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

Genetics in neovascular age-related macular degeneration susceptibility and treatment response to anti-VEGF intravitreal injection: A case series study

BACKGROUND

To identify genotypes associated with neovascular age-related macular degeneration (nAMD) and investigate the associations between genotype variations and anti-vascular endothelial growth factor (VEGF) treatment response.

METHODS

This observational, retrospective, case series study enrolled patients diagnosed with nAMD who received anti-VEGF treatment in National Taiwan University Hospital with at least one-year follow-up between 2012 and 2020. A genome-wide association study (GWAS) was conducted on enrolled patients and controls. Correlations between the genotypes identified from GWAS and the treatment response of functional/anatomical biomarkers, including visual acuity (VA), presence of intraretinal or subretinal fluid (SRF), serous or fibrovascular pigmented epithelium detachment (PED), and disruption of the ellipsoid zone (EZ), were analysed.

RESULTS

In total, 182 patients with nAMD and 1748 controls were enrolled. GWAS revealed 16 single nucleotide polymorphisms (SNPs) as risk loci for nAMD, including seven loci in CFH and ARMS2/HTRA1 and nine novel loci, including rs117517872 and rs79835234(COPB2-DT), rs7525578(RAP1A), rs2123738(LOC105376755), rs1374879(CNTN3), rs3812692(SAR1A), rs117501587(PRKCA), rs9965945(CNDP1), and rs189769231(MATK). Our study revealed rs800292(CFH), rs11200638(HTRA1), and rs2123738(LOC105376755) correlated with poor treatment response in VA (P = 0.005), SRF (P = 0.044), and fibrovascular PED (P = 0.007), respectively. Rs9965945(CNDP1) was correlated with poor response in disruption of EZ (P = 0.046) and serous PED (P = 0.049).

CONCLUSIONS

Among the 16 SNPs found in the GWAS, four loci-CFH, ARMS2/HTRA1, and two novel loci-were correlated with the susceptibility of nAMD and anatomical/functional responses after anti-VEGF treatment.

Considerations for developing regulations for direct-to-consumer genetic testing: a scoping review using the 3-I framework

Direct-to-consumer (DTC) genetic testing exists largely outside of any regulatory schemes, and studies providing a comprehensive overview of the ethical, social, legal, and technological considerations for regulating these types of technologies are lacking. This paper uses the 3-I framework for policy analysis to analyze the ideas, interests, and institutions relevant to policy development for DTC genetic testing in North America and internationally. A scoping review was conducted. Citation databases were searched for papers addressing the ethical, social, legal, and technological implications of DTC genetic testing; stakeholder perspectives on and experiences with DTC genetic testing; or the effect of such testing on the healthcare system. Ninety-nine publications, organizational reports, governmental documents, or pieces of legislation were included. The ideas included are autonomy, informed decision making, privacy, and clinical validity and utility. The interests discussed are those of the public and healthcare providers. The institutions included are regulatory organizations such as the Food and Drug Administration in the United States, laws governing the implementation or delivery of genetic testing in general, and legislation created to protect against genetic discrimination. This analysis clarifies the ethical, social, legal, and technological issues of DTC genetic testing regulation. This information can be used by policy makers to develop or strengthen regulations for DTC genetic testing such as requiring an assessment of the clinical validity of tests before they become publicly available, controlling how tests are marketed, and stipulating requirements for healthcare provider involvement and informed consent.

Public reactions to direct-to-consumer genetic health tests: A comparison across the US, UK, Japan and Australia

While direct to consumer health-related genetic testing (DTCGT) has potential to provide accessible genetic information and empower individuals to make informed healthcare decisions, it attracts concern associated with regulatory gaps, clinical utility and potential for harm. Understanding public reactions to DTCGT is vital to facilitate considered regulatory, health care and consumer protection strategies. Yet little is known, particularly outside the dominant US market, about how the general public view and might engage with DTCGT outside traditional health care systems. This paper addresses this knowledge gap with the first empirical study to investigate general public views across four countries, each at different stages of market development. US (n = 1000), UK (n = 1014), Japanese (n = 1018) and Australian (n = 1000) respondents completed an online experimental survey assessing comprehension, risk perceptions, and potential psychological and behavioural outcomes by type of test (disease pre-disposition and drug sensitivity), severity, lifestyle factors, and family history. Results showed generally low awareness and intention to purchase across countries, highest in the US and lowest in Japan. Results also showed clear preference for within-country purchases (less in Japan), with reports returned via doctors far more important in Japan. All respondents were more likely to act on test results, where there was higher genetic or lifestyle risk of developing a disease. Statistical comparisons of demographic and health-related variables across countries point to the need for further analyses designed to explain much needed cross-cultural, cross-health care system and developed versus developing market differences.

