BACKGROUND: Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models. METHODS: We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants. RESULTS: We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures. CONCLUSIONS: The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC.

Since its beginning more than 75 years ago [1], epigenetics has been an evolving field with growing applications to the study of cancer, aging, and gene expression in response to environmental exposures. The emergence of high-throughput technology for measuring epigenetic markers has enabled population-based studies [2]. The relatively new field of epigenetic epidemiology investigates epigenetic associations from a population perspective for insights into disease risk, prevention, and progression. Unlike genetic variants, epigenetic markers are dynamic, offering epidemiologists a new approach to linking early life and environmental exposures with disease risk [3]. | | Scientific publications on epigenetic epidemiology have been rapidly increasing in number and variety over the past 20 years. The literature now includes studies of epigenetic markers beyond DNA methylation (DNAm), such as histone modification and non-coding RNA, and consists of a variety of study designs including epigenome-wide association studies (EWAS), candidate gene studies, and clinical trials. Epigenetic markers are investigated as risk factors, such as DNAm in association with type 2 diabetes incidence [4], or outcomes, such as DNAm changes in response to air pollution [5]. The objective of the Epigenetic Epidemiology Publications Database (EEPD) is to offer a user-friendly website to explore the expanding literature in epigenetics, epidemiology, and public health.

The fourth author’s name is incorrect. The correct name is Lawrence C. Brody. The correct citation is: Khoury MJ, Feero WG, Chambers DA, Brody LC, Aziz N, Green RC, et al. (2018) A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health. PLoS Med 15(8): e1002631. https://doi.org/10.1371/journal.pmed.1002631.

Precision public health has been defined in many ways [1]. It can be viewed as an emerging multidisciplinary field that uses genomics, big data, and machine learning/artificial intelligence to predict health risks and outcomes and to improve health at the population level. Just like precision medicine seeks to provide the right intervention to the right patient at the right time, the aim of precision public health is to provide the right intervention to the right population at the right time, with the goal of improving health for all.

The opinions expressed in the paper are those of the authors and do not necessarily reflect those of the Centers for Disease Control and Prevention. | | Recognizing the emerging role of genomics as a tool for population screening, the American College of Medical Genetics and Genomics (ACMG) has generated two companion guidance documents on DNA-based screening of healthy individuals that appear in the present issue of Genetics in Medicine.1,2 In this commentary, we offer a brief public health perspective on these documents in the context of recent work from the Centers for Disease Control and Prevention (CDC) Office of Genomics and Precision Public Health (OGPPH).

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Despite growing awareness about the potential for genomic information to improve population health, lingering communication challenges remain in describing the role of genomics in public health programs. Identifying and addressing these challenges provide an important opportunity for appropriate communication to ensure the translation of genomic discoveries for public health benefits. In this commentary, we describe 5 common communication challenges encountered by the Centers for Disease Control and Prevention's Office of Genomics and Precision Public Health based on over 20 years of experience in the field. These include (1) communicating that using genomics to assess rare diseases can have an impact on public health; (2) providing evidence that genetic factors can add important information to environmental, behavioral, and social determinants of health; (3) communicating that although genetic factors are nonmodifiable, they can increase the impact of public health programs and communication strategies; (4) addressing the concern that genomics is not ready for clinical practice; and (5) communicating that genomics is valuable beyond the domain of health care and can be integrated as part of public health programs. We discuss opportunities for addressing these communication challenges and provide examples of ongoing approaches to communication about the role of genomics in public health to the public, researchers, and practitioners.

Muin Khoury and co-authors discuss anticipated contributions of genomics and other forms of large-scale data in public health.

