Potential corporate uses of polygenic indexes: Starting a conversation about the associated ethics and policy issues

Some commercial firms currently sell polygenic indexes (PGIs) to individual consumers, despite their relatively low predictive power. It might be tempting to assume that because the predictive power of many PGIs is so modest, other sorts of firms-such as those selling insurance and financial services-will not be interested in using PGIs for their own purposes. We argue to the contrary. We build this argument in two ways. First, we offer a very simple model, rooted in economic theory, of a profit-maximizing firm that can gain information about a single consumer's genome. We use the model to show that, depending on the specific economic environment, a firm would be willing to pay for statistically noisy PGIs, even if they allow for only a small reduction in uncertainty. Second, we describe two plausible scenarios in which these different kinds of firms could conceivably use PGIs to maximize profits. Finally, we briefly discuss some of the associated ethics and policy issues. They deserve more attention, which is unlikely to be given until it is first recognized that firms whose services affect a large swath of the public will indeed have incentives to use PGIs.

Comparison of Clinical Decision Support Tools to Improve Pediatric Lipid Screening

OBJECTIVE

To test whether different clinical decision support tools increase clinician orders and patient completions relative to standard practice and each other.

STUDY DESIGN

A pragmatic, patient-randomized clinical trial in the electronic health record was conducted between October 2019 and April 2020 at Geisinger Health System in Pennsylvania, with 4 arms: care gap-a passive listing recommending screening; alert-a panel promoting and enabling lipid screen orders; both; and a standard practice-no guideline-based notification-control arm. Data were analyzed for 13 346 9- to 11-year-old patients seen within Geisinger primary care, cardiology, urgent care, or nutrition clinics, or who had an endocrinology visit. Principal outcomes were lipid screening orders by clinicians and completions by patients within 1 week of orders.

RESULTS

Active (care gap and/or alert) vs control arm patients were significantly more likely (P < .05) to have lipid screening tests ordered and completed, with ORs ranging from 1.67 (95% CI 1.28-2.19) to 5.73 (95% CI 4.46-7.36) for orders and 1.54 (95% CI 1.04-2.27) to 2.90 (95% CI 2.02-4.15) for completions. Alerts, with or without care gaps listed, outperformed care gaps alone on orders, with odds ratios ranging from 2.92 (95% CI 2.32-3.66) to 3.43 (95% CI 2.73-4.29).

CONCLUSIONS

Electronic alerts can increase lipid screening orders and completions, suggesting clinical decision support can improve guideline-concordant screening. The study also highlights electronic record-based patient randomization as a way to determine relative effectiveness of support tools.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04118348.

An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability

Traditionally, scientists have placed more emphasis on communicating inferential uncertainty (i.e., the precision of statistical estimates) compared to outcome variability (i.e., the predictability of individual outcomes). Here, we show that this can lead to sizable misperceptions about the implications of scientific results. Specifically, we present three preregistered, randomized experiments where participants saw the same scientific findings visualized as showing only inferential uncertainty, only outcome variability, or both and answered questions about the size and importance of findings they were shown. Our results, composed of responses from medical professionals, professional data scientists, and tenure-track faculty, show that the prevalent form of visualizing only inferential uncertainty can lead to significant overestimates of treatment effects, even among highly trained experts. In contrast, we find that depicting both inferential uncertainty and outcome variability leads to more accurate perceptions of results while appearing to leave other subjective impressions of the results unchanged, on average.

Experiment aversion among clinicians and the public - an obstacle to evidence-based medicine and public health

Background

Randomized controlled trials (RCTs) are essential for determining the safety and efficacy of healthcare interventions. However, both laypeople and clinicians often demonstrate experiment aversion: preferring to implement either of two interventions for everyone rather than comparing them to determine which is best. We studied whether clinician and layperson views of pragmatic RCTs for Covid-19 or other interventions became more positive early in the pandemic, which increased both the urgency and public discussion of RCTs.

Methods

We conducted several survey studies with laypeople (total n=2,909) and two with clinicians (n=895; n=1,254) in 2020 and 2021. Participants read vignettes in which a hypothetical decision-maker who sought to improve health could choose to implement intervention A for all, implement intervention B for all, or experimentally compare A and B and implement the superior intervention. Participants rated and ranked the appropriateness of each decision.

