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C H A P T E R

111Genetics of Alzheimer Disease

Adam C Naj, Regina M Carney, and Susan E Hahn

John P Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA

Michael A Slifer

Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA

Jonathan L Haines

Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA

Margaret A Pericak-Vance

John P Hussman Institute for Human Genomics; Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA

© 2013, Elsevier Ltd. A

111.1 BACKGROUND AND HISTORY

Alzheimer disease (AD) is characterized by an insidious onset and progressive deterioration of memory and at least one other cognitive domain (language, praxis, rec-ognition, or executive functioning). It is the leading cause of dementia in the elderly, affecting more than 5.4 million people in the United States (1,2). Prevalence increases with age, from 0.3–0.5% at age 60 to 11–15% at age 80 (3–5). There is substantial variability in these estimates, with some reports finding the prevalence to be as high as 47% after age 84 (6). Half the beds in long-term care facilities are already devoted to patients with dementia, and a majority of those patients have AD (7). As the population continues to age, the prevalence is expected to increase almost threefold by 2050 (1), making AD a growing public health and economic crisis.

111.1.1 Alzheimer Disease History

In 1906, Bavarian psychiatrist Alois Alzheimer presented the first case of dementia characterized by histopathologi-cal signs of senile (neuritic) plaques and neurofibrillary tangles (NFTs). At the time, Alzheimer thought the dis-ease was a rare cause of senility (8). In the 1960s, Blessed, Tomlinson, and Roth performed a series of autopsies on hundreds of brains affected with “normal senility” (9), and found the majority were affected by the plaque and tangle lesions characteristic of AD. In subsequent decades, it became increasingly clear that most dementias result from a specific pathological process, and are not simply the result of aging.

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Despite advancements in clinical assessment, definitive diagnosis of a symptomatic person still requires either neuropathologic examination of brain tissues obtained postmortem, or pathogenic mutation in a causal AD gene (10–12). Further complicating the diagnosis for clinicians, 6–14% of autopsy confirmed cases have an atypical disease presentation (10,13–15).

Clinically, AD remains a diagnosis of exclusion, gener-ally beginning with slowly progressive memory loss, and advancing to deficits in higher intellectual functions and cognitive abilities (16). Even an apparently clear clinical diagnosis of AD may be overturned later by histopatho-logical investigation, revealing different or additional etio-logic processes contributing to cognitive impairment (17). Standard criteria for the clinical diagnosis of AD were established by the National Institute for Neurological and Communicative Diseases and Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA crite-ria) in 1984 (18) (Table 111-1). In tertiary care facilities, clinical diagnosis is accurate in 85–90% of putative AD cases (19–21). Clinical diagnosis of AD in individuals aged 60 years and older is often complicated by numerous fac-tors including cerebrovascular insults (22) and common, co-occurring neuropsychiatric conditions (23). In a study by Carney et al. (24), 9 of 101 clinically diagnosed cases of AD from a family research study had a different etiology for their dementia, including two within the same multi-plex family. Another 18 cases had unrecognized contribut-ing comorbidities in addition to AD pathology. Most of the cases with disparate clinical and histopathological diagno-ses had significant Lewy body pathology, but Pick disease and progressive supranuclear palsy also contributed.

2 CHAPTER 111 Genetics of Alzheimer Disease

TABLE 111-1 Criteria for Clinical Diagnosis of Alzheimer Disease

I. The criteria for the clinical diagnosis of probable Alzheimer disease include • Dementia established by clinical examination, documented by the Mini-Mental Test, Blessed Dementia Scale, or some similar

examination, and confirmed by neuropsychological tests • Deficits in two or more areas of cognition • Progressive worsening of memory and other cognitive functions • No disturbance of consciousness • Onset between ages 40 and 90, most often after age 65 • Absence of systemic disorders or other brain diseases that in and of themselves could account for the progressive deficits in memory

and cognition II. The diagnosis of probable Alzheimer disease is supported by • Progressive deterioration of specific cognitive functions, such as language (aphasia), motor skills (apraxia), and perception (agnosia) • Impaired activities of daily living and altered patterns of behavior • Family history of similar disorders, particularly if confirmed neuropathologically Laboratory results of

• Normal lumbar puncture as evaluated by standard techniques • Normal pattern or nonspecific changes in electroencephalograph such as increased slow-wave activity • Evidence of cerebral atrophy on computed tomography, with progression documented by serial observations III. Other clinical features consistent with the diagnosis of probable Alzheimer disease, after exclusion of causes of dementia other than

Alzheimer disease, include • Plateaus in the course of progression of the illness • Associated symptoms of depression, insomnia, incontinence, delusions, illusions, hallucinations, catastrophic verbal/emotional/physical

outbursts, sexual disorders, and weight loss • Other neurologic abnormalities in some patients, especially with more advanced disease and including motor signs, such as increased

muscle tone, myoclonus, or gait disorder • Seizures in advanced disease • Computed tomography normal for age IV. Features that make the diagnosis of probable Alzheimer disease uncertain or unlikely include • Sudden, apoplectic onset • Focal neurologic findings such as hemiparesis, sensory loss, visual-field deficits, and incoordination early in the course of the illness • Seizures or gait disturbances at the onset or very early in the course of the illness V. Clinical diagnosis of possible Alzheimer disease • May be made on the basis of the dementia syndrome, in the absence of other neurologic, psychiatric, or systemic disorders sufficient to

cause dementia, and in the presence of variations in the onset, in the presentation, or in the clinical course • May be made in the presence of a second systemic or brain disorder sufficient to produce dementia, which is not considered to be the

cause of the dementia • Should be used in research studies when a single, gradually progressive severe cognitive deficit is identified in the absence of other

identifiable causes VI. Criteria for a diagnosis of definite Alzheimer disease are • The clinical criteria for probable Alzheimer disease • Histopathologic evidence obtained from a biopsy or autopsy VII. Classification of Alzheimer disease for research purposes should specify features that may differentiate subtypes of the disorder, such as • Familial occurrence • Onset before age of 65 • Presence of trisomy 21 • Coexistence of other relevant conditions, such as Parkinson disease

111.1.2 Heritability and Segregation within Families

Several lines of evidence support heritable components of AD. Familial aggregation studies demonstrate the disease clustering within families (25–27), and in large pedigrees with early-onset Alzheimer disease (EOAD), the disease exhibits an autosomal dominant pattern of inheritance (28). Family studies of AD have found that the age at onset (AAO) of AD within families has a bimodal distribution. EOAD families were determined empirically to have a mean AAO less than 58 years (29). EOAD is generally defined as an onset of symptoms prior

to age 60 or 65 (30–32). Twin studies have also demon-strated a genetic component to AD. By their nature, twin studies of the elderly tend to be small, and early studies utilized varying diagnostic criteria; however, each pub-lished study found the concordance rate among mono-zygotic twins (22–83%), who share all their genes, to be higher than the concordance rate among dizygotic twins (0–50%), who share only half their genes on average (33–36). While the higher concordance rate in mono-zygotic twins supports the genetic influence on the dis-ease, environmental factors are also suspected, since concordance was not 100%.

