Part I: FUNDAMENTALS Chapter 1

PAGE 3 of 13

View Table of Contents

On this page

“The findings and conclusions in this book are those of the author(s) and do not
necessarily represent the views of the funding agency.”

These chapters were published with modifications by Oxford University Press (2004)


Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease

Human Genome Epidemiology: Scope and Strategies

Muin J. Khoury, Julian Little, Wylie Burke


The completion of the sequencing of the human genome is viewed as an important milestone in the history of biology and medicine (1,2). Many scientists believe that advances in human genetics and the sequencing of the human genome will play a central role in medicine and public health in the 21st century by providing genetic information for disease prediction and prevention (3,4). Indeed, we are confronted daily with one or more new gene discoveries claimed to be associated with increased risk for some disease and promising a sweeping change in the diagnosis, treatment or prevention of that condition. Table 1-1 shows a sample of stories from web-based headlines (5). These titles illustrate that gene discoveries involve a wide variety of diseases not normally considered “genetic”, and often include information about interactions with non-genetic factors such as cigarette smoking and drugs (5). Although gene discoveries generate excitement and expectations, their contribution to disease prevention is not clear. The central theme of this book is that “translation” of gene discoveries into meaningful actions to improve health depends on scientific information from multiple medical and public health disciplines. The field of epidemiology plays a central role in this effort. In this book, we explore how the applications of the epidemiologic approach to the human genome in relation to health and disease (we call human genome epidemiology or HuGE for short) will form an important scientific foundation for using genetic information to improve health and prevent disease.

Vision of Genomic Medicine

Human genome discoveries have broad potential applications for improving health and preventing disease. For primary prevention, a better understanding of genetic effects and gene-environment interactions in disease processes will allow us to develop better interventions such as avoidance of defined exposures and chemo prevention, and to identify subgroups who are candidates for the interventions. For secondary prevention, we may be able to develop new or tailor existing screening tests for early disease identification based on stratification by genotype and/or family history. For tertiary prevention and therapeutics, advances in human genetics could contribute to the development of better drugs and to tailoring drug uses to maximize benefits and minimize harms.

Nevertheless, many authors have expressed skepticism regarding the value of human genome discoveries to health care and disease prevention (6-10). Concerns cited include the low magnitude of risk for common diseases associated with most genetic variants discovered thus far, the absence of interventions that are specific to different genotypes, and the potential for genetic labeling potentially causing personal and social harms. Furthermore, some public health professionals have expressed concern that too much emphasis on genomics can divert energy and resources from disease prevention strategies that have been proven to be effective on a population basis (e.g., diet, exercise, smoking cessation, 10,11). Nevertheless, leading scientists continue to predict the imminent integration of gene discoveries in medical practice. In 2001, Collins and McKusick stated that: “By the year 2010, it is expected that predictive genetic tests will be available for as many as a dozen common conditions, allowing individuals who wish to know this information to learn their individual susceptibilities and to take steps to reduce those risks for which interventions are or will be available. Such interventions could take the form of medical surveillance, lifestyle modifications, diet, or drug therapy. Identification of persons at highest risk for colon cancer, for example, could lead to targeted efforts to provide colonoscopic screening to those individuals, with the likelihood of preventing many premature deaths” (12). Moreover, commercial marketing of a genomic approach to preventive medicine has already been implemented, albeit prematurely, both in Europe (13) and the United States (14).

This scenario of the practice of medicine is based on the assumption that the use of genetic information at multiple gene loci, perhaps in combination with a person’s family history of disease, can stratify people according to their risks for various diseases for targeted medical and behavioral interventions. In one such hypothetical scenario (Table 1-2), John, a 23-year-old college graduate with a high cholesterol level and a paternal history of early onset of myocardial infarction, undergoes a battery of genetic tests. Because of his increased risk for coronary heart disease, a prophylactic drug chosen for its efficacy in people with John’s genotype is prescribed to reduce his cholesterol level and the risk for coronary artery disease (15). His risk for colon cancer can be addressed by a program of annual colonoscopy starting at the age of 45, which provides an opportunity for early detection and secondary prevention of colon cancer mortality. Finally, his increased risk for lung cancer is addressed through behavior modification for smoking cessation. Although this case is often used to illustrate future use of genetic information for prevention, it raises a number of issues. For example, it is not clear whether or not genetic information is really needed to target certain interventions, such as smoking cessation or treatment of hypercholesterolemia. In the case of smoking, testing for susceptibility may reduce the motivation for smoking cessation in those individuals who test “normal” for the genotype. Even when genetic information provides guidance-as is the case with early initiation of colorectal cancer screening- the need for a DNA-based test versus the more generic information provided by a family history is open to question. Visions of genomic medicine also include gene therapy (12,16), and a whole generation of smart (or designer) drugs and vaccines that can be tailored to individual genetically mediated response (12). As with preventive strategies guided by genotype, potential uses of genomic information requires rigorous evaluation.

