  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.
