Basic Science and Pathogenesis | JoVE Visualize

Area of Science:

Genomics
Proteomics
Bioinformatics
Neurodegenerative Diseases

Background:

Growing availability of large genetic and proteomic datasets necessitates scalable, integrated omics analysis methods for Alzheimer's disease (AD).
Eight distinct genomic and proteomic analysis tools have been developed for the AD community, accessible via the Global Research and Imaging Platform (GRIP).
These tools aim to enhance accuracy and interpretability in diverse AD cases, covering network discovery, confounder removal, ancestry inference, variant annotation, and data harmonization.

Purpose of the Study:

To introduce and highlight two novel methods: generative PCR (gPCR) for phenotypically relevant network detection and FairPCA for confounder effect removal.
To demonstrate the utility of gPCR in learning AD-associated proteomic networks.
To showcase FairPCA's effectiveness in removing confounding factors like sex from omics data and in genome-wide association studies (GWAS).

Main Methods:

gPCR, an improvement over supervised variational autoencoders (SVAEs), generates phenotypically relevant latent components for omics data.
FairPCA, an adversarial learning algorithm, ensures principal components are orthogonal to undesired confounders and scales to large datasets using randomization.
Both methods were evaluated on proteomic data (SomaScan, TMT-MS) from 300 AD samples and in simulated GWAS.

Main Results:

gPCR achieved superior predictive performance (AUC=0.94) compared to traditional PCR (AUC=0.62) and matched elastic net regression (AUC=0.94) while identifying relevant protein networks.
FairPCA demonstrated improved AD status prediction (AUC=0.83) over traditional PCA (AUC=0.64) when removing sex as a confounder.
In GWAS simulations, FairPCA significantly improved the accuracy of effect size estimation (cosine similarity from 0.71 to 0.99).

Conclusions:

gPCR preserves predictive power while enhancing generative capabilities for omics analysis.
FairPCA effectively simplifies the removal of confounding factors in large-scale omics datasets.
These tools, along with others developed for GRIP, are poised to significantly accelerate scientific discovery in Alzheimer's disease research.