Thompson & Thompson Genetics in Medicine, 8th Edition

CHAPTER 18. Application of Genomics to Medicine and Personalized Health Care

The last several chapters have been dedicated to introducing various aspects of the applications of modern genomics to the practice of medicine. In Chapter 15, we described powerful new genomic technologies, such as identifying the mutations present in a tumor and profiling its pattern of RNA expression, that are currently being used for determining prognosis and choosing appropriate targeted therapies for individual cancer patients. In Chapter 16, we discussed how modern genomic approaches are expanding our capabilities in risk assessment and genetic counseling for patients and families dealing with heritable disease. Chapter 17 focused on prenatal genetics and the advances in prenatal diagnosis made possible by genomics.

Finally, in this chapter, we explore other applications of genomics to individualized health care: screening asymptomatic individuals for risk or susceptibility to disease in them or their family members and applying that knowledge to improve health care. We will first describe population screening and present one of the best-established and highly successful forms of genetic screening, the detection of abnormalities in newborns at high risk for preventable illness. We then present some of the basic concepts and applications of pharmacogenomics and how knowledge of individual variation affecting drug therapy can be used to improve therapeutic efficacy and reduce adverse events. Finally, we discuss screening of patients for genetic susceptibility based on their genome sequence and review some of the concepts and methods of genetic epidemiology commonly used to evaluate screening for susceptibility genotypes.

Genetic Screening in Populations

Genetic screening is a population-based method for identifying persons with increased susceptibility for a genetic disease. Screening at the population level is not to be confused with testing for affected persons or carriers within families already identified because of family history, as we explored in Chapter 16. Although family history is a very useful tool (Fig. 18-1), no one except an identical twin has all of the same gene variants that another family member has. Family history is therefore only an indirect means of assessing the contribution that an individual's own combination of genetic variants might make to disease. Family history is also an insensitive indicator of susceptibility because it depends on overt disease actually occurring in the relatives of the individual patient.


FIGURE 18-1 Cumulative incidence (per 10,000) of colon cancer versus age in individuals with and without a family history of the disease.

The challenge going forward is to screen populations, independent of family history and independent of clinical status, for variants relevant to health and disease and to apply this information to make risk assessments that can be used to improve the health care of an individual patient and his or her family.

The objective of population screening is to examine all members of a designated population, regardless of family history. Applying this information requires that we demonstrate that genetic risk factors are valid indicators of actual risk in an individual patient and, if they are valid, how useful such information is in guiding health care. Genetic screening is an important public health activity that will become more significant as more and better screening tests become available for determining genetic susceptibilities for disease.

Newborn Screening

The best-known population screening efforts in genetics are the government-supported or government-mandated programs that identify presymptomatic infants with diseases for which early treatment can prevent or at least ameliorate the consequences (Table 18-1). For newborn screening, the presence of disease is generally not assessed by determining the genotype directly. Instead, in most instances, asymptomatic newborns are screened for abnormalities in the level of various substances in the blood. Abnormalities in these metabolites trigger further evaluation to either confirm or rule out the presence of a disorder. Exceptions to this paradigm of using a biochemical measurement to detect a disease-causing genotype are screening programs for abnormalities in hearing, in which the phenotype itself is the target of screening and intervention (see later).

TABLE 18-1

Some Conditions for Which Newborn Screening Has Been Implemented



(per 100,000 newborns)*

Congenital hearing loss


Sickle cell disease






Congenital adrenal hyperplasia


Severe combined immunodeficiency




Maple syrup urine disease




Biotinidase deficiency


*Approximate values in the United States.

Many of the general issues concerning genetic screening are highlighted by newborn screening programs. A determination of the appropriateness of newborn screening for any particular condition is based on a standard set of criteria involving clinical validity and clinical utility (see Box). The design of newborn screening tests includes keeping false-negative rates low so that true-positive cases are not missed, without making the test so nonspecific as to drive the false-positive rate unacceptably high. False-positive results cause unnecessary anxiety to the parents and also increase the costs, because more unaffected infants have to be recalled for retesting; at the other extreme, false-negative results vitiate the purpose of having a screening program. The criterion that the public health system infrastructure be capable of handling the care of affected newborns identified by screening is often underemphasized in discussions of the clinical utility of screening, but must also be considered in deciding whether to institute screening for any given condition.

The prototype condition that satisfies all of these criteria is phenylketonuria (see Chapter 12). For decades, finding elevated levels of phenylalanine in a spot of blood on filter paper obtained soon after birth has been the mainstay of neonatal screening for phenylketonuria and other forms of hyperphenylalaninemia in the United States, all the provinces of Canada, and nearly all developed countries. A positive screen result, followed by definitive confirmation of the diagnosis, led to the institution of dietary phenylalanine restriction early in infancy, thereby preventing irreversible intellectual disability.

General Criteria for an Effective Newborn Screening Program

Analytic Validity

• A rapid and economic laboratory test is available that detects the appropriate metabolite.

Clinical Validity

• The laboratory test is highly sensitive (no false-negatives) and reasonably specific (few false-positives). Positive predictive value is high.

Clinical Utility

• Treatment is available.

• Early institution of treatment, before symptoms become manifest, reduces or prevents severe illness.

• Routine observation and physical examination will not reveal the disorder in the newborn—a test is required.

• The condition is frequent and serious enough to justify the expense of screening; that is, screening is cost-effective.

• The public health system infrastructure is in place to inform the newborn's parents and physicians of the results of the screening test, to confirm the test results, and to institute effective treatment and counseling.

Two other conditions that are widely targeted for newborn screening are congenital deafness and congenital hypothyroidism. Newborn screening for hearing loss is mandated in 37 states in the United States and three provinces in Canada. Approximately half of all congenital deafness is due to single-gene defects (Case 13). Infants found to have hearing impairments by newborn screening receive intervention with sign language, cochlear implants, and other communication aids early in life, thereby improving their long-term language skills and intellectual abilities beyond those seen if the impairment is discovered later in childhood. Screening for congenital hypothyroidism, a disorder whose genetic basis is known in only 10% to 15% of cases but is easily treatable, is universal in the United States and Canada and is also routine in many other countries. Thyroid hormone replacement therapy started early in infancy completely prevents the severe and irreversible intellectual disability caused by congenital hypothyroidism. Thus both hypothyroidism and deafness easily fulfill the criteria for newborn screening.