Since the completion of the Human Genome Project, progress toward translating genomic research discoveries to address population health issues has been limited. Several meetings of social and behavioral scientists have outlined priority research areas where advancement of translational research could increase population health benefits of genomic discoveries. In this review, we track the pace of progress, study size and design, and focus of genomics translational research from 2012 to 2018 and its concordance with five social and behavioral science recommended priorities. We conducted a review of the literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Guidelines for Scoping Reviews. Steps involved completing a search in five databases and a hand search of bibliographies of relevant literature. Our search (from 2012 to 2018) yielded 4,538 unique studies; 117 were included in the final analyses. Two coders extracted data including items from the PICOTS framework. Analysis included descriptive statistics to help identify trends in pace, study size and design, and translational priority area. Among the 117 studies included in our final sample, nearly half focused on genomics applications that have evidence to support translation or implementation into practice (Centers for Disease Control and Prevention Tier 1 applications). Common study designs were cross-sectional (40.2%) and qualitative (24.8%), with average sample sizes of 716 across all studies. Most often, studies addressed public understanding of genetics and genomics (33.3%), risk communication (29.1%), and intervention development and testing of interventions to promote behavior change (19.7%). The number of studies that address social and behavioral science priority areas is extremely limited and the pace of this research continues to lag behind basic science advances. Much of the research identified in this review is descriptive and related to public understanding, risk communication, and intervention development and testing of interventions to promote behavior change. The field has been slow to develop and evaluate public health-friendly interventions and test implementation approaches that could enable health benefits and equitable access to genomic discoveries. As the completion of the human genome approaches its 20th anniversary, full engagement of transdisciplinary efforts to address translation challenges will be required to close this gap.

With more than 20 million cases of coronavirus disease 2019 (COVID-19) globally and now exceeding 5 million cases in the United States, the COVID-19 pandemic represents one of the greatest public health challenges in more than a century. To succeed against COVID-19, multiple public health tools and interventions will be needed to minimize morbidity and mortality related to COVID-19. Although extreme public health interventions, for example, lockdowns and stay-at-home mandates, were initially critical to flattening the curve, many fundamental questions remain, such as when can employees deemed nonessential return to work, how can children safely return to school, and who should be first to receive a vaccine once it becomes available. Information about who is at highest risk of hospitalization, intensive care unit admission, and death based on age, sex, race/ethnicity, and underlying conditions is now becoming available.1 In addition, the relationship between neighborhood factors (eg, increased neighborhood household crowding rate) and risks for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and COVID-19 disease outcomes are now recognized.2

Emerging data science techniques of predictive analytics expand the quality and quantity of complex data relevant to human health and provide opportunities for understanding and control of conditions such as heart, lung, blood, and sleep disorders. To realize these opportunities, the information sources, the data science tools that use the information, and the application of resulting analytics to health and health care issues will require implementation research methods to define benefits, harms, reach, and sustainability; and to understand related resource utilization implications to inform policymakers. This JACC State-of-the-Art Review is based on a workshop convened by the National Heart, Lung, and Blood Institute to explore predictive analytics in the context of implementation science. It highlights precision medicine and precision public health as complementary and compelling applications of predictive analytics, and addresses future research and training endeavors that might further foster the application of predictive analytics in clinical medicine and public health.

OBJECTIVE: The massive growth in the publication of meta-analyses may cause redundancy and wasted efforts. We performed a meta-epidemiologic study to evaluate the extent of potential redundancy in published meta-analyses in genetic epidemiology. STUDY DESIGN: Using a sample of 38 index meta-analyses of genetic associations published in 2010, we retrieved additional meta-analyses that evaluated identical associations (same genetic variant and phenotype) using the HuGE (Human Genome Epidemiology) Navigator and PubMed databases. We analyzed the frequency of potential duplication and examined whether subsequent meta-analyses cited previous meta-analyses on the exact same association. RESULTS: Based on 38 index meta-analyses, we retrieved a total of 99 duplicate meta-analyses. Only 12 of the index meta-analyses (32%) were unambiguously unique. We found a mean of 2.6 duplicates and median of 2 duplicates per meta-analysis. In case studies, only 29-54% of previously published meta-analyses were cited by subsequent ones. CONCLUSIONS: These results suggest that duplication is common in meta-analyses of genetic associations.

The 2 most common causes of death in the United States are cancer and heart disease. Screening, defined as the process of proactively identifying disease risk, is pursued as part of routine medical care of adults. Standardized imaging-based screening (eg, mammography), procedure-based screening (eg, colonoscopy), and laboratory-based screening (eg, measurement of blood cholesterol levels) exist for cancer and heart disease; however, there is not currently any recommended use of DNA-based screening in these disease areas.

Understanding the drivers of research on human genes is a critical component to success of translation efforts of genomics into medicine and public health. Using publicly available curated online databases we sought to identify specific genes that are featured in translational genetic research in comparison to all genomics research publications. Articles in the CDC's Public Health Genomics and Precision Health Knowledge Base were stratified into studies that have moved beyond basic research to population and clinical epidemiologic studies (T1: clinical and population human genome epidemiology research), and studies that evaluate, implement, and assess impact of genes in clinical and public health areas (T2+: beyond bench to bedside). We examined gene counts and numbers of publications within these phases of translation in comparison to all genes from Medline. We are able to highlight those genes that are moving from basic research to clinical and public health translational research, namely in cancer and a few genetic diseases with high penetrance and clinical actionability. Identifying human genes of translational value is an important step towards determining an evidence-based trajectory of the human genome in clinical and public health practice over time.