How do we get from the sequencing of the human genome to using such knowledge to improve clinical care and disease prevention? Collins and McKusick outlined critical elements of the medical research agenda in the 21st century that include improved laboratory technology, clinical research, evaluation of biologic pathways, and improved drug investigation (pharmacogenomics) (12). However, there will be additional crucial missing gaps in our knowledge base that will be filled by the classical public health sciences (17). This research effort is essential in determining to health risks associated with genetic variants and potential for treatments and prevention. Data in both these areas are also crucial in determining the ethical, legal and social implications of genetic information (18). Table 1-3 illustrates the convergence of several public health fields that will attempt to answer specific questions to allow the effective “translation” of gene discoveries into the realization of a vision of genomic medicine.

Obviously, the first question: “what are the risks?” is a crucial one that falls under the purview of epidemiology. In the following chapters we explore how epidemiologic methods can begin to bridge the knowledge gaps to answer this question. The hypothetical relative risks and lifetime disease probabilities presented in Table 1-2 can be derived only from well-designed population-based studies that assess disease risks and gene-gene and gene-environment interaction. When accurate risk estimates are obtained, other disciplines of medicine and public health will need to be applied to determine the appropriate use of such information, addressing questions related to public policy, economics, communication, and measurement of health outcomes (as shown in Table 1-3 ).

Epidemiology: What Are The Risks?

Epidemiology has been defined in many ways and is often viewed as the scientific core of public health (19). One widely used definition is “the study of the distribution and determinants of health-related states or events in populations and the application of this study to control health problems “(20). Epidemiologists determine risk factors for the occurrence of various diseases, identify segments of the population with highest risk to target prevention and intervention opportunities, and evaluate the effectiveness of health programs and services in improving the health of the population (21). Because of the observational nature of epidemiologic studies, the frequent inability to replicate associations across studies (22), and the inability to adjust for all potential confounding factors that are addressed in experimental designs, epidemiology has been occasionally viewed as having reached its limits (23)

In spite of these perceived weaknesses, epidemiologic methods have grown steadily over the past 3 decades (24) and have become increasingly integrated with those of genetics in the discipline of genetic epidemiology, which seeks to find the role of genetic factors in disease occurrence in populations and families (25). Also, we have seen the emergence of molecular epidemiology that seeks to study disease occurrence using biological markers of exposures, susceptibility and effects (26). Increasingly scientific tools such as micro array technology and gene expression profiles (reviewed in chapter 2) will be used to identify and measure the effects of various chemical and biologic exposures on health status in addition to the underlying genetic susceptibility to such exposures.

Most gene discoveries are based on studies of high-risk families or selected population groups. Once a gene-disease association is documented, well-conducted epidemiologic studies are needed to quantify the impact of gene variants on the risk for disease, death, and disability and to identify and measure the impact of modifiable risk factors that interact with gene variants. Epidemiologic studies are also required in the process of clinical validation of new genetic tests, and to monitor population use of genetic tests and determine the impact of genetic information on the health and well being of different populations. The results of such studies will help medical and public health professionals design clinical trials, ultimately leading to benefit from benefits from medical, behavioral and environmental interventions. To accomplish this goal, there must be collaboration among epidemiologists, clinical geneticists, laboratory scientists, and medical and public health practitioners from government, professional, academic, industry and consumer organizations. The rapid expansion in the number of reported gene-disease associations may lead to pressure to develop commercial tests before validation of research findings. Therefore there is an urgent need for epidemiologic data generated from multidisciplinary collaboration as a basis for developing medical and public health policy. For example, the appropriate use of genetic testing is currently debated in relation to breast cancer and to BRCA1/2 mutations (chapter 27); Alzheimer disease and the apolipoprotein E-E4 allele (chapter 22); and iron overload and HFE gene mutations (chapter 29). Rational and comprehensive health policies on the use of genetic tests will not be possible until robust population-based epidemiologic data are available regarding the frequency of newly discovered gene variants and associated disease risks.

