Evaluation of risk prediction updates from commercial genome-wide scans

Morphogenetic Updating Algorithm and its Application in Updating Probabilistic Risk

The problem of risk update is one of the core problems of dynamic risk analysis. As for updating widely used probabilistic risk, it’s a kind of special problem of pattern recognition which means to transfer from old pattern to new pattern with new information. Aiming at updating probabilistic risk only with the new observations and without the original observations, this paper proposes the morphogenetic updating algorithm to update the probabilistic risk with only new observations and old risk values by reproducing the morphogenesis of new observations and old risk values based on morphogenetic estimation. The form rules hidden in the risk values and new observations are discovered by internal source in Write operation, and the projection of the input and the cosine similarity measure as the basis for defining the weight of updating are computed in Read operation. Based on Resconi theorem, the input is reproduced by its projection on the context which provides the same mathematical space for updating and stores the structural information hidden in the risk. By applying the algorithm to updating the probabilistic risk of typhoon rainstorms occurred in Guangdong Province, it proves that morphogenetic updating algorithm provides us a method to updating probabilistic risk fast and satisfactorily and it’s a sustainable updating method which can be used for dynamic risk analysis.

Reclassification of genetic-based risk predictions as GWAS data accumulate

Background

Disease risk assessments based on common genetic variation have gained widespread attention and use in recent years. The clinical utility of genetic risk profiles depends on the number and effect size of identified loci, and how stable the predicted risks are as additional loci are discovered. Changes in risk classification for individuals over time would undermine the validity of common genetic variation for risk prediction. In this analysis, we quantified reclassification of genetic risk based on past and anticipated future GWAS data.

Methods

We identified disease-associated SNPs via the NHGRI GWAS catalog and recent large scale genome-wide association study (GWAS). We calculated the genomic risk for a simulated cohort of 100,000 individuals based on a multiplicative odds ratio model using cumulative GWAS-identified SNPs at four time points: 2007, 2009, 2011, and 2013. Individuals were classified as Higher Risk (population adjusted odds >2), Average Risk (between 0.5 and 2), and Lower Risk (<0.5) for each time point and we compared classifications between time points for breast cancer (BrCa), prostate cancer (PrCa), diabetes mellitus type 2 (T2D), and cardiovascular heart disease (CHD). We estimated future reclassification using the anticipated number of undiscovered SNPs.

Results

Risk reclassification occurred for all four phenotypes from 2007 to 2013. During the most recent interval (2011-2013), the degree of risk reclassification ranged from 16.3 % for CHD to 24.4 % for PrCa. Many individuals classified as Higher Risk at earlier time points were subsequently reclassified into a lower risk category. From 2011 to 2013, the degree of such downward risk reclassification ranged from 24.9 % for T2D to 55 % for CHD. The percent of individuals classified as Higher Risk increased as more SNPs were discovered, ranging from an increase of 5 % for CHD to 9 % for PrCa from 2007 to 2013. Reclassification continued to occur when we modeled the discovery of anticipated SNPs based on doubling current sample size.

Conclusion

Risk estimates from common genetic variation show large reclassification rates. Identifying disease-associated SNPs facilitates the clinically relevant task of identifying higher-risk individuals. However, the large amount of reclassification that we demonstrated in individuals initially classified as Higher Risk but later as Average Risk or Lower Risk, suggests that caution is currently warranted in basing clinical decisions on common genetic variation for many complex diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-016-0272-5) contains supplementary material, which is available to authorized users.

Internet-Based Direct-to-Consumer Genetic Testing: A Systematic Review

Background

Direct-to-consumer genetic tests (DTC-GT) are easily purchased through the Internet, independent of a physician referral or approval for testing, allowing the retrieval of genetic information outside the clinical context. There is a broad debate about the testing validity, their impact on individuals, and what people know and perceive about them.

Objective

The aim of this review was to collect evidence on DTC-GT from a comprehensive perspective that unravels the complexity of the phenomenon.

Methods

A systematic search was carried out through PubMed, Web of Knowledge, and Embase, in addition to Google Scholar according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist with the key term “Direct-to-consumer genetic test.”

Results

In the final sample, 118 articles were identified. Articles were summarized in five categories according to their focus on (1) knowledge of, attitude toward use of, and perception of DTC-GT (n=37), (2) the impact of genetic risk information on users (n=37), (3) the opinion of health professionals (n=20), (4) the content of websites selling DTC-GT (n=16), and (5) the scientific evidence and clinical utility of the tests (n=14). Most of the articles analyzed the attitude, knowledge, and perception of DTC-GT, highlighting an interest in using DTC-GT, along with the need for a health care professional to help interpret the results. The articles investigating the content analysis of the websites selling these tests are in agreement that the information provided by the companies about genetic testing is not completely comprehensive for the consumer. Given that risk information can modify consumers’ health behavior, there are surprisingly few studies carried out on actual consumers and they do not confirm the overall concerns on the possible impact of DTC-GT. Data from studies that investigate the quality of the tests offered confirm that they are not informative, have little predictive power, and do not measure genetic risk appropriately.

Conclusions

The impact of DTC-GT on consumers’ health perceptions and behaviors is an emerging concern. However, negative effects on consumers or health benefits have yet to be observed. Nevertheless, since the online market of DTC-GT is expected to grow, it is important to remain aware of a possible impact.

