Efficient Genome-wide Association in Biobanks Using Topic Modeling Identifies Multiple Novel Disease Loci

• Main Authors: Thomas H. McCoy (Author), Victor M. Castro (Author), Leslie A. Snapper (Author), Kamber L. Hart (Author), Roy H. Perlis (Author)
• Format: Book
• Published: BMC, 2017-08-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.2119/molmed.2017.00100; 1076-1551; 1528-3658
• Keywords: Therapeutics. Pharmacology; RM1-950; Biochemistry; QD415-436
• Source: Molecular Medicine, Vol 23, Iss 1, Pp 285-294 (2017)

Abstract Biobanks and national registries represent a powerful tool for genomic discovery, but rely on diagnostic codes that can be unreliable and fail to capture relationships between related diagnoses. We developed an efficient means of conducting genome-wide association studies using combinations of diagnostic codes from electronic health records for 10,845 participants in a biobanking program at two large academic medical centers. Specifically, we applied latent Dirichilet allocation to fit 50 disease topics based on diagnostic codes, then conducted a genome-wide common-variant association for each topic. In sensitivity analysis, these results were contrasted with those obtained from traditional single-diagnosis phenome-wide association analysis, as well as those in which only a subset of diagnostic codes were included per topic. In meta-analysis across three biobank cohorts, we identified 23 disease-associated loci with p < 1e-15, including previously associated autoimmune disease loci. In all cases, observed significant associations were of greater magnitude than single phenome-wide diagnostic codes, and incorporation of less strongly loading diagnostic codes enhanced association. This strategy provides a more efficient means of identifying phenome-wide associations in biobanks with coded clinical data.