Diversity In Precision Medicine And Pharmacogenetics: Methodological And Conceptual Considerations For Broadening Participation

Patterns of pharmacogenetic variation in nine biogeographic groups

Frequencies of pharmacogenetic (PGx) variants are known to differ substantially across populations but much of the available PGx literature focuses on one or a few population groups, often defined in nonstandardized ways, or on a specific gene or variant. Guidelines produced by the Clinical Pharmacogenetic Implementation Consortium (CPIC) provide consistent methods of literature extraction, curation, and reporting, including comprehensive curation of allele frequency data across nine defined "biogeographic groups" from the PGx literature. We extracted data from 23 CPIC guidelines encompassing 19 genes to compare the sizes of the populations from each group and allele frequencies of altered function alleles across groups. The European group was the largest in the curated literature for 16 of the 19 genes, while the American and Oceanian groups were the smallest. Nearly 200 alleles were detected in nonreference groups that were not reported in the largest (reference) group. The genes CYP2B6 and CYP2C9 were more likely to have higher frequencies of altered function alleles in nonreference groups compared to the reference group, while the genes CYP4F2, DPYD, SLCO1B1, and UGT1A1 were less likely to have higher frequencies in nonreference groups. PGx allele frequencies and function differ substantially across nine biogeographic groups, all but two of which are underrepresented in available PGx data. Awareness of these differences and increased efforts to characterize the breadth of global PGx variation are needed to ensure that implementation of PGx-guided drug selection does not further widen existing health disparities among populations currently underrepresented in PGx data.

Forging the path to precision medicine in Qatar: a public health perspective on pharmacogenomics initiatives

Pharmacogenomics (PGx) is an important component of precision medicine that promises tailored treatment approaches based on an individual's genetic information. Exploring the initiatives in research that help to integrate PGx test into clinical setting, identifying the potential barriers and challenges as well as planning the future directions, are all important for fruitful PGx implementation in any population. Qatar serves as an exemplar case study for the Middle East, having a small native population compared to a diverse immigrant population, advanced healthcare system, national genome program, and several educational initiatives on PGx and precision medicine. This paper attempts to outline the current state of PGx research and implementation in Qatar within the global context, emphasizing ongoing initiatives and educational efforts. The inclusion of PGx in university curricula and healthcare provider training, alongside precision medicine conferences, showcase Qatar's commitment to advancing this field. However, challenges persist, including the requirement for population specific implementation strategies, complex genetic data interpretation, lack of standardization, and limited awareness. The review suggests policy development for future directions in continued research investment, conducting clinical trials for the feasibility of PGx implementation, ethical considerations, technological advancements, and global collaborations to overcome these barriers.

Open Science Practices in Psychiatric Genetics: A Primer

Open science ensures that research is transparently reported and freely accessible for all to assess and collaboratively build on. Psychiatric genetics has led among the health sciences in implementing some open science practices in common study designs, such as replication as part of genome-wide association studies. However, thorough open science implementation guidelines are limited and largely not specific to data, privacy, and research conduct challenges in psychiatric genetics. Here, we present a primer of open science practices, including selection of a research topic with patients/nonacademic collaborators, equitable authorship and citation practices, design of replicable, reproducible studies, preregistrations, open data, and privacy issues. We provide tips for informative figures and inclusive, precise reporting. We discuss considerations in working with nonacademic collaborators and distributing research through preprints, blogs, social media, and accessible lecture materials. Finally, we provide extra resources to support every step of the research process.

Ethical challenges of conducting and reviewing human genomics research in Malaysia: An exploratory study

Even though there is a significant amount of scholarly work examining the ethical issues surrounding human genomics research, little is known about its footing in Malaysia. This study aims to explore the experience of local researchers and research ethics committee (REC) members in developing it in Malaysia. In-depth interviews were conducted from April to May 2021, and the data were thematically analysed. In advancing this technology, both genomics researchers and REC members have concerns over how this research is being developed in the country especially the absence of a clear ethical and regulatory framework at the national level as a guidance. However, this study argues that it is not a salient issue as there are international guidelines in existence and both researchers and RECs will benefit from a training on the guidelines to ensure genomics research can be developed in an ethical manner.