Results

Compared to our pre-pandemic results, we found no decrease in laypeople's aversion to non-Covid-19 experiments involving catheterization checklists and hypertension drugs. Nor were either laypeople or clinicians less averse to Covid-19 RCTs (concerning corticosteroid drugs, vaccines, intubation checklists, proning, school reopening, and mask protocols), on average. Across all vignettes and samples, levels of experiment aversion ranged from 28% to 57%, while levels of experiment appreciation (in which the RCT is rated higher than the participant's highest-rated intervention) ranged from only 6% to 35%.

Conclusions

Advancing evidence-based medicine through pragmatic RCTs will require anticipating and addressing experiment aversion among both patients and healthcare professionals.

A Randomized Trial of Behavioral Nudges Delivered Through Text Messages to Increase Influenza Vaccination Among Patients With an Upcoming Primary Care Visit

PURPOSE

To evaluate if nudges delivered by text message prior to an upcoming primary care visit can increase influenza vaccination rates.

DESIGN

Randomized, controlled trial.

SETTING

Two health systems in the Northeastern US between September 2020 and March 2021.

SUBJECTS

74,811 adults.

INTERVENTIONS

Patients in the 19 intervention arms received 1-2 text messages in the 3 days preceding their appointment that varied in their format, interactivity, and content.

MEASURES

Influenza vaccination.

ANALYSIS

Intention-to-treat.

RESULTS

Participants had a mean (SD) age of 50.7 (16.2) years; 55.8% (41,771) were female, 70.6% (52,826) were White, and 19.0% (14,222) were Black. Among the interventions, 5 of 19 (26.3%) had a significantly greater vaccination rate than control. On average, the 19 interventions increased vaccination relative to control by 1.8 percentage points or 6.1% (P = .005). The top performing text message described the vaccine to the patient as "reserved for you" and led to a 3.1 percentage point increase (95% CI, 1.3 to 4.9; P < .001) in vaccination relative to control. Three of the top five performing messages described the vaccine as "reserved for you." None of the interventions performed worse than control.

CONCLUSIONS

Text messages encouraging vaccination and delivered prior to an upcoming appointment significantly increased influenza vaccination rates and could be a scalable approach to increase vaccination more broadly.

Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility

In this consensus report by a diverse group of academics who conduct and/or are concerned about social and behavioral genomics (SBG) research, the authors recount the often-ugly history of scientific attempts to understand the genetic contributions to human behaviors and social outcomes. They then describe what the current science-including genomewide association studies and polygenic indexes-can and cannot tell us, as well as its risks and potential benefits. They conclude with a discussion of responsible behavior in the context of SBG research. SBG research that compares individuals within a group according to a "sensitive" phenotype requires extra attention to responsible conduct and to responsible communication about the research and its findings. SBG research (1) on sensitive phenotypes that (2) compares two or more groups defined by (a) race, (b) ethnicity, or (c) genetic ancestry (where genetic ancestry could easily be misunderstood as race or ethnicity) requires a compelling justification to be conducted, funded, or published. All authors agree that this justification at least requires a convincing argument that a study's design could yield scientifically valid results; some authors would additionally require the study to have a socially favorable risk-benefit profile.

Wrestling with Public Input on an Ethical Analysis of Scientific Research

Bioethicists frequently call for empirical researchers to engage participants and community members in their research, but don't themselves typically engage community members in their normative research. In this article, we describe an effort to include members of the public in normative discussions about the risks, potential benefits, and ethical responsibilities of social and behavioral genomics (SBG) research. We reflect on what might-and might not- be gained from engaging the public in normative scholarship and on lessons learned about public perspectives on the risks and potential benefits of SBG research and the responsible conduct and communication of such research. We also provide procedural lessons for others in bioethics who are interested in engaging members of the public in their research.

Public views on polygenic screening of embryos

Understanding moral acceptability and willingness to use is crucial for informing policy.

Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications

Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model’s correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783−0.789], compared with 0.694 [0.690−0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model’s prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823−0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system.

Exploring access to genomic risk information and the contours of the HIPAA public health exception

Considerable resources have been invested in research to identify pathogenic and likely pathogenic variants that cause morbidity and mortality and also in returning these results to patients. The public health impact and cost-effectiveness of these efforts are maximized when probands' relatives are informed of their risk and offered testing. However, such 'Traceback' cascade testing programs face multiple obstacles, including perceived or actual legal and regulatory hurdles. Here, using genetic cancer syndromes as a test case, we explore the contours of the Public Health Exception to the HIPAA Privacy Rule to assess whether it is a viable pathway for implementing a Traceback program. After examining the Privacy Rule as well as state laws and regulations for reportable conditions and genetic privacy, we conclude that this is not currently a viable approach for Traceback programs. We conclude by reflecting on ethical considerations of leveraging HIPAA's public health exception to disclose PHI directly to at-risk relatives and offering insights for how legal hurdles to such a Traceback program could be overcome, if desired.