What is Human Genome Epidemiology?

As shown in figure 1, epidemiology plays an important role in the continuum from gene discovery to the development and applications of genetic tests for diagnosing and treating various diseases or even predicting and preventing future disease in asymptomatic persons. We call this continuum human genome epidemiology (or HuGE) to denote an evolving field of inquiry that uses systematic applications of epidemiologic methods and approaches to the human genome to assess the impact of human genetic variation on health and disease. The terms “genetics” and “genomics” are often used interchangeably but also could be confusing. Guttmacher and Collins refer to “genetics” as “the study of single genes and their effects” and “genomics” as “the study of not just of single genes but of the functions and interactions of all genes in the genome” (27). Because of evolving technologies, we are now able to study many genes simultaneously. Our preference for “genomics” in the context of epidemiology reflects the idea that these methods can be applied to the whole human genome (28), rather than one gene at a time. The intersection of epidemiology with genetics and genomics illustrates the need for common understanding of language and study designs (29)

As shown in figure 1 and Table 1-4, epidemiologic study applications to the human genome include: 1) gene discovery (the traditional domain of genetic epidemiology, 25); 2) population risk characterization (the domain of molecular epidemiology, 26); and 3) evaluation of genetic information for diagnosis and prevention (including genetic tests and family history information), and for genome-based therapies (applied epidemiology and health services research, 30). The evaluation of genetic information can be done in predicting clinical outcomes (clinical epidemiology) or for population screening and public health assessment (public health epidemiology). The continuum of HuGE represents an extension of the concept of “genetic epidemiology with a capital E” proposed by Duncan Thomas (31) because it emphasizes that epidemiologic applications to the human genome go well beyond the quantitative and statistical methods of gene discovery and characterization. Throughout this book, we focus on how epidemiology, as a multidisciplinary field has begun to address issues related to the post gene discovery with increasing emphasis on characterization of gene effects and genetic tests in populations (what do you do with a gene after you find one?). Table 1-5 shows examples of the types of studies encompassed in the spectrum of human genome epidemiology, including methods for gene discovery (such as linkage analysis), assessing the population prevalence of gene variants, evaluating genotype-disease associations, assessing the impact of gene?environment and gene-gene interaction on disease risk; evaluating the usefulness and impact of genetic information (i.e. tests) for individuals, families and populations.

In the past few years, there has been a tremendous increase in the number and scope of human genome epidemiology in the peer-reviewed literature. However, most studies are still in the realm of gene discovery and/or genotype-disease associations. An analysis of abstracts of published HuGE papers for 2001 shows that of the 2402 published articles, 82% reported only on population prevalence of gene variants and gene-disease associations, 15% reported on gene-gene and gene-environment interactions, perhaps reflecting the difficulties in studying interactions, and 3% dealt with evaluation of genetic tests and population screening, reflecting a focus on different types of genes considered for clinical practice or population screening (32). Undoubtedly, in the next few years, the number of papers dealing with evaluation of genetic information in medicine and public health is likely to increase as many gene discoveries progress on the translation pathway. Epidemiologic studies of gene-environment interaction and genetic tests are bound to increase as more genes are discovered, characterized, and used to develop diagnostic and predictive tests.

Finally, we can view the role of epidemiology vis-à-vis the suggested paradigm shifts in biology and medicine that are occurring in conjunction with the human genome revolution (Table 1-6). Peltonen and McKusick (33) outlined the progressive shifts occurring in several areas. As shown in Table 1-6, post genome discovery, there will be an increasing shift from studying genetic diseases to studying all diseases, assessing gene products (proteomics), from mapping and sequencing to discovery of genetic variants, from studying single genes to multiple genes, from studying gene actions to studying gene regulations, and finally from diagnostic testing to testing for susceptibility. Epidemiologic methods should play a role in all these areas.