Pharmacogenomics and health disparities, are we helping?

Pharmacogenomics has been at the forefront of precision medicine during the last few decades. Precision medicine carries the potential of improving health outcomes at both the individual as well as population levels. To harness the benefits of its initiatives, careful dissection of existing health disparities as they relate to precision medicine is of paramount importance. Attempting to address the existing disparities at the early stages of design and implementation of these efforts is the only guarantee of a successful just outcome. In this review, we glance at a few determinants of existing health disparities as they intersect with pharmacogenomics research and implementation. In our opinion, highlighting these disparities is imperative for the purpose of researching meaningful solutions. Failing to identify, and hence address, these disparities in the context of the current and future precision medicine initiatives would leave an already strained health system, even more inundated with inequality.

Communicating Precision Medicine Research: Multidisciplinary Teams and Diverse Communities

INTRODUCTION

Precision medicine research investigates the differences in individuals' genetics, environment, and lifestyle to tailor health prevention and treatment options as part of an emerging model of health care delivery. Advancing precision medicine research will require effective communication across a wide range of scientific and health care disciplines and with research participants who represent diverse segments of the population.

METHODS

A multidisciplinary group convened over the course of a year and developed precision medicine research case examples to facilitate precision medicine research discussions with communities.

RESULTS

A shared definition of precision medicine research as well as six case examples of precision medicine research involving genetic risk, pharmacogenetics, epigenetics, the microbiome, mobile health, and electronic health records were developed.

DISCUSSION/CONCLUSION

The precision medicine research definition and case examples can be used as planning tools to establish a shared understanding of the scope of precision medicine research across multidisciplinary teams and with the diverse communities in which precision medicine research will take place. This shared understanding is vital for successful and equitable progress in precision medicine.

Effect Modification by Social Determinants of Pharmacogenetic Medication Interactions on 90-Day Hospital Readmissions within an Integrated U.S. Healthcare System

The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45−10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61−4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77−3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88−2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47−3.07) (p < 0.0001), median household income (OR = 1.63, 1.03−2.58) (p = 0.035), male gender (OR = 1.47, 1.21−1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18−1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08−1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.

Genetic Variations of the DPYD Gene and Its Relationship with Ancestry Proportions in Different Ecuadorian Trihybrid Populations

Dihydropyrimidine dehydrogenase is one of the main pharmacological metabolizers of fluoropyrimidines, a group of drugs widely used in clinical oncology. Around 20 to 30% of patients treated with fluoropyrimidines experience severe toxicity caused by a partial or total decrease in enzymatic activity. This decrease is due to molecular variants in the DPYD gene. Their prevalence and allelic frequencies vary considerably worldwide, so their description in heterogeneous groups such as the Ecuadorian population will allow for the description of pharmacogenetic variants and proper characterization of this population. Thus, we genotyped all the molecular variants with a predictive value for DPYD in a total of 410 Ecuadorian individuals belonging to Mestizo, Afro-Ecuadorian, and Indigenous ethnic groups. Moreover, we developed a genetic ancestry analysis using 46 autosomal ancestry informative markers. We determined 20 genetic variations in 5 amplified regions, including 3 novel single nucleotide variants. The allele frequencies for DPYD variants c.1627G>A (*5, rs1801159), c.1129-15T>C (rs56293913), c.1218G>A (rs61622928), rs1337752, rs141050810, rs2786783, rs2811178, and g.97450142G>A (chr1, GRCh38.p13) are significantly related to Native American and African ancestry proportions. In addition, the FST calculated from these variants demonstrates the closeness between Indigenous and Mestizo populations, and evidences genetic divergence between Afro-Ecuadorian groups when compared with Mestizo and Indigenous ethnic groups. In conclusion, the genetic variability in the DPYD gene is related to the genetic component of ancestral populations in different Ecuadorian ethnic groups. The absence and low frequency of variants with predictive value for fluoropyrimidine toxicity such as DPYD *2A, HapB3, and c.2846A>T (prevalent in populations with European ancestry) is consistent with the genetic background found.

