Genes Help Identify & Predict Schizophrenia Risk

Genes can be used to generate a genetic risk prediction score to aid schizophrenia diagnostics.
Genes can be used to generate a genetic risk prediction score to aid schizophrenia diagnostics.

Candidate genes, pathways and mechanisms for schizophrenia may be identified earlier than ever in a new testing method developed over the last decade. Schizophrenia affects 24 million people worldwide, and more than 50% of those diagnosed are not receiving proper care.1 Seven out of every 1,000 adults are affected, mostly between the ages of 15 to 35 years.1 Schizophrenia is diagnosed among men 1.4 more times than women.2


According to a study conducted by Mikias Ayalew, Helen Le-Niculescu, and colleagues, published May 15 in Molecular Psychiatry, study researchers found "significant genetic overlap [of schizophrenia] with other major psychiatric disorder domains, suggesting the need for improved nosology."3 Following this discovery, investigators selected nominally significant single-nucleotide polymorphisms (SNPs) from inside the top candidate genes identified by genome-wide association studies (GWAS) and developed a genetic risk prediction panel.

For their translational convergent functional genomics (CFG) data, researchers used earlier data they captured, plus data from published GWAS data sets for schizophrenia. The CFG targets genes that can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics.

Earlier captured data was integrated with the following to identify the top candidate genes:

  • Gene expression data—human postmortem brain gene expression data and human induced pluripotent stem cell-derived neuronal cells
  • Human blood gene expression data—published by the researchers and others, as well as human genetic data (linkage, copy number variant or association)
  • Mouse model genetic evidence

Investigators developed a polygenic GRPS determined by the presence or absence of the alleles of the SNPs associated with schizophrenia. The GRPS was evaluated in independent cohorts for which genotypic and clinical data were available, to compare persons with schizophrenia to normal controls. Two panels were tested: a smaller one containing the single best P-value SNP in the International Schizophrenia Consortium (ISC) in each of the top CFG prioritized genes (n=42), and a larger panel containing all the nominally significant SNPs (n=542) in ISC in the top CFG prioritized genes. Each SNP has two alleles: one associated with schizophrenia (affected), the other not (non-affected), based on the odds ratios from the discovery ISC GWAS. Affected alleles were assigned a point score of 1 and non-affected alleles a score of 0. A two-dimensional matrix of the subjects was constructed and populated according their GRP panel alleles. A SNP in an individual subject could have any permutation of 1 and 0 (1 and 1, 0 and 1, 0 and 0). The scores for all the alleles in the panel were added, averaged, and multiplied by 100, to generate a GRPS for each subject.

According to the study results, researchers demonstrated that the GRPS obtained from the larger panel can identify, at a population level, persons with schizophrenia compared to control subjects. However, researchers state that “at an individual level the margin is minimal,” further suggesting that environmental and other external factors and variables contribute to the risk of schizophrenia. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. This suggests that the latter two may be more environmentally driven or have a somewhat different genetic architecture.

Although the genetic risk test, called the "translational convergent functional genomics approach," is in the early stages, it could be developed commercially within three to five years. The researchers' work maps the "genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics."3


  • Analysis is the best yet. "Our analysis is arguably the most comprehensive integration of genetics and functional genomics to date in the field of schizophrenia, yielding a comprehensive view of genes, blood biomarkers, pathways and mechanisms that may underlie the disorder," researchers wrote.
  • A combined approach can be successful. Integration of functional and genotypic data can be used for complex disorders, both psychiatric and non-psychiatric. "What we are seeing across GWAS of complex disorders are not necessarily the same [single-nucleotide polymorphisms] showing the strongest signal, but rather consistency at the level of genes and biological pathways. The distance from genotype to phenotype may be a bridge too far for genetic-only approaches, given genetic heterogeneity and the intervening complex layers of epigenetics and gene expression regulation," researchers stated.
  • Evidence provided. Researchers say their work prioritizes a number of genes as candidate blood biomarkers for schizophrenia, with an inherited genetic basis. "While prior evidence existed as to alterations in gene expression levels of those genes in whole-blood samples or lymphoblastoid cell lines from schizophrenia patients, it was unclear prior to our analysis whether those alterations were truly related to the disorder or were instead related only to medication effects and environmental factors," they wrote.
  • Major role between gene expression and environment. According to researchers, "full-blown illness occurs when genetic and environmental factors converge, usually in young adulthood for schizophrenia. When they diverge, a stressful/hostile environment may lead to mild or transient illness even in normal genetic load individuals, whereas a favorable environment may lead to supra-normative functioning in certain life areas (such as creative endeavors) for individuals who carry a higher genetic load." 


1. World Health Organization. Schizophrenia. Available at: Accessed June 13, 2012.

2. Picchioni MM, Murray RM. Schizophrenia. BMJ. 2007 July 14; 335(7610): 91-95.

3. Ayalew M, Le-Niculescu H, Levey DF, et al. Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction. Molecular Psychiatry. Advance online publication. May 15, 2012. Available at: Accessed June 13, 2012.