Development of a general logistic model for disease risk prediction using multiple SNPs

Human diseases are usually linked to multiloci genetic alterations, including single‐nucleotide polymorphisms (SNPs). Methods to use these SNPs for disease risk prediction (DRP) are of clinical interest. DRP algorithms explored by commercial companies to date have tended to be complex and led to controversial prediction results. Here, we present a general approach for establishing a logistic model‐based DRP algorithm, in which multiple SNP risk factors from different publications are directly used. In particular, the coefficient β of each SNP is set as the natural logarithm of the reported odds ratio, and the constant coefficient β₀ is comprehensively determined by the coefficient and frequency of each SNP and the average disease risk in populations. Furthermore, homozygous SNP is considered a dummy variable, and the SNPs are updated (addition, deletion and modification) if necessary. Importantly, we validated this algorithm as a proof of concept: two patients with lung cancer were identified as the maximum risk cases from 57 Chinese individuals. Our logistic model‐based DRP algorithm is apparently more intuitive and self‐evident than the algorithms explored by commercial companies, and it may facilitate DRP commercialization in the era of personalized medicine.

Patients' willingness to share digital health and non-health data for research: a cross-sectional study

BACKGROUND

Patients generate large amounts of digital data through devices, social media applications, and other online activities. Little is known about patients' perception of the data they generate online and its relatedness to health, their willingness to share data for research, and their preferences regarding data use.

METHODS

Patients at an academic urban emergency department were asked if they would donate any of 19 different types of data to health researchers and were asked about their views on data types' health relatedness. Factor analysis was used to identify the structure in patients' perceptions of willingness to share different digital data, and their health relatedness.

RESULTS

Of 595 patients approached 206 agreed to participate, of whom 104 agreed to share at least one types of digital data immediately, and 78% agreed to donate at least one data type after death. EMR, wearable, and Google search histories (80%) had the highest percentage of reported health relatedness. 72% participants wanted to know the results of any analysis of their shared data, and half wanted their healthcare provider to know.

CONCLUSION

Patients in this study were willing to share a considerable amount of personal digital data with health researchers. They also recognize that digital data from many sources reveal information about their health. This study opens up a discussion around reconsidering US privacy protections for health information to reflect current opinions and to include their relatedness to health.

Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It?

Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make their own decisions about genetic testing should respect customer autonomy and informed decision making and maximize opportunities for transparency. The algorithm itself is only one piece of the information that is deemed essential for understanding how prediction algorithms are developed and evaluated. Companies should be encouraged to disclose everything else, including the expected risk distribution of the algorithm when applied in the population, using a benchmark DNA dataset. A standardized presentation of information and risk distributions allows customers to compare test offers and scientists to verify whether the undisclosed algorithms could be valid. A new model of oversight in which stakeholders collaboratively keep a check on the commercial market is needed.

The association between complement factor H rs1061170 polymorphism and age-related macular degeneration: a comprehensive meta-analysis stratified by stage of disease and ethnicity

PURPOSE

The strength of association between complement factor H (CFH) rs1061170 polymorphism and age-related macular degeneration (AMD) differs between AMD subtypes and ethnicities. The main aim was to provide a systematic review and an updated meta-analysis stratified by stage of disease and ethnicity.

METHODS

A literature search in the PubMed-Medline, EMBASE and Web of Science databases was conducted to identify epidemiological studies, published before September 2017, that included at least twp comparison groups (a control group with no signs of AMD and a case group of AMD patients). Genotype distribution, phenotype of the cases, ethnicity, mean age and gender ratio were collected. Odds ratios (ORs) and 95%CIs were estimated under the allelic, homozygous and heterozygous models. Sensitivity and subgroup analyses, by AMD subtype and ethnicity, were performed.

RESULTS

The meta-analysis included data of 27 418 AMD patients and 32 843 controls from 76 studies. In Caucasians, the rs1061170 showed a significant association with early AMD (OR: 1.44; 95%CI 1.27-1.63), dry AMD (OR: 2.90; 95%CI 1.89-4.47) and wet AMD (OR: 2.46; 95%CI 2.15-2.83), under an allelic model. In Asians, the rs1061170 showed a significant association with advanced AMD (OR: 2.09; 95%CI 1.67-2.60), especially wet AMD (OR: 2.24; 95%CI 1.81-2.77).

