The Implications of Genomics on the Nursing Care of Adults With Neuropsychiatric Conditions


Dr. Debra L. Schutte, Michigan State University College of Nursing, 1355 Bogue St., Rm #C242, East Lansing, MI 48824. E-mail:


Purpose: Neuropsychiatric disorders contribute substantially to disease burden and quality of life across the lifespan and the globe. The purpose of this article is to review the state of the science regarding genomic contributions to selected common neuropsychiatric conditions and to examine the consequent immediate and future implications for nursing practice and research.

Organizing Construct: Our work is guided by an ecological model that recognizes that common diseases are complex or multifactorial, meaning that multiple genomic and environmental factors contribute to their etiology.

Methods: A review of the literature was conducted to determine the state of the science in relationship to the genomic contributions to selected neuropsychiatric disorders.

Findings: Neuropsychiatric conditions are genomically heterogeneous, both within a single disorder and across groups of disorders. While recent genomic research yields clinically validated and useful information for a small subset of persons (e.g., predictive genetic testing for Huntington disease and early-onset Alzheimer disease), broad clinical application of genetic information is not yet available. In addition, the implications of genomics for the development and targeting of nonpharmacologic treatment strategies is largely unexplored.

Conclusions: Further research is needed to expand knowledge beyond genomic risk for the presence of disease to knowledge about the genomic risk for symptoms, symptom burden, and tailored symptom management interventions.

Clinical Relevance: Knowledge about the genomic influences on neuropsychiatric conditions suggests important implications for practicing nurses in the identification of persons at risk, provision of follow-up support, and in the administration of medications.

The impact of neuropsychiatric disorders on the lives of individuals, families, and communities across the globe is enormous, as evidenced by the large numbers of affected persons. For example, Alzheimer disease (AD) is now among the top 10 leading causes of death and is anticipated to effect 11 to 16 million people in the United States and 115.4 million people worldwide by the year 2050 (World Health Organization, 2012). Further, the phenotypes of these disorders challenge current therapies as well as the informal and formal systems of care. Most neuropsychiatric disorders are chronic and require complex pharmacologic regimens associated with negative side effects.

Their high prevalence, along with complex phenotypes, drive an aggressive research agenda aimed at understanding disease etiology in order to identify targets for pharmacologic and nonpharmacologic interventions. Many neuropsychiatric disorders are etiologically complex, suggesting that multiple interacting genomic and environmental factors contribute to their pathogenesis. As such, genomic research is an integral part of the research agenda for neuropsychiatric conditions. The purpose of this article is to review the state of the science regarding the genomic contributions to selected irreversible dementias and major psychiatric disorders, as examples of common neuropsychiatric conditions, and to examine the immediate and future implications for nursing practice and research.

Genomics and Selected Neuropsychiatric Disorders

Neuropsychiatric disorders represent the intersection between neurology and psychiatry, that is, neurologic conditions that have cognitive or behavioral manifestations. Neuropsychiatric conditions can cross the lifespan, ranging from autism to age-related irreversible dementias. This review focuses on disorders that are typically characterized by adult onset, including two common causes of irreversible dementia and two common major psychiatric disorders.

Irreversible Dementias

Dementia is defined as a loss of cognitive function from a previously attained level to such an extent that it interferes with one's ability to accomplish everyday roles and responsibilities (American Psychiatric Association, 1994). Dementia itself is not a disease but a constellation of symptoms that can be caused by many etiologies, some reversible and some not. AD and Huntington disease (HD) are two common causes of irreversible dementia.

Alzheimer disease AD is an age-related neurodegenerative disorder that is the most common cause of irreversible dementia. AD is multifactorial, exhibiting genomic components to both early-onset AD (EOAD) and late-onset AD (LOAD). The average duration is 8 to 10 years, ranging from 1 to 25 years (Bird, 2010). AD most often manifests after 60 years of age, resulting in cognitive, functional, and behavioral difficulties (National Institute on Aging, 2010). The disease progressively destroys neurons in the cortex and limbic structures of the brain, impacting learning, memory, behavior, emotion, and reasoning (Aderinwale, Ernst, & Mousa, 2010). Symptoms become increasingly severe and eventually incapacitating (Aderinwale et al, 2010; Bird, 2010).