Polygenic risk scores (PRS) are an emerging precision medicine tool based on multiple gene variants that, taken alone, have weak associations with disease risks, but collectively may enhance disease predictive value in the population. However, the benefit of PRS may not be equal among non-European populations, as they are under-represented in genome-wide association studies (GWAS) that serve as the basis for PRS development. In this perspective, we discuss a path forward, which includes: 1) inclusion of underrepresented populations in PRS research; 2) global efforts to build capacity for genomic research; 3) equitable implementation of these tools in clinical practice; and 4) traditional public health approaches to reduce risk of adverse health outcomes as an important component to precision health. As precision medicine is implemented in clinical care, researchers must ensure that advances from PRS research will benefit all.

The field of public health genomics has matured in the past two decades and is beginning to deliver genomic-based interventions for health and health care. In the past few years, the terms precision medicine and precision public health have been used to include information from multiple fields measuring biomarkers as well as environmental and other variables to provide tailored interventions. In the context of public health, "precision" implies delivering the right intervention to the right population at the right time, with the goal of improving health for all. In addition to genomics, precision public health can be driven by "big data" as identified by volume, variety, and variability in biomedical, sociodemographic, environmental, geographic, and other information. Most current big data applications in health are in elucidating pathobiology and tailored drug discovery. We explore how big data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, and efforts to promote uptake of evidence-based interventions, by including more extensive information related to place, person, and time. We use selected examples drawn from child health, cardiovascular disease, and cancer to illustrate the promises of precision public health, as well as current methodologic and analytic challenges to big data to fulfill these promises.

Cardiovascular diseases remain the leading cause of mortality and a major contributor to preventable deaths worldwide. The dominant modifiable risk factors and the social and environmental determinants that increase cardiovascular risk are known, and collectively, are as important in racial and ethnic minority populations as they are in majority populations. Their prevention and treatment remain the foundation for cardiovascular health promotion and disease prevention. Genetic and epigenetic factors are increasingly recognized as important contributors to cardiovascular risk and provide an opportunity for advancing precision cardiovascular medicine. In this review, we explore emerging concepts at the interface of precision medicine and cardiovascular disease in racial and ethnic minority populations. Important among these are the lack of racial and ethnic diversity in genomics studies and biorepositories; the resulting misclassification of benign variants as pathogenic in minorities; and the importance of ensuring ancestry-matched controls in variant interpretation. We address the relevance of epigenetics, pharmacogenomics, genetic testing and counseling, and their social and cultural implications. We also examine the potential impact of precision medicine on racial and ethnic disparities. The National Institutes of Health's All of Us Research Program and the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine Initiative are presented as examples of research programs at the forefront of precision medicine and diversity to explore research implications in minorities. We conclude with an overview of implementation research challenges in precision medicine and the ethical implications in minority populations. Successful implementation of precision medicine in cardiovascular disease in minority populations will benefit from strategies that directly address diversity and inclusion in genomics research and go beyond race and ethnicity to explore ancestry-matched controls, as well as geographic, cultural, social, and environmental determinants of health.

Family history is a strong risk factor for many common chronic diseases and summarizes shared environmental and genetic risk, but how this increased risk is mediated is unknown. We developed a "Family History-Wide Association Study" (FamWAS) to systematically and comprehensively test Clinical and Environmental Quantitative Traits (CEQTs) for their association with family history of disease. We implemented our method on 457 CEQTs for association with family history of diabetes, asthma, and coronary heart disease (CHD) in 42,940 adults spanning 8 waves of the 1999-2014 National Health and Nutrition Examination Survey (NHANES). We conducted pooled analyses of the 8 survey waves and analyzed trait associations using survey-weighted logistic regression. We identified 172 (37.6% of total), 32 (7.0%), and 78 (17.1%) CEQTs associated with family history of diabetes, asthma, and CHD, respectively, in sub-cohorts of individuals without the respective disease. 20 associated CEQTs were shared across family history of diabetes, asthma, and CHD, far more than expected by chance. FamWAS can examine traits not previously studied in association with family history and uncover trait overlap, highlighting a putative shared mechanism by which family history influences disease risk.

