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Genetics/Genomics Advances to Influence Care for Patients with Chronic Disease
Theresa Alice Beery, PhD Carolyn R. Smith, RN

Rapid changes in the field of genetics/genomics are affecting the way we practice nursing. Staying current on this dynamic field is a challenge for all healthcare professionals. This article discusses genetics/genomics issues that are likely to have a strong influence on nurses who care for patients with chronic disease. Complex diseases involving the actions of genes and environment are the most common causes of morbidity and mortality. Pharmacogenetics/pharmacogenomics has the potential to alter the way we choose both medications and appropriate doses. Nutrigenomics promises to help us understand how diet affects gene expression and how genetic variants affect the way we use nutrients. Genetic testing can be purchased through the Internet and currently is being marketed directly to consumers. Each of these topics has present and future implications for all healthcare professionals, especially those caring for people with chronic disease.

Genetics/genomics is moving to the forefront of health care as our understanding of the genetic contributions to common diseases expands. Practice for nurses who care for patients with chronic disease will change, and an understanding of how genetic/genomics will affect nursing practices can guide us as we try to stay current in this rapidly changing field. This article reviews complex disease, pharmacogenetics/pharmacogenomics, nutrigenomics, and genetic testing from a patient care perspective.

Complex Disease

Genetics historically has focused on infants and children with single-gene disorders (monogenic). There are more than 1,500 such disorders, including cystic fibrosis and Marfan syndrome, but common causes of mortality and morbidity rarely are single-gene disorders. Heart failure, coronary artery disease, most cancers, osteoporosis, and diabetes mellitus type 2 are complex (sometimes referred to as multifactorial) diseases. They result from combining the small effects of many gene variants (polygenic) working together, often with the addition of environmental risk factors such as smoking, toxin exposure, poor diet, or lack of exercise.

Tens or hundreds of gene variants may be involved in causing complex disease, with each adding less than 50% to a person’s risk (Lango & Weedon, 2008; Reich & Lander, 2001). Instead of restricting our view to single-gene (monogenic) effects with specific transmission patterns such as dominant and recessive, we now are looking more broadly and considering the interactive effects of the entire genome (all the DNA) of an organism in the study of genomics. The addition of environmental risk makes it more complicated to predict how a disease might be inherited.

We may see clustering of chronic complex diseases without clear lines of inheritance within families. The approximation of how large a role genetics plays in disease transmission is called the heritability estimate. For example, schizophrenia has a heritability estimate of approximately 80%. This means that approximately 80% of the risk for acquiring schizophrenia is attributable to genetics (O’Donovan, Williams, & Owen, 2003). The heritability estimate for coronary artery disease is approximately 50%; this leaves plenty of room for environmental risk factors (Fischer et al., 2005).

Many genomic studies focus on single nucleotide polymorphisms (SNPs). These common variants in DNA are found in more than 1% of the population, and there are approximately 10 million SNPs in the human genome. They are shared more often by people whose ancestors lived in close geographic proximity. These variations can be used to help identify specific parts of the genome that may be associated with disease or the way in which people process medications (Lango & Weedon, 2008).


Pharmacogenetics/pharmacogenomics (PG) is the study of how people respond differently to medications based on common genetic variations. In theory, having knowledge of individual variation in genes controlling drug response would allow clinicians to personalize medicine and select the appropriate drug at the appropriate dose. The promise of PG is the elimination (or mitigation) of adverse responses to drugs through genetic testing conducted before a medication is initiated. Drug response genetic testing can provide data on the effectiveness of metabolic enzymes and the competence of drug transporters, receptors, and drug targets. Drugs could be selected based on whether a person could efficiently process a particular substance at a standard dose, or whether they need a higher or lower dose or a different drug. This is the promise, but practice looks a bit different.