Mutations in three genes are known to cause AD in a small percentage of families exhibiting early-onset and autosomal dominant inheritance patterns, including the amyloid precursor protein (APP) gene as well as the presenilin 1 (PSEN1) and presenilin 2 (PSEN2) genes (Goate et al., 1991; Levy-Lahad et al., 1995; Mullan et al., 1992). The PSEN proteins are components of the gamma-secretase machinery, which cleaves APP into the smaller sections of the Aß protein (Campion et al., 1999). APP is involved in protein trafficking, and Aß protein is a constituent protein of the amyloid plaques found in the brains of persons with AD (Rogaeva et al., 2007).

LOAD comprises the vast majority (∼95%) of all persons with AD (Campion et al., 1999). Variations in the apolipoprotein E (APOE) gene influence risk for developing LOAD. While there are multiple alleles for the APOE gene, including APOE2 and APOE3 and APOE4, the APOE4 allele has the strongest, most consistent evidence for increasing risk for LOAD (Corder et al., 1993; Pericak-Vance et al., 1991) and for shifting onset to a younger age (Borgaonkar et al., 1993; Corder et al., 1993). For example, a meta-analysis of risk associated with the APOE4 allele compared with the APOE3 allele indicates an odds ratio (OR) of 3.68 (confidence interval [CI] 3.30, 4.11) across all studies (Bertram, McQueen, Mullin, Blacker, & Tanzi, 2007). While the role of APOE in risk for LOAD is strong, evidence suggests that APOE4 is neither necessary nor sufficient for the expression of AD, indicating that other genomic and environmental factors are at play (Online Mendelian Inheritance in Man, 2011).

Therefore, both candidate gene and genome-wide association studies (GWAS) continue in an attempt to identify additional susceptibility genes for AD. Notable examples include the TOMM40 and Clusterin genes. Roses and colleagues (2010) recently identified an interactive relationship between TOMM40, which encodes for a protein required in the movement of proteins across mitochondrial membranes, and APOE. Two distinct forms of APOE3 have been distinguished: APOE3 haplotypes linked to TOMM40, increasing LOAD risk with an earlier age of onset, and those that decrease risk (Roses et al., 2010). A specific polymorphism within the Clusterin gene (rs 11136000) is also implicated in risk for AD based on recent GWAS and replication analyses (Carrasquillo et al., 2010). The Clusterin gene encodes for a protein involved in amyloid processing and cell death. Findings from these and other AD genetic association studies are cataloged on the Alzgene database (Bertram et al., 2007) and provide a useful compilation of recent evidence. Risk estimates derived from these studies currently have limited clinical utility in predicting individual risk for AD. Consequently, genetic testing for LOAD is not currently recommended (Alzheimer's Association, 2012).

Huntington disease In contrast to AD, HD is a relatively rare, neuropsychiatric disorder that is caused by a mutation in a single gene, the huntingtin (HTT) gene (Huntington Disease Collaborative Research Group, 1993). HD results from an unstable trinucleotide repeat (CAG) expansion in HTT. The normal range of CAG repeats is less than 26 repeats. The presence of over 40 or more CAG repeats, however, causes HD. HD is an autosomal dominant disorder that is characterized by motor, cognitive, and psychiatric manifestations. The predominant motor symptom in HD is chorea, or a nonrepetitive, nonrhythmic jerking of the limb, face, or trunk, that results in impaired balance and gait, progressing eventually to an inability to walk, speak, and swallow. Global cognitive decline occurs in persons with HD, including decreased attention, concentration, some memory deficits, and impaired visuospatial abilities (Warby, Graham, & Hayden, 2010). Psychiatric manifestations, such as personality changes, depressive symptoms, anxiety, and agitation, also characterize HD. The symptom onset in HD typically occurs between 35 and 44 years of age. A correlation exists between the number of CAG repeats and age at onset (Langbehn, Hayden, & Paulsen, 2010), with more CAG repeats associated with earlier ages at onset.

The common causes of irreversible dementia are a heterogeneous group of conditions, varying in their prevalence, patterns of inheritance, symptoms, and biology. With the exception of HD (a single gene disorder), AD and other causes of irreversible dementia, such as Parkinson disease, are genomically heterogeneous. In these cases, multiple genes and often multiple DNA variants within these genes either cause or are associated with increased risk for disease. In all cases, more genetic variants are likely to be identified for their role in causing, modifying risk, or modifying the symptom onset and progression.