Issues surrounding the health economic evaluation of genomic technologies

AIM

Genomic interventions could enable improved disease stratification and individually tailored therapies. However, they have had a limited impact on clinical practice to date due to a lack of evidence, particularly economic evidence. This is partly because health economists are yet to reach consensus on whether existing methods are sufficient to evaluate genomic technologies. As different approaches may produce conflicting adoption decisions, clarification is urgently required. This article summarizes the methodological issues associated with conducting economic evaluations of genomic interventions.

MATERIALS & METHODS

A structured literature review was conducted to identify references that considered the methodological challenges faced when conducting economic evaluations of genomic interventions.

RESULTS

Methodological challenges related to the analytical approach included the choice of comparator, perspective and timeframe. Challenges in costing centered around the need to collect a broad range of costs, frequently, in a data-limited environment. Measuring outcomes is problematic as standard measures have limited applicability, however, alternative metrics (e.g., personal utility) are underdeveloped and alternative approaches (e.g., cost-benefit analysis) underused. Effectiveness data quality is weak and challenging to incorporate into standard economic analyses, while little is known about patient and clinician behavior in this context. Comprehensive value of information analyses are likely to be helpful.

CONCLUSION

Economic evaluations of genomic technologies present a particular challenge for health economists. New methods may be required to resolve these issues, but the evidence to justify alternative approaches is yet to be produced. This should be the focus of future work in this field.

Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.

Added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review

AIMS

To provide a systematic overview of the added value of novel circulating and genetic biomarkers in predicting type 2 diabetes (T2DM).

METHODS

We searched MEDLINE and EMBASE (January 2000 to September 2012) for studies that reported a measure of improvement in the performance of T2DM risk prediction models subsequent to adding novel biomarkers to traditional risk factors. We extracted data on study methods and metrics of incremental predictive value of novel biomarkers.

RESULTS

We included 34 publications from 30 studies. All studies reported a change in the area under the receiver-operating characteristic curve, which was modest, ranging from -0.004 to 0.1, with claims of statistically significant improvements in eleven studies. The net reclassification index was evaluated in 11 studies, and ranged from -2.2% to 10.2% after inclusion of genetic markers in six studies (statistically significant in two cases), and from -0.5% to 27.5% after inclusion of non-genetic markers in five studies (non-significant in two studies). The integrated discrimination index (0-2.04) was reported in eight studies, being statistically significant in five of these.

CONCLUSIONS

Currently known novel circulating and genetic biomarkers do not substantially improve T2DM risk prediction above and beyond the ability of traditional risk factors.

Personalizing health care: feasibility and future implications

Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.

Clinical utility of genetic risk testing in primary care: the example of Type 2 diabetes

Genetic advances in Type 2 diabetes (T2D) have led to the discovery and validation of multiple markers for this complex disease. Despite low predictive value of current T2D markers beyond clinical risk factors and family history, researchers are exploring the clinical utility and outcomes of implementation in practice, and testing is available via direct-to-consumer markets. Clinical utility research demonstrates high hypothetical utility to patients for motivating behavior change and potentially reducing risk. However, trials to date have not demonstrated improvements in behavioral and clinical outcomes over and above counseling based on traditional risk factors. Ongoing research in T2D genetics and associated risk-prediction models is necessary to refine genetic risk pathways, algorithms for risk prediction and use of this information in clinical care. Further research is also needed to explore care models and support interventions that address the needs of personalized risk information and sustainable preventive behaviors to reduce the rising prevalence of T2D.

Variations in predicted risks in personal genome testing for common complex diseases

PURPOSE

The promise of personalized genomics for common complex diseases depends, in part, on the ability to predict genetic risks on the basis of single nucleotide polymorphisms. We examined and compared the methods of three companies (23andMe, deCODEme, and Navigenics) that have offered direct-to-consumer personal genome testing.

METHODS

We simulated genotype data for 100,000 individuals on the basis of published genotype frequencies and predicted disease risks using the methods of the companies. Predictive ability for six diseases was assessed by the AUC.

RESULTS

AUC values differed among the diseases and among the companies. The highest values of the AUC were observed for age-related macular degeneration, celiac disease, and Crohn disease. The largest difference among the companies was found for celiac disease: the AUC was 0.73 for 23andMe and 0.82 for deCODEme. Predicted risks differed substantially among the companies as a result of differences in the sets of single nucleotide polymorphisms selected and the average population risks selected by the companies, and in the formulas used for the calculation of risks.

CONCLUSION

Future efforts to design predictive models for the genomics of common complex diseases may benefit from understanding the strengths and limitations of the predictive algorithms designed by these early companies.

