MOTIVATION: A plethora of sequenced and genotyped disease cohorts is available to the biomedical research community, spread across many portals and represented in various formats. RESULTS: We have gathered several large studies, including GERA and GRU, and computed population- and disease-specific genetic variant frequencies. In total, our portal provides fast access to genetic variants observed in 84,928 individuals from 39 disease populations. We also include 66,335 controls, such as the 1000 Genomes and Scripps Wellderly. CONCLUSION: Combining multiple studies helps validate disease-associated variants in each underlying data set, detect potential false positives using frequencies of control populations, and identify novel candidate disease-causing alterations in known or suspected genes. AVAILABILITY AND IMPLEMENTATION: https://rvs.u.hpc.mssm.edu/divas CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL). RESULTS: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key 'hub' diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes. CONTACTS: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.