Schizophrenia: From Genetics to Biology to Predictive Medicine
Alexander B. Niculescu, III, MD, PhD
Convergent Functional Genomics
Traditional genetic approaches have had limited success identifying genes involved in psychiatric disorders due to the genetic complexity and heterogeneity of these disorders. 1-3 Convergent functional genomics (CFG) was developed to integrate multiple lines of evidence, genetic and gene expression, from human and animal model studies, in order to increase the ability to detect signal from noise. CFG uses large experimental datasets as well as published data from the field of psychiatric genetics and genomics.
Once candidate genes are identified with CFG, the subsequent biological pathway analyses can lead to the development of mechanistic models. The CFG approach also increases the likelihood that the results will be both predictive and reproducible, an important litmus test for these types of studies.
An Example of CFG in Schizophrenia
A recent publication illustrated the value of CFG in schizophrenia.4 One important aspect of CFG is the ability of this approach to identify a signal where previous analyses have failed, which was the case in the original analysis of these data.5 The original genetic data were integrated with human postmortem brain gene expression data,6 human blood gene expression data,7 animal model brain and blood gene expression data,8 and relevant mouse model genetic evidence. After CFG identified 42 candidate genes, a genetic risk prediction score (GRPS) was developed from the nominally significant single-nucleotide polymorphisms (SNPs) in those genes. This GRPS was able to differentiate schizophrenia subjects from controls in 4 independent cohorts including 2 different ethnicities (AV 1).4
Biomarkers in Schizophrenia
A practical outcome from translational research is blood gene expression biomarkers. The examination of blood for biomarkers is a relatively recent phenomenon. There is the possibility of finding changes in blood cells that may reflect changes in the brain. This method has potential because the same signal transduction machinery can be present in different cells in the body.
The first proof of principle in identifying state biomarkers for psychosis symptoms was revealed in a recent study.7 The approach centered on finding biomarkers that reflected the severity of hallucinations or delusions. The methodology examined blood differences in patients with schizophrenia who had low versus high symptomatology. The goal was to determine if the biomarker panel could subsequently predict the degree of hallucinations and delusions in independent cohorts.
The top candidate biomarkers used in this sample generated predictive scores that demonstrated good sensitivity and negative predictive value in detecting high psychosis states.7 These results were consistent in both the original cohort and 3 additional cohorts. Overall, these results suggest that biomarker tests may be useful with early detection, intervention, and prevention efforts in patients with schizophrenia.
Conclusions and Future Directions in Genomic Research
The CFG approach incorporates multiple types of data in order to identify the best genes and biomarkers. Using CFG to choose and prioritize markers for panels helps ensure generalizability across independent cohorts. Diagnosis will always be a complex undertaking, in which the integration of clinical data, biomarker testing, genetic testing, imaging, and other modalities will be factored in as our knowledge evolves.
The goal of future research in schizophrenia is to strengthen the ability to diagnose patients early and to treat them in an individualized fashion. This approach will be based on a profile of genes, biomarkers, and quantitative phenotypic data and will use rational polypharmacy to get synergistic benefits and minimize side effects.
- Niculescu AB 3rd. Psychiatr Genet. 2006;16(6):241–244. doi:10.1097/01.ypg.0000242195.74268.f9 PubMed
- Niculescu AB, Le-Niculescu H. Neuropsychopharmacology. 2010;35(1):355–356. doi:10.1038/npp.2009.107 PubMed
- Niculescu AB, Lulow LL, Ogden CA, et al. Am J Med Genet B Neuropsychiatr Genet. 2006;141B(6):653–662. doi:10.1002/ajmg.b.30404 PubMed
- Ayalew M, Le-Niculescu H, Levey DF, et al. Mol Psychiatry. 2012;17(9):887–905. doi:10.1038/mp.2012.37 PubMed
- Purcell SM, Wray NR, Stone JL, et al. Nature. 2009;460(7256):748–752.PubMed
- Brennand KJ, Simone A, Jou J, et al. Nature. 2011;473(7346):221–225. doi:10.1038/nature09915 PubMed
- Kurian SM, Le-Niculescu H, Patel SD, et al. Mol Psychiatry. 2011;16(1):37–58. doi:10.1038/mp.2009.117 PubMed
- Le-Niculescu H, Balaraman Y, Patel S, et al. Am J Med Genet B Neuropsychiatr Genet. 2007;144B(2):129–158. doi:10.1002/ajmg.b.30481 PubMed