CHAPTER 111 Genetics of Alzheimer Disease 3

Segregation analysis in families affected by AD further supports the influence of genes on AD risk. Segregation analysis follows the inheritance of a disease through a family and attempts to fit a genetic model to the observed transmission pattern. Models assuming only sporadic AD with no major genes and models limited only to Mendelian inheritance patterns were rejected (37). Using only EOAD families, the pattern of inheritance is often consistent with classic Mendelian autosomal dominant inheritance, while inheritance patterns in late-onset families appear more complex. Late-onset Alzheimer disease (LOAD) follows a pattern of multifactorial inheritance involving a combina-tion of genetic and nongenetic factors (38–40).

In summary, these studies have provided powerful evidence for a genetic contribution to AD, even before any specific genetic variants were identified. In the very rare early-onset autosomal dominant pedigrees, the evi-dence clearly points to Mendelian genes. Additional risk-conferring genetic variants inherited in more complex combinations were believed to contribute to AD in the majority of the population.

111.1.3 Environmental Risk Factors for AD

As noted earlier, the lack of perfect concordance in AD status among identical twins, who share 100% of their genes, strongly implies that not all risk for AD is attribut-able to genetic effects. In fact, a variety of environmental factors have been implicated in AD risk including socio-economic features such as level of education, a multi-tude of risk factors for cardiovascular disease, and still others such as history of head injury and female gender. Immune pathways have also been implicated in AD.

Environmental risk factors of AD have been reviewed in considerable detail elsewhere (41). Major socioeco-nomic factors implicated in increases of AD risk include lower level of income, lower level of completed educa-tion, and lower occupational status (42). These factors are correlated with poor quality of and limited availabil-ity of resources needed during childhood development that tend to be associated with poor growth and physi-cal development (42). These limitations contribute to developmental markers like low birth weight (LBW) and short stature, which have been shown to be associated with AD (43). LBW is often a marker of developmental deficiency and often correlates with limitations in cogni-tive ability that is present in childhood and may contrib-ute to reduced education level. One study observed that limited linguistic ability in young women increased the likelihood of meeting neuropathologic criteria for AD in old age (44). Developmental deficiencies may result in reduced brain development with diminished reserve capacity, which may modify lifelong patterns of growth and risk of AD (42,44).

Early development correlates of LBW include the rapid gain of weight after birth, increased central adi-posity, and metabolic irregularities such as insulin resis-tance. As these are also important early risk factors for

type 2 diabetes mellitus, it is not surprising that connec-tions between features of diabetes and AD pathology have been drawn. Rat models have shown experimen-tally that killing insulin-producing cells in the brain in early life have produced AD-like pathological features. Furthermore, in work using transgenic murine models of AD, increased intake of water supplemented with sucrose advanced amyloidosis and the appearance of cognitive deficits, while caloric restriction was found to be protective (45).

Knowledge is limited about connections between environmental exposures such as infectious and chemi-cal agents and AD. The relationship between envi-ronmental exposures and cognitive decline has been explored in some detail, especially the role of lead expo-sure in reduced cognitive function. One study (46) of blood levels in 172 children showed that even blood lead concentrations below 10 μg/dL are inversely associ-ated with children’s IQ scores at three and five years of age, with declines in IQ greater at smaller than at higher concentrations, suggesting a stronger effect of environ-mental exposure to lead in childhood than previously estimated. Cognitive decline from lead exposure has been found to continue through development and into adulthood, as seen through consistently lower IQ lev-els and behavioral changes (47). Furthermore, occupa-tional lead exposure among adults correlates with lower neurobehavioral test scores and with deficits in several cognitive spheres, including measures of executive func-tion and verbal intelligence and memory (48). Evidence for gene–environment interactions in cognitive decline is evidenced by findings that persons with at least one APOE ε4 allele may be more susceptible to the effects of prolonged lead exposure on the central nervous sys-tem (49). While a direct relationship between cognitive decline from lead exposure and AD development has not been shown in humans, a primate model of early life exposure to lead demonstrated a development of AD-like pathological abnormalities in the brains of infant monkeys; however, monkeys exposed later on in adulthood did not show similar pathology (50).

Head trauma is also associated with AD, but whether trauma occurring earlier or later in life increases risk is unknown. It should be noted that adult head circum-ference has been observed to correlate with cognitive performance; likewise, traumatic brain injury during childhood is associated with reduced head size (42,51), suggesting that head size may mediate the effect of brain injury on AD risk.

111.2 GENETICS OF EOAD

Linkage analysis, followed by positional cloning of can-didate genes, was used to identify genetic variants of the three genes known to cause autosomal dominant EOAD. The three known AD-causative genes are amyloid pre-cursor protein (APP) (52), presenilin 1 (PSEN1), and presenilin 2 (PSEN2) (53–55).


111.2.1 Discovery of the APP Gene

The first AD-causing mutation found was in the APP gene. APP was initially cloned and localized to chro-mosome 21 in 1987 (56,57). Chromosome 21 was an intriguing location, since those with trisomy 21 (Down syndrome) develop the pathologic signs (neuritic plaques and NFTs) and symptoms of AD at an early age (58). Interestingly, an individual who was affected by translo-cation Down syndrome, but did not develop the charac-teristic Alzheimer dementing syndrome, was identified. On autopsy, the brain was free of the characteristic AD plaques and tangles. The individual’s DNA revealed a duplication of the obligate telomeric Down syndrome region of chromosome 21, but the APP gene was not within this duplication (59). Within families affected by APP mutations, functional analyses have demonstrated the likely pathophysiologic processes causing AD. The identified missense mutations lie outside of the beta- amyloid region of the APP gene, and as APP can be cleaved into one of two amino acid sequences, these mutations appear to cause shifts in the proteolytic cleav-age of APP toward the production of beta-amyloid (Aβ) products. In neuroblastoma cell lines one mutation favors the production of a 42 amino acid residue-length Aβ. Aβ is a major component in senile plaques. This 42-residue beta-amyloid (Aβ42) is less soluble than the alternative 40-residue isoform (Aβ40) (60). Aβ42 is also associated with increased aggregation and neurotoxicity (Hilbich et al., 1991). APP mutations consistently show increased production and/or deposition of the pathogenic long-Aβ product (60,61). The long-Aβ isoforms are less soluble than the shorter Aβ40 isoform, and these Aβ deposits are the principal component of amyloid within the charac-teristic plaques of AD-affected brains (62). Transgenic mice expressing the human APP mutation develop senile plaques, but not the tangles, characteristic of AD (63). A second characterized mutation in APP causes double mutation-transfected neuroblastoma cells to secrete five times the Aβ42, compared to control cells with wild-type APP (64). The neurotoxicity of Aβ has been well demon-strated in cell culture (65,66). Aβ also confers increased vulnerability to excitotoxic damage of hippocampal neu-rons in vitro (67). Furthermore, when Aβ is added to neuronal cell cultures, there is a distinct loss of synapses, analogous to the early pathologic processes of AD (68). Image analysis of Aβ load in AD patients and controls demonstrates a positive correlation between the amount of Aβ and severity of cognitive deficit.