Need for Methodologic Standards in Epidemiologic Studies of the Human Genome

As more epidemiologic studies of human genes are conducted and published, it is important to integrate evidence from different studies. Given the large numbers of genes that will be examined in relation to numerous diseases, many spurious findings are likely to emerge. Moreover, variation in study designs and execution will make difficult the synthesis of information across studies. This is always compounded by the potential for publication bias, as positive gene-disease associations may be published preferentially (34). In a literature review of over 600 gene-disease associations, Hirschorn et al. documented that most reported associations are not robust. Of the 166 associations studied three or more times, only 6 were consistently replicated (34). Similarly, Ioannidis et al. conducted a meta-analysis 370 studies addressing 36 genetic associations for various diseases. They showed that the results of the first study correlate only modestly with subsequent studies of the same association. The first study often suggests a stronger genetic effect than is found by subsequent studies (35).

In the past few years, several authors have conducted HuGE reviews (36). Using specified guidelines (37), authors have conducted systematic, peer?reviewed synopses of epidemiologic aspects of human genes, prevalence of allelic variants in different populations, population?based disease risk information, gene?environment interaction, and quantitative data on genetic tests and service. These reviews have uncovered the need for unified guidelines that can be used to synthesize results of the increasing number of such studies. In 2002, Cooper et al. (38) proposed guidelines to promote the publication of scientifically meaningful disease association studies through the introduction of methodological standards (with a focus on discovery of candidate genes). Although several groups have addressed guidelines for the evaluation and synthesis of a number of areas (e.g. controlled clinical trials), no such recommendations exist that cover the full spectrum of HuGE studies. In an analysis of the epidemiologic quality of molecular genetic research, Bogardus et al. (39) used seven methodologic standards to evaluate the quality of studies in four mainstream medical journals. They found that in spite of the major molecular genetic advances, 63% of the articles did not comply with two or more quality standards (39). This finding emphasizes the need for methodologic standards in reporting such studies. Based on an expert panel workshop held in 1997, Stroup et al. published a proposal for reporting results of meta-analysis of observational studies in epidemiology (40) but did not specifically address genetic studies. Bruns et al. provided a checklist for reporting of studies of diagnostic accuracy of medical tests (41). A workshop sponsored by the National Cancer Institute led to a monograph on innovative study designs and analytic approaches to the genetic epidemiology of cancer (42). This series of articles were useful in outlining the spectrum of study designs in gene discovery and characterization in relation to disease, but they do not provide concrete guidance on the evaluation and synthesis of such studies. In 2001, an expert panel sponsored by the Centers for Disease Control and Prevention and the National Institutes of Health developed guidelines and recommendations for the evaluation and integration of data from human genome epidemiology with emphasis on studies of (1) prevalence of gene variants and gene-disease associations, (2) gene-environment and gene-gene interactions, and (3) evaluation of genetic tests. Conclusions and recommendations from this workshop have been published (43,44). Thus, progress is being slowly made in defining quality standards for genetic-epidemiological research, but ongoing evaluation is needed to make sure that such guidelines are implemented.

Finally, there is increasing interest in the evaluation of genetic tests. The National Institutes of Health-Department of Energy Task Force on Genetic Testing (45) and the Secretary’s Advisory Committee on Genetic Testing (46) have proposed detailed evaluation of new genetic tests. Many of the genetic tests that will emerge in the next decades will be used not only for diagnostic purposes but also to predict the risk of developing disease in otherwise healthy people and to make decisions about potentially preventive interventions or therapies. The utility of genetic tests in this context will depend heavily on the quality of epidemiologic information that summarizes the relation between genotypes and disease and how such relation is modulated by the presence of other factors, such as drugs and environmental exposures, and how risk can be reduced by interventions. These concerns clearly apply to the hypothetical case scenario shown in Table 1-2. Such information will have to be based on properly designed epidemiologic information on genotype-disease associations and gene-gene and gene-environment interaction. In addition, this genetic test report will have to include specific information based on appropriate clinical and epidemiologic outcome studies on how the risk for these diseases can be reduced using medical, behavioral and environmental interventions.