Use of genomic panels to determine risk of developing type 2 diabetes in the general population: a targeted evidence-based review

This evidence review addresses whether type 2 diabetes genomic risk panels improve health outcomes (e.g., reduce rates of developing type 2 diabetes) in low- or high-risk adults; two clinical scenarios promulgated by commercial companies offering such testing. Evidence for the analytic validity of available genomic profiles was inadequate. Clinical validity ranged from inadequate to convincing for 30 variants identified on five type 2 diabetes genomic panels and by genome-wide association studies. Eight common variants were identified for general population use; evidence credibility based on published criteria was strong for two variants, moderate for two variants, and weak for four variants. TCF7L2 had the largest per-allele odds ratio of 1.39 (95% confidence interval 1.33-1.46). Models combining the best four, best eight, and all 30 variants used summary effect sizes, reported genotype frequencies, and assumed independent effects. Areas under the curve were 0.547, 0.551, and 0.570, respectively. In high-risk populations, per-allele odds ratios for TCF7L2 alone were similar to those of the general population. TCF7L2, in combination with other variants, yielded minimal improvement in risk reclassification. Evidence on TCF7L2 clinical validity was adequate. Three studies addressed the clinical utility of intervention effectiveness, stratified by TCF7L2 genotype; none found significant interactions. Clinical utility evidence was inadequate. In addition to analytic validity and clinical utility knowledge gaps, additional gaps were identified regarding how to inform, produce, and evaluate models combining multiple variants.

Direct-to-consumer personal genome testing and cancer risk prediction

The last several years has witnessed an explosion in genomics, with the advent of genome-wide association studies revealing hundreds of DNA variants significantly associated with most common diseases, including cancer. On the heels of these scientific advances came the direct-to-consumer (DTC) genetic testing industry. Genome-wide scans for disease have been marketed and sold directly to the public, without the involvement of a health care provider. Unlike genetic testing for mutations in known hereditary cancer susceptibility genes such as BRCA1/2, these genomic profiles examine DNA variants, which typically have a minimal risk impact, and account for only a fraction of the heritable component of cancer. Furthermore, risk information provided to consumers does not account for family history or other known risk factors. The clinical validity and utility of personal genome scans for disease risk prediction remain for the most part unestablished, although some argue lack of evidence of harm and the possibility that positive impacts on health behaviors or genetic awareness may result from consumer use. The DTC genetic testing industry has sparked significant controversy not only among the scientific community, but also among professional societies and government agencies.In this review, we present some of the history and methodological considerations of DTC genomic profiling, with a focus on cancer risk prediction. The literature regarding consumer awareness and utilization is explored, including understanding, expectations, and behavioral and psychological responses to DTC genomic risk prediction. Primary care provider and genetic professional knowledge and perceptions of DTC genomic profiling are also addressed. Ethical and scientific controversy surrounding the DTC genetic testing industry is presented, along with policy recommendations, regulatory actions, and the changing landscape of the DTC genetic testing market in response. Although our understanding of the human genome holds much promise in the realm of cancer prevention and treatment, DTC genomic profiling for cancer risk prediction is unlikely in its current form to have any significant impact on the health of the public. Time will tell if the next venture in genomic medicine, whole genome sequencing, will be accompanied by the translational research and emphasis on public/provider education required to ensure its successful application toward reducing the burden of cancer at a population level.

A randomized trial of genetic information for personalized nutrition

Personal genetic information has become increasingly accessible to the public as a result of direct-to-consumer (DTC) genetic tests; however, concerns have been raised over their value and potential risks. We compared the effects of providing genotype-based dietary advice with general recommendations on behavioral outcomes using a randomized controlled study. Participants were men and women from the Toronto Nutrigenomics and Health Study between the ages of 20-35 years (n = 149) who completed a survey to assess their awareness of DTC genetic tests and nutrigenomics, as well as potential motivations for undergoing genetic testing. Participants were then randomized into an intervention (I) or control (C) group and were given either genotype-based personalized dietary advice or general dietary advice, respectively. A second survey was administered to assess the participants' opinions of the dietary reports they received. A greater proportion of participants in the intervention group agreed that they understood the dietary advice they were given (93% (I) vs. 78% (C); p = 0.009). Participants in the intervention group were more likely to agree that the dietary recommendations they received would be useful when considering their diet (88% (I) vs. 72% (C); p = 0.02) and wanted to know more about the recommendations (95% (I) vs. 76% (C); p < 0.0001). Only 9% of participants in the intervention group reported feeling uneasy about learning their genetic information. These findings suggest that individuals find dietary recommendations based on genetics more understandable and more useful than general dietary advice. Very few feel uneasy about receiving their genetic information that relates to personalized nutrition.

Applying genomics to nutrition and lifestyle modification

Personalized nutrition aims to prevent the onset and development of chronic diseases by targeting dietary recommendations to an individual’s genetic profile. Gene–diet interactions that affect metabolic pathways relevant to disease risk are continuously being uncovered. Discoveries in the field of nutrigenomics demonstrate that some individuals may benefit from adhering to different dietary guidelines than others, depending on their genotype. Certain industries have already begun to capitalize on the anticipation that knowledge of genomic information could help prevent the risk of developing diseases. Although disclosure of genetic information has been associated with the adoption of positive health-related behaviors under certain circumstances, the effect of providing gene-based dietary advice on motivating adherence to favorable dietary changes is largely unknown.

Legislation on direct-to-consumer genetic testing in seven European countries

An increasing number of private companies are now offering direct-to-consumer (DTC) genetic testing services. Although a lot of attention has been devoted to the regulatory framework of DTC genetic testing services in the USA, only limited information about the regulatory framework in Europe is available. We will report on the situation with regard to the national legislation on DTC genetic testing in seven European countries (Belgium, the Netherlands, Switzerland, Portugal, France, Germany, the United Kingdom). The paper will address whether these countries have legislation that specifically address the issue of DTC genetic testing or have relevant laws that is pertinent to the regulatory control of these services in their countries. The findings show that France, Germany, Portugal and Switzerland have specific legislation that defines that genetic tests can only be carried out by a medical doctor after the provision of sufficient information concerning the nature, meaning and consequences of the genetic test and after the consent of the person concerned. In the Netherlands, some DTC genetic tests could fall under legislation that provides the Minister the right to refuse to provide a license to operate if a test is scientifically unsound, not in accordance with the professional medical practice standards or if the expected benefit is not in balance with the (potential) health risks. Belgium and the United Kingdom allow the provision of DTC genetic tests.