Pharmacogenomic Profile of Amazonian Amerindians

Given the role of pharmacogenomics in the large variability observed in drug efficacy/safety, an assessment about the pharmacogenomic profile of patients prior to drug prescription or dose adjustment is paramount to improve adherence to treatment and prevent adverse drug reaction events. A population commonly underrepresented in pharmacogenomic studies is the Native American populations, which have a unique genetic profile due to a long process of geographic isolation and other genetic and evolutionary processes. Here, we describe the pharmacogenetic variability of Native American populations regarding 160 pharmacogenes involved in absorption, distribution, metabolism, and excretion processes and biological pathways of different therapies. Data were obtained through complete exome sequencing of individuals from 12 different Amerindian groups of the Brazilian Amazon. The study reports a total of 3311 variants; of this, 167 are exclusive to Amerindian populations, and 1183 are located in coding regions. Among these new variants, we found non-synonymous coding variants in the DPYD and the IFNL4 genes and variants with high allelic frequencies in intronic regions of the MTHFR, TYMS, GSTT1, and CYP2D6 genes. Additionally, 332 variants with either high or moderate (disruptive or non-disruptive impact in protein effectiveness, respectively) significance were found with a minimum of 1% frequency in the Amazonian Amerindian population. The data reported here serve as scientific basis for future design of specific treatment protocols for Amazonian Amerindian populations as well as for populations admixed with them, such as the Northern Brazilian population.

Applicability of Pharmacogenomically Guided Medication Treatment during Hospitalization of At-Risk Minority Patients

Known disparities exist in the availability of pharmacogenomic information for minority populations, amplifying uncertainty around clinical utility for these groups. We conducted a multi-site inpatient pharmacogenomic implementation program among self-identified African-Americans (AA; n = 135) with numerous rehospitalizations (n = 341) from 2017 to 2020 (NIH-funded ACCOuNT project/clinicaltrials.gov#NCT03225820). We evaluated the point-of-care availability of patient pharmacogenomic results to healthcare providers via an electronic clinical decision support tool. Among newly added medications during hospitalizations and at discharge, we examined the most frequently utilized medications with associated pharmacogenomic results. The population was predominantly female (61%) with a mean age of 53 years (range 19-86). On average, six medications were newly prescribed during each individual hospital admission. For 48% of all hospitalizations, clinical pharmacogenomic information was applicable to at least one newly prescribed medication. Most results indicated genomic favorability, although nearly 29% of newly prescribed medications indicated increased genomic caution (increase in toxicity risk/suboptimal response). More than one of every five medications prescribed to AA patients at hospital discharge were associated with cautionary pharmacogenomic results (most commonly pantoprazole/suboptimal antacid effect). Notably, high-risk pharmacogenomic results (genomic contraindication) were exceedingly rare. We conclude that the applicability of pharmacogenomic information during hospitalizations for vulnerable populations at-risk for experiencing health disparities is substantial and warrants continued prospective investigation.

Participation in genetic research among Latinx populations by Latin America birth-residency concordance: a global study

Latinx populations are underrepresented in DNA-based research, and risk not benefiting from research if underrepresentation continues. Latinx populations are heterogenous; reflect complex social, migration, and colonial histories; and form strong global diasporas. We conducted a global study using a survey tool (Amazon's Mechanical Turk portal) to ascertain willingness to participate in genetic research by Latin America birth-residency concordance. Participants in the global study identified as Latinx (n=250) were classified as the following: (1) born/live outside of Latin America and the Caribbean (LAC), (2) born within/live outside LAC, and (3) born/live within LAC. Latinx were similarly likely to indicated they would participate DNA-based research as their non-Latinx counterparts (52.8% vs. 56.2%, respectively). Latinx born and living in LAC were significantly more willing to participate in DNA-based research than Latinx born and living outside of LAC (OR: 2.5; 95% CI: 1.3, 4.9, p<.01). Latinx indicating they would participate in genetic research were more likely to trust researchers (<.05), believe genetic research could lead to better understanding of disease (<.05), and that genetic research could lead to new treatments (p<.05) when compared with Latinx not interested in participating in genetic research. In summary, significant variation exists in genetic research interest among Latinx based on where they were born and live, suggesting that this context itself independently influences decisions about participation. Cultivating and investing in a research ecosystem that addresses, values, and respects Latinx priorities, circumstances, and researchers would likely increase research participation and, even more importantly, potentially impact the inequitable health disparities disproportionately represented in Latinx communities.

