Performing Gene-Gene Correlation Analysis Across Three Human Age Groups to Improve Biological Age Prediction Models
T. Grapendaal (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Marcel J.T. Reinders – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
I.C. den Hond – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Bram Pronk – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Gerard A. Bouland – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Kaitai Liang – Graduation committee member (TU Delft - Cyber Security)
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Abstract
Aging is the biological process that changes the body over time. When we age our bodies become more prone to disease and other health risks. But not everyone experiences these changes at the same age. This is because the age of our cells (biological age) does not always match our chronological age (time since birth). Being able to predict someone’s biological age and comparing it to their chronological age can be used to infer if someone is indeed more prone to diseases or other health risks.
Other studies have been able to predict the age of cells by using gene expressions. They explore the number of expressions in young and old individuals to identify genes that are affected by age. What has not yet been explored is how the correlation of gene pairs are affected by age. How genes cooperate can change with age, this can be captured by looking at how genes correlate and how that correlation changes with age. This paper will explore these correlations and answer the following question. By performing a correlation analysis between features of young individuals, and on the same features for old individuals, can we interpret any differences and use those to improve current age prediction models?
During this study we found a lot of gene pairs that have a significant difference in correlation from younger to older individuals. We also identified hub genes that change correlation with many other genes. Using these genes to train a linear regression model we were able to predict the age of cells with a Mean Absolute Error of 9.7835.
Using the hub genes we were not able to improve the current existing linear regression model. But we did identify genes that have earlier been linked to aging. Like LIMD2, but also a lot of ribosomal genes and mitochondrial genes, both of which lose functionality with aging.