This year marks the 10th anniversary of Genome Medicine. The journal was launched to meet the need in the community for a platform to publish impactful and open science that advances basic and clinical research—using genetic, genomic, omic, and systems approaches—that has the potential to revolutionize the practice of medicine. We have seen the journal evolve along with the changing landscape of health and disease, including the increasing use of genome-scale approaches in medical research and clinical practice, the generation and analysis of patient- and population-level data, and the clinical implementation of these approaches in precision medicine and public health. Genome Medicine, guided by our renowned Section Editors, continues to serve an ever-growing community of interdisciplinary researchers. Here, our Section Editors discuss the major advances in the field and their applications in genomic medicine during the past decade.

Tracking scientific research publications on the evaluation, utility and implementation of genomic applications is critical for the translation of basic research to impact clinical and population health. In this work, we utilize state-of-the-art machine learning approaches to identify translational research in genomics beyond bench to bedside from the biomedical literature. We apply the convolutional neural networks (CNNs) and support vector machines (SVMs) to the bench/bedside article classification on the weekly manual annotation data of the Public Health Genomics Knowledge Base database. Both classifiers employ salient features to determine the probability of curation-eligible publications, which can effectively reduce the workload of manual triage and curation process. We applied the CNNs and SVMs to an independent test set (n = 400), and the models achieved the F-measure of 0.80 and 0.74, respectively. We further tested the CNNs, which perform better results, on the routine annotation pipeline for 2 weeks and significantly reduced the effort and retrieved more appropriate research articles. Our approaches provide direct insight into the automated curation of genomic translational research beyond bench to bedside. The machine learning classifiers are found to be helpful for annotators to enhance the efficiency of manual curation.

Dr Psaty and colleagues1 compared precision medicine and preventive medicine as 2 distinct models in medicine. They posited that precision medicine is deterministic, often with a gene-centric approach, while preventive medicine is probabilistic and applies to common conditions such as hypertension and hyperlipidemia. The 2 models are complementary and not competitive. Emerging scientific evidence will guide physicians toward preventive and curative interventions that can work best at the population or individual levels. As envisioned by the All of Us research program,2 precision medicine encompasses both treatment and prevention and is more than genetics: “Precision medicine takes into account individual differences in lifestyle, environment, and biology.” Furthermore, precision does not necessarily imply biologic determinism. Most human diseases are due to complex gene-environment interactions that can lead only to probabilistic approaches to prevention.

Social media is increasingly being used as an information source and tool for individuals and organizations to share resources and engage in conversations about health topics. Because the public tends to learn about health topics and genetics from online social media sources, it is imperative to understand the amount, type, and quality of information being shared. We performed a retrospective analysis of tweets related to hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS) between January 1, 2017 and December 31, 2017. A total of 63,770 tweets were included in our dataset. The majority were retweets (59.9%) and users came from 744 different cities. Most tweets were considered "informational" (51.4%) and were designed to provide resources to the public. Online communities (25%), organizations (20%), and providers or researchers (15%) were among the most common contributors. Our results demonstrated that conversations were primarily focused on information and resource sharing, along with individuals discussing their personal stories and testimonials about their experiences with these HBOC and LS. Future studies could consider ways to harness Twitter to help tailor and deliver health communication campaigns and education interventions to improve the public's understanding of these complex topics.

BACKGROUND: The growing availability of genomic information to the public may spur discussion about genetics and genomics on social media. Sites, including Twitter, provide a unique space for the public to access and discuss health information. The objective of this study was to better understand how social media users are sharing information about genetics and genomics in health and healthcare and what information is most commonly discussed among Twitter users. METHODS: We obtained tweets with specific genetics- and genomics-related keywords from Crimson Hexagon. We used Boolean logic to collect tweets containing chosen keywords within the timeframe of October 1, 2016, to October 1, 2017. Features of the software were used to identify salient themes in conversation, conduct an emergent content analysis, and gather key demographic information. RESULTS: We obtained 347,196 tweets from our search. There was a monthly average volume of 28,432 tweets. The five categories of tweets included: genetic disorders/disease (45.3%), health (15.6%), genomics (8%), and genetic testing (7.3%). Top influencers in the conversation included news outlets and universities. CONCLUSIONS: This content analysis provides insight about the types of conversation related to genomics and health. Conversations about genomics are occurring on Twitter, and they frequently emphasize rare genetic diseases and genetic disorders. These discussions tend to be driven by key influencers who primarily include news media outlets. Further understanding of the discussions related to genomics and health in social media may offer insight about topics of importance to the public.

