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Keywords:

  • cancer;
  • gene polymorphism;
  • molecular medicine;
  • risk factors

Abstract

  1. Top of page
  2. Abstract
  3. Genome-Wide Association Studies
  4. Divergent views
  5. Doom and over-enthusiasm are both premature
  6. Translation: to the individual, clinic and public health
  7. Our current approach
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgement
  11. References

Abstract.  Turner AR, Kader AK, Xu J (Wake Forest University, Winston-Salem, NC; and University of California San Diego, La Jolla, CA; USA). Utility of Genome-Wide Association Study findings: prostate cancer as a translational research paradigm (Review). J Intern Med 2012; 271: 344–352.

Genome-Wide Association Studies have identified thousands of consistently replicated associations between genetic markers and complex disease risk, including cancers. Alone, these markers have limited utility in risk prediction; however, when several of these markers are used in combination, the predictive performance appears to be similar to that of many currently available clinical predictors. Despite this, there are divergent views regarding the clinical validity and utility of these genetic markers in risk prediction. There are valid concerns, thus providing a direction for new lines of research. Herein, we outline the debate and use the example of prostate cancer to highlight emerging evidence from studies that aim to address potential concerns. We also describe a translational framework that could be used to guide the development of a new generation of comprehensive research studies aimed at capitalizing on these exciting new discoveries.


Genome-Wide Association Studies

  1. Top of page
  2. Abstract
  3. Genome-Wide Association Studies
  4. Divergent views
  5. Doom and over-enthusiasm are both premature
  6. Translation: to the individual, clinic and public health
  7. Our current approach
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgement
  11. References

Genome-Wide Association Studies (GWAS) provide a comprehensive and unbiased assessment of single nucleotide polymorphisms (SNPs) across the genome and test for their association with disease phenotypes. This study design has resulted in the identification of novel associations of SNPs with a variety of complex diseases such as cancer. The online reference, ‘A Catalog of Published Genome-Wide Association Studies’, now lists approximately 375 SNPs associated with 27 cancer types [1]. Associations listed in the catalogue meet stringent criteria for genome-wide statistical significance and have been validated in independent study populations, thus reducing the likelihood of chance associations.

Although the risk alleles of these SNPs are common (>5%) in the general population, each has a small individual effect on disease risk with odds ratios (ORs) of about 1.2 [2, 3]. However, larger ORs are observed when multiple SNPs are combined, as we have shown in prostate cancer (PCa) and others have shown in breast cancer [4–7], thus supporting their potential use in risk prediction.

Associations identified by GWAS add another class of risk factor that may be particularly useful for diseases in which the majority of cases are not explained by existing risk factors, such as PCa. Furthermore, these germ-line genetic markers are unique in that they can be objectively and accurately measured, do not change with age, and always precede phenotypes and diseases. The discovery of these risk-associated SNPs has spurred efforts to develop clinical applications, although there are controversies.

Divergent views

  1. Top of page
  2. Abstract
  3. Genome-Wide Association Studies
  4. Divergent views
  5. Doom and over-enthusiasm are both premature
  6. Translation: to the individual, clinic and public health
  7. Our current approach
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgement
  11. References

Although the highly significant and confirmed association findings of GWAS are not in question from a statistical perspective, there are divergent views on the clinical validity and clinical utility of the associated SNPs [7, 8]. There are ethical and technical arguments, both for and against clinical applications of these SNPs.

Concerns

Opponents argue that ethically, the balance of benefit versus risk (beneficence/non-maleficence) is unclear (Box 1). For example, if high-risk results create excessive anxiety or if low-risk results create a false sense of security, either of these may lead to inaction or over-reaction in subsequent medical decisions. Additionally, genetic discrimination is a concern. Overall, such testing information may be detrimental to health and well-being, if not at the population level, then perhaps at the individual level.