While it was clear that some early-onset families were affected by APP mutations and showed linkage to chro-mosome 21 (69), many others were found not be affected by APP mutations (70,71). Worldwide, only about two dozen families have been described as carrying muta-tions of the APP gene. Among these families, at least 15 different pathogenic mutations have been characterized (Alzheimer Disease Mutation Database) (72). Therefore,

APP mutations that cause AD display allelic heterogene-ity. Since the majority of early-onset familial AD is not caused by mutations in the APP gene, there is clearly locus heterogeneity for EOAD (73,74).

111.2.2 Discovery of the PSEN1/PSEN2 Genes

Subsequent studies in additional families with autosomal dominant inheritance demonstrated genetic linkage to chromosome 14 (75–77). In 1995, the first AD-causing mutation in the PSEN1 gene was identified (55). Since then, more than 150 different PSEN1 mutations scat-tered throughout the gene have been described (78,79). Interestingly, the majority of the large autosomal domi-nant AD families appear to carry unique PSEN1 mis-sense mutations (Clark et al., 1995). While several families with the same PSEN1 mutation have been iden-tified, these families appear to be unrelated, providing evidence that the same mutation may have arisen more than once (80,81). Phenotypically, PSEN1 familial AD is the most aggressive form of AD, and affected individu-als generally have disease onset in their fourth or fifth decade of life (82). The characteristic amyloid plaques in brains of individuals affected with AD and a PSEN1 mutation show a relative abundance of Aβ42 compared to the plaques in individuals with sporadic AD. Quan-titative image analysis using Aβ42 and Aβ40 antibodies demonstrate a 1.5- to 3-fold relative increase in plaques containing Aβ42 peptides (58,83).

After characterization of APP and PSEN1, there remained EOAD-burdened families under study that did not show segregation of known mutations, and that did not link to either chromosome 21 (APP) or 14 (PSEN1). These included families originating from the Volga River basin of Russia (84) who showed linkage to chromosome 1 (53). Serendipitously, with the cloning of the PSEN1 gene, a larger 7.5-kilobasepair (kb) alternative polyad-enylation message was identified. The product repre-sented a gene homologous to PSEN1. The new gene was mapped to a known linkage region on chromosome 1, and was identified as PSEN2. Mutations in PSEN2 are rare, but have been described in the Volga River basin families and in one Italian pedigree (53,54). Individuals affected by PSEN2 mutations have a variable time to dis-ease onset, ranging from the fourth through the eighth decade of life.

111.2.3 Pathophysiologic Roles of APP and the Presenilins

The physiologic role of the presenilins remains unclear. However, the presenilin mutations do appear to have a pathophysiologic relationship with APP. Assays of Aβ40 and Aβ42 in the plasma and the cultured skin fibroblast media of individuals with presenilin mutations revealed a twofold elevation of Aβ42 levels, despite no mutations of

the APP gene (85). In addition, transgenic mice express-ing PSEN1 and APP mutations demonstrate Aβ42 plaques and an accelerated AD-like phenotype (86). These obser-vations pointed to an interaction between APP and the presenilins. Whether the presenilins interact directly with APP through their putative γ-secretase activity, or act as cofactor for another γ-secretase, remains unclear; how-ever, it is clear that, like APP mutations, the PSEN1 and PSEN2 mutations all lead to increased levels of Aβ42. The precise role of Aβ accumulation in the pathogenesis of AD remains under investigation. Aβ accumulation may be the final common neurotoxic pathway, an additional toxic by-product of the primary neurotoxic pathway, or less likely, an inert by-product (79).

Variants in these three genes (APP, PSEN1, and PSEN2) account for between 30% and 50% of early-onset familial AD (87), yet together they account for less than 2% of all cases of AD (88–91). In addition, while the genetics underlying autosomal-dominant or familial EOAD may appear to be relatively straightforward, the variants in genes responsible for AD in many families have yet to be characterized. Furthermore, the relation-ship between identified causal mutations and AD phe-notype is not necessarily simple. For example, PSEN1 mutations associated with familial EOAD have been iden-tified in individuals with frontotemporal dementia who have no evidence of the Aβ accumulation characteristic of AD (92,93). Nevertheless, each gene discovered has contributed to the elucidation of the pathophysiologic mechanisms underlying AD.

111.3 GENETICS OF LOAD

LOAD cases comprise the majority (90–95%) of indi-viduals afflicted with AD. The prevalence of LOAD increases exponentially with advancing age (5). LOAD also increases the risk for other major common disor-ders (e.g. cardiovascular and cerebrovascular diseases), which reduce life expectancy (94,95). Thus it is clear that LOAD has a significant negative impact on the qual-ity of late life and will represent a major financial and emotional burden to society until effective treatments or preventions are available.

There is compelling evidence that the etiology of LOAD, like EOAD, is also strongly genetic. Family stud-ies (96,97) and twin studies (36,98) have suggested that LOAD may aggregate within families. While the majority of LOAD cases are sporadic, 15–25% are familial (99). Segregation analyses suggest the inheritance of LOAD is consistent with Mendelian inheritance, with incomplete penetrance and an additional multifactorial component (38,40).

Despite strong evidence that LOAD is a genetic dis-ease, the complex etiology of LOAD still poses a great challenge for the identification of susceptibility genes. The presence of co-occurring psychiatric disorders, such as depression, varies from case to case, suggesting the


possibility of heterogeneity in disease etiology. Whether environmental exposures like lead and socioeconomic factors like access to education that factor into risk of disease interact with genetic causes of LOAD remains unclear, and gene–environment interactions may fac-tor into the complex portrait of genetic susceptibility to LOAD.

