Instruct: Identify the main category of Biorxiv papers based on the titles and abstracts
Query: transferGWAS: GWAS of images using deep transfer learning MotivationMedical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations.

ResultsWe validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases.

Availability and ImplementationOur method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/
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The categories of this paper are: cell
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The categories of this paper are: cognition
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The categories of this paper are: paleontology
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The categories of this paper are: biochemistry
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The categories of this paper are: genomics
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The categories of this paper are: cancer
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The categories of this paper are: communication
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The categories of this paper are: education
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The categories of this paper are: molecular
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The categories of this paper are: developmental
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The categories of this paper are: epidemiology
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The categories of this paper are: physiology
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The categories of this paper are: ecology
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The categories of this paper are: neuroscience
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The categories of this paper are: bioinformatics
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The categories of this paper are: systems