The HuGE Map Ahead

As a result of the recognition of the immense gaps in our knowledge base on genes and their relation to health outcomes, we included in this book chapters on epidemiologic methods and approaches that deal with the continuum from gene discovery to health care action. None of the material in the book is scientifically novel, but we have structured it in a way to allow readers to proceed systematically from the fundamentals of genome technology and gene discovery (section I) to epidemiologic approaches to gene characterization (section II) to evaluation of genetic tests and health services (section III). These concepts are then illustrated in a series of disease-specific case studies (section IV).

Section I (fundamentals) describes genomic technologies and their applications (chapter 2), evolving methods of gene discovery (chapter 3), and summarizes the current status of the ethical, legal and social issues for conducting epidemiologic studies of the human genome (with emphasis on informed consent issues).

Section II addresses epidemiologic approaches to the study of genotypes in populations (chapter 5) and their relation to diseases, including the assessment of gene-gene and gene-environment interaction (chapters 6-8). In addition, chapters 9 and 10 address synthesis of these studies, including some methodologic standards.

Section III deals with the application of epidemiologic methods to assessing genetic information for clinical and public health applications. Chapter 11 lays an epidemiologic foundation for using population level information to make individualized risk prediction for clinical use. Chapter 12 explores population-based concepts around the usefulness of genetic tests in population Chapter 13 discusses the evaluation of genetic tests from a combined clinical-laboratory approach. As gene-drug interactions become more prominent in medical practice, chapter 14 explores an epidemiologic framework for the interface between genetics, pharmacology and medicine. Chapter 15 explores the integration of genetics into controlled clinical trials, and chapters 16 and 17 focus on the role of genetics in clinical practice guideline development both in the United States and the United Kingdom.

Finally, section IV uses case studies to illustrate concepts discussed in the first 3 sections in relation to specific disease examples, including gene-environment interactions (pesticides in chapter 18 and oral contraceptive use in chapter 19), chronic diseases (colon cancer in chapter 20, Alzheimer disease in chapter 21, cardiovascular disease in chapter 25, breast cancer in chapter 26 and iron overload in chapter 28), occupational exposures (Berylliosis in chapter 22), newborn screening issues (Fragile X syndrome in chapter 23 and hearing loss in chapter 24), and infectious disease (HIV in chapter 27). These examples are by no means exhaustive, but they do illustrate two points: 1) the spectrum from single gene disorders to complex disorders that involve gene-environment interactions and the application of genomic knowledge to disease prevention, and 2) the need for epidemiologic research to obtain population level information that is crucial for developing health policy and new approaches to practice. Indeed for many of the case studies, the lack of such data represents a primary barrier to developing appropriate health polices related to these of genetic risk information. The readers will discover that our knowledge is rapidly evolving for each one of these examples. Most likely, this information will be outdated soon. Nevertheless, we hope that the basic methodologic foundations for how to approach the process of translation from gene discovery to using genetic information will still apply. Throughout the book, we assume that readers have introductory knowledge of the fields of epidemiology and genetics and we therefore mainly address the interface between the two disciplines. For general readings on epidemiology and epidemiologic methods many excellent books are available (e.g., 24, 47-50). Also, we do not present technical details of genome technologies or statistical methods of gene discoveries using family-based methods but merely an overview of these rapidly changing methods. Our central emphasis is on applying epidemiology to assess the role of genetic variations in health outcomes and to evaluate how such information can be used to improve health and prevent disease.

Finally, there is an increasing emphasis on using family history (with or without the use of genetic testing) as a tool for disease prevention and public health (51). Family history is a consistent risk factor for most common diseases and reflects shared genetic and environmental factors. Family history is often used an initial tool to genetic testing and targeted interventions. While this book does not cover family history directly, we recognize that epidemiologic methods and approaches can be applied to the evaluation of family history with or without genetic testing. For further discussions about family history in the context of public health and disease prevention, the readers are referred to a series of workshop papers published in 2003 (52).

Ultimately, in order to fulfill the promise of the Human Genome Project in improving health, multidisciplinary medical and public health approaches are needed. At the core of these approaches is the simple question: “what are the risks?” followed by the question “what to do with numbers once you get them?” To get there, we have a HuGE map to follow.