There are insufficient data to justify using PG testing to alter dosing of the most commonly prescribed drugs. PG remains in its infancy, although it has been a great help for some applications such as treatment of childhood leukemias (thiopurine methytransferase testing) or targeted treatment (trastuzumab) of HER2/neu-positive breast cancers. A review of PG testing for serotonin reuptake inhibitor administration concluded that despite the existence of potential promising results, there are insufficient data to support clinical utility in treating nonpsychotic depression (Evaluation of Genomic Applications in Practice and Prevention, 2007). More data are being collected on genetic variation in drug response, and new drugs commonly now undergo PG testing before coming to market (U.S. Food and Drug Administration [FDA], 2008).

We know most about the effects of common sequence variations in the genes coding for metabolic enzymes of the cytochrome p-450 (CYP-450) system of liver enzymes (Weinshilboum & Wang, 2004). People tested for CYP-450 polymorphisms can be classified as poor, extensive, or ultrarapid metabolizers based on common genetic variations not currently associated with disease. When an active drug is given, poor metabolizers tend to have more trouble clearing that drug and will have higher than normal blood levels for longer periods of time when given standard doses. Poor metabolizers are more likely to develop adverse reactions and dose-limiting toxicities. Extensive metabolizers manage drugs at the standard dose as expected given other variables like body mass index and liver function. People who are ultrarapid metabolizers will clear an active drug quickly and may experience no therapeutic effect.

When a prodrug is given (such as codeine, which must undergo biotransformation to morphine to have clinical effect), the response is just the opposite. Poor metabolizers will experience little therapeutic effect because they are unable to effectively transform codeine to morphine. The ultrarapid metabolizers will convert codeine efficiently and be more prone to adverse reactions (Prows & Prows, 2004; Weinshilboum & Wang, 2004).

There have been successes and disappointments in the field of pharmacogenetics. Testing for use of the drug trastuzumab (Herceptin) in breast cancer treatment has been an unequivocal success. The drug works well to treat HER2-overexpressing breast cancers, which account for approximately 25% of breast cancers (often those that are more aggressive). When tumor cells are tested, the results determine whether Herceptin is an appropriate treatment (Elledge et al., 1998; Lower, Glass, Blau, & Harman, 2008).

The application of pharmacogenetic testing to warfarin dosing has been somewhat successful, though the extent of its clinical usefulness continues to be a subject of debate. Warfarin is metabolized by the liver enzyme CYP2C9, which is one of the CYP-450 enzymes. People who have polymorphisms in this gene have an increased risk of above- or below-range international normalized ratio (INR). They take longer to stabilize a warfarin dose, have episodes of bleeding earlier during warfarin therapy, and have more frequent and serious episodes of either overcoagulation or life-threatening bleeding (Higashi et al., 2002).

Warfarin works by inhibiting vitamin K epoxide reductase (VKORC1). There are common variations in the DNA sequence of the gene coding for this protein that alter warfarin dose requirements (Geisen et al., 2005). Studies have supported the need to lower warfarin doses in people who have genetic variations in VKORC1 (Schwarz et al., 2008). Based on this mounting evidence, the FDA approved a change in the warfarin label to include information about PG testing. The package insert currently reads, “lower initiation doses should be considered for patients with certain genetic variations in CYP2C9 and VKORC1 enzymes” (FDA, 2007).

A website supported by the National Institutes of Health, American Heart Association, and others provides an algorithm to assist with selecting appropriate dosing for patients taking warfarin (www.warfarindosing.org/Source/Home.aspx; Gage, 2008). In addition to CYP2C9 and VKORC1 results, the site offers fields within which to input lifestyle (e.g., smoking) and clinical (e.g., liver disease) information. Of course, even with the best and most comprehensive assessment for genetic and environmental predictors of response, INR monitoring is essential (Gage). A patient’s genotype for CYP2C9 and VKORC1, combined with age, gender, and weight, account for approximately 45%–60% of variation in response to warfarin dose, so unknown variations also must contribute (McClain, Palomaki, Piper, & Haddow, 2008). A recent study found that it was not cost-effective to use PG testing for warfain dosing in patients with nonvalvular atrial fibrillation The authors noted that PG testing may be useful when initiating warfarin in patients at high risk for hemorrhage (Eckman, Rosand, Greenberg, & Gage, 2009). PG variations illustrate only one way in which genes affect how we use what we ingest.