CONCLUSION

Our work provides a more comprehensive meta-analysis of studies investigating the effect of the CFH rs1061170 polymorphism on AMD risk. These findings not only improve the assessment of disease risk associated with the polymorphism, but also constitute a scientific background to be translated into clinical practice for AMD prevention.

Systems-Mapping of Herbal Effects on Complex Diseases Using the Network-Perturbation Signatures

The herbs have proven to hold great potential to improve people's health and wellness during clinical practice over the past millennia. However, herbal medicine for the personalized treatment of disease is still under investigation owing to the complex multi-component interactions in herbs. To reveal the valuable insights for herbal synergistic therapy, we have chosen Traditional Chinese Medicine (TCM) as a case to illustrate the art and science behind the complicated multi-molecular, multi-genes interaction systems, and how the good practices of herbal combination therapy are applicable to personalized treatment. Here, we design system-wide interaction map strategy to provide a generic solution to establish the links between diseases and herbs based on comprehensive testing of molecular signatures in herb-disease pairs. Firstly, we integrated gene expression profiles from 189 diseases to characterize the disease-pathological feature. Then, we generated the perturbation signatures from the huge chemical informatics data and pharmacological data for each herb, which were represented the targets affected by the ingredients in the herb. So that we could assess the effects of herbs on the individual. Finally, we integrated the data of 189 diseases and 502 herbs, yielding the optimal herbal combinations for the diseases based on the strategy, and verifying the reliability of the strategy through the permutation testing and literature verification. Furthermore, we propose a novel formula as a candidate therapeutic drugs of rheumatoid arthritis and demonstrate its therapeutic mechanism through the systematic analysis of the influencing targets and biological processes. Overall, this computational method provides a systematic approach, which blended herbal medicine and omics data sets, allowing for the development of novel drug combinations for complex human diseases.

Cost Analyses of Genomic Sequencing: Lessons Learned from the MedSeq Project

OBJECTIVE

To summarize lessons learned while analyzing the costs of integrating whole genome sequencing into the care of cardiology and primary care patients in the MedSeq Project by conducting the first randomized controlled trial of whole genome sequencing in general and specialty medicine.

METHODS

Case study that describes key methodological and data challenges that were encountered or are likely to emerge in future work, describes the pros and cons of approaches considered by the study team, and summarizes the solutions that were implemented.

RESULTS

Major methodological challenges included defining whole genome sequencing, structuring an appropriate comparator, measuring downstream costs, and examining clinical outcomes. Discussions about solutions addressed conceptual and practical issues that arose because of definitions and analyses around the cost of genomic sequencing in trial-based studies.

CONCLUSIONS

The MedSeq Project provides an instructive example of how to conduct a cost analysis of whole genome sequencing that feasibly incorporates best practices while being sensitive to the varied applications and diversity of results it may produce. Findings provide guidance for researchers to consider when conducting or analyzing economic analyses of whole genome sequencing and other next-generation sequencing tests, particularly regarding costs.

Complement System and Age-Related Macular Degeneration: Implications of Gene-Environment Interaction for Preventive and Personalized Medicine

Age-related macular degeneration (AMD) is the most common cause of visual loss in developed countries, with a significant economic and social burden on public health. Although genome-wide and gene-candidate studies have been enabled to identify genetic variants in the complement system associated with AMD pathogenesis, the effect of gene-environment interaction is still under debate. In this review we provide an overview of the role of complement system and its genetic variants in AMD, summarizing the consequences of the interaction between genetic and environmental risk factors on AMD onset, progression, and therapeutic response. Finally, we discuss the perspectives of current evidence in the field of genomics driven personalized medicine and public health.

Medicine, market and communication: ethical considerations in regard to persuasive communication in direct-to-consumer genetic testing services

BACKGROUND

Commercial genetic testing offered over the internet, known as direct-to-consumer genetic testing (DTC GT), currently is under ethical attack. A common critique aims at the limited validation of the tests as well as the risk of psycho-social stress or adaption of incorrect behavior by users triggered by misleading health information. Here, we examine in detail the specific role of advertising communication of DTC GT companies from a medical ethical perspective. Our argumentative analysis departs from the starting point that DTC GT operates at the intersection of two different contexts: medicine on the one hand and the market on the other. Both fields differ strongly with regard to their standards of communication practices and the underlying normative assumptions regarding autonomy and responsibility.

