{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T13:06:52Z","timestamp":1780060012391,"version":"3.54.0"},"posted":{"date-parts":[[2026,5,27]]},"group-title":"Health Informatics","reference-count":46,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"funder":[{"DOI":"10.13039\/100015539","name":"the Australian Government","doi-asserted-by":"crossref","award":["IC170100030"],"award-info":[{"award-number":["IC170100030"]}],"id":[{"id":"10.13039\/100015539","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,5,27]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To systematically evaluate pathway-informed polygenic risk score (PRS) strategies and determine which approaches most effectively leverage biological annotations for risk prediction, using brain amyloid-\u03b2 (A\u03b2) positivity as a case study.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>\n                    We systematically benchmarked approaches for integrating pathway information into PRSs construction to predict brain A\u03b2 positivity. Using two cohorts, the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI, n = 969) and Australian Imaging, Biomarkers and Lifestyle (AIBL, n = 251), we compared Apolipoprotein E (\n                    <jats:italic>APOE<\/jats:italic>\n                    ) genetic risk score (GRS), clumping and thresholding (C+T) PRS, pathway-guided single nucleotide polymorphism (SNP) selection PRS, and pathway-specific PRSs ensembled via machine learning. Pathways were derived from manually curated literature or from pathway databases via Functional Mapping and Annotation (FUMA).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    In cross-validation on the ADNI cohort, pathway-informed PRS using a narrow-set of pathways to guide SNP selection (PathPRS-SNP\n                    <jats:sub>Lit<\/jats:sub>\n                    without\n                    <jats:italic>APOE<\/jats:italic>\n                    locus) significantly outperformed the standard PRS model (median AUC = 0.742, p = 0.006) and the APOE locus model (median AUC = 0.736, p = 5.1 \u00d7 10\n                    <jats:sup>-5<\/jats:sup>\n                    ) based on the Mann\u2013Whitney U test, achieving a median AUC of 0.763. This model showed enhanced ability to identify subgroups within the 10% lowest-and highest risk groups compared to the current standard of\n                    <jats:italic>APOE<\/jats:italic>\n                    locus alone (odds ratio = 0.67, 95% CI: 0.56\u20130.81; and OR = 13.23, 95% CI: 10.23\u201317.11), highlighting its clinical potential. Using a focused set\n                    <jats:bold>l<\/jats:bold>\n                    iterature-curated pathways outperformed using a broader set of database-derived pathways across configurations. When contrasting strategies for aggregating information across pathways, we observed that using pathways to guide selection of SNPs and then building a single PRS performed comparably to building PRS for each pathway and using machine learning (ML) to aggregate these, though the latter enabled pathway-level interpretability.\n                    <jats:bold>S<\/jats:bold>\n                    imilar trends were observed in the external AIBL validation dataset\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Interpretation<\/jats:title>\n                  <jats:p>Pathway-informed PRS can meaningfully improve genetic risk enrichment for A\u03b2 positivity beyond APOE and standard C+T approaches, provided pathway definitions are carefully curated. The choice of pathway source has the strongest impact on predictive performance with aggregation strategies or ML model choice having far less impact. Our findings highlight the utility of literature-curated, pathway-informed PRSs for A\u03b2 prediction and offer practical guidance for pathway-informed PRS construction in other polygenic traits.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.05.25.26354071","type":"posted-content","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T19:25:15Z","timestamp":1779909915000},"source":"Crossref","is-referenced-by-count":0,"title":["Comparing Pathway-Informed Polygenic Risk Score Strategies: A multi-cohort evaluation of Amyloid-\u03b2"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9473-2611","authenticated-orcid":false,"given":"Xiyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"Florey Institute, The University of Melbourne, Melbourne, VIC 3010, Australia"},{"name":"ARC Training Centre in Cognitive Computing for Medical Technologies, The University of Melbourne, Melbourne, VIC 3053, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Goudey","sequence":"additional","affiliation":[{"name":"Florey Institute, The University of Melbourne, Melbourne, VIC 3010, Australia"},{"name":"ARC Training Centre in Cognitive