Heteroskedasticity as a Signature of Association for Age-Related Genes

Human aging is a process controlled by both genetics and environment. Many studies have been conducted to identify a subset of genes related to aging from the human genome. Biologists implicitly categorize age-related genes into genes that cause aging and genes that are influenced by aging, which resulted in both causal inference and inference of associations studies. While inference of association is better explored, causal inference and computational causal inference, remains less explored. In this work, we are primarily motivated to tackle the problem of identifying genes associated with aging, while having a brief look into genes with probable causal relations, both from a computational perspective. Specifically, we form a set of hypotheses and accordingly, introduce a data-tailored framework for inference. First we perform linear modeling on the expression values of age-related genes, and then examine the presence of heteroskedastic properties in the residual of the model. We evaluate this framework and our results suggest that, 1) presence of heteroskedasticity in these residuals is a potential signature of association for age-related genes, and 2) consistent heteroskedasticity along the human life span could imply some sort of causality. To our knowledge, along with identifying age-associated genes, this is the first work to propose a framework for computational causal inference on age-related genes, using a dataset of human dermal fibroblast gene expression data. Hence the results of our simple, yet effective approach can be used not only to assess future age-related genes, but also as a possible criterion to select new associative or potential causal genes with respect to aging.