METHODS

Following a short review of the ethical contexts of medical and commercial communication, we provide case examples for persuasive messages of DTC GT websites and briefly analyze their design with a multi-modal approach to illustrate some of their problematic implications.

RESULTS

We observe three main aspects in DTC GT advertising communication: (1) the use of material suggesting medical professional legitimacy as a trust-establishing tool, (2) the suggestion of empowerment as a benefit of using DTC GT services and (3) the narrative of responsibility as a persuasive appeal to a moral self-conception.

CONCLUSIONS

While strengthening and respecting the autonomy of a patient is the focus in medical communication, specifically genetic counselling, persuasive communication is the normal mode in marketing of consumer goods, presuming an autonomous, rational, independent consumer. This creates tension in the context of DTC GT regarding the expectation and normative assessment of communication strategies. Our analysis can even the ground for a better understanding of ethical problems associated with intersections of medical and commercial communication and point to perspectives of analysis of DTC GT advertising.

Impact of Genetic Variants on the Individual Potential for Body Fat Loss

The past decade has witnessed the discovery of obesity-related genetic variants and their functions through genome-wide association studies. Combinations of risk alleles can influence obesity phenotypes with different degrees of effectiveness across various individuals by interacting with environmental factors. We examined the interaction between genetic variation and changes in dietary habits or exercise that influences body fat loss from a large Korean cohort (n = 8840). Out of 673 obesity-related SNPs, a total of 100 SNPs (37 for carbohydrate intake; 19 for fat intake; 44 for total calories intake; 25 for exercise onset) identified to have gene-environment interaction effect in generalized linear model were used to calculate genetic risk scores (GRS). Based on the GRS distribution, we divided the population into four levels, namely, “very insensitive”, “insensitive”, “sensitive”, and “very sensitive” for each of the four categories, “carbohydrate intake”, “fat intake”, “total calories intake”, and “exercise”. Overall, the mean body fat loss became larger when the sensitivity level was increased. In conclusion, genetic variants influence the effectiveness of dietary regimes for body fat loss. Based on our findings, we suggest a platform for personalized body fat management by providing the most suitable and effective nutrition or activity plan specific to an individual.

Utilization of Genetic Counseling after Direct-to-Consumer Genetic Testing: Findings from the Impact of Personal Genomics (PGen) Study

Direct-to-consumer personal genomic testing (DTC-PGT) results lead some individuals to seek genetic counseling (GC), but little is known about these consumers and why they seek GC services. We analyzed survey data pre- and post-PGT from 1026 23andMe and Pathway Genomics customers. Participants were mostly white (91%), female (60%), and of high socioeconomic status (80% college educated, 43% household income of ≥$100,000). After receiving PGT results, 43 participants (4%) made or planned to schedule an appointment with a genetic counselor; 390 (38%) would have used in-person GC had it been available. Compared to non-seekers, GC seekers were younger (mean age of 38 vs 46 years), more frequently had children <18 (26% vs 16%), and were more likely to report previous GC (37% vs 7%) and genetic testing (30% vs 15%). In logistic regression analysis, seeking GC was associated with previous GC use (OR = 6.5, CI = 3.1-13.8), feeling motivated to pursue DTC-PGT for health reasons (OR = 4.3, CI = 1.8-10.1), fair or poor self-reported health (OR = 3.1, CI = 1.1-8.3), and self-reported uncertainty about the results (OR = 1.8, CI = 1.1-2.7). These findings can help GC providers anticipate who might seek GC services and plan for clinical discussions of DTC-PGT results.

Predicting Polygenic Obesity Using Genetic Information

Except in rare cases, obesity tends to be a consequence of both an unhealthy lifestyle and a genetic susceptibility to gain weight. With more than 200 common genetic variants identified, there is a growing interest in developing personalized preventive and treatment strategies to predict an individual's obesity risk. We review the literature on the prediction of obesity and show that models based on the established genetic variants have poorer predictive ability than traditional predictors, such as family history of obesity and childhood obesity. Current findings suggest that opportunities for precision medicine in common obesity may be limited.