A number of other disorders, such as galactosemia, sickle cell disease (Case 42), biotinidase deficiency (see Chapter 12), severe combined immunodeficiency, and congenital adrenal hyperplasia (see Chapter 6), are part of neonatal screening programs in many or most states, but not all. Which disorders should be the target of newborn screening varies from state to state in the United States. However, many states have instituted screening for a group of 32 conditions, following the recommendations of a panel convened by the Secretary of the Department of Health and Human Services.

Standards for newborn screening differ widely across the globe. Which disorders should be the target of newborn screening varies from province to province in Canada without a national consensus. As of 2014, the United Kingdom's national program to screen newborns across all jurisdictions included just five disorders, with the exception of Northern Ireland, which already tests for seven disorders; the United Kingdom is considering adding three additional disorders.

Tandem Mass Spectroscopy

For many years, most newborn screening was performed by a test specific for each individual condition. For example, phenylketonuria screening was based on a microbial or a chemical assay that tested for elevated phenylalanine level (see previous section). This situation has changed dramatically with the application of the technology of tandem mass spectrometry (TMS). Not only can a neonatal blood spot be examined accurately and rapidly for an elevation of phenylalanine, with fewer false positives than with the older testing methods, but TMS analysis can simultaneously detect a few dozen other biochemical disorders as well. Some of these, such as homocystinuria (see Chapter 12) or maple syrup urine disease, were already being screened for by individual tests (Table 18-2). TMS, however, does not replace the disease-specific testing methods for other disorders currently included in newborn screening, such as galactosemia, biotinidase deficiency, congenital adrenal hyperplasia, and sickle cell disease.

TABLE 18-2

Disorders Detectable by Tandem Mass Spectrometry


Cbl, cobalamin; MTHFR, methylene tetrahydrofolate reductase; MTR, 5-methyltetrahydrofolate-homocysteine methyltransferase; MTRR, methionine synthase reductase.

Modified from California Newborn Screening Program, January 2012,

TMS also provides a reliable method for newborn screening for some disorders that fit the criteria for screening but had no reliable newborn screening program in place. For example, medium-chain acyl-CoA dehydrogenase (MCAD) deficiency is a disorder of fatty acid oxidation that is usually asymptomatic but manifests clinically when the patient becomes catabolic. Detection of MCAD deficiency at birth can be lifesaving because affected infants and children are at very high risk for life-threatening hypoglycemia in early childhood during the catabolic stress caused by an intercurrent illness, such as a viral infection, and nearly 25% of children with undiagnosed MCAD deficiency will die with their first episode of hypoglycemia. The metabolic derangement can be successfully managed if it is treated promptly. In MCAD deficiency, alerting parents and physicians to the risk for metabolic decompensation is the primary goal of screening, because the children are healthy between attacks and do not require daily management other than avoidance of prolonged fasting.

In addition to providing a rapid test for many disorders for which newborn screening either is already being done or can easily be justified, TMS also identifies infants with inborn errors, such as methylmalonic acidemia, that have not generally been the targets of newborn screening because of their rarity and difficulty of providing definitive therapy that will prevent the progressive neurological impairment. TMS can also identify abnormal metabolites whose significance for health are uncertain. For example, short-chain acyl-CoA dehydrogenase (SCAD) deficiency, another disorder of fatty acid oxidation, is most often asymptomatic, although a few patients may have difficulties with episodic hypoglycemia. Thus a positive TMS screen result is not particularly predictive of developing symptomatic SCAD later in life. Although TMS can identify many metabolic disorders, does the benefit of detecting disorders such as SCAD deficiency outweigh the negative impact of raising parental concern unnecessarily for most newborns whose test result is positive but who will never be symptomatic? Thus not every disorder detected by TMS fits the criteria for newborn screening. Some public health experts argue, therefore that only those metabolites of proven clinical utility should be reported to parents and physicians.


One area of medicine that is receiving a lot of attention for potential application of genomics to personalized medical care is pharmacogenomics, the study of the many differences between individuals in how they respond to drugs because of allelic variation in genes affecting drug metabolism, efficacy, and toxicity. Drug treatment failures and adverse drug reactions occur in more than 2 million patients each year in the United States alone, resulting in ongoing morbidity and an estimated 100,000 excess deaths. The development of a genetic profile that predicts efficacy, toxicity, or an adverse drug reaction is likely to have immediate benefit in allowing physicians to choose a drug for which the patient will benefit without risk for an adverse event, or to decide on a dosage that ensures adequate therapy and minimizes complications.

The U.S. Food and Drug Administration has recognized the importance of pharmacogenetic variation in individual response to drug treatment by including pharmacogenetic information on the labels that come with a broad range of pharmaceuticals (Table 18-3). As with all other aspects of personalized medicine, however, the cost-effectiveness of such testing must be proved if it is to become part of accepted medical care.

TABLE 18-3

Gene-Drug Combinations for Which There Is Pharmacogenetic Information in Their U.S. Food and Drug Administration Package Inserts*




Clopidogrel, voriconazole, omeprazole, pantoprazole, esomeprazole, diazepam, nelfinavir, rabeprazole


Celecoxib, warfarin


Atomoxetine, venlafaxine, risperidone, tiotropium bromide inhalation, tamoxifen, timolol maleate, fluoxetine, cevimeline, tolterodine, terbinafine, tramadol and acetaminophen, clozapine, aripiprazole, metoprolol, propranolol, carvedilol, propafenone, thioridazine, protriptyline, tetrabenazine, codeine


Capecitabine, fluorouracil


Rasburicase, dapsone, primaquine, chloroquine




Abacavir (Case 1)


Rifampin, isoniazid, and pyrazinamide; isosorbide dinitrate and hydralazine hydrochloride


Azathioprine, thioguanine, mercaptopurine


Irinotecan, nilotinib



*Constitutional variants only; chemotherapy whose usage is affected by somatic mutations are not included.