Informed choice in direct-to-consumer genetic testing (DTCGT) websites: a content analysis of benefits, risks, and limitations

An informed choice about health-related direct-to-consumer genetic testing (DTCGT) requires knowledge of potential benefits, risks, and limitations. To understand the information that potential consumers of DTCGT services are exposed to on company websites, we conducted a content analysis of 23 health-related DTCGT websites. Results revealed that benefit statements outweighed risk and limitation statements 6 to 1. The most frequently described benefits were: 1) disease prevention, 2) consumer education, 3) personalized medical recommendations, and 4) the ability to make health decisions. Thirty-five percent of websites also presented at least one risk of testing. Seventy-eight percent of websites mentioned at least one limitation of testing. Based on this information, potential consumers might get an inaccurate picture of genetic testing which could impact their ability to make an informed decision. Practices that enhance the presentation of balanced information on DTCGT company websites should be encouraged.

Is genetic testing useful to predict type 2 diabetes?

The early identification of individuals at risk for type 2 diabetes (T2D) enables prevention. Recent genome-wide association studies (GWAS) have added at least 40 genetic variants to the list of already well characterized T2D risk predictors, including family history, obesity, and elevated fasting plasma glucose levels. Although these variants can significantly predict T2D alone and as a part of genotype risk scores, they do not yet offer clinical discrimination beyond that achieved with common clinical measurements. Future progress on at least two research fronts may improve the predictive performance of genotype information. First, expanded GWAS efforts in non-European populations will allow targeted sequencing of risk loci and the identification of true causal variants. Second, studies with longer prediction time horizons may demonstrate that genotype information performs better than clinical risk predictors over a longer period of the life course. At present, however, genetic testing cannot be recommended for clinical T2D risk prediction in adults.

Is there a doctor in the house? : The presence of physicians in the direct-to-consumer genetic testing context

Over the last couple of years, many commercial companies, the majority of which are based in the USA, have been advertising and offering direct-to-consumer (DTC) genetic testing services outside of the established health care system, and often without any involvement from a health care professional. In the last year, however, a number of DTC genetic testing companies have changed their provision model such that consumers must now contact a health care professional before being able to order the genetic testing service. In discussing the advent of this new model of service provision, this article also reviews the ethical and social issues surrounding DTC genetic testing and addresses the potential motivations for change, some barriers to achieving truly appropriate medical supervision and the present reality of DTC genetic testing for some psychiatric and neurological disorders. Since the advent of these commercial activities, critics have pointed a finger at the lack of medical supervision surrounding these services. The discussion herein, however, reveals how difficult it may be, despite the addition of a physician, to actually achieve adequate medical supervision within the present context of DTC genetic testing.

Individual risk

Patients don't have an "individual risk" or unique probability of an outcome. Outside Mendelian inheritance, risks are conditional probabilities and differ as the risk factors included differ, at times substantially. This lack of reliability is an inherent limitation and is not resolved by including additional risk factors. Groups of like individuals need to be assembled to measure the probability of an outcome. Many groups, like any individual, can be identified, eg, groups of the same age, sex, race, or any combination of these attributes (or any others). That each of these groups may have different risk means there is no such thing as individual risk. This issue was identified by John Venn in 1866 and is known as the reference class problem. Models relate risk factors to outcomes in populations. The number calculated for an individual should not be reported as their individual or true risk, nor should it be used as the sole criterion for clinical decisions. Instead, Feinstein proposed relying on clinically important subgroups. An example would be utilizing an individual's blood pressure as the primary determinant of hypertension treatment decisions, not an unreliable individual risk estimate.

Health care providers and direct-to-consumer access and advertising of genetic testing in the United States

Marketing pressures, regulatory policies, clinical guidelines, and consumer demand all affect health care providers' knowledge and use of health-related genetic tests that are sold and/or advertised to consumers. In addition, clinical guidelines, regulatory policies, and educational efforts are needed to promote the informed use of genetic tests that are sold and advertised to consumers and health care providers. A shift in culture regarding the regulation of genetic tests that are sold directly to consumers is suggested: by recent actions taken by the US Food and Drug Administration (FDA), including letters sent to direct-to-consumer (DTC) genetic testing companies stating that their tests meet the definition of medical devices; by public meetings held by the FDA to discuss laboratory developed tests; and by the convening of the Molecular and Clinical Genetics Panel to gather input on scientific issues concerning DTC genetic tests that make medical claims. This review provides a brief overview of DTC advertising and the regulation of pharmaceuticals and genetic tests in the United States. It highlights recent changes in the regulatory culture regarding genetic tests that are sold to consumers, and discusses the impact on health care providers of selling and advertising genetic tests directly to consumers.

