Guenther, Felix and Brandl, Caroline and Winkler, Thomas W. and Wanner, Veronika and Stark, Klaus and Kuechenhoff, Helmut and Heid, Iris M. (2020) Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degeneration. GENETIC EPIDEMIOLOGY, 44 (7). pp. 759-777. ISSN 0741-0395, 1098-2272
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Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We establish machine learning-based phenotyping in genetic association analysis as misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automatically classified age-related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artifacts in simulation studies. By combining a GWAS on automatically derived AMD and our MLA in UK Biobank data, we were able to dissect true association (ARMS2/HTRA1,CFH) from artifacts (nearHERC2) and identified eye color as associated with the misclassification. On this example, we provide a proof-of-concept that a GWAS using machine learning-derived disease classification yields relevant results and that misclassification needs to be considered in analysis. These findings generalize to other phenotypes and emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | MISCLASSIFICATION; VALIDATION; IMAGES; RARE; age-related macular degeneration (AMD); genome-wide association study; machine learning-based disease classification; response misclassification; UK Biobank |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Augenheilkunde Medicine > Institut für Epidemiologie und Präventivmedizin Medicine > Institut für Epidemiologie und Präventivmedizin > Lehrstuhl für Genetische Epidemiologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 17 Mar 2021 09:11 |
| Last Modified: | 17 Mar 2021 09:11 |
| URI: | https://pred.uni-regensburg.de/id/eprint/44062 |
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