This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human differences in genomic research including race, ethnicity, and ancestry. Although race and ethnicity are related, race refers to a person’s physical appearance, such as skin color and eye color. Ethnicity, on the other hand, refers to communality in cultural heritage, language, social practice, traditions, and geopolitical factors. Genetic ancestry inferred using ancestry informative markers (AIMs) is based on genetic/genomic data. Phenotype-based race/ethnicity information and data computed using AIMs often disagree. For example, self-reporting African Americans can have drastically different levels of African or European ancestry. Genetic analysis of individual ancestry shows that some self-identified African Americans have up to 99% of European ancestry, whereas some self-identified European Americans have substantial admixture from African ancestry. Similarly, African ancestry in the Latino population varies between 3% in Mexican Americans to 16% in Puerto Ricans. The implication of this is that, in African American or Latino populations, self-reported ancestry may not be as accurate as direct assessment of individual genomic information in predicting treatment outcomes. To better understand human genetic variation in the context of health disparities, we suggest using “ancestry” (or biogeographical ancestry) to describe actual genetic variation, “race” to describe health disparity in societies characterized by racial categories, and “ethnicity” to describe traditions, lifestyle, diet, and values. We also suggest using ancestry informative markers for precise characterization of individuals’ biological ancestry. Understanding the sources of human genetic variation and the causes of health disparities could lead to interventions that would improve the health of all individuals.

Since late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected millions of people worldwide and resulted in more than 200,000 coronavirus disease 2019 (COVID-19) deaths. Emerging data suggest that elderly people as well as individuals with underlying health conditions are at a higher risk of hospitalization and death. 1-3 Interestingly, the Centers for Disease Control and Prevention's list of risk factors for severe COVID-19 (Fig 1) largely overlap with the list of diseases that are known to be worsened by chronic exposure to air pollution, including diabetes, heart diseases, and chronic airway diseases, such as asthma, lung cancer, and chronic obstructive pulmonary disease. 3 In this editorial, we highlight potential links between exposure to air pollution and COVID-19 severity, and we also hypothesize that disparate exposure to air pollution is one of the factors that contribute to the disproportionate impact COVID-19 is having on inner-city racial minorities. Air pollution is a complex mixture of particulate matter smaller than 2.5 or 10 mm (PM 2.5 , PM 10), nitric dioxide (NO 2), carbon monoxide (CO), ozone (O 3), and volatile organic compounds derived from vehicular traffic, industrial emissions, and indoor pollutants. Given overwhelming evidence linking chronic exposure to air pollution with increased morbidity and mortality across a range of cardiopulmonary diseases, 4 there is growing concern that air pollution may also contribute to COVID-19 severity, by directly affecting the lungs' ability to clear pathogens and indirectly by exacerbating underlying cardiovascular or pulmonary diseases. Such a link was reported during the 2003 SARS outbreak in China, where a positive association was observed between both acute and chronic pollution measures from the air pollution index (CO, NO 2 , SO 2 , O 3 , and PM 10) and SARS case-fatality rates. 5 Now, preliminary data are suggesting similar associations for COVID-19. In cities of China's Hubei province, the epicenter

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood. Moreover, a typical progression of children with AD to food allergy, asthma or allergic rhinitis has been reported (“allergic march” or “atopic march”). AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid, and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, might provide the opportunity to capture the flow of information underlying AD disease manifestation. Overlapping genes/candidates derived from multiple omics types include FLG, SPINK5, S100A8, and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFkB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by “big omics data” and their integration.