Table Box 1..   Key ethical and technical concerns for Genome-Wide Association Studies SNPs
1Unclear benefit versus risk
2Potential genetic discrimination
3Causal role not known
4Poor predictive performance
5Risk estimates likely to change
6Unclear health benefit

Underscoring these ethical concerns, a variety of technical concerns have also been raised (Box 1). First, because many of these associated SNPs are in noncoding and intronic areas of the genome, the molecular mechanisms by which most of these SNPs act is poorly understood, thus leaving their causal role in question. Secondly, their predictive performance is generally modest as estimated by the area under the curve (AUC) statistic of the receiver operating characteristic (ROC) [9, 10]. Thirdly, it is believed that only a small fraction of all common risk-associated SNPs have been identified to date, and as a result, current risk estimates are likely to change as the risk prediction models are improved [11]. This could contribute to future risk reclassification and result in varying interpretations at different times. Fourthly, the health benefits are not clear; for example, most of these SNPs cannot distinguish between clinically indolent and aggressive cancer, leading to concerns for overdiagnosis and overtreatment of indolent disease [12–15].

Optimism

Proponents generally focus on the consistent associations between SNPs and disease risk, and independent predictive performance when combining multiple risk-associated SNPs, as evidence in support of SNP-based predictive testing. In terms of ethics, proponents emphasize autonomy, meaning the right of individuals to make informed decisions to access their own personal genetic information, free of paternalism. As for genetic discrimination, proponents counter that safeguards are already in place, including the Genetic Information Non-discrimination Act (GINA). Covering both ethical and technical issues, it can be argued that patients have the ability to seek a third-party interpretation of results and to discuss appropriate medical actions based on their results, because genetic testing and genetic counselling are already well-established aspects of medical care.

Commercial reality

Despite concerns, the commercial testing market has moved forward with a rapid expansion of offerings of multi-SNP tests based on GWAS results, including tests on a direct-to-consumer (DTC) basis [16].

The apparent commercial demand for these tests suggests a subset of patients has a need for the information. At any rate, these commercial offerings represent the most positive view of SNP-based testing that they are ready for use by individuals.

However, change may be coming. Although the newly available tests have experienced very little oversight within the current ‘patchwork’ of federal and state legislation pertaining to genetic testing and DTC testing [17, 18], the US FDA has recently asserted their authority to regulate DTC genetic testing kits on the basis they are medical devices. From May 2010 to August 2011, the US FDA has sent letters to at least 24 companies offering such tests; all were asked to provide evidence of either regulatory clearance or justification for exemption [19].

Doom and over-enthusiasm are both premature

  1. Top of page
  2. Abstract
  3. Genome-Wide Association Studies
  4. Divergent views
  5. Doom and over-enthusiasm are both premature
  6. Translation: to the individual, clinic and public health
  7. Our current approach
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgement
  11. References

We remain at an early stage of judging the clinical validity and utility of GWAS SNPs and the subsequent multi-SNP tests. Current methods to assess the performance of genetic markers may be misleading, resulting in premature, sometimes incorrect, interpretations regarding potential utility or lack thereof. In addition, too little attention has been paid to evaluating the potential health benefits of the new risk prediction tests. As we describe in the following, empirical evidence from prostate cancer studies is beginning to address some of the primary concerns.

Causal role

Whilst GWAS findings have provided many novel biological insights that serve as leads for additional studies, it is well known that most GWAS associations cannot be explained by known causal mechanisms. Traditional molecular genetics approaches, as well as new methods and technology, are expected to illuminate causal links between diseases and the regions that harbour SNPs associated via GWAS. For example, we recently published a study demonstrating the potential interaction between the androgen receptor binding sites and many of the SNPs associated with prostate cancer, suggesting an androgen-dependent pathway by which many of these SNPs act [20]. Future novel study designs such as these together with a more comprehensive assessment of the genome and a better understanding of the role of noncoding regions will result in an appreciation of the functional significance of these SNPs. In particular, next-generation sequencing and proteomics could reveal the functional impact of these sets of variants, which will be important to understand aetiology and to eventually develop targeted therapies.

However, the process of functional characterization and therapeutic development may require many decades to complete and should not be an impediment to risk assessment research. Given the massive public health impact of common diseases such as cancer, it could be argued that we should move forward by utilizing the best currently available information. Although all of the biological mechanisms are not yet understood, we already know that GWAS findings represent true associations in populations, based on consistent observations across independent study populations; this supports research to evaluate the validity and utility of these SNPs for risk prediction. Risk assessment testing does not preclude additional mechanistic research into the causal role of current SNPs or the discovery of additional variants; rather results from GWAS should continue to stimulate additional research in closely related fields.

