Ultra-Accurate Complex Disorder Prediction: Case Study Of Neurodevelopmental Disorders

Abstract

Early prediction of complex disorders (e.g., autism and other neurodevelopmental disorders) is one of the fundamental goals of precision medicine and personalized genomics. An early prediction of complex disorders can have a significant impact on increasing the effectiveness of interventions and treatments in improving the prognosis and, in many cases, enhancing the quality of life in the affected patients. Considering the genetic heritability of neurodevelopmental disorders, we are proposing a novel framework for utilizing rare coding variation for early prediction of these disorders in subset of affected samples. We provide a novel formulation for the Ultra-Accurate Disorder Prediction (UADP) problem and develop a combinatorial framework for solving this problem. The primary goal of this framework, denoted as Odin (Oracle for DIsorder predictioN), is to make prediction for a subset of affected cases while having very low false positive rate prediction for unaffected samples. Note that in the Odin framework we will take advantage of the available functional information (e.g., pairwise coexpression of genes during brain development) to increase the prediction power beyond genes with recurrent variants. Application of our method accurately recovers an additional 8% of autism cases without a sever variant in a known recurrent mutated genes with a less than 1% false positive rate. Furthermore, Odin predicted a set of 391 genes that severe variants in these genes can cause autism or other developmental delay disorders. Odin is publicly available at https://github.com/HormozdiariLab/Odin{dagger}