111.3.1 Discovery of the Apolipoprotein E Gene

The Apolipoprotein E (APOE) gene is the first and most widely accepted susceptibility gene for LOAD. It was initially discovered using affected relative pair linkage analysis in a subset of individuals diagnosed with AD at age 60 or older (100), and the signal was further refined by follow-up association analysis (101). The APOE gene lies on the long arm of chromosome 19 (19q13.2) (102,103) adjacent to another apolipoprotein gene (APOC1), and is also linked to the APOC4, APOC2, and TOMM40 genes (104,105). APOE comprises four exons and three introns, spanning approximately 3.7 kb (102,106). It encodes the serum protein apolipoprotein E (ApoE), which is involved in the transport, storage, and metabolism of lipids and is synthesized in the cen-tral nervous system by astrocytes (107). There are three isoforms for ApoE protein: ApoE2, ApoE3, and ApoE4. These three isoforms result from single amino acid sub-stitutions at residues 112 and 158 (108,109). The ApoE2 isoform is produced when the amino acid sequence con-tains cysteine at both residues 112 and 158. ApoE3, the most common isoform, contains cysteine at residue 112 and arginine at 158. The ApoE4 isoform is seen when the sequence contains arginine at both these two residues. These three isoforms are, therefore, produced by three different combinations of codons at the two loci: TGC (for cysteine) and CGC (for arginine), which result in three different alleles: ε2, ε3, and ε4 alleles (Figure 111-1). The frequencies of these three alleles are estimated to be 8, 78, and 14%, respectively, in most Caucasian popu-lations (110). Other APOE alleles (such as ε1, ε5, and ε7), and therefore the ApoE isoforms they encode, are extremely rare in most populations. The APOE ε4 allele carries the strongest risk of any genetic variation for a complex disorder, and acts in a dose-dependent fashion (111). As such, the APOE ε4/ε4 genotype has a very high penetrance. Furthermore, the APOE ε2 allele appears protective for LOAD (112,113).111.3.1.1 Epidemiologic Features of the APOE Gene.

111.3.1.1.1 The APOE Polymorphism Is Associated with Risk and AAO of LOAD. While the role of APOE in cardiovascular diseases has been well characterized (114,115), its role in neurodegenerative disease has been the focus of investigation for over two decades. The ε4 allele has consistently been associated with risk for both sporadic and familial LOAD (111). In populations of European ancestry, the relative risks of LOAD for ε4


FIGURE 111-1 Structure of the APOE gene and three ApoE isoforms.

homozygotes and for ε3/ε4 heterozygotes are approxi-mately 15 and 3, respectively, compared to ε3 homozy-gotes (116). The ε4 allele has the greatest impact on risk for AD among individuals who are between the ages of 60 and 79 years. Its effect is gradually attenuated as an individual surpasses age 80 (116,117). In contrast to the ε4 allele, the ε2 allele is protective for risk of LOAD with a reduction in risk of up to 20% (112,113,118).

The APOE polymorphisms also modulate the AAO for both familial (111,119–122) and sporadic forms of AD (123–125). The APOE ε4 allele has a dosage effect on AAO. In one family-based study, the estimated mean AAO for subsets consisting of individuals carrying no ε4 allele, one copy of ε4 allele, and two copies of ε4 alleles, were 84.3, 75.5, and 68.8 years, respectively (111). In addition, the effect of ε4 allele on risk for LOAD is also age dependent. Although the APOE ε4 allele appears to determine when an individual develops the first demen-tia-related symptoms, it remains arguable whether the ε4 allele hastens disease progression. Two population-based studies reported that the APOE ε4 allele was associated with a higher rate of memory deterioration in nonde-mented individuals (126,127). However, there is equiv-ocal evidence regarding whether the APOE ε4 allele speeds up (128,129) or slows (130,131) down disease progression in LOAD patients. Other studies report that the APOE ε4 allele has little effect on disease progression (132,133).

111.3.1.1.2 The Effect of APOE Polymorphism May Vary by Race/Ethnicity. Although the APOE ε4 allele is a risk factor for LOAD in nearly every popula-tion, the magnitude of association between the ε4 allele and LOAD may vary by ethnicity. A weaker effect of the APOE ε4 allele on risk of LOAD in African-Americans, compared with Caucasians, has been reported in several studies (134–136). A meta-analysis of 40 studies by Far-rer and colleagues (116) found that the APOE ε4 allele has less influence on risk among African-American and

Hispanic populations, compared with the Caucasian pop-ulation, while the effect of APOE ε4 allele was greater within the Japanese population. In addition, heterogene-ity in APOE ε4 allele effect size has been noted across studies of African-Americans (116) and Hispanics (137), suggesting that the effect of APOE on LOAD risk may depend on additional population-specific characteristics.

Variation in the magnitude of association between the APOE polymorphism and risk of LOAD may also reflect the variation in distributions of APOE ε4 alleles in different racial/ethnic groups. For example, the APOE ε4 allele frequency in cognitively normal individuals was significantly greater in the indigenous Australian popu-lation than in the Caucasian population (138). Another example from a study of three distinct ethnic groups in Malaysia showed that the APOE ε4 allele frequency in the Indian population was higher compared to the Chinese and Malay populations (139). The “thrifty gene hypoth-esis,” initially proposed to study genetic mechanisms of type 2 diabetes (140), may also explain why high APOE ε4 allele frequency does not lead to increased ε4 allele-associated LOAD risk in some populations. High APOE ε4 allele frequency in some indigenous ethnic groups may result from selection favoring the ε4 allele because of its association with enhanced cholesterol uptake in envi-ronments where food supplies are more limited (141). Such a high APOE ε4 allele frequency may not result in an increased risk of AD in these populations until more individuals start to consume a high cholesterol diet (142). The hypothesis may be supported, at least in part, by the findings of no association between the APOE ε4 allele and LOAD in eastern Africa (143,144), while the APOE ε4 allele is a well-established risk factor for LOAD in African-Americans (116,134–136).

111.3.1.1.3 The Effect of APOE Polymorphism May Vary by Gender. Female individuals have a 1.5- to 3-fold higher risk of LOAD compared with male individ-uals (reviewed in (145)). The gender effect on LOAD risk

appears to be most marked among individuals carrying two copies of the APOE ε4 allele (116). Since estrogen is a putative neuroprotectant (reviewed in (146)), some have speculated that the higher risk of LOAD in women may be attributed to “estrogen deprivation” after the age of menopause; however, studies examining the pro-tective effect of estrogen replacement therapy on cogni-tive function have produced inconsistent and sometimes contradictory findings. Inconsistencies may be due to the different study designs (e.g. observational studies versus prospective randomized clinical trials), and may relate to issues of treatment, such as timing of initiation of the therapy and type of therapy (147). The APOE polymor-phism may also modulate the effect of estrogen on risk of LOAD. Decline in cognitive function is slowed by estro-gen replacement therapy, particularly in women carry-ing no APOE ε4 alleles (148). These lines of evidence suggest interactive effects between APOE ε4 allele and gender and/or estrogen on development of LOAD.111.3.1.2 The Role of APOE on the Neuropathology of LOAD. The definite diagnosis of AD is established based on neuropathological findings, including amyloid plaque, intracellular NFTs, and neuronal loss (149). In addition, cerebral amyloid angiopathy (CAA), defined by the accu-mulation of fibrillar Aβ in cerebral blood vessels, is also commonly seen in AD patients (150,151). The APOE poly-morphisms are thought to influence LOAD risk through these amyloid-related neuropathological processes.