Recent dramatic advances in multiomics research coupled with exponentially increasing volume, complexity, and interdisciplinary nature of publications are making it challenging for scientists to stay up-to-date on the literature. Strategies to address this challenge include the creation of online databases and warehouses to support timely and targeted dissemination of research findings. Although most of the early examples have been in cancer genomics and pharmacogenomics, the approaches used can be adapted to support investigators in heart, lung, blood, and sleep (HLBS) disorders research. In this article, we describe the creation of an HLBS population genomics (HLBS-PopOmics) knowledge base as an online, continuously updated, searchable database to support the dissemination and implementation of studies and resources that are relevant to clinical and public health practice. In addition to targeted searches based on the HLBS disease categories, cross-cutting themes reflecting the ethical, legal, and social implications of genomics research; systematic evidence reviews; and clinical practice guidelines supporting screening, detection, evaluation, and treatment are also emphasized in HLBS-PopOmics. Future updates of the knowledge base will include additional emphasis on transcriptomics, proteomics, metabolomics, and other omics research; explore opportunities for leveraging data sets designed to support scientific discovery; and incorporate advanced machine learning bioinformatics capabilities.

Public health plays an important role in ensuring access to interventions that can prevent disease, including the implementation of evidence-based genomic recommendations. We used the Centers for Disease Control and Prevention (CDC) Science Impact Framework to trace the impact of public health activities and partnerships on the implementation of the 2009 Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Lynch Syndrome screening recommendation and the 2005 and 2013 United States Preventive Services Task Force (USPSTF) BRCA1 and BRCA2 testing recommendations.The EGAPP and USPSTF recommendations have each been cited by >300 peer-reviewed publications. CDC funds selected states to build capacity to integrate these recommendations into public health programs, through education, policy, surveillance, and partnerships. Most state cancer control plans include genomics-related goals, objectives, or strategies. Since the EGAPP recommendation, major public and private payers now provide coverage for Lynch Syndrome screening for all newly diagnosed colorectal cancers. National guidelines and initiatives, including Healthy People 2020, included similar recommendations and cited the EGAPP and USPSTF recommendations. However, disparities in implementation based on race, ethnicity, and rural residence remain challenges. Public health achievements in promoting the evidence-based use of genomics for the prevention of hereditary cancers can inform future applications of genomics in public health.

Cancer-related genetic and genomic testing (CGT) is changing cancer care by personalizing care options, leading to an era of precision medicine. Advances in and increased use of CGT add complexity to clinical communication. This landscape analysis assessed published reviews of communication issues related to CGT and discusses implications for practice and behavioral research. A comprehensive electronic literature search was conducted of peer-reviewed literature reviews on studies related to CGT communication published between January 2010 and January 2017, resulting in a final sample of 24 reviews. Reviews were categorized, with overlaps, into four domains across the genetic testing communication continuum. Reviews on CGT-related knowledge, attitudes, and perceptions (n = 8) found that despite substantial public interest, their knowledge and awareness remains low. Providers also reported insufficient knowledge and overall caution, particularly regarding direct-to-consumer (DTC) genetic testing. Reviews of decision-making about CGT and test uptake (n = 8) identified individual, interpersonal, and systems-level barriers to uptake. Reviews of patient-provider CGT communication (n = 8) revealed limited communication and little empirical research on outcomes of communication or efforts at improving clinical and family communication. There were mixed findings in reviews (n = 15) on the psychological and behavioral impact of CGT, and DTC testing particularly had little effect on behaviors. Taken together, there is very little extant research in CGT in minority and underserved communities. In order for scientific advances in CGT to translate into equitable, patient-centered care, behavioral research, including health literacy and communication, plays critical roles.