Genetic risk models: Influence of model size on risk estimates and precision

Disease risk estimation plays an important role in disease prevention. Many studies have found that the ability to predict risk improves as the number of risk single-nucleotide polymorphisms (SNPs) in the risk model increases. However, the width of the confidence interval of the risk estimate is often not considered in the evaluation of the risk model. Here, we explore how the risk and the confidence interval width change as more SNPs are added to the model in the order of decreasing effect size, using both simulated data and real data from studies of abdominal aortic aneurysms and age-related macular degeneration. Our results show that confidence interval width is positively correlated with model size and the majority of the bigger models have wider confidence interval widths than smaller models. Once the model size is bigger than a certain level, the risk does not shift markedly, as 100% of the risk estimates of the one-SNP-bigger models lie inside the confidence interval of the one-SNP-smaller models. We also created a confidence interval-augmented reclassification table. It shows that both more effective SNPs with larger odds ratios and less effective SNPs with smaller odds ratios contribute to the correct decision of whom to screen. The best screening strategy is selected and evaluated by the net benefit quantity and the reclassification rate. We suggest that individuals whose upper bound of their risk confidence interval is above the screening threshold, which corresponds to the population prevalence of the disease, should be screened.

Direct-to-Consumer Genomics: Harmful or Empowering?: It is important to stress that genetic risk is not the same as genetic destiny

The price of whole-genome sequencing is now within the budget of the average American consumer. This has resulted in the commercialization of genome sequencing for a variety of applications, including health-related risk assessment. Direct-to-consumer marketing of personal DNA sequence information uncouples the generation of personal health-related data from the physician-patient relationship. Here, I discuss the status of consumer genomics and the current and potential concerns about bypassing physicians in the analysis and interpretation of personal genomic information and subsequent health care decision-making.

Estimation of the risk of a qualitative phenotype: dependence on population risk

Individual disease risk estimated based on the data from single or multiple genetic loci is generally calculated using the genotypes of a subject, frequencies of alleles of interest, odds ratios and the average population risk. However, it is often difficult to estimate accurately the average population risk, and therefore it is often expressed as an interval. To better estimate the risk of a subject with given genotypes, we built R scripts using the R environment and constructed graphs to examine the change in the estimated risk as well as the relative risk according to the change of the average population risk. In most cases, the graph of the relative risk did not cross the line of y=1, thereby indicating that the order of the relative risk for given genotypes and the population average risk does not change when the average risk increases or decreases. In rare cases, however, the graph of the relative risk crossed the line of y=1, thereby indicating that the order of the relative risk for given genotypes and the population average risk does change owing to the change in the population risk. We propose that the relative risk should be estimated for not only the point average population risk but also for an interval of the average population risk. Moreover, when the graph crosses the line of y=1 within the interval, this information should be reported to the consumer.

Constructing Hypothetical Risk Data from the Area under the ROC Curve: Modelling Distributions of Polygenic Risk

Background

Modeling studies using hypothetical polygenic risk data can be an efficient tool for investigating the effectiveness of downstream applications such as targeting interventions to risk groups to justify whether empirical investigation is warranted. We investigated the assumptions underlying a method that simulates risk data for specific values of the area under the receiver operating characteristic curve (AUC).

Methods

The simulation method constructs risk data for a hypothetical population based on the population disease risk, and the odds ratios and frequencies of genetic variants. By systematically varying the parameters, we investigated under what conditions AUC values represent unique ROC curves with unique risk distributions for patients and nonpatients, and to what extend risk data can be simulated for precise values of the AUC.

Results

Using larger number of genetic variants each with a modest effect, we observed that the distributions of estimated risks of patients and nonpatients were similar for various combinations of the odds ratios and frequencies of the risk alleles. Simulated ROC curves overlapped empirical curves with the same AUC.

Conclusions

Polygenic risk data can be effectively and efficiently created using a simulation method. This allows to further investigate the potential applications of stratifying interventions on the basis of polygenic risk.

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.

Crowdsourced direct-to-consumer genomic analysis of a family quartet

Background

We describe the pioneering experience of a Spanish family pursuing the goal of understanding their own personal genetic data to the fullest possible extent using Direct to Consumer (DTC) tests. With full informed consent from the Corpas family, all genotype, exome and metagenome data from members of this family, are publicly available under a public domain Creative Commons 0 (CC0) license waiver. All scientists or companies analysing these data (“the Corpasome”) were invited to return results to the family.

Methods

We released 5 genotypes, 4 exomes, 1 metagenome from the Corpas family via a blog and figshare under a public domain license, inviting scientists to join the crowdsourcing efforts to analyse the genomes in return for coauthorship or acknowldgement in derived papers. Resulting analysis data were compiled via social media and direct email.

Results

Here we present the results of our investigations, combining the crowdsourced contributions and our own efforts. Four companies offering annotations for genomic variants were applied to four family exomes: BIOBASE, Ingenuity, Diploid, and GeneTalk. Starting from a common VCF file and after selecting for significant results from company reports, we find no overlap among described annotations. We additionally report on a gut microbiome analysis of a member of the Corpas family.