Giving and withholding of information following genomic screening: challenges identified in a study of primary care physicians in Estonia

The use of predictive genomic information to improve medical care remains a contentious topic. However, it is generally agreed that the potential of genomics to improve medicine relies on medical care providers' ability to effectively communicate and put in context the meaning of test results. As the amount of information available increasingly outstrips providers' ability to offer qualified judgments on what the information means, consumers inevitably will be faced with test results of uncertain significance, as well as difficult questions about what they do or do not wish to know. Results of this survey of 64 primary care doctors in Estonia suggests that it may be inherently difficult for physicians to withhold genetic information obtained by genome scans or sequencing, even when they believe that having that information is not in the best interests of their patient. The descriptive data suggest introducing genomic medicine through primary care physicians, as proposed by the Estonian Genome Center of the University of Tartu, will require further genetics education as well as a carefully developed set of guidelines for determining where, when and how to use test results.

Practical considerations to guide development of access controls and decision support for genetic information in electronic medical records

BACKGROUND

Genetic testing is increasingly used as a tool throughout the health care system. In 2011 the number of clinically available genetic tests is approaching 2,000, and wide variation exists between these tests in their sensitivity, specificity, and clinical implications, as well as the potential for discrimination based on the results.

DISCUSSION

As health care systems increasingly implement electronic medical record systems (EMRs) they must carefully consider how to use information from this wide spectrum of genetic tests, with whom to share information, and how to provide decision support for clinicians to properly interpret the information. Although some characteristics of genetic tests overlap with other medical test results, there are reasons to make genetic test results widely available to health care providers and counterbalancing reasons to restrict access to these test results to honor patient preferences, and avoid distracting or confusing clinicians with irrelevant but complex information. Electronic medical records can facilitate and provide reasonable restrictions on access to genetic test results and deliver education and decision support tools to guide appropriate interpretation and use.

SUMMARY

This paper will serve to review some of the key characteristics of genetic tests as they relate to design of access control and decision support of genetic test information in the EMR, emphasizing the clear need for health information technology (HIT) to be part of optimal implementation of genetic medicine, and the importance of understanding key characteristics of genetic tests when designing HIT applications.

Genomic risk profiling: attitudes and use in personal and clinical care of primary care physicians who offer risk profiling

BACKGROUND

Genomic risk profiling involves the analysis of genetic variations linked through statistical associations to a range of disease states. There is considerable controversy as to how, and even whether, to incorporate these tests into routine medical care.

OBJECTIVE

To assess physician attitudes and uptake of genomic risk profiling among an 'early adopter' practice group.

DESIGN

We surveyed members of MDVIP, a national group of primary care physicians (PCPs), currently offering genomic risk profiling as part of their practice.

POPULATION

All physicians in the MDVIP network (N = 356)

RESULTS

We obtained a 44% response rate. One third of respondents had ordered a test for themselves and 42% for a patient. The odds of having ordered personal testing were 10.51-fold higher for those who felt well-informed about genomic risk testing (p < 0.0001). Of those who had not ordered a test for themselves, 60% expressed concerns for patients regarding discrimination by life and long-term/disability insurers, 61% about test cost, and 62% about clinical utility. The odds of ordering testing for their patients was 8.29-fold higher among respondents who had ordered testing for themselves (p < 0.0001). Of those who had ordered testing for patients, concerns about insurance coverage (p = 0.014) and uncertain clinical utility (p = 0.034) were associated with a lower relative frequency of intention to order testing again in the future.

CONCLUSIONS

Our findings demonstrate that respondent familiarity was a key predictor of physician ordering behavior and clinical utility was a primary concern for genomic risk profiling. Educational and interpretive support may enhance uptake of genomic risk profiling.

Genome-based health literacy: a new challenge for public health genomics

So far health literacy has not been sufficiently discussed in the context of public health genomics. Primarily, not genomic but rather genome-based health information needs to be addressed taking into account genome-environment interactions and integrating all health determinants including genomics into a systemic and holistic approach. Translating findings from epigenomics and systems biomedicine will help to understand that individual biological pathways or networks are permanently interacting with environmental networks such as social networks. Thus, in the end also health literacy will become personalized. Genome-based health literacy is challenged by the question of which information is relevant for the individual, for what purpose, and at what time during the lifespan. Public health tools and expertise already in place can and should be used to tackle these huge challenges.

Improved risk prediction for Crohn's disease with a multi-locus approach

Genome-wide association studies have identified numerous loci demonstrating genome-wide significant association with Crohn's disease. However, when many single nucleotide polymorphisms (SNPs) have weak-to-moderate disease risks, genetic risk prediction models based only on those markers that pass the most stringent statistical significance testing threshold may be suboptimal. Haplotype-based predictive models may provide advantages over single-SNP approaches by facilitating detection of associations driven by cis-interactions among nearby SNPs. In addition, these approaches may be helpful in assaying non-genotyped, rare causal variants. In this study, we investigated the use of two-marker haplotypes for risk prediction in Crohn's disease and show that it leads to improved prediction accuracy compared with single-point analyses. With large numbers of predictors, traditional classification methods such as logistic regression and support vector machine approaches may be suboptimal. An alternative approach is to apply the risk-score method calculated as the number of risk haplotypes an individual carries, both within and across loci. We used the area under the curve (AUC) of the receiver operating curve to assess the performance of prediction models in large-scale genetic data, and observed that the prediction performance in the validation cohort continues to improve as thousands of haplotypes are included in the model, with the AUC reaching its plateau at 0.72 at ∼7000 haplotypes, and begins to gradually decline after that point. In contrast, using the SNP as predictors, we only obtained maximum AUC of 0.65. Validation studies in independent cohorts further support improved prediction capacity with multi-marker, as opposed to single marker analyses.