Need for Ethnic and Population Diversity in Psychosis Research

This article aims to evaluate "racial", ethnic, and population diversity-or lack thereof-in psychosis research, with a particular focus on socio-environmental studies. Samples of psychosis research remain heavily biased toward Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. Furthermore, we often fail to acknowledge the lack of diversity, thereby implying that our findings can be generalized to all populations regardless of their social, ethnic, and cultural background. This has major consequences. Clinical trials generate findings that are not generalizable across ethnicity. The genomic-based prediction models are far from being applicable to the "Majority World." Socio-environmental theories of psychosis are solely based on findings of the empirical studies conducted in WEIRD populations. If and how these socio-environmental factors affect individuals in entirely different geographic locations, gene pools, social structures and norms, cultures, and potentially protective counter-factors remain unclear. How socio-environmental factors are assessed and studied is another major shortcoming. By embracing the complexity of environment, the exposome paradigm may facilitate the evaluation of interdependent exposures, which could explain how variations in socio-environmental factors across different social and geographical settings could contribute to divergent paths to psychosis. Testing these divergent paths to psychosis will however require increasing the diversity of study populations that could be achieved by establishing true partnerships between WEIRD societies and the Majority World with the support of funding agencies aspired to foster replicable research across diverse populations. The time has come to make diversity in psychosis research more than a buzzword.

Host pharmacogenetic factors that may affect liver neoplasm incidence upon using direct-acting antivirals for treating hepatitis C infection

Introduction

Direct-acting antivirals (DAAs) represent a breakthrough in hepatitis C virus (HCV) treatment as they directly inhibit HCV nonstructural (NS) proteins (NS3/4A, NS5A, and NS5B). However, ongoing debates exist regarding their relationship with hepatocellular carcinoma (HCC) whose incidence is widely debated among investigators. This study was conducted to identify host pharmacogenetic factors that may influence HCC incidence upon using HCV DAAs.

Materials and methods

Details regarding 16 HCV DAAs were collected from literature and DrugBank database. Digital structures of these drugs were fed into the pharmacogenomics/pharmacovigilance in-silico pipeline (PHARMIP) to predict the genetic factors that may underpin HCC development.

Results

We identified 184 unique genes and 40 unique variants that may have key answers for the DAA/HCC paradox. These findings could be used in different methods to aid in the precise application of HCV DAAs and minimize the proposed risk for HCC. All results could be accessed at: https://doi.org/10.17632/8ws8258hn3.2.

Discussion

All the identified factors are evidence related to HCC and significantly predicted by PHARMIP as DAA targets. We discuss some examples of the methods of using these results to address the DAA/HCC controversy based on the following three primary levels: 1 - individual DAA drug, 2 - DAA subclass, and 3 - the entire DAA class. Further wet laboratory investigation is required to evaluate these results.

African genetic diversity and adaptation inform a precision medicine agenda

The deep evolutionary history of African populations, since the emergence of modern humans more than 300,000 years ago, has resulted in high genetic diversity and considerable population structure. Selected genetic variants have increased in frequency due to environmental adaptation, but recent exposures to novel pathogens and changes in lifestyle render some of them with properties leading to present health liabilities. The unique discoverability potential from African genomic studies promises invaluable contributions to understanding the genomic and molecular basis of health and disease. Globally, African populations are understudied, and precision medicine approaches are largely based on data from European and Asian-ancestry populations, which limits the transferability of findings to the continent of Africa. Africa needs innovative precision medicine solutions based on African data that use knowledge and implementation strategies aligned to its climatic, cultural, economic and genomic diversity.

