Review
Neuroimaging Genetic Risk for Alzheimer’s Disease in Preclinical Individuals: From Candidate Genes to Polygenic Approaches

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Abstract

Better characterization of the preclinical phase of Alzheimer’s disease (AD) is needed to develop effective interventions. Neuropathologic changes in AD, including neuronal loss and the formation of proteinaceous deposits, can begin 20 years before the onset of clinical symptoms. As such, the emergence of cognitive impairment should not be the sole basis used to diagnose AD or to evaluate individuals for enrollment in clinical trials for preventive AD treatments. Instead, early preclinical biomarkers of disease and genetic risk should be used to determine the most likely prognosis and to enroll individuals in appropriate clinical trials. Neuroimaging-based biomarkers and genetic analysis together present a powerful system for classifying preclinical pathology in patients. Disease-modifying interventions are more likely to produce positive outcomes when administered early in the course of AD. This review examines the utility of the neuroimaging genetics field as it applies to AD and early detection during the preclinical phase. Neuroimaging studies focused on single genetic risk factors are summarized. Particular focus is on the recent increased interest in polygenic methods, and the benefits and disadvantages of these approaches are discussed. Challenges in the neuroimaging genetics field, including limitations of statistical power arising from small effect sizes and the overuse of cross-sectional designs, are also discussed. Despite the limitations, neuroimaging genetics has already begun to influence clinical trial design and is expected to play a major role in the prevention of AD.

Section snippets

Neuroimaging of Preclinical AD

A common approach for studying preclinical AD is to use a group at increased risk for AD as a potential preclinical cohort and compare them with a cohort of controls without the risk factor. Increased risk can be defined by the presence of a particular genetic risk variant, such as the apolipoprotein E ε4 (APOEε4) allele; a positive family history of AD; subjective memory impairment; and the presence of an early neuroimaging or cerebrospinal fluid biomarker. Well-validated neuroimaging-based

Neuroimaging and AD Candidate Genes

In 2000, the first study to combine neuroimaging and genetic risk for AD in healthy subjects found that carriers of the APOEε4 allele had higher activation across several cortical regions during a memory task compared with noncarriers (Figure 1A) (23). This approach, examining a selected variant within a single gene and the association of that variant with brain structure and function, is a type of candidate gene study. Candidate gene studies in neuroimaging are common, but they are

Polygenic Risk Scores

Combining multiple genetic risk loci into a single metric or score is an attractive way to modernize the candidate gene approach by using the metric or score as the “candidate” rather than a single gene. Associations between a risk score and, for example, an imaging endophenotype cannot be attributed to a single gene, but these associations may be clinically useful in the effort to characterize preclinical AD better (39). Such metrics are designed on one of two main theoretical bases: first,

Regression Approaches to Polygenic Risk

The use of predictive regression models in clinical biostatistics is common (45). Neuroimaging genetics presents a unique problem with millions of genetic markers (in whole-genome data) that can be used as predictors and many outcome phenotypes of interest. Furthermore, linkage disequilibrium, or the tendency of certain genetic loci to be inherited together, must be considered when using any regression method because many of these models assume that predictors are independent (46). The numerous

Power: Effect Sizes and Variant Frequency

A major challenge in neuroimaging genetics is sufficiently powering studies to detect hypothesized effects. One problem is the low effect size of common genetic associations with disease in human polygenic disorders (57, 58). An exception to this pattern is the APOE locus, where a commonly occurring variant is strongly associated with increased AD risk. APOE accounts for a larger amount of the variance in AD heritability than any single known genetic locus in another human neurobehavioral,

Implications for Clinical Trials

Despite major challenges related to statistical power, polygenic risk modeling, and generalizability, the field of neuroimaging genetics is poised to play a major role in the development of effective treatments for AD. All phase 3 AD treatment trials in humans have had negative outcomes, not meeting end points despite promising data in model organisms and in preceding trial phases (68, 69). This high failure rate may be partly the result of heterogeneity across the study participants enrolled

Conclusions

A neuroimaging genetics approach uses minimally invasive technologies to characterize the earliest pathophysiologic changes in preclinical AD. In the effort to prevent and treat AD, the short-term goal of combining multiple genetic factors, neuroimaging biomarkers, and other measures to estimate AD risk is to preselect clinical trial and research participants. The long-term goal is to provide more detailed prognoses in the clinic during the preclinical phase that can be used to create optimized

Acknowledgments and Disclosures

This work was supported by the National Institute on Aging Grant Nos. 5R01AG013308 (SYB) and 1F31AG047041 (TMH).

We thank Ms. Therese Vasagas for her help preparing the figures included in this manuscript.

The authors report no biomedical financial interests or potential conflicts of interest.

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