Predictive performance as assessed by AUC and PPV

A major criticism of GWAS SNPs is the modest level of risk prediction, as assessed by AUC [21]. In the case of PCa, an AUC of 62% can be obtained when using the very best baseline clinical parameters in combination (age, family history, free/total prostate specific antigen (PSA) ratio, number of cores at prestudy entry biopsy and prostate volume) to predict PCa amongst repeat biopsies in the REDUCE study, which is 12% higher than chance (50%) (A. R. Turner, A. K. Kader, J. Xu, unpublished data). When 33 PCa risk-associated SNPs (Table 1) are added to these clinical parameters, we observed a 66% AUC that is statistically significant. Although this AUC only represents a 4% absolute increase, it represents a 33% (4%/12%) relative improvement over the best clinical risk prediction model.

Table 1. Summary of SNPs reproducibly associated with PCa
CHRSNPsRegionPositionKnown genesm/M alleleRisk allele
 2rs14656182p2143 407 453THADAA/GA
 2rs7210482p1562 985 235EHBP1A/GA
 2rs126212782q31.1173 019 799ITGA6G/AA
 3rs26607533p1287 193 364T/CT
 3rs109348533q21.3129 521 063EEFSECA/CA
 4rs170219184q22.395 781 900PDLIM5T/CC
 4rs76796734q24106 280 983TET2A/CC
 6rs93645546q25160 753 654SLC22A3T/CT
 7rs104865677p1527 943 088JAZF1A/GG
 7rs64656577q2197 654 263LMTK2T/CC
 8rs29286798p21.223 494 920SLC25A37A/GA
 8rs15122688p21.223 582 408NKX3.1T/CT
 8rs100869088q24 (5)128 081 119C/TT
 8rs169019798q24 (2)128 194 098A/CA
 8rs169020948q24.21128 389 528N/AG
 8rs6208618q24 (4)128 404 855A/GG
 8rs69832678q24 (3)128 482 487G/TG
 8rs14472958q24 (1)128 554 220A/CA
 9rs15718019q33123 467 194DAB2ICG/AA
10rs1099399410q1151 219 502MSMBT/CT
10rs496241610q26126 686 862CTBP2C/TC
11rs712790011p15.52 190 150IGF2, IGF2AS, INS, THG/AA
11rs1241845111q13 (2)68 691 995A/GA
11rs1089644911q13 (1)68 751 243MYEOVA/GG
17rs1164974317q12 (2)33 149 092HNF1BA/GG
17rs443079617q12 (1)33 172 153HNF1BA/GA
17rs185996217q24.366 620 348G/TG
19rs810247619q13.243 427 453PPP1R14AT/CC
19rs88739119q1346 677 464C/TT
19rs273583919q1356 056 435KLK3A/GG
22rs962311722q1338 782 065TNRC6BC/TC
22rs575916722q13.241 830 156TTLL1, BIK, MCAT, PACSIN2T/GG
23rs5945619Xp1151 258 412NUDT10, NUDT11, LOC340602C/TC

When assessing predictive performance, a more fundamental question is whether the findings have clinical meaning, such as the detection rate. Unfortunately, AUC is an abstract value that has no inherent clinical meaning. AUC assesses the ability to distinguish risk across all risk strata. However, if the goal is to identify men at considerably elevated risk, then methods based on a risk cut-off, such as positive predictive value (PPV), offer more clinical meaning. When we evaluated 28 PCa risk-associated SNPs within a Swedish population-based PCa case–control study, the PPV of this test was 36% when a threefold increased risk over population median risk was used to define high risk; this is comparable to PSA screening based on a 4-ng mL−1 cut-off [22]. This result has clinical meaning, because PPV is the disease detection rate amongst subjects predicted to be at risk based on disease biomarkers. This also reinforces our belief that AUC should not be viewed in isolation, but rather, in context.

Risk estimates and the plateau effect

The discovery of additional risk-associated SNPs and their later inclusion in risk prediction models are another source of concern because of the potential impact on individual test results. Risk-associated SNPs discovered to date most likely represent common genetic variants with a relatively larger effect that were relatively easy to identify. However, additional risk-associated SNPs with a smaller effect and/or rare risk-associated SNPs will almost certainly be discovered in future from new GWAS, meta-analyses of GWAS and re-sequencing studies. The question is whether the addition of smaller-effect and/or rare risk-associated SNPs would significantly improve the predictive performance of multi-SNP risk prediction models. Statistical modelling has suggested additional genetic markers may significantly improve predictive performance [11]. However, an empirical analysis comparing the first 5, 14 and 28 PCa risk-associated SNPs discovered from GWAS suggests that PPV reaches a plateau after the most important SNPs have been included in the risk prediction model [22]. Whilst this result is promising, the impact on risk prediction and reclassification owing to the addition of new genetic markers warrants further study, particularly as new high-risk variants are identified by whole-genome sequencing.