There are two ways that genetic variation affects drug therapy. The first is the effect of variation on pharmacokinetics, that is, the rate at which the body absorbs, transports, metabolizes, or excretes drugs or their metabolites. The second is the variation affecting pharmacodynamics, that is, differences in the way the body responds to a drug. Thus, most broadly, pharmacogenetics encompasses any genetically determined variation in “what the body does to the drug” and in “what the drug does to the body,” whereas pharmacogenomics refers to the sum total of all relevant genetic variation affecting drug therapy.

Variation in Pharmacokinetic Response

Variation in Drug Metabolism: Cytochrome P-450

The human cytochrome P-450 proteins are a large family of 56 different functional enzymes, each encoded by a different CYP gene. The cytochromes P-450 are grouped into 20 families according to amino acid sequence homology. Three of these families—CYP1, CYP2, and CYP3—contain enzymes that are promiscuous in the substrates they will act on and that participate in metabolizing a wide array of substances from outside the body (xenobiotics), including drugs. Six cytochrome P-450 genes (CYP1A1, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4) are especially important because the enzymes they encode are responsible for the metabolism of more than 90% of all commonly used drugs (Fig. 18-2).


FIGURE 18-2 Contribution of individual cytochrome P-450 enzymes to drug metabolism.

For many drugs, the action of a cytochrome P-450 is to begin the process of detoxification through a series of reactions that render the drug less active and easier to excrete. Some drugs, however, are themselves inactive prodrugs whose conversion into an active metabolite by a cytochrome P-450 is required for the drug to have any therapeutic effect.

Many of the CYP genes important for drug metabolism (including CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4) are highly polymorphic, with alleles that result in absent, decreased, or increased enzyme activity, thereby affecting the rate at which many drugs are metabolized, with real functional consequences for how individuals respond to drug therapy (see Table 18-3). As one example, CYP2D6, the primary cytochrome in the metabolism of more than 70 different drugs, has dozens of reduced, absent, or increased activity alleles, leading to normal, poor, or ultrafast metabolism (see Table on metabolizer phenotypes later). Missense mutations decrease the activity of this cytochrome; alleles with no activity are caused by splicing or frameshift mutations. In contrast, the CYP2D6*1XN allele is actually a series of copy number variation alleles in which the CYP2D gene is present in three, four, or more copies on one chromosome. Predictably, these copy number polymorphisms produce high levels of the enzyme. There are dozens more alleles that do not affect the function of the protein and are therefore considered to be wild-type. Various combinations of these four classes of alleles produce quantitative differences in metabolizing activity, resulting in three main phenotypes: normal (also called “extensive”) metabolizers, poor metabolizers, and ultrafast metabolizers (Fig. 18-3).


FIGURE 18-3 Serum drug levels after repeated doses of a drug (arrows) in three individuals with different phenotypic profiles for drug metabolism. A, Poor metabolizer accumulates drug to toxic levels. B, Normal (extensive) metabolizer reaches steady-state levels within the therapeutic range. C,Ultrafast metabolizer fails to maintain serum levels within the therapeutic range.

Depending on whether a drug is itself an active compound or is a prodrug that requires activation by a cytochrome P-450 enzyme to have its pharmacological effect, poor metabolizers may either accumulate toxic levels of the drug or fail to have therapeutic efficacy because of poor activation of a prodrug. In contrast, ultrafast metabolizers are at risk for being undertreated by a drug with doses inadequate to maintain blood levels in the therapeutic range, or they may suffer overdose due to too rapid conversion of a prodrug to its active metabolite. For example, codeine is a weak narcotic drug that exerts most of its analgesic effect on conversion to morphine, a bioactive metabolite with a 10-fold higher potency. This conversion is carried out by the CYP2D6 enzyme. Poor metabolizers, quite common in some populations, carrying loss-of-function alleles at the CYP2D6 locus fail to convert codeine to morphine and thereby receive little therapeutic benefit; in contrast, ultrafast metabolizers can become rapidly intoxicated with low doses of codeine. A number of children have died from codeine overdoses due to having an ultrafast metabolizer phenotype.

Metabolizer Phenotypes Arising from Various Combinations of CYP2D6 Alleles


As with many forms of genetic variation (see Chapter 9), the frequency of many of the alleles in the cytochromes P-450 differs among different populations (Table 18-4). For example, a slow metabolizing phenotype for CYP2D6 that is present in 1 in 14 whites is rare in Asia and nearly absent in Native Americans and Pacific Islanders. Similarly, slow metabolizing alleles at CYP2C19 show striking ethnic variability, with 1 in 33 whites but nearly 1 in 6 Asians having slow metabolism. These ethnic differences in the frequency of poor and ultrafast metabolizers are important for the delivery of personalized genetic medicine to ethnically heterogeneous populations.

TABLE 18-4

Frequency of Poor CYP2D6 and CYP2C19 Metabolizers in Various Population Groups


Population Frequency of Poor Metabolizers (%)

Ethnic Origin of Population



Sub-Saharan Africa



Native American









Middle Eastern/North Africa



Pacific Islander



Data from Burroughs VJ, Maxey RW, Levy RA: Racial and ethnic differences in response to medicines: towards individualized pharmaceutical treatment, J Natl Med Assoc 94(Suppl):1-26, 2002.

Variation in Pharmacodynamic Response

Malignant Hyperthermia

Malignant hyperthermia is a rare autosomal dominant condition in which there may be a dramatic adverse response to the administration of many commonly used inhalational anesthetics (e.g., halothane) and depolarizing muscle relaxants (e.g., succinylcholine). Soon after induction of anesthesia, the patients develop life-threatening fever, sustained muscle contraction, and attendant hypercatabolism. The fundamental physiological abnormality in the disease is an elevation of the level of ionized calcium in the sarcoplasm of muscle. This increase leads to muscle rigidity, elevation of body temperature, rapid breakdown of muscle (rhabdomyolysis), and other abnormalities. The condition is an important if not a common cause of death during anesthesia. The incidence is 1 in 50,000 adults undergoing anesthesia but for unknown reasons is 10-fold higher in children.