Conclusions

This study presents an analysis of a diverse set of tools and methods offered by four DTC companies. The striking discordance of the results mirrors previous findings with respect to DTC analysis of SNP chip data, and highlights the difficulties of using DTC data for preventive medical care. To our knowledge, the data and analysis results from our crowdsourced study represent the most comprehensive exome and analysis for a family quartet using solely DTC data generation to date.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1973-7) contains supplementary material, which is available to authorised users.

Patient-centric trials for therapeutic development in precision oncology

An enhanced understanding of the molecular pathology of disease gained from genomic studies is facilitating the development of treatments that target discrete molecular subclasses of tumours. Considerable associated challenges include how to advance and implement targeted drug-development strategies. Precision medicine centres on delivering the most appropriate therapy to a patient on the basis of clinical and molecular features of their disease. The development of therapeutic agents that target molecular mechanisms is driving innovation in clinical-trial strategies. Although progress has been made, modifications to existing core paradigms in oncology drug development will be required to realize fully the promise of precision medicine.

The impact of direct-to-consumer personal genomic testing on perceived risk of breast, prostate, colorectal, and lung cancer: findings from the PGen study

Background

Direct access to genomic information has the potential to transform cancer risk counseling. We measured the impact of direct-to-consumer genomic risk information on changes to perceived risk (ΔPR) of breast, prostate, colorectal and lung cancer among personal genomic testing (PGT) customers. We hypothesized that ΔPR would reflect directionality of risk estimates, attenuate with time, and be modified by participant characteristics.

Methods

Pathway Genomics and 23andMe customers were surveyed prior to receiving PGT results, and 2 weeks and 6 months post-results. For each cancer, PR was measured on a 5-point ordinal scale from “much lower than average” to “much higher than average.” PGT results, based on genotyping of common genetic variants, were dichotomized as elevated or average risk. The relationship between risk estimate and ΔPR was evaluated with linear regression; generalized estimating equations modeled this relationship over time.

Results

With the exception of lung cancer (for which ΔPR was positive regardless of result), elevated risk results were significantly associated with positive ΔPR, and average risk results with negative ΔPR (e.g., prostate cancer, 2 weeks: least squares-adjusted ΔPR = 0.77 for elevated risk versus −0.21 for average risk; p-value_difference < 0.0001) among 1154 participants. Large changes were rare: for each cancer, <4 % of participants overall reported a ΔPR of ±3 or more units. Effect modification by age, cancer family history, and baseline interest was observed for breast, colorectal, and lung cancer, respectively. A pattern of decreasing impact on ΔPR over time was consistently observed, but this trend was significant only in the case of colorectal cancer.

Conclusions

We have quantified the effect on consumer risk perception of returning genetic-based cancer risk information directly to consumers without clinician mediation. Provided via PGT, this information has a measurable but modest effect on perceived cancer risk, and one that is in some cases modified by consumers’ non-genetic risk context. Our observations of modest marginal effect sizes, infrequent extreme changes in perceived risk, and a pattern of diminishing impact with time, suggest that the ability of PGT to effect changes to cancer screening and prevention behaviors may be limited by relatively small changes to perceived risk.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0140-y) contains supplementary material, which is available to authorized users.

Multigene panels in prostate cancer risk assessment: a systematic review

PURPOSE

Single-nucleotide polymorphism (SNP) panel tests have been proposed for use in the detection of, and prediction of risk for, prostate cancer and as prognostic indicator in affected men. A systematic review was undertaken to address three research questions to evaluate the analytic validity, clinical validity, clinical utility, and prognostic validity of SNP-based panels.

METHODS

Data sources comprised MEDLINE, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, and EMBASE; these were searched from inception to April 2013. The gray-literature searches included contact with manufacturers. Eligible studies included English-language studies evaluating commercially available SNP panels. Study selection and risk of bias assessment were undertaken by two independent reviewers.

RESULTS

Twenty-one studies met eligibility criteria. All focused on clinical validity and evaluated 18 individual panels with 2 to 35 SNPs. All had poor discriminative ability (overall area under receiver-operator characteristic curves, 58-74%; incremental gain resulting from inclusion of SNP data, 2.5-11%) for predicting risk of prostate cancer and/or distinguishing between aggressive and asymptomatic/latent disease. The risk of bias of the studies, as assessed by the Newcastle Ottawa Scale (NOS) and Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tools, was moderate.

