Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes-Reference-Cited by-同舟云学术

Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes

Published:2024-02 Issue:1 Volume:10 Page:
ISSN:2376-7839
Container-title:Neurology Genetics
language:en
Short-container-title:Neurol Genet

Author:

Gunter Nathaniel B.¹^ORCID,Gebre Robel K.¹^ORCID,Graff-Radford Jonathan¹^ORCID,Heckman Michael G.¹^ORCID,Jack Clifford R.¹^ORCID,Lowe Val J.¹^ORCID,Knopman David S.¹^ORCID,Petersen Ronald C.¹^ORCID,Ross Owen A.¹^ORCID,Vemuri Prashanthi¹^ORCID,Ramanan Vijay K.¹^ORCID,

Affiliation:

1. From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Link

https://www.neurology.org/doi/pdfdirect/10.1212/NXG.0000000000200120

Reference48 articles.

1. Measuring heritable contributions to Alzheimer’s disease: polygenic risk score analysis with twins

2. Genetics of Alzheimer’s Disease: the Importance of Polygenic and Epistatic Components

3. Common polygenic variation enhances risk prediction for Alzheimer’s disease

4. Gene Dose of Apolipoprotein E Type 4 Allele and the Risk of Alzheimer's Disease in Late Onset Families

5. New insights into the genetic etiology of Alzheimer’s disease and related dementias

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