Malignant hyperthermia is most frequently associated with mutations in a gene called RYR1, encoding an intracellular calcium ion channel. However, mutations in RYR1 account for only approximately half of cases of malignant hyperthermia. At least five other loci have now been identified, one of which is the CACNL1A3 gene, which encodes the α1 subunit of a dihydropyridine-sensitive calcium channel. Precisely why the abnormalities in calcium handling in muscle found with RYR1 or CACNL1A3 mutations make the muscle sensitive to inhalation anesthetics and muscle relaxants and precipitate malignant hyperthermia is unknown.

The need for special precautions when at-risk persons require anesthesia is obvious. Cooling blankets, muscle relaxants, and cardiac antiarrhythmics may all be used to prevent or reduce the severity of the response if an unsuspected attack occurs, and alternative anesthetics can be given to patients at risk.

Adverse Drug Reactions

The majority (75% to 80%) of adverse drug events result from predictable, nonimmunological drug toxicities such as overdoses caused by medication errors, renal or hepatic disease, or drug-drug interactions. The remaining adverse drug events are mostly unpredictable reactions to the drugs; of these, approximately 25% to 50% are true IgE-mediated drug hypersensitivity reactions, including life-threatening anaphylaxis characterized by sudden onset of laryngeal edema, leading to occlusion of the airway, marked hypotension, and cardiac arrhythmias.

The remaining 50 to 75% of adverse drug reactions are genetically determined nonallergic immune reactions, manifesting as widespread damage to skin and mucous membranes, referred to as Stevens-Johnson syndrome (SJS) and, in its more serious extreme form, toxic epidermal necrolysis (TEN). Although rare, TEN is a very serious adverse drug reaction that causes denuding of large areas of skin and carries a mortality rate of 30% to 40%. There is a strong correlation between particular drugs and certain human leukocyte antigen (HLA) alleles in the major histocompatibility complex (see Chapters 4 and 8) that results in SJS and TEN. For example, individuals who take the retroviral drug abacavir (Case 1) and carry the HLA-B*5701 allele have a 50% risk for SJS or TEN, whereas those without it never develop this skin reaction in response to the drug. Because approximately 5% of Europeans carry the HLA-B*5701 allele, the risk for a severe drug reaction in abacavir-treated patients from this ethnic background is especially significant. The allele is less frequent in Asian populations (≈1%) and even less frequent in Africans (<1%). HLA typing is therefore standard of care for any patient for whom one is contemplating beginning abacavir. A similar situation exists with the use of the antiseizure medication carbamazepine and HLA-B*1502, which is present in 10% to 20% of certain Chinese populations (see Table 18-3).

Pharmacogenomics as a Complex Trait

The examples of pharmacogenomics provided in this chapter primarily involve variation at single genes and its effect on drug treatment. In truth, most drug response is a complex trait. A drug may have its effect directly or through more active metabolites, each of which may then be metabolized by different pathways and exert its effects on various targets. Thus variants at more than one locus may interact, synergistically or antagonistically, either to potentiate or to reduce the effectiveness of a drug or to increase its toxic side effects. A comprehensive pharmacogenomic profile that takes into account multiple genetic variants as well as environmental effects, including other drugs, for their aggregate impact on the outcome of drug therapy is necessary before we can have highly precise and predictive information to guide drug therapy. If a pharmacogenomic profile is sufficiently predictive of drug response, it could be used to predict the probable efficacy or side effects of the medication in an individual before the drug is administered, and to identify those patients who should be treated more aggressively and monitored to be sure that the drug achieves therapeutic levels. The ultimate goal is that patients receive the best drug at the right dose and avoid potentially dangerous side effects. We expect pharmacogenomics to become increasingly more important in the delivery of personalized, precision medicine in the years ahead.

Screening for Genetic Susceptibility to Disease

Genetic Epidemiology

Epidemiological studies of risk factors for disease rely on population studies that measure disease prevalence or incidence and determine whether certain risk factors (e.g., genetic, environmental, social) are present in individuals with versus without disease. Genetic epidemiology is concerned with how genotypes and environmental factors interact to increase or decrease susceptibility to disease. Epidemiological studies generally follow one of three different strategies: case-control, cross-sectional, and cohort design (see Box).

Strategies Used in Genetic Epidemiology

• Case-control: Individuals with and without the disease are selected, and the genotypes and environmental exposures of individuals in the two groups are determined and compared.

• Cross-sectional: A random sample of the population is selected and divided into those with and without the disease, and their genotypes and environmental exposures are determined and compared.

• Cohort: A sample of the population is selected and observed for some time to ascertain who does or does not develop disease, and their genotypes and environmental exposures are determined and compared. The cohort may be selected at random or may be targeted to individuals who share a genotype or an environmental exposure.

Cohort and cross-sectional studies not only capture information on the relative risk conferred by different genotypes but, if they are random population samples, also provide information on the prevalence of the disease and the frequency of the various genotypes under study. A randomly selected cohort study, in particular, is the most accurate and complete approach in that phenotypes that take time to appear have a better chance of being detected and scored; they are, however, more expensive and time-consuming. Cross-sectional studies, on the other hand, suffer from underestimation of the frequency of the disease. First, if the disease is rapidly fatal, many of the patients with disease and carrying a risk factor will be missed. Second, if the disease shows age-dependent penetrance, some patients carrying a risk factor will actually not be scored as having the disease. Case-control studies, on the other hand, allow researchers to efficiently target individuals, particularly with relatively rare phenotypes that would require very large sample sizes in a cross-sectional or cohort study. However, unless a study is based on complete ascertainment of individuals with a disease (e.g., in a population register or surveillance program) or uses a random sampling scheme, a case-control study cannot capture information on the population prevalence of the disease.

Disease Association

A genetic disease association is the relationship in a population between a susceptibility or protective genotype and a disease phenotype (see Chapter 10). The susceptibility or protective genotype can be an allele (in either a heterozygote or a homozygote), a genotype at one locus, a haplotype containing alleles at neighboring loci, or even combinations of genotypes at multiple unlinked loci. Whether a disease association between genotype and phenotype is statistically significant can be determined from standard statistical tests, such as the chi-square test, whereas how strongly associated the genotype and phenotype are is given by the odds ratio or relative risk, as discussed in Chapter 10. The relationship between some of these concepts is best demonstrated by means of a 2 × 2 table.

