Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Intern Med. Author manuscript; available in PMC 2013 August 27.
Published in final edited form as:
PMCID: PMC3753782

Utility of Genome-Wide Association Study findings: prostate cancer as a translational research paradigm


Genomewide Association Studies (GWAS) 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 currently many 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 which could be used to guide the development of a new generation of comprehensive research studies aimed at capitalizing on these exciting new discoveries.

Keywords: cancer, gene polymorphism, molecular medicine, risk factors

Genome-Wide Association Studies

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 catalog 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 is controversy.

Divergent views

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.


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.

Box 1

Key ethical and technical concerns for GWAS SNPs

  1. Unclear benefit versus risk
  2. Potential genetic discrimination
  3. Causal role not known
  4. Poor predictive performance
  5. Risk estimates likely to change
  6. Unclear 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 non-coding 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. Second, their predictive performance is generally modest as estimated by the area under the curve (AUC) statistic of the receiver operating characteristic (ROC) [9, 10]. Third, 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. Fourth, 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].


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 third-party interpretation of results and to discuss appropriate medical actions based on their results, because genetic testing and genetic counseling 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 USFDA 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

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, empiric evidence from prostate cancer studies is beginning to address some of the primary concerns.

Causal role

While 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 harbor 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 prostate cancer associated SNPs, 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 non-coding 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 etiology 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 with 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 PSA ratio, number of cores at pre-study entry biopsy, and prostate volume) to predict PCa among repeat biopsies in the REDUCE study, which is 12% higher than chance (50%) (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.

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 cutoff, 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 (CAPS), the PPV of this test was 36% when 3-fold increased risk over population median risk was used to define high risk; this is comparable to PSA screening based on a 4 ng/mL cutoff [22]. This result has clinical meaning, because PPV is the disease detection rate among 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 is 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 the 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 modeling 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]. While this result is promising, the impact on risk prediction and reclassification due 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 tumors 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 PCa patients 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 (PCPT) 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 U.S. Surgeon General and the CDC, and is widely used in clinical settings to assess individual cancer risk and to guide clinical management [31, 32]. For PCa, the strength of association with the disease is stronger for PCa risk-associated SNPs than family history surveys, in prospective studies [33] and in the REDUCE study (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

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, while also defining a series of intermediate phases that comprise TR [34-38]. Genomic TR is essentially a progression through several stages; T1) confirms association and establishes clinical validity; T2) clinical utility; T3) practice-based implementation research; and finally T4) population/community wide outcomes assessment.


After 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 due 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 due 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 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 through the T2 phase [39-40], 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” [41]. By comparing genomics 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 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 motivation to offer tests, patient uptake of tests, patient interpretation of results, physician 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 versus specialty or academic centers) or implementation scenarios (e.g. population screening versus targeting to high risk families). Because T3 research is so wide-ranging, it may be necessary to direct research toward 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 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 toward T1, the early discovery phase of TR [40]. 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 [42].

Our current approach

Analytic 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 (figure 1). First, we will assess the clinical validity of each SNP association identified by GWAS as well as the analytic 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 1) all study subjects were felt not to have PCa at study entry based on a prior negative biopsy, 2) all subjects underwent protocol-required biopsies at years two and four, regardless of PSA levels or other clinical variables, and 3) 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 analytic validity of a risk model that is based on the combined panel of SNPs.

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

Clinical utility

After establishing analytic and clinical validity, the next goal is to understand the clinical utility of the SNP panel, i.e. 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 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 behavioral outcomes. Subject recruitment in the trial consists of men age 40-49 years, Caucasian, and never had prior PSA screening or PCa diagnosis. 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 three months we will evaluate behavior 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 behavioral outcomes. While 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). While 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 non-targeted 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.


Based on sets of SNPs identified by GWAS, there exists an opportunity to estimate disease risk earlier and more accurately. Although DTC genomics 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 behavior [43-46]. 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.


This work was partially supported by National Cancer Institute grants CA140262-02 and CA148463-02.