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Computational ethics

Technological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework - computational ethics - that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions.

Allocation of Opportunities to Participate in Clinical Trials during the Covid-19 Pandemic and Other Public Health Emergencies

Covid-19 raised many novel ethical issues including regarding the allocation of opportunities to participate in clinical trials during a public health emergency. In this article, we explore how hospitals that have a scarcity of trial opportunities, either overall or in a specific trial, can equitably allocate those opportunities in the context of an urgent medical need with limited therapeutic interventions. We assess the three main approaches to allocating trial opportunities discussed in the literature: patient choice, physician referral, and randomization/lottery. As, we argue, none of the three typical approaches are ethically ideal for allocating trial opportunities in the pandemic context, many hospitals have instead implemented hybrid solutions. We offer practical guidance to support those continuing to face these challenges, and we analyze options for the future.

Resource profile and user guide of the Polygenic Index Repository

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Consequences of rush to emergency use authorization of bamlanivimab

In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Effect of Targeted Behavioral Science Messages on COVID-19 Vaccination Registration Among Employees of a Large Health System: A Randomized Trial

This randomized trial evaluates whether individually addressed emails designed with behaviorally informed features increase COVID-19 vaccination rates.

Problems with Using Polygenic Scores to Select Embryos

Companies have recently begun to sell a new service to patients considering in vitro fertilization: embryo selection based on polygenic scores (ESPS). These scores represent individualized predictions of health and other outcomes derived from genomewide association studies in adults to partially predict these outcomes. This article includes a discussion of many factors that lower the predictive power of polygenic scores in the context of embryo selection and quantifies these effects for a variety of clinical and nonclinical traits. Also discussed are potential unintended consequences of ESPS (including selecting for adverse traits, altering population demographics, exacerbating inequalities in society, and devaluing certain traits). Recommendations for the responsible communication about ESPS by practitioners are provided, and a call for a society-wide conversation about this technology is made. (Funded by the National Institute on Aging and others.).

Genomic medicine and the "loss of chance" medical malpractice doctrine

As genomic medicine expands, interest in how medical malpractice law will apply to such questions as whether and when to return new or updated genomic results has grown. Given that access to some genomic results (such as those pertaining to minors or those for which scientific interpretations are unsettled) is delayed for years, the “loss of chance” (LOC) doctrine is of particular potential relevance. Yet it has received relatively little attention among scholars of law and genomics. We performed legal research to determine the status of this malpractice doctrine across the United States and consider its potential applicability to genomic medicine. We further examined known genomic medicine malpractices to assess whether this doctrine had yet been invoked in that context. We identified a trend toward adoption of the LOC doctrine, finding 29 states (58%) have adopted, 15 states (30%) have rejected, and six states (12%) have deferred or not yet addressed the doctrine. Attempts to invoke or apply the doctrine in the known genomic medical malpractice cases were also found. While our findings do not provide cause for substantial concern, the availability of the LOC medical malpractice doctrine is a potentially important factor to consider when making programmatic decisions for genomic medicine. Future research examining whether liability risks posed by this doctrine prompt defensive medicine practices would be useful.

An ethics framework for consolidating and prioritizing COVID-19 clinical trials

Given the dearth of established safe and effective interventions to respond to COVID-19, there is an urgent ethical imperative to conduct meaningful clinical research. The good news is that interventions to be tested are not in short supply. Unfortunately, the human and material resources needed to conduct these trials are finite. It is essential that trials be robust and meet enrollment targets and that lower-quality studies not be permitted to displace higher-quality studies, delaying answers to critical questions. Yet, with few exceptions, existing research review bodies and processes are not designed to ensure these conditions are satisfied. To meet this challenge, we offer guidance for research institutions about how to ethically consolidate and prioritize COVID-19 clinical trials, while recognizing that consolidation and prioritization should also take place upstream (among manufacturers and funders) and at a higher level (e.g. nationally). In our proposed three-stage process, trials must first meet threshold criteria. Those that do are evaluated in a second stage to determine whether the institution has sufficient capacity to support all proposed trials. If it does not, the third stage entails evaluating studies against two additional sets of comparative prioritization criteria: those specific to the study and those that aim to advance diversification of an institution's research portfolio. To implement these criteria fairly, we propose that research institutions form COVID-19 research prioritization committees. We briefly discuss some important attributes of these committees, drawing on the authors' experiences at our respective institutions. Although we focus on clinical trials of COVID-19 therapeutics, our guidance should prove useful for other kinds of COVID-19 research, as well as non-pandemic research, which can raise similar challenges due to the scarcity of research resources.

