{"doi":"10.1002/alz.70886","title":"Integrative multi‐omics approaches identify molecular pathways and improve Alzheimer's disease risk prediction","abstract":"INTRODUCTION: Alzheimer's disease (AD) is a complex neurodegenerative disorder with heterogeneous genetic and molecular underpinnings. Polygenic scores (PGS) capture little of this complexity. METHODS: We conducted genome-, transcriptome-, and proteome-wide association studies (G/T/PWAS) on 15,480 individuals from the Alzheimer's Disease Sequencing Project R4 (ADSP) to identify AD-associated signals, followed by pathway enrichment analysis. Integrative risk models (IRMs) 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 PGS. RESULTS: Known and novel signals were identified via G/T/PWAS. Enrichment analyses highlighted cholesterol and immune signaling pathways. The best-performing IRM, random forest with transcriptomic and covariate features, achieved area under the receiver operating characteristic (AUROC) of 0.703 and area under the precision-recall curve (AUPRC) of 0.622, significantly outperforming PGS and baseline models. DISCUSSION: Integrating univariate discovery approaches with multivariate modeling enhances AD risk prediction and offers novel insights into underlying biological processes. HIGHLIGHTS: Identified novel contributions to Alzheimer's disease (AD) from a multi-omics perspective. Integrated genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), and proteome-wide association studies (PWAS) in a unified association study framework. Developed a method for predicting heritable risk of late-onset AD. Demonstrated that ancestry-aware modeling improves AD risk prediction accuracy.","journal":"Alzheimer s & Dementia","year":2025,"id":522329,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8631,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1205133,"name":"Katie M. Cardone","orcid":null,"position":1,"is_corresponding":false},{"id":98505,"name":"Yuki Bradford","orcid":null,"position":2,"is_corresponding":false},{"id":438425,"name":"Anni Moore","orcid":"0000-0003-1953-6449","position":3,"is_corresponding":false},{"id":651628,"name":"Rachit Kumar","orcid":"0000-0002-7736-3307","position":4,"is_corresponding":false},{"id":14812,"name":"Jason H. Moore","orcid":"0000-0002-5015-1099","position":5,"is_corresponding":false},{"id":988143,"name":"Li Shen","orcid":"0000-0001-6520-1418","position":6,"is_corresponding":false},{"id":633926,"name":"Dokyoon Kim","orcid":"0000-0002-4592-9564","position":7,"is_corresponding":false},{"id":22049,"name":"Marylyn D. Ritchie","orcid":"0000-0002-1208-1720","position":8,"is_corresponding":false},{"id":42535,"name":"Rasika Venkatesh","orcid":"0009-0003-4471-4291","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-19T02:49:54.058858Z","pmid":"41231230","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}