Nutrigenomics (or nutritional genomics) is the study of how specific nutrients and other components of our diets affect gene expression or gene structure and how genetic variants affect how we respond to foods (Kaput & Rodriquez, 2006; Lovegrove & Gitau, 2008; Roche, 2006). It has been known for some time that food intake, also called nutrient exposure, has an effect on health status. By understanding the gene-nutrient connection, healthcare professionals may be able to personalize diet recommendations based on genotype rather than providing standard recommended daily allowances (Ronteltap, van Trijp, & Renes, 2009).

The Center of Excellence for Nutritional Genomics at the University of California–Davis suggests the following five principles to describe the major concepts implicit in this new science of nutrigenomics: (1) in some situations and for some people, diet is an important risk factor for disease; (2) common chemicals found in our diet can change gene expression or gene structure either directly or indirectly; (3) a person’s individual genotype can alter the extent to which diet affects health; (4) gene variants are involved in whether people have chronic disease as well as its course and severity; and (5) a diet based on knowledge of an individual’s genotype can prevent or lessen chronic disease (National Center on Minority Health and Health Disparities, 2009).

Nutritional genomics provides another avenue for personalized medicine, but diet customization may not be likely any time in the near future. The genome, health status, disease, and constituents of diet are very complex, making it difficult to conduct rigorous scientific experiments that can easily be replicated. Standardization of ingredient composition in recommended foods also would be a challenge, and consumer acceptance of nutritional recommendations cannot be assumed (Ronteltap et al., 2009).

The link between diet and cardiovascular disease (CVD) is well known, and this has been a natural area for work in nutritional genomics. Ordovas (2006) found that women with certain gene variants (or alleles) on chromosome 11 who increased their intake of polyunsaturated fatty acids (PUFAs; also known as omega-3 and omega-6 fatty acids) raised their levels of high-density lipoprotein (HDL; a protective factor for CVD). Women with different alleles experienced a decrease in HDL levels, sparking the conclusion that dietary interventions aimed at increasing PUFAs only may apply to those with a specific genetic make-up (Ordovas).

Multiple studies have explored the gene-nutrient interaction to cholesterol-lowering diets. Schaefer and colleagues (1997) found that cholesterol-lowering diets were not effective for everyone. Evidence suggests that variations among cholesterol metabolism regulating genes such as ApoA1, ApoA4, ApoB, and ApoE may explain why some people respond well to dietary interventions to reduce blood cholesterol levels and others do not (Roche, 2006). Dietary interventions to reduce blood cholesterol levels and the risk of CVD could take into account the genetic make-up of an individual to maximize the intended effect.

Nutrigenomic studies also have advanced the understanding of weight management. Diets tailored by nutrigenomic analysis have been shown to improve long-term body mass index reduction and stabilize fasting blood sugars in patients with histories of failure at weight management (Arkadianos et al., 2007). Risk of abdominal obesity has been shown to vary with both saturated fat intake and genetic polymorphisms (in the gene STAT3; Phillips et al., 2009). Variations in two genes related to inflammation (RIPK3 and RNF216) have been associated with regain of weight lost in obese subjects (Goyenechea, Crujeiras, Abete, & Martinez, 2009).