Trends in Health Care Worker Intentions to Receive a COVID-19 Vaccine and Reasons for Hesitancy

This survey study queried employees of a health care system before COVID-19 vaccine distribution to assess their intentions to receive a vaccine and to understand their reasons for hesitancy to do so.

Informing patients that they are at high risk for serious complications of viral infection increases vaccination rates

For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status. Patients (N=39,717) were evenly randomized to (1) a control condition (exposure only to standard direct mail or patient portal vaccine promotion efforts) or to be told via direct mail, patient portal, and/or SMS that they were (2) at high risk for influenza and its complications if not vaccinated; (3) at high risk according to a review of their medical records; or (4) at high risk according to a computer algorithm analysis of their medical records. Patients in the three treatment conditions were 5.7% more likely to get vaccinated during the 112 days post-intervention (p < .001), and did so 1.4 days earlier (p < .001), on average, than those in the control group. There were no significant differences among risk messages, suggesting that patients are neither especially averse to nor uniquely appreciative of learning their records had been reviewed or that computer algorithms were involved. Similar approaches should be considered for COVID-19 vaccination campaigns.

Self-experimentation, ethics, and regulation of vaccines

DIY COVID-19 vaccines raise legal and ethical questions

Pediatric reporting of genomic results study (PROGRESS): a mixed-methods, longitudinal, observational cohort study protocol to explore disclosure of actionable adult- and pediatric-onset genomic variants to minors and their parents

BACKGROUND

Exome and genome sequencing are routinely used in clinical care and research. These technologies allow for the detection of pathogenic/likely pathogenic variants in clinically actionable genes. However, fueled in part by a lack of empirical evidence, controversy surrounds the provision of genetic results for adult-onset conditions to minors and their parents. We have designed a mixed-methods, longitudinal cohort study to collect empirical evidence to advance this debate.

METHODS

Pediatric participants in the Geisinger MyCode® Community Health Initiative with available exome sequence data will have their variant files assessed for pathogenic/likely pathogenic variants in 60 genes designated as actionable by MyCode. Eight of these genes are associated with adult-onset conditions (Hereditary Breast and Ovarian Cancer Syndrome (HBOC), Lynch syndrome, MUTYH-associated polyposis, HFE-Associated Hereditary Hemochromatosis), while the remaining genes have pediatric onset. Prior to clinical confirmation of results, pediatric MyCode participants and their parents/legal guardians will be categorized into three study groups: 1) those with an apparent pathogenic/likely pathogenic variant in a gene associated with adult-onset disease, 2) those with an apparent pathogenic/likely pathogenic variant in a gene associated with pediatric-onset disease or with risk reduction interventions that begin in childhood, and 3) those with no apparent genomic result who are sex- and age-matched to Groups 1 and 2. Validated and published quantitative measures, semi-structured interviews, and a review of electronic health record data conducted over a 12-month period following disclosure of results will allow for comparison of psychosocial and behavioral outcomes among parents of minors (ages 0-17) and adolescents (ages 11-17) in each group.

DISCUSSION

These data will provide guidance about the risks and benefits of informing minors and their family members about clinically actionable, adult-onset genetic conditions and, in turn, help to ensure these patients receive care that promotes physical and psychosocial health.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03832985. Registered 6 February 2019.

There Oughta Be a Law: When Does(n't) the U.S. Common Rule Apply?

Using mobile health (mHealth) research as an extended example, this article provides an overview of when the Common Rule "applies" to a variety of activities, what might be meant when one says that the Common Rule does or does not "apply," the extent to which these different meanings of "apply" matter, and, when the Common Rule does apply (however that term is defined), how it applies.