Personal genome testing: test characteristics to clarify the discourse on ethical, legal and societal issues

Background

As genetics technology proceeds, practices of genetic testing have become more heterogeneous: many different types of tests are finding their way to the public in different settings and for a variety of purposes. This diversification is relevant to the discourse on ethical, legal and societal issues (ELSI) surrounding genetic testing, which must evolve to encompass these differences. One important development is the rise of personal genome testing on the basis of genetic profiling: the testing of multiple genetic variants simultaneously for the prediction of common multifactorial diseases. Currently, an increasing number of companies are offering personal genome tests directly to consumers and are spurring ELSI-discussions, which stand in need of clarification. This paper presents a systematic approach to the ELSI-evaluation of personal genome testing for multifactorial diseases along the lines of its test characteristics.

Discussion

This paper addresses four test characteristics of personal genome testing: its being a non-targeted type of testing, its high analytical validity, low clinical validity and problematic clinical utility. These characteristics raise their own specific ELSI, for example: non-targeted genetic profiling poses serious problems for information provision and informed consent. Questions about the quantity and quality of the necessary information, as well as about moral responsibilities with regard to the provision of information are therefore becoming central themes within ELSI-discussions of personal genome testing. Further, the current low level of clinical validity of genetic profiles raises questions concerning societal risks and regulatory requirements, whereas simultaneously it causes traditional ELSI-issues of clinical genetics, such as psychological and health risks, discrimination, and stigmatization, to lose part of their relevance. Also, classic notions of clinical utility are challenged by the newer notion of 'personal utility.'

Summary

Consideration of test characteristics is essential to any valuable discourse on the ELSI of personal genome testing for multifactorial diseases. Four key characteristics of the test - targeted/non-targeted testing, analytical validity, clinical validity and clinical utility - together determine the applicability and the relevance of ELSI to specific tests. The paper identifies and discusses four areas of interest for the ELSI-debate on personal genome testing: informational problems, risks, regulatory issues, and the notion of personal utility.

Type 2 diabetes and obesity: genomics and the clinic

Type 2 diabetes (T2D) and obesity represent major challenges for global public health. They are at the forefront of international efforts to identify the genetic variation contributing to complex disease susceptibility, and recent years have seen considerable success in identifying common risk-variants. Given the clinical impact of molecular diagnostics in rarer monogenic forms of these diseases, expectations have been high that genetic discoveries will transform the prospects for risk stratification, development of novel therapeutics and personalised medicine. However, so far, clinical translation has been limited. Difficulties in defining the alleles and transcripts mediating association effects have frustrated efforts to gain early biological insights, whilst the fact that variants identified account for only a modest proportion of observed familiarity has limited their value in guiding treatment of individual patients. Ongoing efforts to track causal variants through fine-mapping and to illuminate the biological mechanisms through which they act, as well as sequence-based discovery of lower-frequency alleles (of potentially larger effect), should provide welcome acceleration in the capacity for clinical translation. This review will summarise recent advances in identifying risk alleles for T2D and obesity, and existing contributions to understanding disease pathology. It will consider the progress made in translating genetic knowledge into clinical utility, the challenges remaining, and the realistic potential for further progress.

Motivations and perceptions of early adopters of personalized genomics: perspectives from research participants

BACKGROUND/AIMS

To predict the potential public health impact of personal genomics, empirical research on public perceptions of these services is needed. In this study, 'early adopters' of personal genomics were surveyed to assess their motivations, perceptions and intentions.

METHODS

Participants were recruited from everyone who registered to attend an enrollment event for the Coriell Personalized Medicine Collaborative, a United States-based (Camden, N.J.) research study of the utility of personalized medicine, between March 31, 2009 and April 1, 2010 (n = 369). Participants completed an Internet-based survey about their motivations, awareness of personalized medicine, perceptions of study risks and benefits, and intentions to share results with health care providers.

RESULTS

Respondents were motivated to participate for their own curiosity and to find out their disease risk to improve their health. Fewer than 10% expressed deterministic perspectives about genetic risk, but 32% had misperceptions about the research study or personal genomic testing. Most respondents perceived the study to have health-related benefits. Nearly all (92%) intended to share their results with physicians, primarily to request specific medical recommendations.

CONCLUSION

Early adopters of personal genomics are prospectively enthusiastic about using genomic profiling information to improve their health, in close consultation with their physicians. This suggests that early users (i.e. through direct-to-consumer companies or research) may follow up with the health care system. Further research should address whether intentions to seek care match actual behaviors.

Users' motivations to purchase direct-to-consumer genome-wide testing: an exploratory study of personal stories

The relatively rapid growth of the direct-to-consumer (DTC) genetic testing market in the last few years has led to increasing attention from both the scientific community and policy makers. One voice often missing in these debates, however, is that of the actual user of these genetic testing services. In order to gain a better picture of the motivations and expectations that propel individuals to purchase DTC genome-wide testing, we conducted an exploratory study based on users' personal stories. Through qualitative content analysis of users' personal stories found on Internet blogs and DTC genetic testing companies' websites, we identified five major sets of motivations and expectations towards DTC genome-wide testing. These themes are related to (1) health, (2) curiosity and fascination, (3) genealogy, (4) contributing to research, and (5) recreation. Obtaining such information can help us to understand how users consider genome-wide testing and forms the basis for further research.

