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Chances and challenges of machine learning‐based disease classification in genetic association studies illustrated on age‐related macular degeneration
Author(s) -
Guenther Felix,
Brandl Caroline,
Winkler Thomas W.,
Wanner Veronika,
Stark Klaus,
Kuechenhoff Helmut,
Heid Iris M.
Publication year - 2020
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22336
Subject(s) - biobank , genome wide association study , macular degeneration , machine learning , computer science , genetic association , artificial intelligence , association (psychology) , bioinformatics , medicine , biology , single nucleotide polymorphism , genotype , genetics , psychology , ophthalmology , psychotherapist , gene
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 (near HERC2 ) 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.