Longevity Genomics Integrative genomic resource to develop translational strategies to promote longevity

Project 3 - LAG identification from gene expression or model organisms

LAG identification from gene expression

Rationale

Based on muscle gene expression from 15 young (25 year old) and 15 old (65 year old) participants, Sood et al. identified a 150 probe set that accurately classified young and old individuals in external studies with gene expression data collected from tissues other than muscle (Sood, Gallagher et al. 2015). A gene score based on the classifier was associated with better renal function, increased survival time over follow-up, and decreased Alzheimer’s Disease prevalence.

A large-scale study of gene expression from whole blood in 14,983 individuals of European ancestry identified 1497 genes that are differentially expressed with chronological age.

Approach

Once LAGs are identified using gene expression, at least two analyses are performed:

  1. Create genetic Instrumental Variables (gIVs) using GTEx for the Sood 150 probe set or the Peters 1497 gene set to test using Mendelian Randomization. See Project 4.

  2. Identify candidate Longevity-Associated Drugs (LADs) associated with the gene expression pattern found in younger individuals. See Project 5.

Data

Sood 150 probe set

Peters 1497 gene set

Software

longevityTools (R Package on GitHub)

Expected results

Prioritized LAGs.


LAG identification from model organisms

Human orthologs of genes associated with lifespan extension in model organisms are candidate LAGs.

More updates to come


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