Abstract
Patterns of disease co-occurrence that deviate from statistical independence may represent important constraints on biological mechanism, which sometimes can be explained by shared genetics. In this work we study the relationship between disease co-occurrence and commonly shared genetic architecture of disease. Records of pairs of diseases were combined from two different electronic medical systems (Columbia, Stanford), and compared to a large database of published disease-associated genetic variants (VARIMED); data on 35 disorders were available across all three sources, which include medical records for over 1.2 million patients and variants from over 17,000 publications. Based on the sources in which they appeared, disease pairs were categorized as having predominant clinical, genetic, or both kinds of manifestations. Confounding effects of age on disease incidence were controlled for by only comparing diseases when they fall in the same cluster of similarly shaped incidence patterns. We find that disease pairs that are overrepresented in both electronic medical record systems and in VARIMED come from two main disease classes, autoimmune and neuropsychiatric. We furthermore identify specific genes that are shared within these disease groups.
Highlights
When two diseases occur together in the same individuals more or less often than would be expected by chance, this may signal the operation of important biological processes
When a disease pair occurs more often than would be expected by chance given the frequencies of each disease separately, that may signal the presence of shared causal factors
As noted in the paper, because VARIMED is proprietary, the relevant subset of 35 diseases, with associated genes, chromosome number, and PubMed ID of the source of each association were extracted and used for the analysis we report here
Summary
When two diseases occur together in the same individuals more or less often than would be expected by chance, this may signal the operation of important biological processes. Pairs of diseases occurring more than expected are called synergistic; such interactions are familiar from clinical practice when the occurrence of a disease may raise the risk of a second disease. Pairs occurring less than expected are called protective; these interactions, sometimes called “inverse comorbidities,” are less common, but intriguing. Because discovery of disease patterns has been haphazard, it is attractive to systematically search for these patterns across a wide range of diseases, without adhering to prior conceptions of disease class, associated features, or expected comorbidities. We integrate clinical and genomic data across diseases to systematically assess their co-occurrence
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