Benchmarking Alzheimer’s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies
Abstract Background The success of selecting high risk or early-stage Alzheimer’s disease individuals for the delivery of clinical trials depends on the design and the appropriate recruitment of participants. Polygenic risk scores (PRS) show potential for identifying individuals at risk for Alzheime...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-024-01664-9 |
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| author | Eftychia Bellou Woori Kim Ganna Leonenko Feifei Tao Emily Simmonds Ying Wu Niklas Mattsson-Carlgren Oskar Hansson Michael W. Nagle Valentina Escott-Price the Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | Eftychia Bellou Woori Kim Ganna Leonenko Feifei Tao Emily Simmonds Ying Wu Niklas Mattsson-Carlgren Oskar Hansson Michael W. Nagle Valentina Escott-Price the Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | Eftychia Bellou |
| collection | DOAJ |
| description | Abstract Background The success of selecting high risk or early-stage Alzheimer’s disease individuals for the delivery of clinical trials depends on the design and the appropriate recruitment of participants. Polygenic risk scores (PRS) show potential for identifying individuals at risk for Alzheimer’s disease (AD). Our study comprehensively examines AD PRS utility using various methods and models. Methods We compared the PRS prediction accuracy in ADNI (N = 568) and BioFINDER (N = 766) cohorts using five disease risk modelling approaches, three PRS derivation methods, two AD genome-wide association study (GWAS) statistics and two sets of SNPs: the whole genome and microglia-selective regions only. Results The best prediction accuracy was achieved when modelling genetic risk by using two predictors: APOE and remaining PRS (AUC = 0.72–0.76). Microglial PRS showed comparable accuracy to the whole genome (AUC = 0.71–0.74). The individuals’ risk scores differed substantially, with the largest discrepancies (up to 70%) attributable to the GWAS statistics used. Conclusions Our work benchmarks the best PRS derivation and modelling strategies for AD genetic prediction. |
| format | Article |
| id | doaj-art-a674f3d0a2e84c5188933f81cc8bb576 |
| institution | Kabale University |
| issn | 1758-9193 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj-art-a674f3d0a2e84c5188933f81cc8bb5762025-01-12T12:11:01ZengBMCAlzheimer’s Research & Therapy1758-91932025-01-0117111110.1186/s13195-024-01664-9Benchmarking Alzheimer’s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studiesEftychia Bellou0Woori Kim1Ganna Leonenko2Feifei Tao3Emily Simmonds4Ying Wu5Niklas Mattsson-Carlgren6Oskar Hansson7Michael W. Nagle8Valentina Escott-Price9the Alzheimer’s Disease Neuroimaging InitiativeUK Dementia Research Institute at Cardiff, Cardiff UniversityEisai IncUK Dementia Research Institute at Cardiff, Cardiff UniversityEisai IncUK Dementia Research Institute at Cardiff, Cardiff UniversityEisai IncClinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund UniversityClinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund UniversityEisai IncUK Dementia Research Institute at Cardiff, Cardiff UniversityAbstract Background The success of selecting high risk or early-stage Alzheimer’s disease individuals for the delivery of clinical trials depends on the design and the appropriate recruitment of participants. Polygenic risk scores (PRS) show potential for identifying individuals at risk for Alzheimer’s disease (AD). Our study comprehensively examines AD PRS utility using various methods and models. Methods We compared the PRS prediction accuracy in ADNI (N = 568) and BioFINDER (N = 766) cohorts using five disease risk modelling approaches, three PRS derivation methods, two AD genome-wide association study (GWAS) statistics and two sets of SNPs: the whole genome and microglia-selective regions only. Results The best prediction accuracy was achieved when modelling genetic risk by using two predictors: APOE and remaining PRS (AUC = 0.72–0.76). Microglial PRS showed comparable accuracy to the whole genome (AUC = 0.71–0.74). The individuals’ risk scores differed substantially, with the largest discrepancies (up to 70%) attributable to the GWAS statistics used. Conclusions Our work benchmarks the best PRS derivation and modelling strategies for AD genetic prediction.https://doi.org/10.1186/s13195-024-01664-9Alzheimer’s diseasePolygenic risk scoreRisk prediction |
| spellingShingle | Eftychia Bellou Woori Kim Ganna Leonenko Feifei Tao Emily Simmonds Ying Wu Niklas Mattsson-Carlgren Oskar Hansson Michael W. Nagle Valentina Escott-Price the Alzheimer’s Disease Neuroimaging Initiative Benchmarking Alzheimer’s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies Alzheimer’s Research & Therapy Alzheimer’s disease Polygenic risk score Risk prediction |
| title | Benchmarking Alzheimer’s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies |
| title_full | Benchmarking Alzheimer’s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies |
| title_fullStr | Benchmarking Alzheimer’s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies |
| title_full_unstemmed | Benchmarking Alzheimer’s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies |
| title_short | Benchmarking Alzheimer’s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies |
| title_sort | benchmarking alzheimer s disease prediction personalised risk assessment using polygenic risk scores across various methodologies and genome wide studies |
| topic | Alzheimer’s disease Polygenic risk score Risk prediction |
| url | https://doi.org/10.1186/s13195-024-01664-9 |
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