Conflict of interest statement: No conflict of interest was declared


1. Hindorff LA, Junkins HA, Hall PN, Mehta JP, Manolio TA. [Accessed August 15, 2011];A Catalog of Published Genome-Wide Association Studies. Available at:
2. Easton DF, Eeles RA. Genome-wide association studies in cancer. Hum Mol Genet. 2008 Oct 15;17(R2):R109–15. [PubMed]
3. Foulkes WD. Inherited Susceptibility to Common Cancers. N Engl J Med. 2008;359:2143–53. [PubMed]
4. Zheng SL, Sun J, Wiklund F, et al. Cumlative association of five genetic variants with prostate cancer. N Engl J Med. 2008;358:910–9. [PubMed]
5. Xu J, Sun J, Kader AK, Lindström S, Wiklund F, Hsu FC, Johansson JE, Zheng SL, Thomas G, Hayes RB, Kraft P, Hunter DJ, Chanock SJ, Isaacs WB, Grönberg H. Estimation of absolute risk for prostate cancer using genetic markers and family history. Prostate. 2009 Oct 1;69(14):1565–72. [PMC free article] [PubMed]
6. Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet. 2002;31:33–6. [PubMed]
7. Wacholder S, Hartge P, Prentice R, et al. Performance of Common Genetic Variants in Prevention of Breast Cancer. NEJM. 2010 Mar 18;362:986–93. [PMC free article] [PubMed]
8. Devilee, Rookus A Tiny Step Closer to Personalized Risk Prediction for Breast Cancer. NEJM. 2010 Mar 18;362:1043–5. [PubMed]
9. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004;159:882–90. [PubMed]
10. Ioannidis JP. Personalized genetic prediction: too limited, too expensive, or too soon? Ann Intern Med. 2009;150:139–141. [PubMed]
11. Kraft P, Hunter DJ. Genetic risk prediction--are we there yet? N Engl J Med. 2009;360:1701–3. [PubMed]
12. Wiklund F. Prostate cancer genomics: can we distinguish between indolent and fatal disease using genetic markers? Genome Med. 2010 Jul 29;2(7):45. [PMC free article] [PubMed]
13. Penney KL, Pyne S, Schumacher FR, Sinnott JA, Mucci LA, Kraft PL, Ma J, Oh WK, Kurth T, Kantoff PW, Giovannucci EL, Stampfer MJ, Hunter DJ, Freedman ML. Genome-wide association study of prostate cancer mortality. Cancer Epidemiol Biomarkers Prev. 2010 Nov;19(11):2869–76. Epub 2010 Oct 26. [PMC free article] [PubMed]
14. Kader AK, Sun J, Isaacs SD, Wiley KE, Yan G, Kim ST, Fedor H, DeMarzo AM, Epstein JI, Walsh PC, Partin AW, Trock B, Zheng SL, Xu J, Isaacs W. Individual and cumulative effect of prostate cancer risk-associated variants on clinicopathologic variables in 5,895 prostate cancer patients. Prostate. 2009 Aug 1;69(11):1195–205. [PMC free article] [PubMed]
15. Xu J, Zheng SL, Isaacs SD, Wiley KE, Wiklund F, Sun J, Kader AK, Li G, Purcell LD, Kim ST, Hsu FC, Stattin P, Hugosson J, Adolfsson J, Walsh PC, Trent JM, Duggan D, Carpten J, Grönberg H, Isaacs WB. Inherited genetic variant predisposes to aggressive but not indolent prostate cancer. Proc Natl Acad Sci U S A. 2010 Feb 2;107(5):2136–40. Epub 2010 Jan 11. [PubMed]
16. The Genetics and Public Policy Center. [Accessed June 1, 2010]; Published May 28, 2010.
17. Javit, Hudson . Issues in Science and Technology. Spring; 2006. Federal Neglect: Regulation of Genetic Testing.
18. Secretary's Advisory Committee on Genetics, Health and Society. 2008 Apr 30;
20. Feng J, Sun J, Kim ST, Lu Y, Wang Z, Zhang Z, Gronberg H, Isaacs WB, Zheng SL, Xu J. A genome-wide survey over the ChIP-on-chip identified androgen receptor-binding genomic regions identifies a novel prostate cancer susceptibility locus at 12q13.13. Cancer Epidemiol Biomarkers Prev. 2011 Nov;20(11):2396–403. [PMC free article] [PubMed]
21. Jakobsdottir, et al. Interpretation of Genetic Association Studies: Markers with Replicated Highly Significant Odds Ratios May be Poor Classifiers. PLOS Genetics. 2009 Feb; [PMC free article] [PubMed]
22. Sun J, Kader AK, Hsu FC, Kim ST, Zhu Y, Turner AR, Jin T, Zhang Z, Adolfsson J, Wiklund F, Zheng SL, Isaacs WB, Grönberg H, Xu J. Inherited genetic markers discovered to date are able to identify a significant number of men at considerably elevated risk for prostate cancer. Prostate. 2011 Mar 1;71(4):421–30. doi: 10.1002/pros.21256. Epub 2010 Sep 28. [PMC free article] [PubMed] [Cross Ref]
23. FitzGerald Liesel M, Kwon Erika M, Conomos Matthew P, Kolb Suzanne, Holt Sarah K, Levine David, Feng Ziding, Ostrander Elaine A, Stanford Janet L. Genome-wide Association Study Identifies a Genetic Variant Associated with Risk for More Aggressive Prostate Cancer. Cancer Epidemiol Biomarkers Prev. 2011 Jun;20:1196. [PMC free article] [PubMed]