 Top of Page


 Top of Page


  1. Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome.. Science 2001;291:1304-51.
  2. Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature 2001;409:860-921.
  3. Collins FS, Guttmacher AE. Genetics moves into the medical mainstream. JAMA 2001;286:2322-4.
  4. King RA. Editorial. Genetics in Medicine 1998;2:1-2.
  5. CDC Office of Genomics and Disease Prevention Weekly Update, Atlanta, 2002.
  6. Holtzman NA, Marteau TM. Will genetics revolutionize medicine? New Engl J Med 2000;343(2):141-4.
  7. Zimmern R, Emery J, Richards T. Putting genetics in perspective. BMJ. 2001;322:1005-6.
  8. Willet WC. Balancing lifestyle and genomics research for disease prevention. Science 2002;296:695-8.
  9. Evans JP, Skrzynia C, Burke W. The complexities of predictive genetic testing. BMJ 2001;322:1052-6.
  10. Vineis P, Schulte P, McMichael AJ. Misconceptions about the use of genetic tests in populations. Lancet 2001;357:709-12.
  11. Rockhill B, Kawachi I, Colditz GA. Individual risk prediction and population-wide disease prevention.
    Epidemiol Rev 2000;22:176-80.
  12. Collins FS, McKusick VA. Implications of the Human Genome Project for medical science. JAMA. 2001;285:540-4.
  13. Sciona.. Discover the relationship between you and your genes.
  14. Genovations™ : the advent of truly personalized healthcare.
  15. Collins FS. Shattuck lecture–medical and societal consequences of the Human Genome Project.
    N Engl J Med. 1999;341:28-37.
  16. Wadhwa PD, Zielske SP, Roth JC, et al. Cancer gene therapy: scientific basis. Annu Rev Med 2002;53:437-52.
  17. Khoury MJ, Beskow L, Gwinn ML. Translation of genomic research into health care.
    JAMA. 2001;285:2447-8.
  18. Burke W, Pinsky LE, Press NA. Categorizing genetic tests to identify their ethical, legal, and social implications.
    Am J Med Genet. 2001;106:233-40.
  19. Institute of Medicine. Committee for the Study of the Future of Public Health. National Academy of Sciences, Washington, DC, 1988.
  20. Last JM. A Dictionary of Epidemiology, third edition. New York: Oxford University Press, 1995.
  21. Koplan JP, Thacker SB, Lezin NA. Epidemiology in the 21st century: calculation, communication, and intervention.
    Am J Public Health. 1999;89:1153-5.
  22. Ioannidis JP, Ntzani EE, Trikalinos TA, et al. Replication validity of genetic association studies. Nature Genetics 2001;3:306-9.
  23. Taubes G. Epidemiology faces it limits. Science 1995;269:164-165.
  24. Rothman KJ, Greenland S. Modern epidemiology. Second edition, Lippincot, Raven, Philadelphia, 1998.
  25. Khoury MJ, Beaty TH, Cohen BH. Fundamentals of Genetic Epidemiology. New York, Oxford University Press, 1993.
  26. Schulte, P.A., Perera, F.P. (eds). Molecular Epidemiology: Principles and Practice, Academic Press, New York, 1993.
  27. Guttmacher AE, Collins FS. Genomic medicine: a primer. New Engl J Med 2002;347:1512-20.
  28. Millikan R. The changing face of epidemiology in the genomics era. Epidemiology 2002;13:472-80.
  29. Potter JD. At the interfaces of epidemiology, genetics and genomics. Nature Reviews Genetics 2001;2:142-7.
  30. Brownson RC, Petitti DB. Applied epidemiology: Theory to Practice, Oxford University Press, New York, 1998.
  31. Thomas DC. Genetic epidemiology with a capital E. Genet Epidemiol 2000;19:289-300.
  32. CDC Human Genome Epidemiology Network Published Literature Database
  33. Peltonen L, McKusick VA. Genomics and medicine. Dissecting human disease in the postgenomic era.
    Science 2001;291:1224-9.
  34. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. Comprehensive review of genetic association studies.
    Genetics in Medicine 2002;4:45-61.
  35. Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG. Replication validity of genetic association studies.
    Nat Genet 2001;29:306-9
  36. Khoury MJ, Little MJ. Human genome epidemiologic reviews:the beginning of something huge. Am J Epidemiol 2000;151:2-3.