111.3.1.2.1 Amyloid Plaque and NFTs. Several studies report that individuals carrying two copies of APOE ε4 alleles have a higher amyloid plaque density than individuals carrying one or no APOE ε4 alleles (152–157). Two other studies did not confirm this find-ing (158,159). Similarly, there have been conflicting find-ings regarding whether the APOE ε4 allele is associated with the density of NFTs. Several studies have reported an association between the APOE ε4 allele and NFT density (153,156,160,161), but these have been refuted by other studies (154,157–159,162).

111.3.1.2.2 Cerebral Amyloid Angiopathy. CAA is present in more than 90% of individuals affected with LOAD. There is an association of CAA and the APOE ε4 allele (156,163–165), and there is a dose-response relationship between the APOE ε4 allele and the extent of CAA (163,166); however, a recent study has shown that APOE polymorphisms have no effect on the severity of CAA (Tian et al., 2004). Interestingly, although CAA is a risk factor for lobar cerebral hemorrhage, APOE ε2 allele carriers have a higher rate of such hemorrhage related to CAA, compared with individuals carrying no APOE ε2 alleles (167,168). This counterintuitive finding is likely due to the association of the APOE ε2 allele and microangiopathic changes (e.g. fibrinoid necrosis and concentric splitting) occurring at the vessel wall (169).111.3.1.3 The Relationship of the APOE Gene with Anatomic Findings in LOAD. The typical anatomic pathological change in brains of LOAD individuals is


whole brain atrophy. The medial temporal lobe, and in particular the hippocampus, manifests some of the ear-liest and most marked atrophy. Mild cognitive impair-ment (MCI) is generally understood to be an intermediate stage between the cognitive changes of normal aging and the serious cognitive declines related to AD. MCI is more likely to progress to AD in the presence of hippocampal atrophy (170). Several studies have shown that hippo-campal volume is inversely associated with the number of APOE ε4 alleles (171–173), but this finding was not supported by a different study (174). Other research has shown that the APOE ε4 allele hastens hippocampal atrophy in nondemented individuals (175). In addition, the APOE ε4 allele appears to exert a greater effect on hippocampal atrophy in women than in men (176).

The mechanism by which APOE polymorphisms influence risk and AAO in LOAD is yet to be uncovered. Several hypotheses have been posited, mostly centered on the relationship between the APOE polymorphisms and Aβ. One widely tested hypothesis proposes that the APOE polymorphisms may have an impact on the production, distribution, and clearance of Aβ. Support-ing this hypothesis, the APOE polymorphisms influence AAO, particularly in individuals carrying the β-amyloid precursor protein (βAPP) Val717Ile mutation, suggest-ing a possible interaction between the APOE gene and the APP gene (122). The density of Aβ peptide plaque is greater in individuals carrying at least one copy of the APOE ε4 allele, compared to individuals carrying no copies of the APOE ε4 allele (156). In addition, the hip-pocampal atrophy resulting from neuronal loss may, at least in part, stem from an impaired ability to clear extra-cellular Aβ due to the APOE ε4 allele (177).

An alternative hypothesis proposes that the APOE polymorphisms influence risk of LOAD by altered cho-lesterol metabolism. Intracellular cholesterol availability impacts transport and storage of βAPP, and therefore the reduction in intracellular cholesterol may lead to decreased Aβ formation (178). The cholesterol hypoth-esis is supported by observations that taking statin drugs to treat hypercholesterolemia may decrease risk of LOAD (179–183); however, a longitudinal community-based study refutes this finding (184).

111.3.2 Additional Candidate Susceptibility Loci for Late-Onset AD

111.3.2.1 Overview. While APOE is critically important, it does not account for all the genetic variations observed in AD. More than a third of AD cases do not have a single APOE ε4 allele. The sibling relative risk (λ) for the APOE locus is estimated to be about two, suggesting that APOE accounts for, at most, 50% of the total genetic effect in AD (116,123,185). To date, efforts to identify the remaining AD loci have taken several forms: regional and whole-genome scans for linkage in multiplex families, association tests of


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didate genes, the combining of findings across ltiple studies through linkage and association meta-lyses, genome-wide association studies (GWAS), and emerging technology of deep resequencing..3.2.2 Linkage Analysis. Genome-wide linkage

ns (GWLS) for LOAD have been published on many ependent datasets since 1997 (137,186–195). Many the scans (186–191) have focused on Caucasian fami- from the general population, while other scans have used on special populations (137,189,190,193) or y large single inbred families (192,194,195). All these dies have used microsatellite markers with, at best, average spacing of 8 cM. Some chromosomal regions e been studied extensively (most notably chromo-es 9, 10, and 12), although no consistently replicated

gene has yet been identified within these regions. It uld also be noted that while the majority of linkage

follow-up studies have focused on these chromosomes, there are numerous other linkage regions identified in more than one study that have been virtually ignored (Table 111-2).

For several reasons, reliance solely on linkage data has produced limited results and problematic inconsistencies across studies (196). One reason is that linkage analysis is more powerful than association analysis for identify-ing rare, high-risk disease alleles, but association analysis is more powerful for detecting common disease alleles conferring modest disease risk (197). Yet another expla-nation is that criteria for suggestive linkage may allow for too many false-positive regions; because of multiple hypothesis testing considerations, it is suggested that fine-mapping suggestive linkage peaks is only sensible when there is clear evidence for an excess of suggestive linkage across the genome (198).

TABLE 111-2 Genetic Classification of Alzheimer Dementias

Type Chromosome Gene

I. The Alzheimer diseasesA. Early-onset familial, autosomal dominant mutations (AD1) 21 APPB. Late-onset familial and sporadic associated, susceptibility gene (AD2) 19 APOEC. Early-onset familial, autosomal dominant (AD3) 14 PSEN1D. Early-onset familial, autosomal dominant (AD4) 1 PSEN2E. Late-onset familial (AD5) 12 ?F. Late-onset familial (AD6) 10 ?G. Alzheimer disease, Lewy body variant 19, other? APOE, other?H. Other confirmed late-onset susceptibility genes (sporadic AD) 1 CR1

2 BIN16 CD2AP7 EPHA18 CLU11 MS4A gene cluster11 PICALM11 SORL119 ABCA719 CD33

II. Other causes of presenile dementias 17 MAPTA. Frontotemporal dementia with parkinsonism (FTDP-17) (Picks disease,

nonspecific dementia, familial subcortical gliosis, frontal lobe degeneration)4 HD

B. Huntington disease 21 APPC. Hereditary cerebral hemorrhage with amyloidosis, Dutch 20 CST3D. Hereditary cerebral hemorrhage with amyloidosis, Icelandic 19 NOTCH3E. Hereditary multi-infarct type dementia (CADISIL) 13 ITMB2F. Familial dementia, British (FDB) and Danish (FDD) 3 ?G. Familial nonspecific dementia 4 SNCAH. Familial Parkinson disease, type 1 ? ?I. Adult-onset Parkinson disease, familial and sporadic