Background -Familial hypercholesterolemia (FH) and other extreme elevations in low-density lipoprotein cholesterol significantly increase the risk of atherosclerotic cardiovascular disease; however, recent data suggest that prescription rates for statins remain low in these patients. National rates of screening, awareness, and treatment with statins among individuals with FH or severe dyslipidemia are unknown. Methods -Data from the 1999 to 2014 National Health and Nutrition Examination Survey were used to estimate prevalence rates of self-reported screening, awareness, and statin therapy among US adults (n=42 471 weighted to represent 212 million US adults) with FH (defined using the Dutch Lipid Clinic criteria) and with severe dyslipidemia (defined as lowdensity lipoprotein cholesterol levels >/=190 mg/dL). Logistic regression was used to identify sociodemographic and clinical correlates of hypercholesterolemia awareness and statin therapy. Results -The estimated US prevalence of definite/probable FH was 0.47% (standard error, 0.03%) and of severe dyslipidemia was 6.6% (standard error, 0.2%). The frequency of cholesterol screening and awareness was high (>80%) among adults with definite/probable FH or severe dyslipidemia; however, statin use was uniformly low (52.3% [standard error, 8.2%] of adults with definite/probable FH and 37.6% [standard error, 1.2%] of adults with severe dyslipidemia). Only 30.3% of patients with definite/probable FH on statins were taking a high-intensity statin. The prevalence of statin use in adults with severe dyslipidemia increased over time (from 29.4% to 47.7%) but not faster than trends in the general population (from 5.7% to 17.6%). Older age, health insurance status, having a usual source of care, diabetes mellitus, hypertension, and having a personal history of early atherosclerotic cardiovascular disease were associated with higher statin use. Conclusions -Despite the high prevalence of cholesterol screening and awareness, only approximately 50% of adults with FH are on statin therapy, with even fewer prescribed a high-intensity statin; young and uninsured patients are at the highest risk for lack of screening and for undertreatment. This study highlights an imperative to improve the frequency of cholesterol screening and statin prescription rates to better identify and treat this high-risk population. Additional studies are needed to better understand how to close these gaps in screening and treatment.

Genomic and other related big data (Big Genomic Data, BGD for short) are ushering a new era of precision medicine. This overview discusses whether principles of evidence-based medicine (EBM) hold true for BGD and how they should be operationalized in the current era. Major EBM principles include the systematic identification, description and analysis of the validity and utility of BGD, the combination of individual clinical expertise with individual patient needs and preferences, and the focus on obtaining experimental evidence, whenever possible. BGD emphasize information of single patients with an overemphasis on N-of-1 trials to personalize treatment. However, large-scale comparative population data remain indispensable for meaningful translation of BGD personalized information. The impact of BGD on population health depends on its ability to affect large segments of the population. While several frameworks have been proposed to facilitate and standardize decision-making for use of genomic tests, there are new caveats that arise from BGD that extend beyond the limitations that were applicable for more simple genetic tests. Non-evidence-based use of BGD may be harmful and result in major waste of health care resources. Randomized controlled trials (RCTs) will continue to be the strongest arbitrator for the clinical utility of genomic technologies, including BGD. Research on BGD needs to focus not only on finding robust predictive associations (clinical validity), but more importantly on evaluating the balance of health benefits and potential harms (clinical utility), as well as implementation challenges. Appropriate features of such useful research on BGD are discussed.

In this paper, we review the evolution of the field of public health genomics in the United States in the past two decades. Public health genomics focuses on effective and responsible translation of genomic science into population health benefits. We discuss the relationship of the field to the core public health functions and essential services, review its evidentiary foundation, and provide examples of current US public health priorities and applications. We cite examples of publications to illustrate how Genetics in Medicine reflected the evolution of the field. We also reflect on how public-health genomics is contributing to the emergence of "precision public health" with near-term opportunities offered by the US Precision Medicine (AllofUs) Initiative.GENETICS in MEDICINE advance online publication, 14 December 2017; doi:10.1038/gim.2017.211.

n response to Drs Hoosien and Elshazly, we reiterate that we are not opposed to the principle of precision medicine as a concept. In our Viewpoint, we were simply making the case that, like other promising areas of new technologies, the new tools of genomics, big data analytics, artificial intelligence, and machine learning need to be evaluated not only with respect to their ability to integrate complex data but in their ability to improve health outcomes for patients and populations. | | It is known that changing individuals’ behavior is extremely difficult, whether it is based on genetic or other personal information. Current data suggest that genetic information may not be a strong driver for behavior change, but this is based on recent meta-analyses of published studies that have methodologic limitations.1 Perhaps more contemporary approaches to behavior modification using genetic and nongenetic information could work. Nevertheless, new approaches need to be evaluated for their utility before integrating them into clinical practice. | | Although precision medicine may hold promise for the future and has immediate applications today, the point of our article was to build a bridge between those who are skeptical of individualized approaches to improving population health and those who believe that with more “precision,” the health of populations automatically will be improved.