Determination of the Predictive Value of a Test


*The values of a, b, c, and d are derived from a random sample of the population, divided into those with and without the susceptibility genotype, and then examined for the disease (with or without longitudinal follow-up, depending on whether it is a cross-sectional or cohort study) (see later).

Frequency of the susceptibility genotype = (a + b)/N

Disease prevalence = (a + c)/N (with random sampling or a complete population survey)

Relative Risk Ratio:



Sensitivity: Fraction of individuals with disease who have the susceptibility genotype = a/(a + c)

Specificity: Fraction without disease who do not have the susceptibility genotype = d/(b + d)

Positive predictive value: Proportion of individuals with the susceptibility genotype who have or will develop a particular disease = a/(a + b)

Negative predictive value: Proportion of individuals without the susceptibility genotype who do not have or will not develop a particular disease = d/(c + d)

Clinical Validity and Utility

Finding the genetic contributions to health and disease is of obvious importance for research into underlying disease etiology and pathogenesis, as well as for identifying potential targets for intervention and therapy. In medical practice, however, whether to screen individuals for increased susceptibilities to illness depends on the clinical validity and clinical utility of the test. That is, how predictive of disease is a positive test, and how useful is it to have this information?

Clinical Validity

Clinical validity is the extent to which a test result is predictive for disease. Clinical validity is captured by the two concepts of positive predictive value and negative predictive value. The positive predictive value is the frequency with which a group of individuals who test positive have or will develop the disease. For mendelian disorders, the positive predictive value of a genotype is the penetrance. Conversely, the negative predictive value is the frequency with which a group of individuals who test negative are free of disease and remain so. When faced with an individual patient, the practitioner of personalized genetic medicine needs to know more than just whether there is an association and its magnitude (i.e., relative risk or odds ratio). It is important to know clinical validity (i.e., how well the test predicts the presence or absence of disease).

Susceptibility Testing Based on Genotype

The positive predictive value of a genotype that confers susceptibility to a particular disease depends on the relative risk for disease conferred by one genotype over another and on the prevalence of the disease. Figure 18-4 provides the positive predictive value for genotype frequencies ranging from 0.5% (rare) to 50% (common), which confer a relative risk that varies from low (twofold) to high (100-fold), when the prevalence of the disease ranges from relatively rare (0.1%) to more common (5%). As the figure shows, the value of the test as a predictor of disease increases substantially when one is dealing with a common disorder due to a relatively rare susceptibility genotype that confers a high relative risk, compared with the risk for individuals who do not carry the genotype. The converse is also clear; testing for a common genotype that confers a modest relative risk is of limited value as a predictor of disease.


FIGURE 18-4 Theoretical positive predictive value calculations for a susceptibility genotype for a disease, over a range of genotype frequencies, disease prevalences, and relative risks for disease conferred by the genotype.

We will illustrate the use of the 2 × 2 table in assessing the role of susceptibility alleles in a common disorder, colorectal cancer. Shown in the following Box are data from a population-based study of colorectal cancer risk conferred by a polymorphic variant in the APC gene (see Chapter 15(Case 15) that changes isoleucine to lysine at position 1307 of the protein (Ile1307Lys). This variant has an allele frequency of approximately 3.1% among Ashkenazi Jews, which means that approximately 1 in 17 individuals is a heterozygote (and 1 in 1000 are homozygous) for the allele. The prevalence of colon cancer among Ashkenazi Jews is 1%. The Ile1307Lys variant, common enough to be heterozygous in approximately 1 in 17 Ashkenazi Jews, confers a 2.4-fold increased risk for colon cancer relative to individuals without the allele. However, the small positive predictive value (≈2%) means that an individual who tests positive for this allele has only a 2% chance of developing colorectal cancer. If this had been a cohort study that allowed complete ascertainment of everyone in whom colorectal cancer was going to develop, the penetrance would, in effect, be only 2%.

The Ile1307Lys Allele of the APC Gene and Colon Cancer


• image

• Sensitivity: Fraction of individuals with colon cancer who have the Lys1307 allele = 7/45 = 16%

• Specificity: Fraction without colon cancer who do not have the Lys1307 allele = 4142/4452 = 93%

• Positive predictive value: Fraction of individuals with the Lys1307 allele who develop colon cancer = 7/317 = 2%

• Negative predictive value: Fraction of individuals without the Lys1307 allele who do not develop colon cancer = 99%

Data from Woodage T, King SM, Wacholder S, et al: The APCl1307K allele and cancer risk in a community-based study of Ashkenazi Jews. Nat Genet 20:62-65, 1998.

Clinical Utility

The clinical utility of a test is more difficult to assess than clinical validity, because it has different meanings for different people. In its narrowest sense, the clinical utility of a test is that the result is medically actionable, that is, the result will change what medical care an individual receives and, as a consequence, will improve the outcome of care, both medically and economically. At the other end of the spectrum is clinical utility broadly defined as any piece of information an individual patient might be interested in having, for any reason, including simply for the sake of knowing.

In a patient who tests positive for the APC Ile1307Lys allele, how does a positive predictive value of 2% translate into clinical utility for medical practice? One critical factor is a public health economic one: can the screening be shown to be cost-effective? Is the expense of the testing outweighed by improving health outcomes while reducing health care costs, disability, and loss of earning power? In the example of screening for the APC Ile1307Lys allele in Ashkenazi Jews, more frequent screening or the use of different approaches to screening for colon cancer may be effective. Screening methods (occult stool blood testing versus fecal DNA testing, or sigmoidoscopy versus full colonoscopy) differ in expense, sensitivity, specificity, and potential for hazard, and so deciding which regimen to follow has important implications for the patient's health and health care costs.