Major Psychiatric Disorders

Like the irreversible dementias, major psychiatric disorders such as major depressive disorder (MDD) and schizophrenia (SZ) present major challenges for genetic researchers due to their biological complexity, phenotypic variability, and lack of objective, laboratory-based diagnostic tests. Despite these challenges, recent human genomic studies indicate substantial progress toward identifying the genomic causes of these disorders.

Major Depressive Disorder MDD is a mood disorder, characterized by an overall low mood. MDD affects approximately 17% of the population (Kessler et al., 1994) and is associated with high levels of morbidity and mortality (Lohoff, 2010). Family, twin, and adoption studies have documented the genomic influence of MDD (Kendler, 2001; Lau & Eley, 2010), whose heritability is estimated to be between 31% and 42% (Bienvenu, Davydow, & Kendler, 2011).

Linkage studies in MDD primarily served to identify areas on the genome for further research (Lohoff, 2010). As a result, several hypothesized etiological pathways, such as imbalances in serotonin, norepinephrine, or dopamine, guided subsequent candidate gene association studies (Bleakley & Brodie, 2009; Drago, Crisafulli, Sidoti, & Serretti, 2011). Unfortunately, despite numerous candidate gene association studies, the genes associated with MDD remain elusive (see Elder & Mosack [2011] for a review). Recently, the glutaminergic theory, which suggests that the neurotransmitter glutamate causes depression by influencing neuroplasticity or neurotoxicity (Drago, Crisafulli, Sidoti, et al., 2011) and the gamma-amino butyric acid (GABA) theory, which hypothesizes that MDD is related to reduced GABAergic system activity (Drago, Crisafulli, Sidoti, et al., 2011), guide genomic research. Results from linkage studies and GWAS support this theory by implicating genes related to NMDA (a glutamatergic receptor type) and GRM7 (which encodes the protein metabotropic glutamate receptor 7-MGLU7; Breen et al., 2011; Pergadia et al., 2011; Sullivan et al., 2009). In addition, functional studies of expression patterns and agonist-antagonist profiles of glutamatergic receptors have led to the development of drugs with glutamatergic properties (Drago, Crisafulli, & Serretti, 2011). The GABA genes that are implicated in the etiology of MDD include the GAD1 gene and genetic variants coding for GABA-A receptor subunits (Hasler & Northoff, 2011). Recent hypotheses suggest that MDD involves a complex interactional system involving both the monaminergic and glutamatergic systems (Drago, Crisafulli, Sidoti, et al., 2011; Hasler & Northoff, 2011).

Several large GWAS have been conducted to take a hypothesis-free approach to gene discovery in MDD (see Lohoff [2011] for a review; Wray et al., 2012), yielding no significant associations. While this lack of findings is discouraging, it likely reflects both the phenotypic and genomic heterogeneity of the disorder. Notably, one meta-analysis (McMahon et al., 2010) pooled GWAS data from five case-control cohorts with over 13,600 individuals with MDD and bipolar disease (BD) and found that single nucleotide polymorphisms (SNPs) at 3p21.1 were associated with these major mood disorders. Until genetic test(s) are found that clearly identify a predisposition to depression or a diagnosis of depression, future genomic studies will require these very large sample sizes to allow for population stratification by phenotypic or genomic variability.

Schizophrenia Scientists also continue to unravel the neurobiology of SZ, a complex psychiatric disorder characterized by hallucinations, delusions, reduced affect, and cognitive deficits (Purcell et al., 2009). Heritability estimates for SZ are high (80%–85%; Bienvenu et al., 2011). Genomic evidence indicates a polygenic model, in which common alleles of very small effect and rare mutations interact with environmental factors to confer risk for illness (Gejman, Sanders, & Kendler, 2011; Purcell et al., 2009).

Over 1,000 genes have been examined in candidate gene association studies of SZ (Gejman et al., 2011). Early studies focused on genes hypothesized to play a role in etiologic pathways (e.g., dopaminergic or serotonergic hyperfunction) or those encoding the receptor targets of antipsychotic drugs (Almoguera et al., 2012; O’Connell, Lawrie, McIntosh, & Hall, 2011). Findings from early studies have proved difficult to replicate, likely due to small sample size, differing statistical thresholds, and variability in phenotypic definition (Gejman et al., 2011). More recent candidate gene studies have focused on the glutamate hypofunction hypothesis and GABA deficit pathways (Charych, Liu, Moss, & Brandon, 2009; O’Connell et al., 2011).

