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.

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Organisms frequently encounter different environmental conditions. The physiological and behavioral responses to these conditions depend on the genetic make up of individuals. Genotype generally remains constant from one environment to another, although occasional spontaneous mutations may occur which cause it to change. However, when the same genotype is subjected to different environments, it can produce a wide range of phenotypes. These phenotypic variations are attributable to the effect of the environment on the expression and function of genes influencing the trait. Changes in the relative performance of genotypes across different environments are referred to as genotype–environment interactions (GEI). A general argument for research on the impact of GEI in common diseases is that it provides insights into disease processes at the population, individual and molecular levels. In humans, GEI is complicated by multiple factors including phenocopies, genocopies, epigenetics and imprinting. A better unde...

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.