Schizophrenia (SZ) is a severely debilitating mental illnesses. It is highly heritable, with concordance rates of 50-80% in monozygotic twins and substantial familial clustering. Phenomenology-based diagnostic systems in psychiatry have serious limitations resulting in classifications lacking clear boundaries and biological basis. With respect to SZ, there is extensive overlap with other disorders on many dimensions, including symptoms, neurophysiology, brain imaging, cognition, and pharmacotherapy. Our eventual goal is to develop a novel product termed """"""""Phyziotype"""""""", which will substantially improve the diagnosis of SZ by accessing the substantial contribution (50-80%) of genetic factors to the disease. The Phyziotype consists of a multi-gene ensemble of single nucleotide polymorphisms (SNPs) which, interpreted with a biomathematical algorithm, may predict the onset of SZ, more clearly delineate its diagnosis from related disorders, and distinguish between potential etiological subtypes. This Phase II Program is concerned with the discovery of the MRI DNA markers that form the foundation of the Phyziotype. The discovery of predictive biomarkers through association studies based on conventional psychiatric phenotypes has been limited by clinical confounders and the small effect sizes for individual markers. We hypothesize that """"""""endophenotypes"""""""", known subclinical vulnerability phenotypes, are more strongly associated to individual genes than clinical observations, and provide a powerful tool to discover predictive biomarkers. Functional magnetic resonance imaging (fMRI) of the brain is a versatile technique for measuring important psychiatric endophenotypes. This Program will utilize fMRI endophenotypes for association screening with total genome SNP arrays to identify MRI DNA markers relevant to the diagnosis of SZ. These biomarkers will advance our understanding of genetic risk factors and neurophysiology and may stimulate novel approaches to pharmaceutical development by refining psychiatric diagnosis. This Phase II program integrates the substantial physiogenomic capabilities of Genomas with the leading fMRI research of Dr. Godfrey Pearlson of Hartford Hospital's Institute of Living and the advanced neuroinformatics capabilities of Dr. Vincent Calhoun at the MIND Institute and the University of New Mexico. With ready access to a rich patient population at the Institute of Living and the MIND Research Network, the team has already integrated neuroimaging and physiogenomics as a novel platform for molecular analysis of mental illness, leading to two publications in 'Human Brain Mapping'and 'Annals of Biomedical Engineering'. This Phase II SBIR Program extends these studies to include 500 SZ patients and 250 healthy controls. All participants will be genotyped for 1,072,820 SNPs and 13,298 copy number variants using total genome arrays. Focused and Hypothesis-free Modes of physiogenomic analysis with fMRI endophenotypes will be used to discover MRI DNA markers for SZ. Physiogenomic models incorporating multiple SNPs will be developed as research prototypes for a Phyziotype system.

Public Health Relevance

Schizophrenia (SZ) is a severely debilitating mental illnesses, currently afflicting 2.4 million American adults. The Phase II program will utilize fMRI endophenotypes and total genome SNP screening to identify MRI DNA markers relevant to schizophrenia. MRI DNA markers will eventually allow the development of systems that refine the clinical diagnosis of mental illness and may predict the individual susceptibility to schizophrenia and other disorders. The development of the actual diagnostic product will be pursued during Phase III as an FDA-approved diagnostic system for personalized mental health.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-ETTN-C (11))
Program Officer
Grabb, Margaret C
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Genomas, Inc.
United States
Zip Code
Tandon, Neeraj; Nanda, Pranav; Padmanabhan, Jaya L et al. (2017) Novel gene-brain structure relationships in psychotic disorder revealed using parallel independent component analyses. Schizophr Res 182:74-83
Mokhtari, Mohammadreza; Narayanan, Balaji; Hamm, Jordan P et al. (2016) Multivariate Genetic Correlates of the Auditory Paired Stimuli-Based P2 Event-Related Potential in the Psychosis Dimension From the BSNIP Study. Schizophr Bull 42:851-62
Narayanan, Balaji; Ethridge, Lauren E; O'Neil, Kasey et al. (2015) Genetic Sources of Subcomponents of Event-Related Potential in the Dimension of Psychosis Analyzed From the B-SNIP Study. Am J Psychiatry 172:466-78
Narayanan, B; Soh, P; Calhoun, V D et al. (2015) Multivariate genetic determinants of EEG oscillations in schizophrenia and psychotic bipolar disorder from the BSNIP study. Transl Psychiatry 5:e588
Meda, Shashwath A; Ruaño, Gualberto; Windemuth, Andreas et al. (2014) Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia. Proc Natl Acad Sci U S A 111:E2066-75
Book, Gregory A; Anderson, Beth M; Stevens, Michael C et al. (2013) Neuroinformatics Database (NiDB)--a modular, portable database for the storage, analysis, and sharing of neuroimaging data. Neuroinformatics 11:495-505
Jagannathan, Kanchana; Calhoun, Vince D; Gelernter, Joel et al. (2010) Genetic associations of brain structural networks in schizophrenia: a preliminary study. Biol Psychiatry 68:657-66
Liu, Jingyu; Pearlson, Godfrey; Windemuth, Andreas et al. (2009) Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA. Hum Brain Mapp 30:241-55