Results from two GWAS support the hypothesis of an association between the major histocompatibility complex (MHC) region on chromosome 6p21.3–22.1 and SZ (Smeraldi, Bellodi, & Cazzullo, 1976; Stefansson et al., 2009), suggesting an immune function role in SZ. When researchers combined data sets from single GWAS, they were more successful in identifying specific susceptibility loci (Purcell et al., 2009; Shi et al., 2009). GWAS in SZ have also suggested new etiologic mechanisms. For example, Ripke and colleagues (2011) found significance for a genetic variant related to microRNA 137 (MIR137) using a genome-wide approach, suggesting an unknown etiological mechanism.

The genomic contributions to major psychiatric disorders have been challenging to dissect. Several genes and biologic pathways have been implicated and remain the target of further inquiry. More susceptibility genes are expected to emerge as genomic technologies evolve.

Implications for Clinical Practice

The advances in knowledge generated through ongoing genomic research in the area of neuropsychiatric disorders highlight important clinical implications for today's practicing nurses. These immediate and emerging implications include the consideration of genomic information in risk assessment and pharmacologic care.

Identification, Referral, and Support of At-risk Individuals

The assessment and interpretation of family history data remain of utmost importance for today's generalist and advanced practice nurses. The collection of a comprehensive, or at least disease-targeted, family history provides important clues about the extent to which an individual or family is in need of specialty genetics services, particularly as more and more genetic tests for diagnosis and risk prediction are clinically available. For example, persons with EOAD, especially in the presence of autosomal dominant patterns of inheritance, are candidates for referral to genetics clinics to consider the potential benefits of genetic testing for risk clarification. While several biotechnology firms use direct-to-consumer marketing of genetic tests purported to predict or diagnose psychiatric conditions, such as MDD, BD, and SZ, these tests are often not robust and require additional validation of their clinical utility (Mitchell et al., 2010). Nurses will play an increasingly important role in providing decision-making and emotional support for persons who are considering genomic information in their healthcare activities (Jenkins & Calzone, 2007).

Genotype-directed Pharmacotherapeutics

Another important, emerging practice implication of genomic knowledge and technology is the potential role of genotype-directed pharmacotherapeutics in the care of persons with neuropsychiatric disorders. Most pharmacogenomic studies of major psychiatric disorders focus on genes encoding the molecular targets of psychoactive drugs (i.e., neurotransmitters) or on genes related to drug-metabolizing enzymes within the P450 system (Steimer, 2010). To date, the U.S. Food and Drug Administration has approved only the AmpliChipr CYP450 test (Malhotra, Zhang, & Lencz, 2011), which assesses alleles in the CYP2D6 and CYP219 genes (de Leon, 2009). However, the genomics-related therapeutic effects and side effects of the following three classes of medications are active areas of research that will eventually inform the care of persons with major psychiatric disorders: antidepressants, mood stabilizers, and second-generation antipsychotics (SGAs).

Therapeutic effects Genomic variants associated with the therapeutic responses of antidepressant medications is a robust area of research. Specifically, evidence suggests that genes within the serotonin system (5-HTT, HTR1A, and HTR2A), tryptophan hydroxylase 1 (TPH1) and brain-derived neurotrophic factor (BDNF) are associated with therapeutic response to antidepressant medications (Kato & Serretti, 2010; Porcelli et al, 2011). A GWAS (Ising et al., 2009) identified 41 novel genes related to antidepressant medication response that could be clustered into three interrelated categories: (a) metabolic pathways and brain development, (b) metabolic and cardiovascular disorders, and (c) genes related to neurotransmitter targets. Drug-metabolizing enzymes, such as CYP2D6 and CYP2C19, have also been studied in relationship to the therapeutic response of antidepressants. Rudberg and colleagues (Rudberg, Mohebi, Hermann, Refsum, & Molden, 2008) found that an ultrarapid metabolizer isoform, named CYP2C19*17, was associated with a reduced selective serotonin reuptake inhibitor drug concentration, while a deletion of this gene was associated with an almost sixfold increase in drug concentration.