Risk assessment tools for identifying individuals at risk of developing type 2 diabetes

Trials have demonstrated the preventability of type 2 diabetes through lifestyle modifications or drugs in people with impaired glucose tolerance. However, alternative ways of identifying people at risk of developing diabetes are required. Multivariate risk scores have been developed for this purpose. This article examines the evidence for performance of diabetes risk scores in adults by 1) systematically reviewing the literature on available scores and 2) their validation in external populations; and 3) exploring methodological issues surrounding the development, validation, and comparison of risk scores. Risk scores show overall good discriminatory ability in populations for whom they were developed. However, discriminatory performance is more heterogeneous and generally weaker in external populations, which suggests that risk scores may need to be validated within the population in which they are intended to be used. Whether risk scores enable accurate estimation of absolute risk remains unknown; thus, care is needed when using scores to communicate absolute diabetes risk to individuals. Several risk scores predict diabetes risk based on routine noninvasive measures or on data from questionnaires. Biochemical measures, in particular fasting plasma glucose, can improve prediction of such models. On the other hand, usefulness of genetic profiling currently appears limited.

Use of reclassification for assessment of improved prediction: an empirical evaluation

BACKGROUND

An increasing number of studies evaluate the ability of predictors to change risk stratification and alter medical decisions, i.e. reclassification performance. We examined the reported design and analysis of recent studies of reclassification and the robustness of their claims for improved reclassification.

METHODS

Two independent investigators searched PubMed and citations to the article that introduced the currently most popular reclassification metric (net reclassification index, NRI) to identify studies performing reclassification analysis (January 2006-January 2010). We focused on articles that included any analyses comparing the performance of a baseline predictive model vs the baseline model plus some additional predictor for a prospectively assessed outcome. We recorded information on the baseline model used, outcomes assessed, choice of risk thresholds and features of reclassification analyses.

RESULTS

Of 58 baseline models used in 51 eligible papers, only 14 (24%) were previously described, used as described and had same outcomes as originally intended. Calibration was examined in 53% of the studies. Sixteen studies (31%) provided a reference for the choice of risk thresholds and only six used the previously proposed categories or justified the use of alternative thresholds. Only 14 studies (27%) stated that the chosen risk thresholds had different therapeutic intervention implications. NRI was calculated in 38 studies and was smaller in studies with adequately referenced or justified risk thresholds vs others (P < 0.0001).

CONCLUSIONS

Reclassification studies would benefit from more rigorous methodological standards; otherwise claims for improved reclassification may remain spurious.

Size of the direct-to-consumer genomic testing market

There has been enormous interest in the recent development of consumer genomics services, but very little is known about their impact. Using publicly available information, we estimate that the market for genetic susceptibility tests for complex diseases is much smaller than previously suggested, and hence consider that regulation through restrictive statutory legislation may be excessive.

The sense and nonsense of direct-to-consumer genetic testing for cardiovascular disease

Expectations are high that increasing knowledge of the genetic basis of cardiovascular disease will eventually lead to personalised medicine—to preventive and therapeutic interventions that are targeted to at-risk individuals on the basis of their genetic profiles. Most cardiovascular diseases are caused by a complex interplay of many genetic variants interacting with many non-genetic risk factors such as diet, exercise, smoking and alcohol consumption. Since several years, genetic susceptibility testing for cardiovascular diseases is being offered via the internet directly to consumers. We discuss five reasons why these tests are not useful, namely: (1) the predictive ability is still limited; (2) the risk models used by the companies are based on assumptions that have not been verified; (3) the predicted risks keep changing when new variants are discovered and added to the test; (4) the tests do not consider non-genetic factors in the prediction of cardiovascular disease risk; and (5) the test results will not change recommendations of preventive interventions. Predictive genetic testing for multifactorial forms of cardiovascular disease clearly lacks benefits for the public. Prevention of disease should therefore remain focused on family history and on non-genetic risk factors as diet and physical activity that can have the strongest impact on disease risk, regardless of genetic susceptibility.

Role of genomics in cardiovascular medicine

As all branches of science grow and new experimental techniques become readily accessible, our knowledge of medicine is likely to increase exponentially in the coming years. Recently developed technologies have revolutionized our analytical capacities, leading to vast knowledge of many genes or genomic regions involved in the pathogenesis of congenital heart diseases, which are often associated with other genetic syndromes, coronary artery disease and non-ischemic cardiomyopathies and channelopathies. The knowledge-base of the genesis of cardiovascular diseases is likely going to be further revolutionized in this new era of genomic medicine. Here, we review the advances that have been made over the last several years in this field and discuss different genetic mechanisms that have been shown to underlie a variety of cardiovascular diseases.