Health benefit

Even if GWAS SNPs allow accurate prediction of overall risk, questions of health benefit remain.

This is particularly important in a disease such as PCa, where most PCa tumours are not aggressive or life-threatening, and thus, treatment can cause more harm than good. Unfortunately, most GWAS SNPs identified to date are not associated with aggressiveness or survival and are unable to predict these clinical features. This is not surprising, given the original studies primarily used early-stage cases for association discovery and validation. Recent reports identified SNPs (rs4054823 at 17p12, rs6497287 at 15q13, rs2735839 at 19q13 and rs7679673 at 4q24) specifically associated with PCa progression or survival [15, 23–25]. Confirmation of these initial results is inconsistent and likely limited by different definitions of progression, differing study designs and small numbers of advanced PCa cases. However, the initial findings offer important leads and design guidance for future studies in this important area.

When assessing the potential health benefit of SNP-based tests, a thorough understanding of the complexity of disease is critical. For example, SNP association with PCa is consistent with an alternative hypothesis of association with higher PSA levels and not PCa risk per se (i.e. PSA detection bias) [26–28]. This is because most patients with PCa have higher PSA levels than controls in case–control studies conducted in developed countries where PSA screening is commonly used. Only well-designed prospective studies or clinical trials such as Prostate Cancer Prevention Trial and Reduction by Dutasteride of Prostate Cancer Events (REDUCE), where all study subjects undergo prostate biopsy regardless of PSA levels and other clinical parameters, can be used to dissect whether these SNPs are associated with PSA level or PCa risk, or both. [29, 30]. As described later, we are actively pursuing this line of research.

Health benefit may also be demonstrated by comparative analyses, which provide context to new findings on risk-associated SNPs. For example, both family history and GWAS SNPs reflect the genetic risk of individuals, and each modestly predicts an individual’s genetic risk for most complex diseases. Family history is promoted by the US Surgeon General and the CDC and is widely used in clinical settings to assess individual cancer risk and to guide clinical management (https://familyhistory.hhs.gov/ and http://www.cdc.gov/genomics/famhistory/). For PCa, the strength of the association with the disease is stronger in PCa risk-associated SNPs than family history surveys in prospective studies [31] and in the REDUCE study (A. R. Turner, A. K. Kader, J. Xu, unpublished data). We recognize that family history has advantages, such as the ability to obtain basic information by questionnaire or interview, versus SNP-based markers that require a laboratory test. However, neither is a perfect assessment of risk, and thus, the overall balance of risks and benefits is key in determining health benefit. If the balance of risks and benefits of family history are acceptable for clinical risk assessment, then genetic score should likewise be considered as a compliment to family history, for improved PCa risk prediction.

Translation: to the individual, clinic and public health

  1. Top of page
  2. Abstract
  3. Genome-Wide Association Studies
  4. Divergent views
  5. Doom and over-enthusiasm are both premature
  6. Translation: to the individual, clinic and public health
  7. Our current approach
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgement
  11. References

With valid arguments rebutting the raised technical concerns, it may be time to consider how these discoveries may be further evaluated and then brought from the bench to the patient. For this, we again use prostate cancer to highlight one possible translational research (TR) framework that will allow us to capitalize upon the exciting results from multi-SNP tests.

Contemporary notions of TR have extended this definition to include translation into the community whilst also defining a series of intermediate phases that comprise TR [32–36]. Genomic TR is essentially a progression through several stages (T1) confirming association and establishing clinical validity; (T2) clinical utility; (T3) practice-based implementation research; and finally (T4) population-/community-wide outcomes assessment.