Computing for Medical Technologies, The University of Melbourne, Melbourne, VIC 3053, Australia"},{"name":"Australia BioCommons, The University of Melbourne, Melbourne, VIC3051, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon M.","family":"Laws","sequence":"additional","affiliation":[{"name":"Centre for Precision Health, Edith Cowan University, Perth, WA 6027, Australia"},{"name":"Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Perth, WA 6027, Australia"},{"name":"Curtin Medical School, Curtin University, Perth, WA 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin L.","family":"Masters","sequence":"additional","affiliation":[{"name":"Florey Institute, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Baldwin","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noel G.","family":"Faux","sequence":"additional","affiliation":[{"name":"Florey Institute, The University of Melbourne, Melbourne, VIC 3010, Australia"},{"name":"ARC Training Centre in Cognitive Computing for Medical Technologies, The University of Melbourne, Melbourne, VIC 3053, Australia"},{"name":"Melbourne Data Analytics Platform, The University of Melbourne, Parkville, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026052905100505000_2026.05.25.26354071v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(09)70299-6"},{"key":"2026052905100505000_2026.05.25.26354071v1.2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1997.03550160069041"},{"key":"2026052905100505000_2026.05.25.26354071v1.3","unstructured":"Kunkle BW , Grenier-Boley B , Sims R , et al. Genetic meta-analysis of diagnosed Alzheimer\u2019s disease identifies new risk loci and implicates A\u03b2, tau, immunity and lipid processing."},{"key":"2026052905100505000_2026.05.25.26354071v1.4","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-020-0353-1"},{"key":"2026052905100505000_2026.05.25.26354071v1.5","doi-asserted-by":"publisher","DOI":"10.1002\/ana.25530"},{"key":"2026052905100505000_2026.05.25.26354071v1.6","doi-asserted-by":"publisher","DOI":"10.1002\/ANA.25918"},{"issue":"4","key":"2026052905100505000_2026.05.25.26354071v1.7","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1002\/ana.26588","article-title":"Polygenic Effect on Tau Pathology Progression in Alzheimer\u2019s Disease","volume":"93","year":"2023","journal-title":"Ann Neurol"},{"issue":"2","key":"2026052905100505000_2026.05.25.26354071v1.8","first-page":"473","article-title":"Pathway-Specific Polygenic Risk Scores as Predictors of Amyloid-\u03b2 Deposition and Cognitive Function in a Sample at Increased Risk for Alzheimer\u2019s Disease","volume":"55","year":"2017","journal-title":"J Alzheimers Dis"},{"issue":"1","key":"2026052905100505000_2026.05.25.26354071v1.9","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s13195-022-01079-4","article-title":"Association of Alzheimer\u2019s disease polygenic risk scores with amyloid accumulation in cognitively intact older adults","volume":"14","year":"2022","journal-title":"Alzheimers Res Ther"},{"key":"2026052905100505000_2026.05.25.26354071v1.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-24082-z"},{"key":"2026052905100505000_2026.05.25.26354071v1.11","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18534-1"},{"key":"2026052905100505000_2026.05.25.26354071v1.12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2015.12.056"},{"key":"2026052905100505000_2026.05.25.26354071v1.13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1010624"},{"key":"2026052905100505000_2026.05.25.26354071v1.14","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181cb3e25"},{"key":"2026052905100505000_2026.05.25.26354071v1.15","doi-asserted-by":"publisher","DOI":"10.1017\/S1041610209009405"},{"key":"2026052905100505000_2026.05.25.26354071v1.16","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.114.148981"},{"key":"2026052905100505000_2026.05.25.26354071v1.17","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-021-00836-1"},{"key":"2026052905100505000_2026.05.25.26354071v1.18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.08.044"},{"key":"2026052905100505000_2026.05.25.26354071v1.19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2015.05.009"},{"issue":"1","key":"2026052905100505000_2026.05.25.26354071v1.20","doi-asserted-by":"crossref","first-page":"443","DOI":"10.3233\/ADR-210005","article-title":"Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer\u2019s Disease","volume":"5","year":"2021","journal-title":"J Alzheimers Dis Rep"},{"key":"2026052905100505000_2026.05.25.26354071v1.21","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3656"},{"key":"2026052905100505000_2026.05.25.26354071v1.22","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3679"},{"key":"2026052905100505000_2026.05.25.26354071v1.23","doi-asserted-by":"publisher","DOI":"10.1038\/nature15393"},{"key":"2026052905100505000_2026.05.25.26354071v1.24","doi-asserted-by":"crossref","unstructured":"Chang CC , Chow CC , Tellier LC , Vattikuti S , Purcell SM , Lee JJ . Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.","DOI":"10.1186\/s13742-015-0047-8"},{"issue":"2","key":"2026052905100505000_2026.05.25.26354071v1.25","first-page":"801","article-title":"Pathway-Specific Genetic Risk for Alzheimer\u2019s Disease Differentiates Regional Patterns of Cortical Atrophy in Older Adults","volume":"30","year":"2020","journal-title":"Cereb Cortex"},{"key":"2026052905100505000_2026.05.25.26354071v1.26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2018.01.005"},{"issue":"1","key":"2026052905100505000_2026.05.25.26354071v1.27","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1038\/s41398-020-01018-7","article-title":"Immune response and endocytosis pathways are associated with the resilience against Alzheimer\u2019s disease","volume":"10","year":"2020","journal-title":"Transl Psychiatry"},{"key":"2026052905100505000_2026.05.25.26354071v1.28","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-017-01261-5"},{"key":"2026052905100505000_2026.05.25.26354071v1.29","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2802"},{"key":"2026052905100505000_2026.05.25.26354071v1.30","unstructured":"Kingma DP , Ba J. Adam: a method for stochastic optimization. Published online December 22, 2014. Accessed February 20, 2025. arXiv:1412.6980."},{"key":"2026052905100505000_2026.05.25.26354071v1.31","unstructured":"Maas AL , Hannun AY , Ng AY . Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th International Conference on Machine Learning (ICML 2013), Deep Learning Workshop; 2013; Atlanta, GA."},{"key":"2026052905100505000_2026.05.25.26354071v1.32","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2010.04.018"},{"key":"2026052905100505000_2026.05.25.26354071v1.33","unstructured":"Paszke A , Gross S , Massa F , et al. PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NeurIPS); 2019; Vancouver, Canada."},{"key":"2026052905100505000_2026.05.25.26354071v1.34","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"2026052905100505000_2026.05.25.26354071v1.35","doi-asserted-by":"publisher","DOI":"10.1016\/0197-2456(86)90046-2"},{"key":"2026052905100505000_2026.05.25.26354071v1.36","doi-asserted-by":"publisher","DOI":"10.1016\/j.tem.2023.05.002"},{"key":"2026052905100505000_2026.05.25.26354071v1.37","doi-asserted-by":"crossref","unstructured":"Cai Y , Liu J , Wang B , Sun M , Yang H . Microglia in the Neuroinflammatory Pathogenesis of Alzheimer\u2019s Disease and Related Therapeutic Targets. Front Immunol. 2022;13:856376.","DOI":"10.3389\/fimmu.2022.856376"},{"key":"2026052905100505000_2026.05.25.26354071v1.38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2020.10.008"},{"key":"2026052905100505000_2026.05.25.26354071v1.39","doi-asserted-by":"publisher","DOI":"10.1016\/S0021-9258(17)32449-3"},{"key":"2026052905100505000_2026.05.25.26354071v1.40","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.3487-13.2013"},{"issue":"8","key":"2026052905100505000_2026.05.25.26354071v1.41","doi-asserted-by":"crossref","first-page":"e1011543","DOI":"10.1371\/journal.pgen.1011543","article-title":"Pathway polygenic risk scores (pPRS) for the analysis of gene-environment interaction","volume":"21","year":"2025","journal-title":"PLoS Genet"},{"key":"2026052905100505000_2026.05.25.26354071v1.42","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a008888"},{"key":"2026052905100505000_2026.05.25.26354071v1.43","doi-asserted-by":"publisher","DOI":"10.1038\/nature20413"},{"key":"2026052905100505000_2026.05.25.26354071v1.44","doi-asserted-by":"publisher","DOI":"10.1007\/s00401-017-1685-y"},{"key":"2026052905100505000_2026.05.25.26354071v1.45","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1016\/j.neuroimage.2016.04.001","article-title":"A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging","volume":"134","year":"2016","journal-title":"Neuroimage"},{"key":"2026052905100505000_2026.05.25.26354071v1.46","doi-asserted-by":"crossref","unstructured":"Kivis\u00e4kk P , Carlyle BC , Sweeney T , et al. Plasma biomarkers for diagnosis of Alzheimer\u2019s disease and prediction of cognitive decline in individuals with mild cognitive impairment. Front Neurol. 2023;14:1069411.","DOI":"10.3389\/fneur.2023.1069411"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.05.25.26354071","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T12:10:18Z","timestamp":1780056618000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.05.25.26354071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,27]]},"references-count":46,"URL":"https:\/\/doi.org\/10.64898\/2026.05.25.26354071","relation":{},"subject":[],"published":{"date-parts":[[2026,5,27]]},"subtype":"preprint"}}