Genetic commonalities of autism spectrum disorder and schizophrenia

Abstract

The characterization of a disease through the detection of differentially expressed genes is of interest for early detection, diagnosis, prognosis, and prediction. Autism Spectrum Disorder (ASD) and Schizophrenia (SCZ) have been correlated in literature for sharing similar symptoms and behaviors, however, not at the genetic level. This work aims to correlate both conditions through the analysis of microarray datasets. Multiple Criteria Optimization was used to find the most significant gene expression changes common to both conditions. Thirty-six genes resulted from this first step. Subsequently, the most correlated structures were identified through the Traveling Salesman Problem and the Minimum Spanning Tree. Mathematical optimization, as a driver for gene selection and structuring, defines an analysis point of view advocated by our group that is different to biostatistics and bioinformatics. ASD and SCZ are analyzed simultaneously for the first time in this work owing to the capabilities of our group’s strategy.