Better cancer-prevention and treatment strategies are always of interest, and dietary factors have long been identified as contributing to risk of cancer (e.g., the effect of grilling meat at high temperature on production of heterocyclic amines and polycyclic aromatic hydrocarbons, which are known carcinogens; Harvard, 2007). Epidemiologic studies have demonstrated that eating cruciferous vegetables such as broccoli and cauliflower is associated with lowered risk for lung, colorectal, stomach, breast, and prostate cancers, but studies have been inconsistent. This inconsistency could be due to nutrigenomic variations in the studies’ participants. Overexpression of some proteins involved in phase II metabolism protects cells from DNA damage caused by carcinogens, so people with these “helpful” gene variants could be protected against cancer risk without eating cruciferous vegetables; however, the biochemistry involved in associating cancer and dietary intake is complex. Despite the marketing of genetic testing for cancer risk, knowledge about genotype variations in how diet and cancer are related is quite limited (Ambrosone & Tang, 2009).

Nutritional genomics offers a promising future including the development of tailored dietary interventions to prevent and treat chronic diseases. Evidence is mounting to support increased use of genetic testing to develop individualized nutritional, pharmacological, and general medical advice to reduce risk for chronic diseases (Ordovas, 2006). The following discussion focuses on the types and purpose of genetic testing commonly ordered by medical professionals and direct-to-consumer (DTC) genetic tests.

Genetic Testing

Clinical genetic tests are available for more than 1,500 diseases; an additional 277 tests are available for participants in research studies (GeneTests, 2009). Genetic tests are used for various purposes. Diagnostic testing is used to confirm or rule out a known or suspected genetic disorder in a symptomatic patient. This type of test may alter medical management of a patient and may have reproductive or psychosocial implications for other family members (GeneTests).

Predictive genetic tests are given to asymptomatic patients with family histories of a genetic disorder. Predictive tests can be presymptomatic or predispositional. Predispositional predictive tests are positive if the person has an increased risk for developing the genetic disease. Testing for risk of breast and ovarian cancer is an example of predispositonal testing. If a person tests positive for disease-associated mutations in BRCA1 or BRCA2, for example, that person has an 85% cumulative risk for a breast cancer diagnosis by age 70. Sometimes genetic test results are reported as “inconclusive.” This means that a gene variant (mutation) was found, but it is not known if it has any clinical significance. This type of test result can be confusing and distressing to the person being tested (Petrucelli, Daly, Culver, & Feldman, 2007).

A positive presymptomatic predictive test indicates that the person being tested eventually will develop the genetic disease (GeneTests, 2009). Testing for the genetic mutations that cause Huntington disease (HD) is an example of presymptomatic testing. A person who tests positive for mutations in the HD gene will get HD (usually between the ages of 35–55) unless they die of something else first (Warby, Graham, & Hayden, 2007).

A third type of genetic testing is carrier testing. Carrier testing is done for people (usually asymptomatic) who are at risk of passing on an autosomal recessive or X-linked recessive disease to their children. This type of testing allows identified carriers of genetic diseases to make appropriate reproductive decisions (GeneTests, 2009). A man and woman who have relatives with sickle-cell disease, a trait that typically is transmitted in an autosomal recessive fashion, may choose to have carrier testing before they conceive children. If they learn they both are carriers, they can make an informed choice. They can use assisted reproductive technologies such as preimplantation genetic diagnosis and in vitro fertilization to select and implant an embryo without the disease-causing mutations (Bender & Hobbs, 2009).

Other common genetic tests include prenatal testing, newborn screenings, and preimplantation testing. Prenatal testing is provided during pregnancy to assess the genetic risk of the fetus. Newborn screening tests routinely are performed at birth to screen for a variety of treatable diseases including metabolic disorders. Positive results on a newborn screening would indicate the need for further diagnostic testing of the baby (GeneTests, 2009).

DTC genetic testing is relatively new and quite concerning. These tests are available for purchase by consumers, with results reported directly to the buyer with no independent healthcare provider serving as an intermediary (American Society of Human Genetics [ASHG], 2007). Widely advertised via print media, television, and the Internet, DTC tests are marketed to consumers who want to know information ranging from their ancestry to their risk for a certain disease. Companies such as 23andMe, Navigenics, DNA Direct, and Knome provide a wide variety of DTC tests ranging in cost from hundreds to hundreds of thousands of dollars (Federal Trade Commission [FTC], 2006; Tarini, Singer, Clark, & Davis, 2008).