CONCLUSION

The evidence on currently available SNP panels is insufficient to assess analytic validity, and at best the panels assessed would add a small and clinically unimportant improvement to factors such as age and family history in risk stratification (clinical validity). No evidence on the clinical utility of current panels is available.Genet Med 18 6, 535-544.

Management of neovascular age-related macular degeneration: current state-of-the-art care for optimizing visual outcomes and therapies in development

Contemporary management of neovascular age-related macular degeneration (AMD) has evolved significantly over the last few years. The goal of treatment is shifting from merely salvaging vision to maintaining a high quality of life. There have been significant breakthroughs in the identification of viable drug targets and gene therapies. Imaging tools with near-histological precision have enhanced our knowledge about pathophysiological mechanisms that play a role in vision loss due to AMD. Visual, social, and vocational rehabilitation are all important treatment goals. In this review, evidence from landmark clinical trials is summarized to elucidate the optimum modern-day management of neovascular AMD. Therapeutic strategies currently under development, such as gene therapy and personalized medicine, are also described.

Highly penetrant alleles in age-related macular degeneration

Age-related macular degeneration (AMD) is a complex disease caused by a combination of genetic and environmental factors. Genome-wide association studies have identified several common genetic variants associated with AMD, which together account for 15%-65% of the heritability of AMD. Multiple hypotheses to clarify the unexplained portion of genetic variance have been proposed, such as gene-gene interactions, gene-environment interactions, structural variations, epigenetics, and rare variants. Several studies support a role for rare variants with large effect sizes in the pathogenesis of AMD. In this work, we review the methods that can be used to detect rare variants in common diseases, as well as the recent progress that has been made in the identification of rare variants in AMD. In addition, the relevance of these rare variants for diagnosis, prognosis, and treatment of AMD is highlighted.

Summarizing polygenic risks for complex diseases in a clinical whole-genome report

PURPOSE

Disease-causing mutations and pharmacogenomic variants are of primary interest for clinical whole-genome sequencing. However, estimating genetic liability for common complex diseases using established risk alleles might one day prove clinically useful.

METHODS

We compared polygenic scoring methods using a case-control data set with independently discovered risk alleles in the MedSeq Project. For eight traits of clinical relevance in both the primary-care and cardiomyopathy study cohorts, we estimated multiplicative polygenic risk scores using 161 published risk alleles and then normalized them using the population median estimated from the 1000 Genomes Project.

RESULTS

Our polygenic score approach identified the overrepresentation of independently discovered risk alleles in cases as compared with controls using a large-scale genome-wide association study data set. In addition to normalized multiplicative polygenic risk scores and rank in a population, the disease prevalence and proportion of heritability explained by known common risk variants provide important context in the interpretation of modern multilocus disease risk models.

CONCLUSION

Our approach in the MedSeq Project demonstrates how complex trait risk variants from an individual genome can be summarized and reported for the general clinician and also highlights the need for definitive clinical studies to obtain reference data for such estimates and to establish clinical utility.

Surveying Recent Themes in Translational Bioinformatics: Big Data in EHRs, Omics for Drugs, and Personal Genomics

OBJECTIVE

To provide a survey of recent progress in the use of large-scale biologic data to impact clinical care, and the impact the reuse of electronic health record data has made in genomic discovery.

METHOD

Survey of key themes in translational bioinformatics, primarily from 2012 and 2013.

RESULT

This survey focuses on four major themes: the growing use of Electronic Health Records (EHRs) as a source for genomic discovery, adoption of genomics and pharmacogenomics in clinical practice, the possible use of genomic technologies for drug repurposing, and the use of personal genomics to guide care.

CONCLUSION

Reuse of abundant clinical data for research is speeding discovery, and implementation of genomic data into clinical medicine is impacting care with new classes of data rarely used previously in medicine.

Implementation and utilization of genetic testing in personalized medicine

Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient’s clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.

Incorporation of personal single nucleotide polymorphism (SNP) data into a national level electronic health record for disease risk assessment, part 1: an overview of requirements

BACKGROUND

Personalized medicine approaches provide opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world including Turkey.

OBJECTIVE

The objective of this study was to concentrate on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluate the performance of various predictive models for prostate cancer cases. We present our work as a miniseries containing three parts: (1) an overview of requirements, (2) the incorporation of SNP into the NHIS-T, and (3) an evaluation of SNP incorporated NHIS-T for prostate cancer.