Population Whole Exome Screening: Primary Care Provider Attitudes About Preparedness, Information Avoidance, and Nudging

Compared to clinicians previously surveyed, primary care providers employed in a health system known for clinical genomics were more likely to have ordered or referred a patient for genetic testing, but had only modestly more genetics training and reported similarly low levels of comfort answering patient questions about genetic risk. Most supported population genomic screening, reported willingness to get screened themselves, and judged a hypothetical patient's decision to be screened favorably relative to a similar patient's decision to decline screening. Stakeholder perceptions of the ethical appropriateness of nudging at-risk patients to discuss testing with counselors were mixed.

Objecting to experiments that compare two unobjectionable policies or treatments

Randomized experiments have enormous potential to improve human welfare in many domains, including healthcare, education, finance, and public policy. However, such "A/B tests" are often criticized on ethical grounds even as similar, untested interventions are implemented without objection. We find robust evidence across 16 studies of 5,873 participants from three diverse populations spanning nine domains-from healthcare to autonomous vehicle design to poverty reduction-that people frequently rate A/B tests designed to establish the comparative effectiveness of two policies or treatments as inappropriate even when universally implementing either A or B, untested, is seen as appropriate. This "A/B effect" is as strong among those with higher educational attainment and science literacy and among relevant professionals. It persists even when there is no reason to prefer A to B and even when recipients are treated unequally and randomly in all conditions (A, B, and A/B). Several remaining explanations for the effect-a belief that consent is required to impose a policy on half of a population but not on the entire population; an aversion to controlled but not to uncontrolled experiments; and a proxy form of the illusion of knowledge (according to which randomized evaluations are unnecessary because experts already do or should know "what works")-appear to contribute to the effect, but none dominates or fully accounts for it. We conclude that rigorously evaluating policies or treatments via pragmatic randomized trials may provoke greater objection than simply implementing those same policies or treatments untested.

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Patient-Centered Precision Health In A Learning Health Care System: Geisinger's Genomic Medicine Experience

Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.

Of Parachutes and Participant Protection: Moving Beyond Quality to Advance Effective Research Ethics Oversight

There are several reasons to believe that Institutional Review Boards (IRBs) and Human Research Protection Programs (HRPPs) contribute to ethical research and the protection of research participants, but there are also important reasons to interrogate this belief. Determining whether IRBs and HRPPs "work" requires empirical evaluation of whether and how well they actually achieve what they were designed to do. In other words, it is critical to examine their outcomes and not only their procedures and structures. In this response to Tsan, we argue that the concept of IRB and HRPP quality entails three dimensions: (1) effectiveness, (2) procedures and structures likely to promote effectiveness, and (3) features unrelated to effectiveness but nonetheless essential, such as efficiency, fairness, and proportionality. Because not all types of quality necessarily guarantee or entail effectiveness, we suggest that broad quality assessments, including such features as regulatory compliance and other procedural measures suggested by Tsan, are unhelpful as the first step in evaluating IRBs and HRPPs. Instead, we must start with outcomes relevant to effectiveness. To do this, we launched the Consortium to Advance Effective Research Ethics Oversight (AEREO), with a mission to define and specify ways to measure relevant outcomes for research ethics oversight, empirically evaluate whether those outcomes are achieved, test new approaches to achieving them, and ultimately, develop and implement empirically-based policy and practice to advance IRB and HRPP effectiveness. We describe several anticipated AEREO projects and call for collaboration between various stakeholders to more meaningfully evaluate IRB and HRPPs.

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Practical Tips for Ethical Data Sharing

This Tutorial provides practical dos and don’ts for sharing research data in ways that are effective, ethical, and compliant with the federal Common Rule. I first consider best practices for prospectively incorporating data-sharing plans into research, discussing what to say—and what not to say—in consent forms and institutional review board applications, tools for data de-identification and how to think about the risks of re-identification, and what to consider when selecting a data repository. Turning to data that have already been collected, I discuss the ethical and regulatory issues raised by sharing data when the consent form either was silent about data sharing or explicitly promised participants that the data would not be shared. Finally, I discuss ethical issues in sharing “public” data.

Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.

Genome-wide association study identifies 74 loci associated with educational attainment

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

GWAS of 126,559 individuals identifies genetic variants associated with educational attainment

A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R(2) ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.

The more things change: the new NIH Guidelines on human stem cell research

Many assumed that the Obama administration would usher in a sea change from the previous administration by expanding NIH support for human embryonic stem cell (hESC) research and reducing the patchwork of state and federal regulations that currently governs it. This article examines the extent to which NIH's new Guidelines are likely to accomplish these goals.