Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction

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RESEARCH ARTICLE Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction

Abstract

INTRODUCTION: Alzheimer's disease is a complex neurodegenerative disorder with heterogeneous genetic and molecular underpinnings. Polygenic scores capture little of this complexity.

METHODS: We conducted genome-, transcriptome-, and proteome-wide association studies on fifteen thousand four hundred eighty individuals from the Alzheimer's Disease Sequencing Project R Four to identify Alzheimer's disease-associated signals, followed by pathway enrichment analysis. Integrative risk models were developed using genetically regulated components of gene and protein expression and clinical covariates. Elastic-net logistic regression and random forest classifiers were evaluated using standard metrics and compared against baseline polygenic scores.

RESULTS: Known and novel signals were identified via genome-, transcriptome-, and proteome-wide association studies. Enrichment analyses highlighted cholesterol and immune signaling pathways. The best-performing integrative risk model, random forest with transcriptomic and covariate features, achieved area under the receiver operating characteristic curve of zero point seven zero three and area under the precision-recall curve of zero point six two two, significantly outperforming polygenic scores and baseline models.

DISCUSSION: Integrating univariate discovery approaches with multivariate modeling enhances Alzheimer's disease risk prediction and offers novel insights into underlying biological processes.

Highlights

· Identified novel contributions to Alzheimer's disease from a multi-omics perspective.

· Integrated genome-wide association studies, transcriptome-wide association studies, and proteome-wide association studies in a unified association study framework.

· Developed a method for predicting heritable risk of late-onset Alzheimer's disease.

· Demonstrated that ancestry-aware modeling improves Alzheimer's disease risk prediction accuracy.

One BACKGROUND

RESEARCH IN CONTEXT

Two METHODS

Two point two. Genome-wide association studies

Two point three. Transcriptome-wide association studies

Two point four. Proteome-wide association studies

Two point five. Gene-set enrichment analysis

Two point six. Baseline models

Two point six point two Genetic similarity ablation study

Two point seven Integrative Risk Models

Two point seven point two Random forest models with imputed data modalities

Three RESULTS

Three point two Association study and pathway enrichment analyses

Three point three IRM model performance

Three point four Feature importance

Four DISCUSSION

CONFLICT OF INTEREST STATEMENT

CONSENT STATEMENT

ORCID

Overview

The study addresses the complexity of Alzheimer's disease through integrative multi-omics approaches to identify genetic and molecular factors contributing to risk prediction. By analyzing data from the Alzheimer's Disease Sequencing Project, it proposes new models that outperform traditional polygenic scores in predicting Alzheimer's risk.

Key Points

1Multi-omics integration enhances understanding of Alzheimer's disease risk
2The study analyzed data from 15,480 individuals to identify risk factors
3Cholesterol and immune signaling pathways were highlighted through enrichment analysis
4Integrative risk models significantly outperformed baseline models and polygenic scores
5Ancestry-aware modeling improved risk prediction accuracy

Details

Authors: Rasika Venkatesh, Katie M. Cardone, Yuki Bradford, Anni K. Moore, Rachit Kumar, Jason H. Moore, Li Shen, Dokyoon Kim, Marylyn D. Ritchie
Category: Health and Medicine

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