METHODS

For the first article of this miniseries, the scientific literature is reviewed and the requirements of SNP data integration into EMRs/EHRs are extracted and presented.

RESULTS

In the literature, basic requirements of genomic-enabled EMRs/EHRs are listed as incorporating genotype data and its clinical interpretation into EMRs/EHRs, developing accurate and accessible clinicogenomic interpretation resources (knowledge bases), interpreting and reinterpreting of variant data, and immersing of clinicogenomic information into the medical decision processes. In this section, we have analyzed these requirements under the subtitles of terminology standards, interoperability standards, clinicogenomic knowledge bases, defining clinical significance, and clinicogenomic decision support.

CONCLUSIONS

In order to integrate structured genotype and phenotype data into any system, there is a need to determine data components, terminology standards, and identifiers of clinicogenomic information. Also, we need to determine interoperability standards to share information between different information systems of stakeholders, and develop decision support capability to interpret genomic variations based on the knowledge bases via different assessment approaches.

Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies

Background: There is increasing interest in investigating genetic risk models in empirical studies, but such studies are premature when the expected predictive ability of the risk model is low. We assessed how accurately the predictive ability of genetic risk models can be estimated in simulated data that are created based on the odds ratios (ORs) and frequencies of single-nucleotide polymorphisms (SNPs) obtained from genome-wide association studies (GWASs).

Methods: We aimed to replicate published prediction studies that reported the area under the receiver operating characteristic curve (AUC) as a measure of predictive ability. We searched GWAS articles for all SNPs included in these models and extracted ORs and risk allele frequencies to construct genotypes and disease status for a hypothetical population. Using these hypothetical data, we reconstructed the published genetic risk models and compared their AUC values to those reported in the original articles.

Results: The accuracy of the AUC values varied with the method used for the construction of the risk models. When logistic regression analysis was used to construct the genetic risk model, AUC values estimated by the simulation method were similar to the published values with a median absolute difference of 0.02 [range: 0.00, 0.04]. This difference was 0.03 [range: 0.01, 0.06] and 0.05 [range: 0.01, 0.08] for unweighted and weighted risk scores.

Conclusions: The predictive ability of genetic risk models can be estimated using simulated data based on results from GWASs. Simulation methods can be useful to estimate the predictive ability in the absence of empirical data and to decide whether empirical investigation of genetic risk models is warranted.

Age-related macular degeneration: genetics and biology coming together

Genetic and genomic studies have enhanced our understanding of complex neurodegenerative diseases that exert a devastating impact on individuals and society. One such disease, age-related macular degeneration (AMD), is a major cause of progressive and debilitating visual impairment. Since the pioneering discovery in 2005 of complement factor H (CFH) as a major AMD susceptibility gene, extensive investigations have confirmed 19 additional genetic risk loci, and more are anticipated. In addition to common variants identified by now-conventional genome-wide association studies, targeted genomic sequencing and exome-chip analyses are uncovering rare variant alleles of high impact. Here, we provide a critical review of the ongoing genetic studies and of common and rare risk variants at a total of 20 susceptibility loci, which together explain 40-60% of the disease heritability but provide limited power for diagnostic testing of disease risk. Identification of these susceptibility loci has begun to untangle the complex biological pathways underlying AMD pathophysiology, pointing to new testable paradigms for treatment.

PATH-SCAN: a reporting tool for identifying clinically actionable variants

The American College of Medical Genetics and Genomics (ACMG) recently released guidelines regarding the reporting of incidental findings in sequencing data. Given the availability of Direct to Consumer (DTC) genetic testing and the falling cost of whole exome and genome sequencing, individuals will increasingly have the opportunity to analyze their own genomic data. We have developed a web-based tool, PATH-SCAN, which annotates individual genomes and exomes for ClinVar designated pathogenic variants found within the genes from the ACMG guidelines. Because mutations in these genes predispose individuals to conditions with actionable outcomes, our tool will allow individuals or researchers to identify potential risk variants in order to consult physicians or genetic counselors for further evaluation. Moreover, our tool allows individuals to anonymously submit their pathogenic burden, so that we can crowd source the collection of quantitative information regarding the frequency of these variants. We tested our tool on 1092 publicly available genomes from the 1000 Genomes project, 163 genomes from the Personal Genome Project, and 15 genomes from a clinical genome sequencing research project. Excluding the most commonly seen variant in 1000 Genomes, about 20% of all genomes analyzed had a ClinVar designated pathogenic variant that required further evaluation.