Even with demonstrable clinical validity and actionable clinical utility, demonstrating that testing improves health care is not always straightforward. For example, 1 in 200 to 1 in 250 white individuals are homozygous for a Cys282Tyr mutation in the HFE gene associated with hereditary hemochromatosis, a disorder characterized by iron overload that can silently lead to extensive liver damage and cirrhosis (Case 20). A simple intervention—regular phlebotomy to reduce total body iron stores—can prevent hepatic cirrhosis. The susceptibility genotype is common, and 60% to 80% of Cys282Tyr homozygotes show biochemical evidence of increased body iron stores, which suggests that screening would be a reasonable and cost-effective measure to identify asymptomatic individuals who should undergo further testing and, if indicated, the institution of regular phlebotomy. However, most Cys282Tyr homozygotes (>90% to 95%) remain clinically asymptomatic, leading to the argument that the positive predictive value of HFE gene testing for liver disease in hereditary hemochromatosis is too low to justify population screening. Nonetheless, some of these largely asymptomatic patients do have signs of clinically occult fibrosis and cirrhosis on liver biopsy, indicating that the Cys282Tyr homozygote may actually be at a higher risk for liver disease than previously thought. Thus some would argue for population screening to identify individuals in whom regular prophylactic phlebotomy should be instituted. The clinical utility of such population screening remains controversial and will require additional research to determine the natural history of the disease and whether the silent fibrosis and cirrhosis seen on liver biopsy represent the early stages of what will be a progressive illness.

APOE testing in Alzheimer disease (AD) (see Chapter 12(Case 4) is another example of the role of a careful assessment of clinical validity and clinical utility in applying genetic testing to personalized medicine. As discussed in Chapter 8, heterozygotes for the ε4 allele of the APOE gene are at a two- to threefold increased risk for development of AD compared with individuals without an APOE ε4 allele. APOE ε4/ε4 homozygotes are at a eightfold increased risk. An analysis of both the clinical validity and clinical utility of APOE testing, including calculation of the positive predictive value for asymptomatic and symptomatic individuals, is shown later (Table 18-5).

TABLE 18-5

Clinical Validity and Utility of APOE Population Screening and Diagnostic Testing for Alzheimer Disease


Positive predictive value (PPV) calculations are based on a population prevalence of Alzheimer disease (AD) of approximately 3% in individuals aged 65 to 74 years, an allele frequency for the ε4 allele in whites of 10% to 15%, a relative risk of approximately 3 for one ε4 allele, and a relative risk of approximately 20 for two ε4 alleles.

As can be seen from these positive predictive values for asymptomatic people in the age bracket 65 to 74 years, the presence of a single ε4 allele is not a strong predictor of whether AD will develop, despite the threefold increased risk for the disease conferred by the ε4 allele compared with those without an ε4 allele. Thus AD will not develop in the majority of individuals heterozygous for an ε4 allele identified through APOE testing as being at increased risk. Even with two ε4 alleles, which occurs in approximately 1.5% of the population and is associated with a 8-fold increased risk relative to genotypes without ε4 alleles, there is still less than a one in four chance of developing AD. APOE testing for the ε4 allele, is therefore not recommended in asymptomatic individuals but is being used by some practitioners in the evaluation of individuals with symptoms and signs of dementia.

The utility of testing asymptomatic individuals at their APOE locus to assess risk for AD is also controversial. First, knowing that one is at increased risk for AD through APOE testing does not lead to any preventive or therapeutic options. Thus, under a strict definition of clinical utility—that is, the result is actionable and leads to changes in medical management—there would appear to be little value in APOEtesting for AD risk.

There may be, however, positive and negative outcomes of testing that are psychological or economic in nature and more difficult to assess than the purely clinical factors. For example, testing positive for a susceptibility genotype could empower patients with knowledge of their risks as they make important lifestyle decisions. On the other hand, it has been suggested that knowing of an increased risk through APOEtesting might cause significant emotional and psychological distress. However, careful studies of the impact of receiving APOE genotype information have shown little harm in appropriately counseled individuals with a family history of AD who wished to know if they were at increased risk.

Finally, patients who test negative for the ε4 alleles could be falsely reassured that they are at no increased risk for the disorder, despite having a positive family history or other risk factors for dementia. Balancing all of these considerations, APOE testing is still not recommended in asymptomatic individuals even in light of such a strong genotype-disease association, because of the low positive predictive value and lack of clinical utility, rather than because such information is clearly harmful.

As in all of medicine, the benefits and costs for each component of personalized genetic medicine need to be clearly demonstrated but also continually reassessed. The requirement for constant reevaluation is obvious; imagine how the recommendations for APOE testing, despite its low positive predictive value, might change if a low-risk and inexpensive medical intervention were discovered that could prevent or significantly delay the onset of dementia.

Heterozygote Screening

In contrast to screening for genetic disease in newborns or for genetic susceptibility in patients, screening for carriers of mendelian disorders has, as its main purpose, the identification of individuals who are themselves healthy but are at substantial (25% or higher) risk for having children with a severe autosomal recessive or X-linked illness. The principles of heterozygote screening are shown in the accompanying Box.

Criteria for Heterozygote Screening Programs

• High frequency of carriers, at least in a specific population

• Availability of an inexpensive and dependable test with very low false-negative and false-positive rates

• Access to genetic counseling for couples identified as heterozygotes

• Availability of prenatal diagnosis

• Acceptance and voluntary participation by the population targeted for screening

To provide a sufficient yield of carriers, current heterozygote screening programs have typically focused on particular ethnic groups in which the frequency of mutant alleles is high. In contrast to newborn screening, as discussed previously in this chapter, heterozygote screening is voluntary and focuses on individuals who identify themselves as being members of a particular high-risk ethnic group. Heterozygote screening has been used extensively for a battery of disorders for which carrier frequency is relatively high: Tay-Sachs disease (Case 43) (the prototype of carrier screening) (see Chapter 12), Gaucher disease, and Canavan disease in the Ashkenazi Jewish population; sickle cell disease (Case 42) in the African American population of North America; and β-thalassemia (Case 44) in high-incidence areas, especially in Cyprus and Sardinia or in extended consanguineous families from Pakistan (see Chapter 11).

