{"doi":"10.1101/2022.01.03.22268705","title":"An integrated genome and phenome-wide association study approach to understanding Alzheimer’s disease predisposition","abstract":"ABSTRACT Background Genome-wide association studies (GWAS) have identified common, heritable alleles that increase late-onset Alzheimer’s disease (LOAD) risk. We recently published an analytic approach to integrate GWAS and phenome-wide association study (PheWAS) data, enabling identification of candidate traits and trait-associated variants impacting disease risk, and apply it here to LOAD. Methods PheWAS was performed for 23 known LOAD-associated single nucleotide polymorphisms (SNPs) and 4:1 matched control SNPs using UK Biobank data. Traits enriched for association with LOAD SNPs were ascertained and used to identify trait-associated candidate SNPs to be tested for association with LOAD risk (17,008 cases; 37,154 controls). Results LOAD-associated SNPs were significantly enriched for associations with 6/778 queried traits, including three platelet traits. The strongest enrichment was for platelet distribution width (PDW) (P=1.2×10 −5 ), but no consistent direction of effect was observed between increased PDW and LOAD susceptibility across variants or in Mendelian randomization analysis. Of 384 PDW-associated SNPs identified by prior GWAS, 36 were nominally associated with LOAD risk and 5 survived false-discovery rate correction for multiple testing. Associations confirmed known LOAD risk loci near PICALM, CD2AP, SPI1 , and NDUFAF6 , and identified a novel risk locus in the epidermal growth factor receptor ( EGFR ) gene. Conclusions Through integration of GWAS and PheWAS data, we identify substantial pleiotropy between genetic determinants of LOAD and of platelet morphology, and for the first time implicate EGFR – a mediator of β-amyloid toxicity – in Alzheimer’s disease susceptibility.","journal":"medRxiv","year":2022,"id":302449,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8454,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":655100,"name":"Courtney E. Wimberly","orcid":"0000-0002-7396-3479","position":1,"is_corresponding":false},{"id":338821,"name":"Eleanor C. Semmes","orcid":"0000-0003-2595-1948","position":2,"is_corresponding":false},{"id":325723,"name":"Jillian H. Hurst","orcid":"0000-0001-5079-9920","position":3,"is_corresponding":false},{"id":338823,"name":"Kyle M. Walsh","orcid":"0000-0002-5879-9981","position":4,"is_corresponding":false},{"id":912562,"name":"Archita S. Khaire","orcid":"0000-0001-6913-5323","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:32:16.279991Z","pmid":null,"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":[]}