Challenges in Diversity, Equity, and Inclusion in Research and Clinical Oncology

Disparities are common and well-known in the field of clinical oncology and cancer research. In patient care, poor access and a number of other factors disadvantage patients and this can lead to inadequate screening, prevention or treatment of cancer and poor patient outcomes. World-wide, socioeconomic status, health care expenditures and a number of other challenges contribute to disparities in cancer care and patient outcomes. Access to cancer clinical trials remains inadequate for underrepresented minorities as well as non-white racial and ethnic groups. There are also disparities and many challenges in the biomedical research enterprise that can limit innovation and that must be addressed as part of active interventions.

Defining precision health: a scoping review protocol

INTRODUCTION

Precision health is a nascent field of research that would benefit from clearer operationalisation and distinction from adjacent fields like precision medicine. This clarification is necessary to enable precision health science to tackle some of the most complex and significant health problems that are faced globally. There is a pressing need to examine the progress in human precision health research in the past 10 years and analyse this data to first, find similarities and determine discordances in how precision health is operationalised in the literature and second, identify gaps and future directions for precision health research.

METHODS AND ANALYSIS

To define precision health and map research in this field, a scoping review will be undertaken and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Scoping Review Extension guidelines. Systematic searches of scientific databases (Medline, Embase, Scopus, Web of Science and PsycINFO) and grey literature sources (Google Scholar, Google Patents) identified 8053 potentially eligible articles published from 1 January 2010 to 30 June 2020. Following removal of duplicates, a total of 3190 articles were imported for screening. Article data will be extracted using a customised extraction template on Covidence and analysed descriptively using narrative synthesis.

ETHICS AND DISSEMINATION

Ethics approval is not required. Findings will be disseminated through professional networks, conference presentations and publication in a scientific journal.

Combination of Genome-Wide Polymorphisms and Copy Number Variations of Pharmacogenes in Koreans

For predicting phenotypes and executing precision medicine, combination analysis of single nucleotide variants (SNVs) genotyping with copy number variations (CNVs) is required. The aim of this study was to discover SNVs or common copy CNVs and examine the combined frequencies of SNVs and CNVs in pharmacogenes using the Korean genome and epidemiology study (KoGES), a consortium project. The genotypes (N = 72,299) and CNV data (N = 1000) were provided by the Korean National Institute of Health, Korea Centers for Disease Control and Prevention. The allele frequencies of SNVs, CNVs, and combined SNVs with CNVs were calculated and haplotype analysis was performed. CYP2D6 rs1065852 (c.100C>T, p.P34S) was the most common variant allele (48.23%). A total of 8454 haplotype blocks in 18 pharmacogenes were estimated. DMD ranked the highest in frequency for gene gain (64.52%), while TPMT ranked the highest in frequency for gene loss (51.80%). Copy number gain of CYP4F2 was observed in 22 subjects; 13 of those subjects were carriers with CYP4F2*3 gain. In the case of TPMT, approximately one-half of the participants (N = 308) had loss of the TPMT*1*1 diplotype. The frequencies of SNVs and CNVs in pharmacogenes were determined using the Korean cohort-based genome-wide association study.

Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks

BACKGROUND

Traditional methods for disease risk prediction and assessment, such as diagnostic tests using serum, urine, blood, saliva or imaging biomarkers, have been important for identifying high-risk individuals for many diseases, leading to early detection and improved survival. For pancreatic cancer, traditional methods for screening have been largely unsuccessful in identifying high-risk individuals in advance of disease progression leading to high mortality and poor survival. Electronic health records (EHR) linked to genetic profiles provide an opportunity to integrate multiple sources of patient information for risk prediction and stratification. We leverage a constellation of temporally associated diagnoses available in the EHR to construct a summary risk score, called a phenotype risk score (PheRS), for identifying individuals at high-risk for having pancreatic cancer. The proposed PheRS approach incorporates the time with respect to disease onset into the prediction framework. We combine and contrast the PheRS with more well-known measures of inherited susceptibility, namely, the polygenic risk scores (PRS) for prediction of pancreatic cancer.

