Journal of Alzheimer's disease : JAD · 2022
Using Polygenic Hazard Scores to Predict Age at Onset of Alzheimer's Disease in Nordic Populations.
СтатьяПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Motazedi Ehsan, Cheng Weiqiu, Thomassen Jesper Q, Frei Oleksandr, Rongve Arvid, Athanasiu Lavinia, Bahrami Shahram, Shadrin Alexey, Ulstein Ingun, Stordal Eystein, Brækhus Anne, Saltvedt Ingvild, Sando Sigrid B, O'Connell Kevin S, Hindley Guy, van der Meer Dennis, Bergh Sverre, Nordestgaard Børge G, Tybjærg-Hansen Anne, Bråthen Geir, Pihlstrøm Lasse, Djurovic Srdjan, Frikke-Schmidt Ruth, Fladby Tormod, Aarsland Dag, Selbæk Geir, Seibert Tyler M, Dale Anders M, Fan Chun C, Andreassen Ole A
Аннотация
BACKGROUND: Polygenic hazard scores (PHS) estimate age-dependent genetic risk of late-onset Alzheimer's disease (AD), but there is limited information about the performance of PHS on real-world data where the population of interest differs from the model development population and part of the model genotypes are missing or need to be imputed.
OBJECTIVE: The aim of this study was to estimate age-dependent risk of late-onset AD using polygenic predictors in Nordic populations.
METHODS: We used Desikan PHS model, based on Cox proportional hazards assumption, to obtain age-dependent hazard scores for AD from individual genotypes in the Norwegian DemGene cohort (n = 2,772). We assessed the risk discrimination and calibration of Desikan model and extended it by adding new genotype markers (the Desikan Nordic model). Finally, we evaluated both Desikan and Desikan Nordic models in two independent Danish cohorts: The Copenhagen City Heart Study (CCHS) cohort (n = 7,643) and The Copenhagen General Population Study (CGPS) cohort (n = 10,886).
RESULTS: We showed a robust prediction efficiency of Desikan model in stratifying AD risk groups in Nordic populations, even when some of the model SNPs were missing or imputed. We attempted to improve Desikan PHS model by adding new SNPs to it, but we still achieved similar risk discrimination and calibration with the extended model.
CONCLUSION: PHS modeling has the potential to guide the timing of treatment initiation based on individual risk profiles and can help enrich clinical trials with people at high risk to AD in Nordic populations.
OBJECTIVE: The aim of this study was to estimate age-dependent risk of late-onset AD using polygenic predictors in Nordic populations.
METHODS: We used Desikan PHS model, based on Cox proportional hazards assumption, to obtain age-dependent hazard scores for AD from individual genotypes in the Norwegian DemGene cohort (n = 2,772). We assessed the risk discrimination and calibration of Desikan model and extended it by adding new genotype markers (the Desikan Nordic model). Finally, we evaluated both Desikan and Desikan Nordic models in two independent Danish cohorts: The Copenhagen City Heart Study (CCHS) cohort (n = 7,643) and The Copenhagen General Population Study (CGPS) cohort (n = 10,886).
RESULTS: We showed a robust prediction efficiency of Desikan model in stratifying AD risk groups in Nordic populations, even when some of the model SNPs were missing or imputed. We attempted to improve Desikan PHS model by adding new SNPs to it, but we still achieved similar risk discrimination and calibration with the extended model.
CONCLUSION: PHS modeling has the potential to guide the timing of treatment initiation based on individual risk profiles and can help enrich clinical trials with people at high risk to AD in Nordic populations.