An epidemiological perspective on the future of direct-to-consumer personal genome testing

Personal genome testing is offered via the internet directly to consumers. Most tests that are currently offered use data from genome-wide scans to predict risks for multiple common diseases and traits. The utility of these tests is limited, predominantly because they lack predictive ability and clear benefits for disease prevention that are specific for genetic risk groups. In the near future, personal genome tests will likely be based on whole genome sequencing, but will these technological advances increase the utility of personal genome testing? Whole genome sequencing theoretically provides information about the risks of both monogenic and complex diseases, but the practical utility remains to be demonstrated. The utility of testing depends on the predictive ability of the test, the likelihood of actionable test results, and the options available for the reduction of risks. For monogenic diseases, the likelihood of known mutations will be extremely low in the general population and it will be a challenge to recognize new causal variants among all rare variants that are found using sequencing. For complex diseases, the predictive ability of genetic tests will be mainly restricted by the heritability of the disease, but also by the genetic complexity of the disease etiology, which determines the extent to which the heritability can be understood. Given that numerous genetic and non-genetic risk factors interact in the causation of complex diseases, the predictive ability of genetic models will likely remain modest. Personal genome testing will have minimal benefits for individual consumers unless major breakthroughs are made in the near future.

The prospect of genome-guided preventive medicine: a need and opportunity for genetic counselors

One of the major anticipated benefits of genomic medicine is the area of preventive medicine. Commercially available genomic profiling is now able to generate risk information for a number of common conditions several of which have recognized preventive guidelines. Similarly, family history assessment affords powerful health risk prediction based on the shared genetic, physical and lifestyle environments within families. Thus, with the ability to help predict disease risk and enable preemptive health plans, genome-guided preventive medicine has the potential to improve population health on an individualized level. To realize this potential, steps to broaden access to accurate genomic health information must be considered. With expertise in genetic science, risk assessment and communication, and a patient-centered practice approach, genetic counselors are poised to play a critical role in facilitating the incorporation of genomic health risks into the burgeoning field of genome-guided preventive medicine.

Coriell Personalized Medicine Collaborative®: a prospective study of the utility of personalized medicine

There is a dearth of large prospective studies to determine if genetic risk factors are useful predictors of health outcomes and if reporting them to individuals or physicians changes health behavior. The Coriell Personalized Medicine Collaborative® (CPMC, NJ, USA) is a prospective observational study with three cohorts – community, cancer and chronic disease cohorts. Participants provide detailed medical history through a dynamic internet-based portal. DNA is tested and personalized risk reports are provided for potentially actionable health conditions. To date, the community cohort has enrolled 4372 participants. The internet-based portal supplies educational content, captures phenotypic data and delivers customized risk reports. The Informed Cohort Oversight Board has approved 16 health conditions to date, and risk reports with genetic and nongenetic risks for six conditions have been released. The majority (87%) of participants who completed requisite questionnaires viewed at least one report. The CPMC is a cohort study delivering customized risk reports for actionable conditions using a web interface and measuring outcomes longitudinally.

Informational content, literacy demands, and usability of websites offering health-related genetic tests directly to consumers

PURPOSE

As direct-to-consumer genetic testing becomes more available, a diverse group of consumers, including those with limited health literacy, may consider testing. In light of concerns raised about direct-to-consumer genetic testing, this study sought to critically examine whether the informational content, literacy demands, and usability of health-related direct-to-consumer websites met existing recommendations.

METHODS

A content analysis was performed on 29 health-related direct-to-consumer websites. Two coders independently evaluated each website for informational content (e.g., benefits, limitations), literacy demands (e.g., reading level), and usability (e.g., ease of navigation).

RESULTS

Most sites presented health conditions and some markers for which they tested, benefits of testing, a description of the testing process, and their privacy policy. Fewer cited scientific literature, explained test limitations, or provided an opportunity to consult a health professional. Key informational content was difficult to locate on most sites. Few sites gave sample disease risk estimates or used common language and explained technical terms consistently. Average reading level was grade 15.

CONCLUSION

The quality of informational content, literacy demands, and usability across health-related direct-to-consumer websites varied widely. Many users would struggle to find and understand the important information. For consumers to better understand the content on these sites and evaluate the meaning of the tests for their health, sites should lower the demands placed on users by distilling and prioritizing the key informational content while simultaneously attending to the reading level and usability elements. In the absence of regulation compelling such changes, government agencies or professional organizations may need to increase consumer and provider awareness of these issues.

Practical issues in building risk-predicting models for complex diseases

Recent genome-wide association studies have identified many genetic variants affecting complex human diseases. It is of great interest to build disease risk prediction models based on these data. In this article, we first discuss statistical challenges in using genome-wide association data for risk predictions, and then review the findings from the literature on this topic. We also demonstrate the performance of different methods through both simulation studies and application to real-world data.

Evaluation of genetic tests for susceptibility to common complex diseases: why, when and how?

Recent research into the human genome has generated a wealth of scientific knowledge and increased both public and professional interest in the concept of personalised medicine. Somewhat unexpectedly, in addition to increasing our understanding about the genetic basis for numerous diseases, these new discoveries have also spawned a burgeoning new industry of 'consumer genetic testing'. In this paper, we present the principles learnt though the evaluation of tests for single gene disorders and suggest a comparable framework for the evaluation of genetic tests for susceptibility to common complex diseases. Both physicians and the general public will need to be able to assess the claims made by providers of genetic testing services, and ultimately policy-makers will need to decide if and when such tests should be offered through state funded healthcare systems.