{"doi":"10.1002/alz.043922","title":"Polygenic risk scores can predict AD‐related pathologies","abstract":"<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Alzheimer’s disease is defined by the presence of amyloid plaques and tau tangles in the brain leading to cognitive decline. Risk for AD is multifactorial with genetic factors playing a large role in disease risk. Specifically, presence of the <jats:italic>APOE</jats:italic> e4 allele is a strong risk factor for late onset AD. Beyond <jats:italic>APOE</jats:italic>, many variants have been identified as risk factors for AD, however with smaller effects.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>Polygenic risk scores are a well‐established method for combining information across genetic factors with individually small effects on disease risk to predict an individual’s disease risk. We use this approach to develop genetic predictors of AD‐related pathologies such as amyloid. Specifically, we developed a PRS, that in combination with age and sex, is predictive of the presence of amyloid in an individual’s brain. We use pathological measures of protein aggregates in postmortem brain tissue as our training data set for model development however our tests can be applied antemortem via a blood test. We evaluate performance of our PRS and previously published scores in all individuals and subset by <jats:italic>APOE</jats:italic> e4 status.</jats:p></jats:sec><jats:sec><jats:title>Result</jats:title><jats:p>We show the performance of our PRSs to predict AD related pathologies in independent validation datasets. We also show performance metrics compared to PET and CSF measurements within the ADNI dataset as a comparison to established methods used in AD patients. Finally, in comparison to published AD PRSs, we found our pathology PRSs performed as well or better than in predicting specific pathology phenotypes. When used as a screening tool for clinical trial enrollment, our PRS assays can reduce the overall cost of patient enrollment by reducing the number of screen failures after a negative amyloid PET scan.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our PRSs are predictive of AD related pathologies and can be useful as a screening tool within the context of a clinical trial. The cost savings for clinical trial enrollment varies based on the size of the trial. Thus, incorporating genetics early within a clinical trial will be valuable for enrollment. This information can also be used to identify important biomarkers of response as the trial progresses.</jats:p></jats:sec>","journal":"Alzheimer's &amp; Dementia","year":2020,"id":629492,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1630330,"name":"Julie Collens","orcid":null,"position":1,"is_corresponding":false},{"id":107088,"name":"Thomas G. Beach","orcid":"0000-0003-3296-6128","position":2,"is_corresponding":false},{"id":230680,"name":"Carlos Cruchaga","orcid":"0000-0002-0276-2899","position":3,"is_corresponding":false},{"id":66448,"name":"Lon Schneider","orcid":null,"position":4,"is_corresponding":false},{"id":1630333,"name":"Kaanan P. Shah","orcid":null,"position":5,"is_corresponding":false},{"id":1630328,"name":"Jared Cara","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Polygenic risk scores can predict AD‐related pathologies","abstract":"Abstract Background Alzheimer’s disease is defined by the presence of amyloid plaques and tau tangles in the brain leading to cognitive decline. Risk for AD is multifactorial with genetic factors playing a large role in disease risk. Specifically, presence of the APOE e4 allele is a strong risk factor for late onset AD. Beyond APOE , many variants have been identified as risk factors for AD, however with smaller effects. Method Polygenic risk scores are a well‐established method for combining information across genetic factors with individually small effects on disease risk to predict an individual’s disease risk. We use this approach to develop genetic predictors of AD‐related pathologies such as amyloid. Specifically, we developed a PRS, that in combination with age and sex, is predictive of the presence of amyloid in an individual’s brain. We use pathological measures of protein aggregates in postmortem brain tissue as our training data set for model development however our tests can be applied antemortem via a blood test. We evaluate performance of our PRS and previously published scores in all individuals and subset by APOE e4 status. Result We show the performance of our PRSs to predict AD related pathologies in independent validation datasets. We also show performance metrics compared to PET and CSF measurements within the ADNI dataset as a comparison to established methods used in AD patients. Finally, in comparison to published AD PRSs, we found our pathology PRSs performed as well or better than in predicting specific pathology phenotypes. When used as a screening tool for clinical trial enrollment, our PRS assays can reduce the overall cost of patient enrollment by reducing the number of screen failures after a negative amyloid PET scan. Conclusion Our PRSs are predictive of AD related pathologies and can be useful as a screening tool within the context of a clinical trial. The cost savings for clinical trial enrollment varies based on the size of the trial. Thus, incorporating genetics early within a clinical trial will be valuable for enrollment. This information can also be used to identify important biomarkers of response as the trial progresses.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W3112930927","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.09930963,"influential_citations":0,"citation_trend":[],"oa_status":"bronze","license":null,"oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/alz.043922","host_type":"journal"},{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/alz.043922","host_type":"publisher"},{"url":"https://doi.org/10.1002/alz.043922","host_type":"journal"}],"fields_of_study":["Bioinformatics and Genomic Networks","Alzheimer's disease research and treatments","Gene expression and cancer classification"],"mesh_terms":[],"keywords":["Apolipoprotein E","Disease","Medicine","Pathological","Polygenic risk score","Alzheimer's Disease Neuroimaging Initiative","Alzheimer's disease","Bioinformatics","Internal medicine","Genotype","Biology","Single-nucleotide polymorphism","Gene","Genetics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T18:49:10.252861Z","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":[]}