Beery Figure 1Although the FDA oversees direct marketing of pharmaceuticals, it does not oversee DTC genetic testing. Twenty-six states allow unrestricted access to DTC testing; however, access to DTC tests is limited in 10 states and prohibited in 14 states (Figure 1). In addition to the inconsistent state regulation of DTC tests, many professional groups including the American College of Medical Genetics (ACMG) and the ASHG have published position statements raising concerns about the quality and validity of DTC tests, the potential for misinterpretation of results, and the privacy/security of results (ACMG, 2008; ASHG, 2007).

Due to the lack of federal regulation, companies offering DTC genetic tests do not need to provide scientific evidence of the quality or validity of DTC test results. Similarly, no federal regulations exist to monitor the quality and performance of clinical labs performing DTC tests (ACMG, 2008). The lack of oversight and regulation raises the questions: Are the consumers of DTC tests receiving valid and meaningful genetic ¨results for their money? If so, do consumers know what their results truly mean?

Another major concern about DTC tests is the lack of any requirements for consumers before or after receiving DTC genetic test results. Depending on the state, consumers may not be required to speak with a genetic counselor or healthcare professional before purchasing a DTC genetic test (ACMG, 2008; ASHG, 2007). Similarly, results of many DTC tests are delivered directly to the consumer, with no intermediary healthcare provider or genetic counselor assistance in reviewing and interpreting results (ASHG; FTC, 2006).

This lack of professional involvement may increase the potential for consumers to misinterpret test results and make unwarranted medical decisions (e.g., termination of pregnancy, forgoing needed treatment). Similarly, medical professionals express concern that some consumers may pursue DTC testing instead of seeking assistance from a healthcare provider (FTC, 2006). The belief is that substituting a DTC genetic test for a complete medical examination rather than supplementing it may leave consumers with an incomplete or inaccurate picture of their current health status.

Last, medical professionals express concerns about the privacy and security of DNA samples and DTC test results (ACMG, 2008; FTC, 2006). Companies that offer DTC tests may not be subject to the Health Information Portability and Accountability Act (commonly known as HIPPA) and may not be required to protect a consumer’s health information (ASHG, 2007). Consumers of DTC tests lack assurance that their test results will remain private and their DNA sample will not be used for other purposes.

Beery Table 1Conclusion

Rapid changes in genetics/genomics are affecting the way we care for patients. Years ago, genetics focused on the actions of single-gene variants and was seen as the purview of those caring for infants and young children. Today those who care for patients at all stages of life must be attentive to genetic issues. Now we have expanded the discussion to include complex chronic diseases that are a result of the actions of many genes working together combined with environmental factors. The PG movement into the clinical arena and the increasing prevalence of DTC genetic testing make it essential that rehabilitation nurses caring for those with chronic diseases be well-versed in genetics/genomics concepts and terminology. More patients will have genetic testing and bring genetic test results with them to clinic appointments and hospitals. As PG advances, more drugs will be selected using genetic information. We will need to explain to our patients the reasons why they and their spouses may be taking different drugs for the same condition.

We suggest that all nurses stay current on these and myriad other genetics/genomics issues. A list of websites that nurses can use to update their genetics/genomics knowledge can be found in Table 1.

About the Authors

Theresa Alice Beery, PhD, is professor and director of the Center for Education Research, Scholarship, and Innovation, Institute of Nursing Research and Scholarship at the University of Cincinnati College of Nursing in Cincinnati, OH. Address correspondence to her at Theresa.beery@uc.edu.

Carolyn R. Smith, RN, is a doctoral candidate at the University of Cincinnati College of Nursing in Cincinnati, OH.


Ambrosone, C. B., & Tang, L. (2009). Cruciferous vegetable intake and cancer prevention: Role of nutrigenetics. Cancer Prevention Research (Philadelphia, Pa), 2(4), 298–300.

