Model-based gene selection shows engrailed 1 is associated with antipsychotic response.
OBJECTIVE
Schizophrenia is an highly heritable complex disorder with a significant impact on public health. A variety of antipsychotics are available for treatment of the disorder and individual response to treatment is variable. To date, only a limited number of potential candidate genes have been examined for genetic association with treatment response. As there is lack of understanding of disease etiology and variation in treatment response, a large number of additional genes are potential targets for investigation. A variety of strategies for selecting candidate genes for further investigation are available and in most cases information used is weighed and ranked intuitively by the investigator. We sought to find genes that may influence treatment response in a less biased manner, after integrating heterogeneous data sources related to schizophrenia.


METHODS
A method to select liability and treatment response candidate genes for schizophrenia using multiple data sets was constructed. The method successfully selected DTNBP1, a strong candidate gene for schizophrenia. We then evaluated novel genes picked by the method. Thirty-six single nucleotide polymorphisms in two genes engrailed 1 (EN1) and secretin receptor (SCTR) were genotyped in the Clinical Antipsychotic Trials of Intervention Effectiveness study sample. Outcomes analyzed were the Positive and Negative Syndrome Scale and six different neurocognitive measures.


RESULTS
Several of the seven single nucleotide polymorphisms genotyped in the EN1 gene were associated with schizophrenia symptoms (smallest P value=0.0061) and the effects of antipsychotics on symptoms (smallest P value=2.4x10). The estimated probabilities of being a false discovery were 0.14 for symptoms and 0.0012 for drug response.


CONCLUSIONS
These findings show that EN1 may influence individual variation in response to antipsychotics. In addition, model-based data integration of schizophrenia-related data seems to improve the prior probability of selecting genes that have an effect on antipsychotics response.
Webb
Bradley Todd
BT
Center for Biomarker Research and Personalized Medicine, Department of Pharmacy, Medical College of Virginia Campus, Virginia Commonwealth University, Richmond, VA 23298, USA. btwebb@vcu.edu
Sullivan
Patrick F
PF
Skelly
Tara
T
van den Oord
Edwin J C G
EJ
D014150:Antipsychotic Agents / Q000627:therapeutic use*; D018398:Homeodomain Proteins / Q000235:genetics*; D006801:Humans; D008954:Models, Biological; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D043562:Receptors, G-Protein-Coupled / Q000235:genetics*; D011964:Receptors, Gastrointestinal Hormone / Q000235:genetics*; D012559:Schizophrenia / Q000188:drug therapy* / Q000235:genetics*; D012641:Selection, Genetic
D014150:Antipsychotic Agents; C513076:EN1 protein, human; D018398:Homeodomain Proteins; D043562:Receptors, G-Protein-Coupled; D011964:Receptors, Gastrointestinal Hormone; C029937:secretin receptor