III. Mutations at the prion locusA. Gerstmann–Straussler–Schenker (GSS) 20 PRNPB. Creutzfeldt–Jakob, hereditary (CJD) 20 PRNPC. Creutzfeldt–Jakob (transmitted) 20 PRNP

IV. Nongenetic dementiasA. Infectious (e.g., AIDS, syphilis)B. VasculitisC. Metabolic/nutritional (e.g., thyroid, B12 deficiency)D. Vascular (e.g., multi-infarct)E. Drug toxocityF. Tumors

In linkage studies of AD, initial dataset sizes were quite small and had inadequate power to detect all relevant loci. While the datasets have subsequently increased, the microsatellite marker sets have recovered less than 50% of the potential linkage information in the families (199). Thus, many regions of true linkage may remain unde-tected, and false-positive results may have been inflated. Even under the best of circumstances, the regions identi-fied through GWLS still contain hundreds of locational candidate genes. Thus, while GWLS data have been, and will continue to be, useful, they do not represent a definitive, comprehensive, nor sufficiently detailed exam-ination of the whole genome. Despite these caveats, con-siderable analyses have been done following up GWLS regions without confirmation of new genes identified.111.3.2.3 Candidate Gene Association Analyses. The candidate gene approach for identifying suscepti-bility genes focuses on specific candidate genes selected because of their known (or more often, hypothesized) bio-logical function relevant to AD. Hundreds of genes have been individually tested for association with LOAD, and nearly 150 genes have been reported to be associated, but few of these have gained wide acceptance (200,201). There are several reasons for this. First, our knowledge of gene function is still very limited, and it has been diffi-cult to make direct observations of altered gene function or expression in Alzheimer tissues. Second, the sample sizes and single-stage study designs have generally been too small for the small-to-moderate effect sizes and locus heterogeneity that we now suspect underlie AD. Third, the use of clinical details and clinical subtypes to define clinical (and by proxy, genetic) heterogeneity has only recently been recognized, and only sporadically imple-mented. Fourth, the level of genomic detail that could be interrogated was low. All these issues conspire to make replication of any true effect difficult, and false-positive results rampant. Thus, while some of these reported associations could be and are real, it is not surprising that the evidence for these loci has been mixed. Meta-analyses and GWAS are two association approaches that have been used to overcome the limitations of the candidate gene approach for identifying genes, the first by combining statistical power to detect effects across multiples datasets, and the second by interrogating most of the genome agnostically with high-density coverage.111.3.2.4 Candidate Gene Meta-Analyses. Given the limitation that sample sizes in candidate gene study designs have generally been too small to detect small- to-moderate effect sizes, one approach that benefits from the availability of genetic data in multiple samples is meta-analysis. A meta-analysis by Bertram et al. (201) served not only to catalog association results across many can-didate gene association studies in the AlzGene database, as described before, but also to identify associations with AD by systematically combining data on genetic variants that had been genotyped commonly across multiple stud-ies. This study performed meta-analyses systematically


on variants genotyped in three or more samples and identified variants with small-to-modest effect sizes in 13 candidate genes for further investigation (including ACE, CHRNB2, CST3, R1, GAPDHS, IDE, MTHFR, NCSTN, PRNP, PSEN1, TF, TFAM and TNF).

PSEN1 is one particular gene known to be involved in familial, EOAD, suggesting that this approach may have identified valuable candidates for further exami-nation. The authors noted a major limitation of this approach: candidate gene studies tend to examine genes with hypothetical functional roles in disease or proxim-ity to strong linkage signals, and this has the potential to introduce bias from several sources into meta-analytic examinations. This limitation is partially addressed by the introduction of GWAS, which deal with several potential biases by examining high-density genotyping of markers capturing most common variations across the genome, reducing overrepresentation of variant data from particular genomic regions. The application of meta-analytic approaches to GWAS data represents an additional improvement upon meta-analysis in candi-date genes by improving sample sizes and allowing for small-to-modest effects to be observed with fewer poten-tial biases inherent, provided appropriate adjustment is made for differences between high-density genotyping platforms and for relevant issues of study design.111.3.2.5 Genome-Wide Association Analyses. More than 13 studies have tested association with LOAD on high-density, genome-spanning panels of SNPs. Grupe et al. (202) pooled samples and tested association with more than 17,000 gene-based putative functional SNPs across the genome, finding a signal at APOE that reached study-wide statistical significance, with multiple weaker associations observed elsewhere, many occurring in regions of known linkage. Coon et al. (203) reported results of association with half-million SNPs across the genome genotyped on over 1000 histo-pathologically verified AD cases and controls, identifying only APOE as a major susceptibility gene. A follow-up study (204) stratifying cases by APOE genotype detected strong associations with GAB2 SNPs, and in follow-up work observed altered GAB2 transcript levels in vul-nerable neurons, and an effect of GAB2 levels on tau phosphorylation; replication studies have observed mixed results (205–209). Abraham et al. (210) geno-typed approximately 550,000 SNPs in more than 1000 pooled cases and 1200 pooled controls, and in testing genetic associations, observed genome-wide statistical significance only for SNPs in or near APOE. Following up on the strongest signals that did not attain statistical significance with more individual genotyping identified the gene LRAT (MIM:604863), which is involved in the vitamin A (retinoid) cascade, a system previously impli-cated in AD. Bertram et al. (211) analyzed 500,000 SNPs in 410 families, reporting an SNP associated with AD AAO on chromosome 14q31, and providing additional evidence of associations near APOE and near GAB2.


Beecham et al. (212) reported a GWAS on approxi-mately 550,000 SNPs in nearly 500 AD cases and 500 cognitive controls. This study confirmed associations at APOE and identified an SNP on chromosome 12q13 meeting a genome-wide statistical significance threshold corresponding to a false-discovery rate (FDR) < 0.20. The strongest signals with less than genome-wide statistical significance in this study were identified in regions with prior linkage evidence, suggesting that some of these are likely to underlie true associations (213). A GWAS (214), genotyping 314,000 SNPs on 844 cases and 1255 controls, once again verified APOE associations, and in second-stage replication analysis, identified a novel signal on the X chromosome (combined P = 3.9 × 10−12) in the gene PCDH11X (MIM:300246), encoding a pro-tocadherin, a cell–cell adhesion molecule expressed in the brain. Finally, a study (215) using the original set of cases and controls from Beecham et al. (212) and additional newly identified cases and controls with high-density genotyping examined associations in a genome-wide set of markers (483,399 SNPs) and identified an association with LOAD of genome-wide significance (P = 4.70 × 10−8) at 151.2 Mb of chromosome 6q25.1 in the gene MTHFD1L, which encodes the methylenetet-rahydrofolate dehydrogenase (NADP + dependent) 1-like protein, a gene involved in the pathway synthesizing methionine from homocysteine, and thus may influence homocysteine levels, known risk factors for AD.