T1

After the initial discovery of candidate associations, T1 research verifies associations and assesses analytical and clinical validity. A fundamental goal of T1 is to minimize the possibility of spurious associations owing to both statistical and clinical causes. Statistical causes are addressed by utilizing independent populations with large numbers of samples for confirmation analyses, reducing the possibility of false positives attributable to to chance. Clinical sources of spurious association are difficult to address in case–control studies, as described above for PSA detection bias and may be addressed by prospective studies in which potential bias is minimized. T1 research aims to answer questions such as ‘Are these SNPs truly associated with PCa or rather with PSA levels that lead to the detection of most PCa cases’? By answering this type of question, we can establish the validity of associations.

T2

T2 research addresses whether the valid associations from T1 have clinical utility. The necessary approaches in T2 include prospective studies, either observational or interventional (clinical trials), and comparative effectiveness research (CER). Unfortunately, very few of the initially promising associations are tested in prospective studies that can pave the way for the T2 phase [37, 38], in part because prospective studies are costly and require many years. One efficient approach is to utilize previously completed prospective studies, by examining predictors at baseline (e.g. clinical parameters and genotypes) in relation to outcome data. This approach is particularly appropriate for genetic studies in which genetic markers are practically blinded to patients and observers, reducing potential bias. CER is defined by the Institute of Medicine as ‘The generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care’ [39]. By comparing genomic tests to existing clinical markers, CER gives clinical context to statistics. If T2 research is successful, then we have answered questions such as ‘How does the PPV of a combined SNP test for PCa risk compare to the PPV of family history or PSA’? Clear answers to these types of questions can inform the public discourse and the subsequent development of professional guidelines and public policy.

T3

T3 research aims to maximize the utility that is established by T2 research, by examining the practical issues impacting clinical usage. These studies could examine physician’s motivation to offer tests, patient’s uptake of tests, patient’s interpretation of results, physician’s recommendations based on tests and the downstream decisions of those receiving (or not) test results. T3 research may also explore the differential impact when testing is applied in various clinical settings (e.g. private practice vs. specialty or academic centres) or implementation scenarios (e.g. population screening vs. targeting to high-risk families). Because T3 research is so wide-ranging, it may be necessary to direct research towards the most pressing issues in clinical implementation. Again, CER can be used to weigh a genomic test against existing methods. T3 research can ask ‘Do genomic test results for PCa risk alter perception and accordingly patterns of PSA screening or willingness to opt for chemoprevention’? Answers to these types of questions are intended to help capitalize on the potential positive health impacts of tests and may further guide the development of public policy.

T4

T4 research focuses on health outcomes amongst a wide community or population, following the introduction of a new intervention. Going beyond the well-defined groups of patients typically studied in T3 research, T4 examines real-world impact. For example, when new genomic tests are introduced, it is possible to monitor disease incidence using population-based registries; if a decrease is observed, then this may be attributable to the test, particularly if evidence from T1 through T3 would predict the observed effect. Formal cost-effectiveness analysis is also an important component of T4, utilizing real-world data on cost, test usage and outcomes. Questions addressed in T4 could include ‘Following the widespread introduction of a new genomic risk assessment test, how many cases of PCa are prevented in a population and at what financial cost’? By answering these questions, we can monitor whether the test is having the expected effects.

Currently, NCI-funded projects are heavily skewed towards T1, the early discovery phase of TR [38]. If we are to reap the full benefit of the heavy investment in discovery approaches such as GWAS, then it is imperative that scientists and clinicians commit to carrying out T2, T3 and T4 research. Fortunately, the NIH intends to promote translational research that is aimed at areas in which the FDA likewise intends to step up the regulation of tests, and this will promote research across the TR continuum [40].

Our current approach

  1. Top of page
  2. Abstract
  3. Genome-Wide Association Studies
  4. Divergent views
  5. Doom and over-enthusiasm are both premature
  6. Translation: to the individual, clinic and public health
  7. Our current approach
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgement
  11. References

Analytical and clinical validity

Here, we outline our approach to a new study that covers many stages of TR to evaluate a multi-SNP panel in PCa risk prediction (Fig. 1). First, we will assess the clinical validity of each SNP association identified by GWAS as well as the analytical validity of risk prediction for PCa based on combined SNPs (T1). These analyses will utilize the existing REDUCE trial, a large prospective cohort [29]. This study population is well suited to assess validity because (i) all study subjects were felt not to have PCa at study entry based on a prior negative biopsy, (ii) all subjects underwent protocol-required biopsies at years two and four, regardless of PSA levels or other clinical variables, and (iii) associations of SNPs with PCa have not been previously evaluated in this study population. The design of this population allows us to independently confirm SNP associations as well as the more critical issue of whether each SNP is associated with PSA rather than PCa per se. This also allows us to assess the analytical validity of a risk model that is based on the combined panel of SNPs.

image

Figure 1. Alignment of study aims with translational stages T1–T4.

