Advancing aging research: from computational tools to molecular and cellular insights
Embargo Date
2026-05-27
OA Version
Citation
Abstract
Aging is associated with dynamic changes across multiple biological systems. This project aims to advance aging research by investigating genotype information, molecular characteristics, and cell type composition.The first section of the project presents a computational pipeline for testing genotype-phenotype associations through the identification of quantitative trait loci (QTL). This pipeline integrates key steps in the QTL discovery process, including input data quality control, association testing, and results aggregation and visualization. It is applicable to datasets with or without family structure, leveraging linear and linear mixed-effects models. Developed using Nextflow, the pipeline supports process parallelization and automated workflow management.
The second section examines the relationship between aging and molecular features, with a particular focus on gene expression. RNA sequencing data from the Long Life Family Study (LLFS) was analyzed using linear mixed-effects models to identify transcriptomic signatures associated with aging, extreme longevity, and mortality risk.
Enrichment analysis revealed multiple gene sets reflecting key hallmarks of aging, including increased inflammatory signaling and reduced protein synthesis. Additionally, a transcriptomic aging clock was developed using an elastic net model, with its predicted biological age showing a significant association with mortality risk.
The final section of this thesis project investigates age-related changes in cell type composition using flow cytometry data from the same cohort. Additionally, cell type deconvolution was applied to the gene expression data mentioned above to evaluate state- of-the-art deconvolution methods. Notable trends in cell type composition changes were observed, including a decrease in naïve CD4 T cells and an increase in monocytes with age.
An important deliverable of the project was the derivation and annotation of signatures of age-associated phenotypes, and their comparison and integration with previous results. To address challenges in storing and organizing biological signatures across research projects, an R6 object, OmicSignature, was developed to streamline the management and retrieval of biological signatures in R programming environment. OmicSignature facilitates structured organization and efficient access to pre-existing signatures, enhancing workflow efficiency in biological research.
In summary, this project advances our understanding of the molecular and cellular mechanisms underlying human aging, and provides computational and analytical resources for future aging research.
Description
2025
License
Attribution-NonCommercial 4.0 International