While many of the aforementioned non-APOE associ-ations attained genome-wide significance and were found to be associated in replication datasets, associations of these variants have not been found to be statistically sig-nificant in other independent studies, and for this reason, it remains unclear whether these genetic variations are truly associated with risk. A notable limitation of these studies is the lack of statistical power to detect small or even modest associations (odds ratio (OR) < 1.5) with samples sizes of ~1000 cases and ~1000 controls, or less. A second generation of GWAS with greatly increased sam-ple sizes were able to detect the first set of associations of variants with small effects on LOAD and that replicated in multiple studies. The first of these by the European Alzheimer Disease Initiative (EADI) (216) examined associations in a total of 6010 LOAD cases and 8625 controls, and observed highly significant associations in CLU, a chromosome 8 p21.1 gene that encodes clusterin or apolipoprotein J, and in CR1, a chromosome 1q32.2 gene encoding the complement component (3b/4b) recep-tor 1. Effect sizes for associations of the minor alleles at these loci were either slightly deleterious (for CR1, OR = 1.21) or slightly protective (for CLU, OR = 0.86). The Genetic and Environmental Risk in Alzheimer Disease (GERAD) consortium (217) also observed genome-wide statistical significance for associations of non-APOE genomic variants in their independent data-set including a total of 5964 cases and 10,188 controls. These included strong associations in CLU (OR = 0.86),

one of the genes with significantly associated variants in the Lambert et al. study, and novel associations in the PICALM gene (OR = 0.86). A third study by the Cohorts for Heart and Aging Research in Genomic Epidemiol-ogy (CHARGE) consortium (218), which incorporated data from both the EADI and GERAD consortia to investigate an overall 9511 cases and 28,174 controls, identified associations with genome-wide statistical sig-nificance adjacent to two sets of previously unreported loci, near BIN1 on chromosome 2q14.3 (OR = 1.13) and near EXOC3L2/BLOC1S3/MARK4 on chromosome 19q13.3 (OR = 1.18). BIN1 encodes the bridging inte-grator 1 protein, which is a nucleocytoplasmic adaptor protein that is heavily expressed in brain and muscle. Of the three genes proximal to the significant associations on chromosome 19q13.3 (signals that are independent of and not in linkage disequilibrium with associations in the APOE region), only two are potential biological candidates involved in AD-related pathways: BLOC1S3, which encodes subunit 3 of the biogenesis of lysosomal organelles complex-1 protein, influences endosomal to lysosomal routing, and is expressed in the brain; and MARK4, which encodes the MAP (mitogen activated protein)/microtubule affinity-regulating kinase 4 protein, is expressed exclusively in the brain, and participates in neuronal differentiation.

Most recently, two even larger GWAS, one from the Alzheimer Disease Genetics Consortium (ADGC) (219) and another from a joint GERAD/EADI/CHARGE analysis (GERAD+) (220), confirmed the genetic asso-ciations at CLU, CR1, PICALM, and BIN1, and further identified and confirmed several additional AD suscep-tibility loci. The ADGC examined data on nearly 2.5 million genotyped and imputed SNPs from 11,840 cases and 10,931 controls in 15 datasets using a three-stage design, all on samples from individuals of European ancestry. These novel SNP associations with LOAD at MS4A4A, CD2AP, EPHA1, CD33, and ABCA7 bring the total of consistently replicated loci associated with LOAD risk to 10 (APOE, CR1, CLU, PICALM, BIN1, EPHA1, MS4A, CD33, CD2AP, and ABCA7), confirm-ing the emerging consensus that common genetic varia-tion plays a significant role in the etiology of LOAD. These two studies estimated that, with the inclusion of genetic effects contributed by the nine non-APOE loci, as much as 50% of the genetic effect of LOAD may now be explained.

The next generation of genetic studies will likely take several forms. Some will combine and meta-analyze find-ings from large GWAS consortia, and with the increase in sizes of these datasets to many tens of thousands of LOAD cases and controls, much greater power will be available to identify and replicate newfound genetic associations with LOAD, and to uncover the biological mechanisms underlying disease pathology. Others will integrate annotation data on biochemical pathways to perform associations of variants at multiple related loci


through pathway analyses, while still others will inte-grate data on important biomarkers like Aβ in order to be able to capture genes more closely related to specific pathological features of disease.

111.3.3 Clinical Implications of Genetic Discoveries for AD

111.3.3.1 Genetic Counseling and Testing. Increased media attention and access to genetic testing through clinical laboratories and direct-to-consumer testing have enhanced public awareness of the role genetic factors play in the etiology of AD, but the personal and clinical utility of such information has thus far been unclear. It remains controversial whether genetic testing for AD is appropriate, particularly since effective treatments and preventions for AD are still lacking (88,221,222). In light of these facts, the National Society of Genetic Counselors and the American College of Medical Genetics published practice guidelines for clinicians to assess their patients’ risk for AD, to determine when genetic testing is appropriate, and to counsel their patients accordingly (223).

Using information collected from a three-generation pedigree, risk should be assessed based on average AAO in the family (early or late onset) and pattern of inheri-tance observed (sporadic, familial, or autosomal domi-nant). Genetic counseling should include information about the patient’s risk relative to the population and the current status of treatment and prevention options. For families in which an autosomal dominant form of AD is a possibility, it is recommended that genetic test-ing be offered for causative forms of dementia using the Guidelines for Genetic Testing for Huntington’s Disease (HD) set forth by The Huntington’s Disease Society of America (224). In particular, pretest genetic counsel-ing should include a discussion about the likelihood of identifying a mutation in the family and, if so, risk to the patient and offspring, motivations and consider-ations for pursuing testing, permanence of the informa-tion, and the potential impact of insurance, psyche, life plans, and relationships, among others. Genetic testing for causative mutations in a referral-based series of AD cases was proved to be cost-effective (225), and when accompanied by appropriate genetic counseling, resulted in effective coping skills and absence of untoward psy-chological responses (e.g. severe depression, anxiety, or suicidal ideation) (226–230).

On the other hand, while guidelines recommend assessing and counseling about risk, genetic testing for those with familial or sporadic LOAD is not recom-mended in most instances. In addition to a lack of clini-cal utility for those who carry APOE ε4 allele(s), APOE testing has low sensitivity and specificity, and it is dif-ficult to convey probabilistic risk based on the presence or absence of an ε4 allele (231). Based on data from the REVEAL study, which suggest that APOE testing

on a self-selected, highly motivated, and educated group does not result in significant short-term psychological distress, guidelines further state that testing may be con-sidered at the clinician’s discretion if the patient wishes to pursue testing despite genetic counseling and recom-mendations to the contrary (232). In such instances, the same testing protocol used for causative genes should be used.111.3.3.2 Pharmacogenomics. Genetic information may help illuminate disease mechanisms and facilitate developing targeted treatments. Diverse genetic influ-ences in AD may drive biochemical pathways that result in the clinical heterogeneity observed in AD. Therefore, individuals affected with different subtypes of AD or other dementias may be characterized by different dis-ease courses, prognosis, and responses to treatments. Pharmacogenomics research may shed some light on heterogeneity in individuals’ response to pharmaceutical compounds.