  37. American Journal of Epidemiology: Revised guidelines for publishing HuGE reviews. Am J Epidemiol 2000:131:4-6.
  38. Cooper DN, Nussbaum RL, Krawczak M. Proposed guidelines for papers describing DNA polymorphism-disease associations. Human Genetics 2002;110:207-8.
  39. Bogardus ST, Concato J, Feinstein AR. Clinical epidemiological quality in molecular genetic research: the need for methodologic standards. JAMA 1999;281:1919-26.
  40. Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283:2008-16.
  41. Bruns DE, Huth EJ, Magid E, Young DS. Toward a checklist for reporting of studies of diagnostic accuracy of medical tests. Clin Chem. 2000;46:893-5.
  42. National Cancer Institute- Innovative study designs and analytic approaches to the genetic epidemiology of cancer. Monographs of JNCI 1999;26:1-105.
  43. Little J et al. HuGE paper 1, Am J Epidemiol 2002;156:300-310.
  44. Burke W et al. HuGE paper 3, Am J Epidemiol 2002;156:311-318.
  45. Holtzman NA, Watson M (eds). Promoting Safe and Effective Genetic Testing in the United States. Final Report of the Task Force on Genetic Testing (1997) available at
  46. Secretary’s Advisory Committee on Genetic Testing. Enhancing the Oversight of Genetic Tests: Recommendations of the SACGT (2000).
  47. Lilienfeld DE, Stolley P. Foundations of Epidemiology (Third Edition). Oxford University Press, New York (1994).
  48. Rothman KJ. Epidemiology: An Introduction. Oxford University Press, New York, 2002.
  49. Kelsey JL, Whitemore AS, Evans AS, Thompson WD. Methods in Observational Epidemiology (Second Edition). Oxford University Press, New York (1996).
  50. Elwood JM. Critical appraisal of epidemiological studies and clinical trials. Second edition. Oxford University press, Oxford, UK, 1998.
  51. Yoon PW, Scheuner MT, Peterson-Oehlke P et al. Can family history be used as a tool for public health and preventive medicine 2002;4:304-310.
  52. Yoon PW, Scheuner M, Khoury MJ. Research priorities for using family history as tool for public health and disease prevention. Am J Prev Med (in press, 2003)
  53. Wu X, Cooper RS, Borecki I, et al. A Combined Analysis of Genomewide Linkage Scans for Body Mass Index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program. Am J Hum Genet 2002;70:1247-56.
  54. Fisher SE, Francks C, McCracken JT, et al. A genomewide scan for Loci involved in attention-deficit/hyperactivity disorder. Am J Hum Genet 2002;70:1183-96.
  55. Lindgren CM, Mahtani MM, Widen E, et al. Genomewide search for type 2 diabetes mellitus susceptibility loci in Finnish families: the Botnia study. Am J Hum Genet 2002;70:509-16.
  56. Liu J, Juo SH, Holopainen P, Terwilliger J, et al. Genomewide linkage analysis of celiac disease in Finnish families. Am J Hum Genet 2002;70:51-9.
  57. Steinberg KK, Cogswell ME, Chang JC, et al. Prevalence of C282Y and H63D mutations in the hemochromatosis (HFE) gene in the United States. JAMA 2001;285:2216-22.
  58. Beranek M, Kankova K, Bene P, et al. Polymorphism R25P in the gene encoding transforming growth factor-beta (TGF-beta1) is a newly identified risk factor for proliferative diabetic retinopathy. Am J Med Genet 2002;109:278-83.
  59. Cortessis V, Siegmund K, Chen Q, et al. A case-control study of microsomal epoxide hydrolase, smoking, meat consumption, glutathione S-transferase M3, and risk of colorectal adenomas. Cancer Res 2001;61(6):2381-5.
  60. Greenstein G, Hart TC. A critical assessment of interleukin-1 (IL-1) genotyping when used in a genetic susceptibility test for severe chronic periodontitis. J Periodontol 2002;73:231-47.
  61. Frank TS, Deffenbaugh AM, Reid JE, et al. Clinical characteristics if individuals with germline mutations in BRCA1 and BRCA2: analysis of 10,000 individuals. J Clin Oncol 2002;20:1480-1490.
  62. Burke W, Thomson E, Khoury MJ, et al. Hereditary hemochromatosis: gene discovery and its implications for population-based screening. JAMA 1998; 280:172-8.

 Top of Page