METHODOLOGY

We first calculated pairwise, unadjusted associations between pancreatic cancer diagnosis and all possible other diagnoses across the medical phenome. We call these pairwise associations co-occurrences. After accounting for cross-phenotype correlations, the multivariable association estimates from a subset of relatively independent diagnoses were used to create a weighted sum PheRS. We constructed time-restricted risk scores using data from 38,359 participants in the Michigan Genomics Initiative (MGI) based on the diagnoses contained in the EHR at 0, 1, 2, and 5 years prior to the target pancreatic cancer diagnosis. The PheRS was assessed for predictability in the UK Biobank (UKB). We tested the relative contribution of PheRS when added to a model containing a summary measure of inherited genetic susceptibility (PRS) plus other covariates like age, sex, smoking status, drinking status, and body mass index (BMI).

RESULTS

Our exploration of co-occurrence patterns identified expected associations while also revealing unexpected relationships that may warrant closer attention. Solely using the pancreatic cancer PheRS at 5 years before the target diagnoses yielded an AUC of 0.60 (95% CI = [0.58, 0.62]) in UKB. A larger predictive model including PheRS, PRS, and the covariates at the 5-year threshold achieved an AUC of 0.74 (95% CI = [0.72, 0.76]) in UKB. We note that PheRS does contribute independently in the joint model. Finally, scores at the top percentiles of the PheRS distribution demonstrated promise in terms of risk stratification. Scores in the top 2% were 10.20 (95% CI = [9.34, 12.99]) times more likely to identify cases than those in the bottom 98% in UKB at the 5-year threshold prior to pancreatic cancer diagnosis.

CONCLUSIONS

We developed a framework for creating a time-restricted PheRS from EHR data for pancreatic cancer using the rich information content of a medical phenome. In addition to identifying hypothesis-generating associations for future research, this PheRS demonstrates a potentially important contribution in identifying high-risk individuals, even after adjusting for PRS for pancreatic cancer and other traditional epidemiologic covariates. The methods are generalizable to other phenotypic traits.

A Tutorial for Pharmacogenomics Implementation Through End-to-End Clinical Decision Support Based on Ten Years of Experience from PREDICT

Vanderbilt University Medical Center implemented pharmacogenomics (PGx) testing with the Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT) initiative in 2010. This tutorial reviews the laboratory considerations, technical infrastructure, and programmatic support required to deliver panel-based PGx testing across a large health system with examples and experiences from the first decade of the PREDICT initiative. From the time of inception, automated clinical decision support (CDS) has been a critical capability for delivering PGx results to the point-of-care. Key features of the CDS include human-readable interpretations and clinical guidance that is anticipatory, actionable, and adaptable to changes in the scientific literature. Implementing CDS requires that structured results from the laboratory be encoded in standards-based messages that are securely ingested by electronic health records. Translating results to guidance also requires an informatics infrastructure with multiple components: (1) to manage the interpretation of raw genomic data to "star allele" results to expected phenotype, (2) to define the rules that associate a phenotype with recommended changes to clinical care, and (3) to manage and update the knowledge base. Knowledge base management is key to processing new results with the latest guidelines, and to ensure that historical genomic results can be reinterpreted with revised CDS. We recommend that these components be deployed with institutional authorization, programmatic support, and clinician education to govern the CDS content and policies around delivery.

Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations

Genome-wide association studies have revolutionized our understanding of the genetic underpinnings of cardiometabolic disease. Yet, the inadequate representation of individuals of diverse ancestral backgrounds in these studies may undercut their ultimate potential for both public health and precision medicine. The goal of this review is to describe the imperativeness of studying the populations who are most affected by cardiometabolic disease, to the aim of better understanding the genetic underpinnings of the disease. We support this premise by describing the current variation in the global burden of cardiometabolic disease and emphasize the importance of building a globally and ancestrally representative genetics evidence base for the identification of population-specific variants, fine-mapping, and polygenic risk score estimation. We discuss the important ethical, legal, and social implications of increasing ancestral diversity in genetic studies of cardiometabolic disease and the challenges that arise from the (1) lack of diversity in current reference populations and available analytic samples and the (2) unequal generation of health-associated genomic data and their prediction accuracies. Despite these challenges, we conclude that additional, unprecedented opportunities lie ahead for public health genomics and the realization of precision medicine, provided that the gap in diversity can be systematically addressed. Achieving this goal will require concerted efforts by social, academic, professional and regulatory stakeholders and communities, and these efforts must be based on principles of equity and social justice.