Donepezil, galantamine, rivastigmine, and tacrine are cholinesterase inhibitors (ChEIs) used to alleviate AD symptoms, with evidence of weak-to-moderate efficacy (233–235). Response to ChEIs may differ by APOE gen-otype (236). Specifically, carriers of at least one copy of the APOE ε4 polymorphism have been shown to be the less responsive to the ChEI tacrine (237).

The ChEIs are metabolized primarily by the cyto-chrome P450 drug metabolism proteins (238). Their baseline metabolic activity varies greatly from individual to individual, largely because of polymorphisms in the genes encoding them. One such polymorphism in the CYP2D6 gene has been found to contribute to 15% of cases of efficacy and/or adverse drug response in AD (239). There are ethnic and geographic differences in the frequency of these polymorphisms. For example, the CYP2Adel mutation is present in 15% of Asians, but only 1% of Caucasians, and produces a protein product with no metabolic activity (240).

The clinical significance of P450 gene polymorphisms as related to response to ChEIs has not been thoroughly elucidated. Similarly, pharmacogenomic approaches to developing drug targets in AD have not been extensively utilized in clinical practice (241).

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FURTHER READING

Holtzman, D. M.; Morris, J. C.; Goate, A. M. Alzheimer’s Disease: The Challenge of the Second Century. Sci. Transl. Med. 2011, 3 (77), 77sr1.

O’Brien, R. J.; Wong, P. C. Amyloid Precursor Protein Processing and Alzheimer’s Disease. Annu. Rev. Neurosci. 2011, 34, 185–204.

Lambert, J. C.; Amouyel, P. Genetics of Alzheimer’s Disease: New Evidences for an Old Hypothesis? Curr. Opin. Genet. Dev. 2011, 21 (3), 295–301.

Ertekin-Taner, N. Genetics of Alzheimer Disease in the Pre- and Post-GWAS Era. Alzheimer Res. Ther. 2010, 2 (1), 3.

Berkis, L. M.; Yu, C. -E.; Bird, T. D.; Tsuang, D. W. Review Arti-cle: Genetics of Alzheimer Disease. J. Geriatr. Psychiatr Neu-rol. 2010, 23 (4), 213–227.

Citron, M. Alzheimer’s Disease: Strategies for Disease Modifica-tion. Nat. Rev. Drug Discov. 2010, 9, 387–398.

Bettens, K.; Sleegers, K.; Van Broeckhoven, C. Current Status on Alzheimer Disease Molecular Genetics: From Past to Present, to Future. Hum. Mol. Genet. 2010, 19 (R1), R4–R11.

Hardy, J.; Guerreiro, R.; Wray, S.; Ferrari, R.; Momeni, P. The Genetics of Alzheimer’s Disease and Other Tauopathies. J. Alzheimers. Dis. 2011, 23, S33–S39.

Avramopoulos, D. Genetics of Alzheimer’s Disease: Recent Advances. Genom. Med. 2009, 1, 34.


Biographies

Margaret A Pericak-Vance, PhD, is the Associate Dean for Human Genomic Programs and Director of the John P Hussman Institute for Human Genomics at the University of Miami Miller School of Medicine. She is a genetic epidemiologist and a founding fellow of the Ameri-can College of Medical Genetics and a board-certified PhD medical geneticist. She has extensive experience in many types of genetic analysis. She directs the statistical and molecular genet-ics screening laboratory. Her particular area of interest is identifying genetic risk factors for complex traits. She was the first to report the linkage of late-onset Alzheimer disease (AD) to chromosome 19. She was responsible for the association studies of APOE-4 and AD. It was her laboratory that first reported the dosage effect of the APOE-4 allele on both risk and age- of-onset in AD, as well as the protective effect of the APOE-2 allele.

Jonathan Haines, PhD, is a genetic epidemiologist, Louise B McGavock Professor of Human Genetics, founder and Director of the Center for Human Genetics Research (CHGR), and Professor and Chief of the Division of Human Genomics within the Department of Molecular Physiology and Biophysics at Vanderbilt University. Dr Haines has extensive experience in all aspects of genetic epidemiology including clinical ascertainment and statistical and molecular analysis. He has supervised genomic studies for many complex disorders including autism, Alzheimer disease (AD), multiple sclerosis (MS), and macular degeneration. Most recently, he has been involved in multiple genome-wide association studies (GWAS) including those for autism, AD, MS, and breast cancer.

Susan Hahn, MS, CGC, is a board-certified genetic counselor and the Assistant Director of Communications, Compliance, and Ethics at the Hussman Institute for Human Genomics at the University of Miami Miller School of Medicine; a Director of the American Board of Genetic Counselors; current Chair of the Public Policy Committee of National Society of Genetic Coun-selors (NSGC); and a member of the Practice Guidelines Committee of the ACMG. She is an author of the recent practice guidelines regarding genetic counseling and testing for Alzheimer disease. She is knowledgeable on a variety of topics related to genomic medicine including clini-cal discoveries, genomic education initiatives, genomic medicine tools such as family history tools and algorithms, outcome measurements, and ethical, legal, and social issues related to genomic medicine, including impact on health disparities.


Adam Naj, PhD is a postdoctoral associate investigating late-onset Alzheimer disease (LOAD) with Margaret Pericak-Vance at the John P Hussman Institute for Human Genomics. He has performed multiple research projects identifying genetic risk factors for LOAD and related phenotypes. These include two genome-wide association studies (GWAS) of LOAD cases and cognitive controls, including work for the Alzheimer Disease Genetics Consortium, with which he recently published a study newly identifying four genes that contribute to genetic susceptibil-ity to LOAD. His ongoing work includes adapting existing linkage and association techniques for the analysis of next-generation sequence data on LOAD cases and controls.

Regina Carney, MD, is a consultant on the Hussman Institute’s Alzheimer study. Dr Carney has been involved in genetic research projects in hereditary spastic paraplegia, Parkinson’s disease, tuberculosis, autism spectrum disorders, and Alzheimer’s disease. Having completed her clinical training in psychiatry, she has decided to focus her efforts on Alzheimer’s dis-ease. Dr Carney has participated in the Alzheimer’s disease genetic study in multiple capacities, including clinical project management, achieving recruitment and enrollment goals, organizing and overseeing team efforts, participating in clinical case conferences, and managing clinical research staff.

Michael A Slifer, MD, PhD, is a Psychiatrist who served as the Chief Resident of Psychiatry and Behavioral Sciences at Duke University. He has research experience in Alzheimer disease genetics, and focuses on the molecular and clinical subphenotyping of genetically complex heterogeneous disorders.

emery and rimoin's principles and practice of medical genetics || genetics of alzheimer disease

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