Mood stabilizers, such as lithium, are an important therapeutic option in persons with major psychiatric disorders (Grof, 2010). In 2008, the Consortium on Lithium Genetics ( was founded to establish the largest dataset for genome-wide studies of lithium response in BD (Schulze et al., 2010). Other research has been directed toward anticonvulsants as mood stabilizers. For example, evidence supports an association between response to valproate (an anticonvulsant) and the XP1–116C/G polymorphism (related to endoplasmic reticulum stress response; Kim, Kim, Lee, & Joo, 2009). Similarly, evidence suggests that polymorphisms within the genes for two enzymes (UGT1A4 and UGT2B7) that metabolize the anticonvulsant lamotrigine explain interindividual variability in drug response.

Over the past 2 decades, SGAs, such as aripiprazole, clozapine, olanzapine, quetiapine, and risperidone, have become the standard treatment for the management of SZ. In this case, gene variants within the serotonin and dopamine system are associated with the efficacy of SGAs (Arranz, Rivera, & Munro, 2011; Zhang, Lencz, & Malhotra, 2010). Drug-metabolizing enzymes have also been examined in relationship to the therapeutic effect of SGAs. Evidence suggests that the serum concentrations of risperidone and aripiprazole are significantly affected by CYP2D6 genotype; specifically, poor metabolizers may need lower doses to achieve a similar steady-state serum concentration than extensive metabolizers (Hendset, Hermann, Lunde, Refsum, & Molden, 2007).

Medication side effects The side effects (e.g., weight gain, suicidal ideation, and mania) of medications used for the treatment of psychiatric disorders are common, problematic, and negatively impact medication adherence (Kozuki & Schepp, 2005). An ability to predict risk for side effects, based on genotype, would be a helpful advancement in the field. Some strides in achieving this goal have been made. For example, evidence suggests an association between weight gain and the serotonin receptor gene (HTR2C) and leptin genes (Lett et al., 2012). Antidepressant-induced suicidal ideation and antidepressant-induced mania are additional troubling side effects of antipsychotic medications that have been the foci of several pharmacogenomic studies, implicating CREB1, BDNF, ADRA2A, GRIA3, and other genes (Perroud, 2011). The short variant of the serotonin-transporter promoter region (5-HTTLPR) is also associated with antidepressant-induced mania (Daray, Thommi, & Ghaemi, 2010).

In summary, findings from pharmacogenomic research for neuropsychiatric disorders hold promise. However, more research is needed before genotype-directed pharmacotherapies can be broadly applied to the treatment of neuropsychiatric disorders in order to provide the optimal medication dose while minimizing the risk for side effects prior to the initiation of pharmacotherapy.

Future Opportunities and Directions

Despite the discoveries that have been made about the genomic etiologies of major neuropsychiatric disorders since the advent of the Human Genome Project, substantial gaps exist in knowledge. Certainly not all of the heritability of these disorders has yet been identified. In fact, missing heritability is recognized as a critical challenge facing scientists today (Manolio et al., 2009). Further substantial gains need to be made before clinically useful diagnoses and genotype-directed pharmacologic interventions are widely available in the clinical setting. Finally, the development and testing of genotype-directed nonpharmacologic interventions to improve the lives of persons with neuropsychiatric disorders remain largely unexplored.

Finding the Missing Heritability

With advancing technologies and improving statistical models, there is hope that researchers will be able to either refine previous estimates of heritability or find the missing heritability in neuropsychiatric disorders. Several research strategies may help achieve this goal, including (a) refining the phenotype, (b) combining the phenotypes through cross-boundary studies, (c) employing increasingly powerful population-based approaches, (d) employing hybrid population and family based approaches, and (e) examining gene-environment (GxE) interactions.

Refining the phenotype One explanation for the relative lack of findings from genetic association studies is the phenotypic variability of neuropsychiatric and major psychiatric disorders. One strategy for addressing this challenge is refining the phenotype or defining relevant phenotypic subgroups. Phenotypic subgroups might be based on family history, inheritance pattern, or particular aspects of the clinical picture, such as age at onset, severity, phenotypic subgroups, or response to medications (Elder & Mosack, 2011). Biomarkers, such as serum protein levels, and advanced clinical assessment strategies, such as magnetic resonance imaging and positron emission tomography, may also refine the phenotypes (Scharinger, Rabl, Pezawas, & Kasper, 2011).

Combining the phenotype Symptoms overlap across neuropsychiatric disorders (Ivleva et al., 2010). In some cases, genetic studies suggest an overlap in the genetic etiology of these disorders as well (Cherlyn et al., 2010). Efforts to examine etiologic overlap, through cross-boundary studies such as the Cross-Diagnostic Group of the Psychiatric GWAS Consortium, are important areas of inquiry (Cichon et al, 2009; Gejman et al., 2011).