Carrier screening for cystic fibrosis (Case 12) has become standard of care for couples contemplating a pregnancy. As discussed in Chapter 12, more than 1000 different disease-causing mutations have been described in the CFTR gene. Although the vast majority of disease-causing mutations in the CFTR gene can be readily detected with greater than 99% sensitivity when the entire gene is sequenced, sequencing the entire gene in every couple seeking preconception carrier testing is expensive if carried out on a population-wide basis, particularly in individuals with low prior probability of carrying a mutation (Table 18-6). Instead, panels of specific mutations have been designed to detect just the most common mutations in various ethnic groups using a relatively inexpensive platform. The sizes of these panels range from one proposed by the American College of Medical Genetics and Genomics, which contains the most common 23 mutations found in ethnic groups with the highest frequency of the disease, such as non-Hispanic whites, to more extensive panels of more than 60 different mutations that include mutations found more commonly in populations with lower frequencies of disease, such as Africans or Asians (see Table 18-6). Because these allele-specific methods are designed to detect only the most common mutations, their sensitivity is less than 100%, ranging from 88% to 90% in non-Hispanic whites, and from 64% to 72% in African Americans. One may anticipate that as the cost of comprehensive sequencing falls, allele-specific methods with less than 100% sensitivity may be superseded, but, for the near future, the cost-effectiveness of allele-specific methods remains a reasonable argument for their continued use in the appropriate setting.

TABLE 18-6

Cystic Fibrosis Carrier Frequencies by Ethnic Group before and after Negative Carrier Test with Standard and Expanded Allele-Specific Panel


ACMG, American College of Medical Genetics and Genomics.

As the cost of mutation detection using allele-specific detection methods has fallen, it is becoming much less compelling that carrier screening needs to be restricted to a small number of mutant alleles common in certain ethnic groups in genes that are known to be associated with disease. It is possible now to obtain expanded carrier screening beyond disorders common to particular ethnic groups, such as cystic fibrosis, sickle cell trait, or thalassemia, to include carrier status for more than 100 additional autosomal recessive and X-linked disorders. With the use of sequencing instead of allele-specific detection methods, there is no longer any limit on which genes and which mutant alleles in these genes can theoretically be detected. Rare mutant alleles in genes associated with known disease will be found, thereby raising the sensitivity of carrier detection methods. Sequencing, however, also has the ability to uncover variants, particularly missense changes, of unknown pathogenicity in disease genes as well as in genes whose role in the disease is unknown (see Chapter 16). Unless great care is taken in assessing the clinical validity of rare variants detected by sequencing, the frequency of false-positive carrier test results will increase.

The impact of carrier screening in lowering the incidence of a genetic disease can be dramatic. Carrier screening for Tay-Sachs disease in the Ashkenazi Jewish population has been carried out since 1969. Screening followed by prenatal diagnosis, when indicated, has already lowered the incidence of Tay-Sachs disease by 65% to 85% in this ethnic group. In contrast, attempts to screen for carriers of sickle cell disease in the U.S. African American community have been less effective and have had little impact on the incidence of the disease so far. The success of carrier screening programs for Tay-Sachs disease, as well as the relative failure for sickle cell anemia, underscores the importance of community consultation, community engagement, and the availability of genetic counseling and prenatal diagnosis as critical requirements for an effective program.

Personalized Genomic Medicine

More than a century ago, the British physician-scientist Archibald Garrod proposed the concept of chemical individuality, in which each of us differs in our health status and susceptibility to various illnesses because of our individual genetic makeup. Indeed, in 1902, he wrote:

…the factors which confer upon us our predisposition and immunities from disease are inherent in our very chemical structure, and even in the molecular groupings which went to the making of the chromosomes from which we sprang.

The goal of personalized genomic medicine is to use knowledge of an individual's genetic variants relevant to maintaining health or treating illness as a routine part of medical care.

Now, more than a hundred years after Garrod's visionary pronouncement, in the era of human genomics, we have the means to assess an individual's genotype at all relevant loci by whole-genome sequencing (WGS) or, less comprehensively, by whole-exome sequencing (WES) to characterize the genetic underpinnings of each person's unique “chemical individuality.” In addition to genomic approaches to prenatal screening of the fetus for aneuploidy by maternal cell-free DNA, as described in Chapter 17, WGS and WES are being studied for analyzing fetal DNA obtained by invasive procedures, newborn screening, screening asymptomatic adults for increased predisposition to various diseases, identifying couples that are heterozygotes for autosomal recessive or X-linked diseases that could affect their children before conception, and for finding pharmacogenetic variants relevant to drug therapy.

The National Health Service of the United Kingdom is preparing to sequence the genomes of 100,000 people by 2017, with the eventual aim of having the sequence of every individual in the country in a database to use for developing personalized prevention and treatment. Hospitals, pharmaceutical companies, and the U.S. Department of Veterans Affairs are also beginning large-scale sequencing of hundreds of thousands of individuals. Although these efforts are focusing initially on mining the data for genetic variants that contribute to disease or for finding novel drug targets, they are also proposing to study how to use the genomic information to design personalized prevention and treatment strategies.

The application of WGS and WES to personalized medicine is not without controversy, however. One issue is cost. Although sequencing per se is many orders of magnitude less expensive now than when the original Human Genome Project was being carried out, the interpretation of such sequences remains very time consuming and expensive. Despite the time and effort put into interpretation, we are still unable to assign any clinical significance to the vast majority of all variants found through sequencing. There is widespread concern that individuals and their health care providers, when confronted with variants of uncertain significance (see Chapter 16), will seek additional expensive and unnecessary testing, with all the attendant expense and potential for complications that result from any medical test. There is the additional concern that even when a variant is known to be pathogenic and shown to be highly penetrant in families with multiple affected individuals, the actual penetrance when the variant is found through population screening in individuals with a negative family history may be much less.

Personalized genomic medicine is only one component of precision medicine, which, in its broadest sense, requires health care providers to merge genomic information with other kinds of information, such as physiological or biochemical measures, developmental history, environmental exposures, and social experiences. The ultimate goal is to provide more precise diagnosis, counseling, preventive intervention, management, and therapy. This effort has begun, but a lot of work remains before personalized genomic medicine becomes a part of mainstream medicine.