24. Pomerantz Mark M, Werner Lillian, Xie Wanling, Regan Meredith M, Lee Gwo-Shu Mary, Sun Tong, Evan Carolyn, Petrozziello Gillian, Nakabayashi Mari, Oh William K, Kantoff Philip W, Freedman Matthew L. Association of Prostate Cancer Risk Loci with Disease Aggressiveness and Prostate Cancer–Specific Mortality. Cancer Prev Res. 2011 May;4:719. [PMC free article] [PubMed]
25. Gallagher David J, Vijai Joseph, Cronin Angel M, Bhatia Jasmine, Vickers Andrew J, Gaudet Mia M, Fine Samson, Reuter Victor, Scher Howard I, Halldén Christer, Dutra-Clarke1 Ana, Klein Robert J, Scardino Peter T, Eastham James A, Lilja Hans, Kirchhoff Tomas, Offit Kenneth. Susceptibility Loci Associated with Prostate Cancer Progression and Mortality. Clin Cancer Res. 2010 May 15;16:2819. [PMC free article] [PubMed]
26. Wiklund F. Association of reported prostate cancer risk alleles with PSA levels among men without a diagnosis of prostate cancer. Prostate. 2009 Mar 1;69(4):419–27. [PMC free article] [PubMed]
27. Ahn J, et al. Variation in KLK genes, prostate-specific antigen and risk of prostate cancer. Nat Genet. 2008;40:1032–1034. [PMC free article] [PubMed]
28. Gudmundsson J, et al. Genetic correction of PSA values using sequence variants associated with PSA levels. Sci Transl Med. 2010 Dec 15;2(62):62ra92. [PMC free article] [PubMed]
29. Andriole GA, Bostwick D, Brawley OW. The influence of dutasteride on the risk of biopsy-detectable prostate cancer: Outcomes of the REduction by DUtasteride of Prostate Cancer Events (REDUCE) study. N Engl J Med. 2010;362(13):1192–202. [PubMed]
30. Thompson IM, Goodman PJ, Tangen CM, et al. The influence of finasteride in the development of prostate cancer. N Engl J Med. 2003;349:215–24. [PubMed]
33. Aly M, Wiklund F, Xu J, Isaacs WB, Eklund M, D'Amato M, Adolfsson J, Grönberg H. Polygenic Risk Score Improves Prostate Cancer Risk Prediction: Results from the Stockholm-1 Cohort. Study. 2011 Jan;60(1):e1–e8. [PMC free article] [PubMed]
34. Khoury MJ, Gwinn M, Yoon PW, Dowling N, Moore CA, Bradley L. The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention? Genet Med. 2007 Oct;9(10):665–74. Review. [PubMed]
35. Woolf SH. The Meaning of Translational Research and Why It Matters. JAMA. 2008;299:211–213. [PubMed]
36. Lean M, Mann J, Hoek J, Elliot R, Schofield G. From evidence based medicine to sustainable solutions for public health problems. BMJ. 2008;337:a863. [PubMed]
37. NIH TRWG. [Accessed June 14, 2010];
38. Hiss RG. From clinical trials to community: the science of translating diabetes and obesity research. Natcher Conference Center, National Institutes of Health; Bethesda, Maryland, USA: 2004. Fundamental issues in translational research Translational research—two phases of a continuum; pp. 11–4.
39. Ioannidis JP. Materializing research promises: opportunities, priorities and conflicts in translational medicine. J Transl Med. 2004;2:5. [PMC free article] [PubMed]
40. Schully SD, Benedicto CB, Gillanders EM, Wang SS, Khoury MJ. Translational Research in Cancer Genetics: The Road Less Traveled. Public Health Genomics. 2009 Dec 29; [PMC free article] [PubMed]
41. Committee on Comparative Effectiveness Research Prioritization, Institute of Medicine (IOM) Initial National Priorities for Comparative Effectiveness Research. Washington , DC: The National Academies Press; 2009.
42. Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med. 2010 Jul 22;363(4):301–4. Epub 2010 Jun 15. [PubMed]
43. Chao S, Roberts JS, Marteau TM, Silliman R, Cupples LA, Green RC. Health behavior changes after genetic risk assessment for Alzheimer disease: The REVEAL Study. Alzheimer Dis Assoc Disord. 2008 Jan-Mar;22(1):94–7. [PMC free article] [PubMed]
44. Green Robert C, Roberts J Scott, Cupples L Adrienne, Relkin Norman R, Whitehouse Peter J, Brown Tamsen, Eckert Susan LaRusse, Butson Melissa, Sadovnick A Dessa, Quaid Kimberly A, Chen Clara, Cook-Deegan Robert, Farrer Lindsay A. for the REVEAL Study Group. Disclosure of APOE Genotype for Risk of Alzheimer's Disease. NEJM. 2009 Jul 16;361:245–254. Number 3. [PMC free article] [PubMed]
45. Sanderson SC, O'Neill SC, White DB, Bepler G, Bastian L, Lipkus IM, McBride CM. Responses to online GSTM1 genetic test results among smokers related to patients with lung cancer: a pilot study. Cancer Epidemiol Biomarkers Prev. 2009 Jul;18(7):1953–61. [PMC free article] [PubMed]
46. Stack CB, Gharani N, Gordon ES, Schmidlen T, Christman MF, Keller MA. Genetic risk estimation in the Coriell Personalized Medicine Collaborative. Genet Med. 2011 Feb;13(2):131–9. [PubMed]