Dataset Information

Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling.

ABSTRACT: Late-onset Alzheimer's Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods to generate predictive models have been suggested. In this research, we compared three machine learning methods that have been proved to construct powerful predictive models (genetic algorithms, LASSO, and step-wise) and propose the inclusion of markers from misclassified samples to improve overall prediction accuracy. Our results show that the addition of markers from an initial model plus the markers of the model fitted to misclassified samples improves the area under the receiving operative curve by around 5%, reaching ~0.84, which is highly competitive using only genetic information. The computational strategy used here can help to devise better methods to improve classification models for AD. Our results could have a positive impact on the early diagnosis of Alzheimer's disease.

SUBMITTER: Romero-Rosales BL

PROVIDER: S-EPMC7179850 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling.

Romero-Rosales Brissa-Lizbeth BL Tamez-Pena Jose-Gerardo JG Nicolini Humberto H Moreno-Treviño Maria-Guadalupe MG Trevino Victor V

PloS one 20200423 4

Late-onset Alzheimer's Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods ...[more]

PMID: 32324812

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Dataset Information

Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling.

Publications

Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling.

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