Increasing the power of population-based designs In order to increase the power of population-based genetic association studies, such as GWAS, very large cohorts, well-characterized cases (by both genomic and environmental factors), and prospective designs are required (Chen et al., 2011). The replication of genetic associations identified through GWAS represent another critical need in genomic research (Gejman et al., 2011).

Expanding the range of genomic variation under study New technologies and knowledge of the architecture of the human genome provide for an expanded range of genomic variation to explore. Researchers can search for rare coding variants (mutations and polymorphisms) using high-throughput sequencing techniques (such as targeted resequencing, exome sequencing, or whole genome sequencing) and copy number variants (structural variants such as microdeletions, microinsertions, duplications, and transpositions) using microarray techniques within the genomic and mitochondrial DNA. The massive amounts of resulting data require the further development of bioinformatics approaches for testing associations between rare variants and increasingly large samples of individuals with neuropsychiatric disorders (De la Vega, Bustamante, & Leal, 2011).

Hybrid population and family-based approaches A current hypothesis about the “missing heritability” of common disease is that it can be found in rare SNPs that have a moderate effect (Manolio et al., 2009; Zeggini, 2011). Such SNPs cannot be found by classic family-based studies because their effect is too small. And they cannot be found by classic GWAS because their frequency is too low. Hybrid study designs will be required that combine the complementary powers of family-based and population-based studies. Population isolates provide a potential resource for this hybrid design (Peltonen, Palotie, & Lange, 2000). This approach takes advantage of the whole population, but because the population is isolated, all (or most) members belong to an extended family, increasing the likelihood of identifying rare variants.

Gene-environment interactions Examining the impact of the human genome on disease as it interacts with a complex environment is a critical need of future genomic research. Epidemiological studies suggest that environmental risk factors are associated with the development of major psychiatric disorders (Nugent, Tyrka, Carpentaer, & Price, 2011; Brown, 2011; Hamdani, Tamouza, & Leboyer, 2012) and that GxE interactions occur in mouse models of AD (Arendash et al., 2004). However, few genetic studies directly examine GxE interactions in humans. GxE studies are challenging, requiring large samples and careful conceptualization of the relevant environmental constructs. However, these interactions may yield key opportunities for new and tailored interventions that consider both the genomic and environmental characteristics of our clients.

Toward Tailored, Genotype-driven, Nonpharmacologic Strategies

While the clinical application of new genetic and genomic information has focused on genetic testing for diagnosis, disease prediction, susceptibility testing, and genotype-driven pharmacotherapeutics, the development and testing of tailored, genotype-driven, nonpharmacologic interventions are exciting, yet relatively unexplored, frontiers in nursing research and subsequent practice. For example, in the case of AD, pharmacotherapeutics such as antipsychotics are associated with substantial side effects, including increased mortality (U.S. Department of Agriculture, 2005), highlighting the importance of nonpharmacologic interventions. Can we identify persons with AD who differentially respond to a behavioral intervention for agitation based on their genotype? Are environmental modifications to limit wandering in persons with dementia more likely to benefit one group over another? What role does genotype or other individual characteristics play in this differential effect? Nurse researchers and clinicians are in critical positions to ask these questions, test them, and translate findings into improved care for persons with neuropsychiatric conditions.


Neuropsychiatric disorders, such as the major psychiatric disorders and irreversible dementias, are highly prevalent, devastating conditions that present immense challenges for family and formal caregivers worldwide. Genomic research has provided many new insights into the pathogenesis of these conditions toward the ultimate, but likely distant, goal of prevention and cure. However, further research is needed to expand knowledge beyond genomic risk for the presence of disease to knowledge about the genomic risk for symptoms, symptom burden, and tailored symptom management interventions. In the meantime, nurses who care for persons with neuropsychiatric conditions in home or institutional settings are responsible for staying abreast of genomic discoveries relevant to their clients and for the integration of genetic information ascertained through a comprehensive family history into their practice. Further, nurses are in key positions for advocating for their clients who may benefit from relevant information about the potential impact of genomics on their health and well-being.


Goris is supported by a John A. Hartford Foundation Building Academic Geriatric Faculty Capacity Predoctoral Scholarship (2011–2013).

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