American College of Medical Genetics. (2008). ACMG statement on direct-to-consumer genetic testing. Bethesda, MD: Author.

American Society of Human Genetics. (2007). ASHG statement on direct-to-consumer genetic testing in the United States. The American Journal of Human Genetics, 81, 635–637.

Arkadianos, I., Valdes, A. M., Marinos, E., Florou, A., Gill, R. D., & Grimaldi, K. A. (2007). Improved weight management using genetic information to personalize a calorie controlled diet. Nutrition Journal, 6, 29.

Bender, M. A., & Hobbs, W. (2009). Sickle cell disease. Retrieved December 7, 2010, from www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book=gene∂=sickle.

Eckman, M. H., Rosand, J., Greenberg, S. M., & Gage, B. F. (2009). Cost-effectiveness of using pharmacogenetic information in warfarin dosing for patients with nonvalvular atrial fibrillation. Annals of Internal Medicine, 150(2), 73–83.

Elledge, R. M., Green, S., Ciocca, D., Pugh, R., Allred, D. C., Clark, G. M., et al. (1998). HER-2 expression and response to tamoxifen in estrogen receptor-positive breast cancer: A Southwest Oncology Group study. Clinical Cancer Research, 4(1), 7–12.

Evaluation of Genomic Applications in Practice and Prevention. (2007). Recommendations from the EGAPP working group: Testing for cytochrome P450 polymorphisms in adults with nonpsychotic depression treated with selective serotonin reuptake inhibitors. Genetics in Medicine, 9(12), 819–825.

Federal Trade Commission. (2006). At-home genetics tests: A healthy dose of skepticism may be the best prescription. Retrieved December 7, 2010, from www.ftc.gov/bcp/edu/pubs/consumer/health/hea02.pdf.

Fischer, M., Broeckel, U., Holmer, S., Baessler, A., Hengstenberg, C., Mayer, B., et al. (2005). Distinct heritable patterns of angiographic coronary artery disease in families with myocardial infarction. Circulation, 111(7), 855–862.

Gage, B. (2008). Warfarin dosing. Retrieved December 7, 2010, from www.warfarindosing.org/Source/Home.aspx.

Geisen, C., Watzka, M., Sittinger, K., Steffens, M., Daugela, L., Seifried, E., et al. (2005). VKORC1 haplotypes and their impact on the inter-individual and inter-ethnical variability of oral anticoagulation. Thrombosis Haemostasis, 94(4), 773–779.

GeneTests. (2009). GeneTests. Retrieved December 7, 2010, from www.genetests.org.

Goyenechea, E., Crujeiras, A. B., Abete, I., & Martinez, J. A. (2009). Expression of two inflammation-related genes (RIPK3 and RNF216) in mononuclear cells is associated with weight-loss regain in obese subjects. Journal of Nutrigenetics and Nutrigenomics, 2(2), 78–84.

Harvard. (2007). Meat in the hot seat. Cooking meat at high temperatures produces cancer-causing chemicals, but grilling can be made safer. Harvard Health Letter, 32(8), 1–2.

Higashi, M. K., Veenstra, D. L., Kondo, L. M., Wittkowsky, A. K., Srinouanprachanh, S. L., Farin, F. M., et al. (2002). Association between CYP2C9 genetic variants and anticoagulation-related outcomes during warfarin therapy. Journal of the American Medical Association, 287(13), 1690–1698.

Kaput, J., & Rodriquez, R. L. (Eds.). (2006). Nutritional genomics. Hoboken, NJ: John Wiley & Sons.

Lango, H., & Weedon, M. N. (2008). What will whole genome searches for susceptibility genes for common complex disease offer to clinical practice? Journal of Internal Medicine, 263(1), 16–27.

Lovegrove, J. A., & Gitau, R. (2008). Personalized nutrition for the prevention of cardiovascular disease: A future perspective. Journal of Human Nutrition and Dietetics, 21, 306–316.