General References

Feero WG, Guttmacher AE, Collins FS. Genomic medicine—an updated primer. N Engl J Med. 2010;362:2001–2011.

Ginsburg G, Willard HF. Genomic and personalized medicine. ed 2. Elsevier: New York; 2012 [(vols 1 & 2); 1305 pp].

Kitzmiller JP, Groen DK, Phelps MA, et al. Pharmacogenomic testing: relevance in medical practice. Cleve Clin J Med. 2011;78:243–257.

Schrodi SJ, Mukherjee S, Shan Y, et al. Genetic-based prediction of disease traits: prediction is very difficult, especially about the future. Frontiers Genet. 2014;5:1–18.

References for Specific Topics

Amstutz U, Carleton BC. Pharmacogenetic testing: time for clinical guidelines. Pharmacol Therapeutics. 2011;89:924–927.

Bennett MJ. Newborn screening for metabolic diseases: saving children's lives and improving outcomes. Clin Biochem. 2014;47(9):693–694.

Dorschner MO, Amendola LM, Turner EH, et al. Actionable, pathogenic incidental findings in 1,000 participants’ exomes. Am J Hum Genet. 2013;93:631–640.

Ferrell PB, McLeod HL. Carbamazepine, HLA-B*1502 and risk of Stevens-Johnson syndrome and toxic epidermal necrolysis: US FDA recommendations. Pharmacogenomics. 2008;9:1543–1546.

Green RC, Roberts JS, Cupples LA, et al. Disclosure of APOE genotype for risk of Alzheimer's disease. N Engl J Med. 2009;361:245–254.

Kohane IS, Hsing M, Kong SW. Taxonomizing, sizing, and overcoming the incidentalome. Genet Med. 2012;14:399–404.

Mallal S, Phillips E, Carosi G, et al. HLA-B*5701 screening for hypersensitivity to abacavir. N Engl J Med. 2008;358:568–579.

McCarthy JJ, McLeod HL, Ginsburg GS. Genomic medicine: a decade of successes, challenges and opportunities. Sci Transl Med. 2013;5:189sr4.

Topol EJ. Individualized medicine from prewomb to tomb. Cell. 2014;157:241–253.

Urban TJ, Goldstein DB. Pharmacogenetics at 50: genomic personalization comes of age. Sci Transl Med. 2014;6:220ps1.


1. In a population sample of 1,000,000 Europeans, idiopathic cerebral vein thrombosis (iCVT) occurred in 18, consistent with an expected rate of 1 to 2 per 100,000. All the women were tested for factor V Leiden (FVL). Assuming an allele frequency of 2.5% for FVL, how many homozygotes and how many heterozygotes for FVL would you expect in this sample of 1,000,000 people, assuming Hardy-Weinberg equilibrium?

Among the affected individuals, two were heterozygotes for FVL and one was homozygous for FVL. Set up a 3 × 2 table for the association of the homozygous FVL genotype, the heterozygous FVL genotype, and the wild-type genotype for iCVT.

What is the relative risk for iCVT in a FVL heterozygote versus the wild-type genotype? What is the risk in a FVL homozygote versus wild-type? What is the sensitivity of testing positive for either one or two FVL alleles for iCVT? Finally, what is the positive predictive value of being homozygous for FVL? heterozygous?

2. In a population sample of 100,000 European women taking oral contraceptives, deep venous thrombosis (DVT) of the lower extremities occurred in 100, consistent with an expected rate of 1 per 1,000. Assuming an allele frequency of 2.5% for factor V Leiden (FVL), how many homozygotes and how many heterozygotes for FVL would you expect in this sample of 100,000 women, assuming Hardy-Weinberg equilibrium?

Among the affected individuals, 58 were heterozygotes for FVL and three were homozygous for FVL. Set up a 3 × 2 table for the association of the homozygous FVL genotype, the heterozygous FVL genotype, and the wild-type genotype for DVT of the lower extremity.

What is the relative risk for DVT in a FVL heterozygote using oral contraceptives versus women with the wild-type genotype taking oral contraceptives? What is the risk in a FVL homozygote versus wild-type? What is the sensitivity of testing positive for either one or two FVL alleles for DVT while taking oral contraceptives? Finally, what is the positive predictive value for DVT of being homozygous for FVL while taking oral contraceptives? Heterozygous?

3. What steps should be taken when a phenylketonuria (PKU) screening test comes back positive?

4. Newborn screening for sickle cell disease can be performed by hemoglobin electrophoresis, which separates hemoglobin A and S, thereby identifying individuals who are heterozygotes as well as those who are homozygotes for the sickle cell mutation. What potential benefits might accrue from such testing? What harms?

5. Toxic epidermal necrolysis (TEN) and the Stevens-Johnson syndrome (SJS) are two related, life-threatening skin reactions that occur in approximately 1 per 100,000 individuals in China, most commonly as a result of exposure to the antiepileptic drug carbamazepine. These conditions carry a significant mortality rate of 30% to 35% (TEN) and 5% to 15% (SJS). It was observed that individuals who suffered this severe immunological reaction carried a particular major histocompatibility complex class 1 allele, HLA-B*1502, as do 8.6% of the Chinese population. In a retrospective cohort study of 145 patients who received carbamazepine therapy, 44 developed either TEN or SJS. Of these, all 44 carried the HLA-B*1502 allele, whereas only three of the patients who received the drug without incident were HLA-B*1502positive. What is the sensitivity, specificity, and positive predictive value of this allele for TEN or SJS in patients receiving carbamazepine?

6. In 1997, a young female college student died suddenly of cardiac arrhythmia after being startled by a fire alarm in her college dormitory in the middle of the night. She had recently been prescribed an oral antihistamine, terfenadine, for hay fever by a physician at the school. Her parents reported that she would take her medications every morning with breakfast, which consisted of grapefruit juice, toast, and caffeinated coffee. Her only other medication was oral itraconazole, which she was given by a dermatologist in her home town to treat a stubborn toenail fungus that she considered unsightly. Terfenadine was removed from the U.S. market in 1998.

Do a literature search on sudden cardiac death associated with terfenadine, relating possibly genetic and environmental factors that might have interacted to cause this young woman's death.