Download figure to PowerPoint

Clinical utility

After establishing analytical and clinical validity, the next goal is to understand the clinical utility of the SNP panel, that is, whether it adds value to existing clinical markers in predicting positive prostate biopsy (T2). Using a CER approach, we will compare the performance of the combined SNP panel with that of existing clinical markers in predicting positive biopsy, using clinically meaningful measurements such as PPV. Again, this is possible because both clinical parameters and genetic markers at baseline are available and all study subjects were systematically biopsied in the REDUCE study. In addition, the REDUCE study randomized subjects to dutasteride versus placebo, providing an opportunity to explore whether men at higher estimated PCa risk based on SNPs and family history respond better to chemoprevention with dutasteride.

Practice-based research

Our next step is to evaluate some of the practical issues impacting clinical usage (T3). This will be accomplished by a new prospective randomized clinical trial to assess the impact of the SNP panel on risk perception and behavioural outcomes. Caucasian men aged 40–49 years who never had prior PSA screening or PCa diagnosis are recruited in this study. Baseline surveys will collect data on their perception of PCa risk, numeracy and health attitudes. Subjects will then be randomized, with half to receive a standard risk assessment (family history and age) and the other half to receive a risk assessment based on SNPs plus standard risk assessment. Immediately following the disclosure of the risk assessment based on these two methods, we will assess the perception of risk in each group. After 3 months, we will evaluate behavioural outcomes such as discussion of results with family members, engaging in medical appointments, discussion of PCa screening options with a medical provider, engaging in PCa screening such as PSA and DRE, and uptake of preventative measures such as chemoprevention. By comparing the two randomization groups, we can measure the impact of the SNP panel on risk perception and behavioural outcomes. Whilst there are many additional aspects that will remain to be addressed in future T3 studies, this prospective randomized clinical trial represents an important first step.

Population outcomes

Finally, we will begin to examine the potential health outcomes if the SNP panel is implemented in clinical practice (T4). Whilst realizing that it is too early to assess the impact of SNP-based risk prediction on incidence and mortality of PCa, we will focus on a cost-effectiveness analysis of genomic-targeted chemoprevention. Utilizing the data from T2 (PCa reduction rate using genomic-targeted and nontargeted approaches) and T3 (willingness to opt for chemoprevention based on perceived risk), we can estimate the cost of obtaining one quality-adjusted life-year. These results will be important in understanding the likely impact of SNP-based risk assessment on medical practice and the community-level outcomes.

Conclusions

  1. Top of page
  2. Abstract
  3. Genome-Wide Association Studies
  4. Divergent views
  5. Doom and over-enthusiasm are both premature
  6. Translation: to the individual, clinic and public health
  7. Our current approach
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgement
  11. References

Based on sets of SNPs identified by GWAS, there exists an opportunity to estimate disease risk earlier and more accurately. Although DTC genomic companies are already up and running, the divergent views on ethical and technical issues highlight several key areas in which additional research is still needed. We believe this early-stage technology holds great promise and needs to be fully evaluated and developed. Additional work remains if we are to responsibly bring GWAS discoveries to bear on health outcomes. For this to happen, all stages of the TR continuum need to be pursued, which means placing additional emphasis on the later stages of TR. To this end, there are promising early results from a few studies that suggest individuals have used genomic profile information to make positive changes in their behaviour [41–44]. It is crucial to extend these initially promising results to outcomes that are further down the pathway to clinical outcomes. As scientists and clinicians, we should embrace the opportunity to pursue TR as a means to improve human health.

References

  1. Top of page
  2. Abstract
  3. Genome-Wide Association Studies
  4. Divergent views
  5. Doom and over-enthusiasm are both premature
  6. Translation: to the individual, clinic and public health
  7. Our current approach
  8. Conclusions
  9. Conflict of interest statement
  10. Acknowledgement
  11. References