Lower, E. E., Glass, E., Blau, R., & Harman, S. (2008). HER-2/neu expression in primary and metastatic breast cancer. Breast Cancer Research and Treatment, 113(2), 301–306.

McClain, M. R., Palomaki, G. E., Piper, M., & Haddow, J. E. (2008). A rapid-ACCE review of CYP2C9 and VKORC1 alleles testing to inform warfarin dosing in adults at elevated risk for thrombotic events to avoid serious bleeding. Genetics in Medicine, 10(2), 89–98.

National Center on Minority Health and Health Disparities. (2009). Nutrional genomics. Retrieved December 7, 2010, from http://nutrigenomics.ucdavis.edu/.

O’Donovan, M. C., Williams, N. M., & Owen, M. J. (2003). Recent advances in the genetics of schizophrenia. Human Molecular Genetics, 12 Spec No 2, R125–R133.

Ordovas, J. M. (2006). Genetic interactions with diet influence the risk of cardiovascular disease. American Journal of Clinical Nutrition, 83(2), 443s–446s.

Petrucelli, N., Daly, M. B., Culver, J. O., & Feldman, G. L. (2007). BRCA1 and BRCA2 hereditary breast/ovarian cancer. Retrieved December 7, 2010, from www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book=gene∂=brca1.

Phillips, C. M., Goumidi, L., Bertrais, S., Field, M. R., Peloso, G. M., Shen, J., et al. (2009). Dietary saturated fat modulates the association between STAT3 polymorphisms and abdominal obesity in adults. Journal of Nutrition, 139(11), 2011–2017.

Prows, C. A., & Prows, D. R. (2004). Medication selection by genotype: How genetics is changing drug prescribing and efficacy. American Journal of Nursing, 104(5), 60–70; quiz 71.

Reich, D. E., & Lander, E. S. (2001). On the allelic spectrum of human disease. Trends in Genetics, 17(9), 502–510.

Roche, H. M. (2006). Nutrigenomics—New approaches for human nutrition research. Journal of the Science of Food and Agriculture, 86, 1156–1163.

Ronteltap, A., van Trijp, J. C., & Renes, R. J. (2009). Consumer acceptance of nutrigenomics-based personalised nutrition. British Journal of Nutrition, 101(1), 132–144.

Schaefer, E. J., Lamon-Fava, S., Ausman, L. M., Ordovas, J. M., Cevidence, B. A., Judd, J. J., et al. (1997). Individual variability in liprprotein cholesterol response to National Cholesterol Education Program Step 2 diets. American Journal of Clinical Nutrition, 65, 823–830.

Schwarz, U. I., Ritchie, M. D., Bradford, Y., Li, C., Dudek, S. M., Frye-Anderson, A., et al. (2008). Genetic determinants of response to warfarin during initial anticoagulation. New England Journal of Medicine, 358(10), 999–1008.

Tarini, B. A., Singer, D., Clark, S. J., & Davis, M. M. (2008). Parents’ concern about their own and their children’s genetic disease risk: Potential effects of family history vs genetic test results. Archives of Pediatrics & Adolescent Medicine, 162(11), 1079–1083.

U.S. Food and Drug Administration. (2007). FDA approves updated warfarin (Coumadin) prescribing information. Retrieved December 7, 2010, from www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2007/ucm108967.htm.

U.S. Food and Drug Administration. (2008). Pharmacogeneomics and its role in drug safety. Retrieved December 7, 2010, from www.fda.gov/Drugs/DrugSafety/DrugSafetyNewsletter/ucm119991.htm.

Warby, S. C., Graham, R. K., & Hayden, M. R. (2007). Huntington disease. Retrieved December 7, 2010, from www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book=gene∂=Huntington.

Weinshilboum, R., & Wang, L. (2004). Pharmacogenomics: Bench to bedside. Nature Reviews Drug Discovery, 3(9), 739–748.

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