Covariance-Based Sample Selection for Heterogeneous Data: Applications to Gene Expression and Autism Risk Gene Detection

Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, guilt by association methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to affect the gene expression's covariance, previous research has focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called covariance-based sample selection (COBS) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBSs effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWNs ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Automated summary

Genetic mutations can influence the risk of autism, with genes showing highly correlated expressions often being functionally interrelated. The COBS method aims to find a more homogeneous subset of samples to improve the detection of risk genes. By utilizing genetic risk scores from different data freezes, COBS has shown to enhance DAWN's ability to predict risk genes in newer datasets.