in SCZ, as well as structural imaging data assessing brain structural features. Furthermore, genome-wide SNP data are available for over half of the subjects, and the remaining subjects have DNA available for genotyping. Nearly all of the data utilized by this project are already collected and available to the investigators. Subject recruitment, collection of phenotypic data and DNA, and genome-wide SNP data, as well as GWAS meta- analyses that identified the SCZ risk variants we will examine, were all funded under other NIH grants or by other agencies. The funds requested in this application for nominal genotyping and data analysis are negligible in comparison to these previous efforts, yet will exponentially build upon and add enormous value to those efforts. Our plan to seize upon seminal new findings in the SCZ genetics field is expected to obtain critical insight into the genetic underpinnings of neural dysfunctions at the core of SCZ pathophysiology.

Public Health Relevance

This work is relevant to public health because it is expected to improve knowledge of the cause of abnormal brain function and cognitive impairment in schizophrenia patients. This information may aid the development of early intervention and treatment approaches that will improve outcome for patients.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH092380-03
Application #
8717729
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Senthil, Geetha
Project Start
2012-09-10
Project End
2016-07-31
Budget Start
2014-08-08
Budget End
2015-07-31
Support Year
3
Fiscal Year
2014
Total Cost
$413,624
Indirect Cost
$111,559
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
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Konishi, Jun; Del Re, Elisabetta C; Bouix, Sylvain et al. (2018) Abnormal relationships between local and global brain measures in subjects at clinical high risk for psychosis: a pilot study. Brain Imaging Behav 12:974-988
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Margulies, Daniel S; Ghosh, Satrajit S; Goulas, Alexandros et al. (2016) Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A 113:12574-12579
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