Studying the ability of finding single and interaction effects with Random Forest, and its application in psychiatric genetics

Abstract

Psychotic disorders such as schizophrenia and bipolar disorder have a strong genetic component. The aetiology of psychoses is known to be complex, including additive effects from multiple susceptibility genes, interactions between genes, environmental risk factors, and gene by environment interactions. With the development of new technologies such as genome-wide association studies and imputation of ungenotyped variants, the amount of genomic data has increased dramatically leading to the necessary use of Machine Learning techniques. Random Forest has been widely used to study the underlying genetic factors of psychiatric disorders such as epistasis and gene-gene interactions. Several authors have investigated the ability of this algorithm in finding single and interaction effects, but have reported contradictory results. Therefore, in order to examine Random Forest ability of detecting single and interaction effects based on different variable importance measures, I conducted a simulation study assessing whether the algorithm was able to detect single and interaction models under different correlation conditions. The results suggest that the optimal Variable Importance Measures to use in real situations under correlation is the unconditional unscaled permutation variable importance measure. Several studies have shown bias in one of the most popular variable importance measures, the Gini importance. Hence, in a second simulation study I study whether the Gini variable importance is influenced by the variability of predictors, the precision of measuring them, and the variability of the error. Evidence of other biases in this variable importance was found. The results from the first simulation study were used to study whether genes related to 29 molecular biomarkers, which have been associated with schizophrenia, influence risk for schizophrenia in a case-control study of 26476 cases and 31804 controls from 39 different European ancestry cohorts. Single effects from ACAT2 and TNC genes were detected to contribute risk for schizophrenia. ACAT2 is a gene in the chromosome 6 which is related to energy metabolism. Transcriptional differences have been shown in schizophrenia brain tissue studies. TNC is expressed in the brain where is involved in the migration of the neurons and axons. In addition, we also used the simulation results to examine whether interactions between genes associated with abnormal emotion/affect behaviour influence risk for psychosis and cognition in humans, in a case-control study of 2049 cases and 1794 controls. Before correcting for multiple testing, significant interactions between CRHR1 and ESR1, and between MAPT and ESR1, and among CRHR1, ESR1 and TOM1L2, and among MAPT, ESR1 and TOM1L2 were observed in abnormal fear/anxiety-related behaviour pathway. There was no evidence for epistasis after Bonferroni correction.