Recently, a small number of studies have used genome-wide association (GWA) to search for novel genetic variants associated with AD endophenotypes. Discovering new risk genes would be extremely beneficial to the study of AD. Clinical trials could then selectively enroll, or perform sub-analyses on risk allele carriers, who are more likely to decline than noncarriers. Those at heightened genetic risk might also benefit the most from early treatment. Additionally, using AD risk genes as covariates would boost power in AD-related studies since modeling the identified genetic risk factors reduces otherwise unexplained variance in the disease trajectory, making other influential factors easier to detect.
Several initiatives, such as Alzheimer's Disease Neuroimaging Initiative (ADNI) (http://www.loni.ucla.edu/ADNI
), are now searching for new gene risk variants using neuroimaging traits that are highly heritable, easily measured in a reliable way, and associated with AD [83
]. This may be a valuable way to overcome some of the obstacles inherent in diagnosis-based searches for risk polymorphisms. For instance, one might use as an endophenotype the baseline regional neuroimaging measures known to predict longitudinal cognitive decline in amnestic MCI or early AD. Such measures make specific diagnoses unnecessary because they focus on symptoms, namely the confluence of longitudinally decreased cognitive ability with specific functional or structural brain deficits that predict that decrease. Also, as continuous measures that vary across the continuum of normalcy from MCI to AD, neuroimaging measures may offer greater statistical power for genetic analysis than binary diagnostic categories. Suggested criteria for endophenotypes are that the measures are associated with illness, are heritable, are apparent in an individual regardless of whether the illness is active, and that they co-segregate with illness within families [84
]. Some neuroimaging measures, such as hippocampal and ventricular volume largely meet these criteria as endophenotypes for AD. Both increased ventricular volume [85
] and decreased volume of medial temporal lobe structures, especially the hippocampus [87
] predict cognitive decline, are moderately to highly heritable [93
], and are associated with AD and genetic risk for AD (). Other measures that show promise in predicting cognitive decline are brain amyloid burden as measured using Pittsburgh Compound B [96
] and white matter integrity (in general and perhaps more specifically in the parietal lobe) as measured with diffusion tensor imaging [97
] both of which are also highly heritable [98
]. Some neuroimaging measures may not yet be considered endophenotypes. For instance, glucose metabolism as measured with FDG-PET [100
], and cerebral perfusion as measured with arterial spin labeling [103
] also may predict cognitive decline, but large-scale heritability studies of these measures in healthy older adults are needed to ascertain their potential for identifying genetic influences. These guidelines may be useful when evaluating the utility of a measure as an endophenotype.
One recent GWA study by Shen and associates (2010) evaluated genetic associations with brain structure using a large number of nonspecific phenotypes. They studied 733 AD and MCI patients and normal controls from the ADNI cohort and controlled for age, sex, education, handedness, and baseline intracranial volume [104
]. The authors examined 142 regions of interest and found that the well-known variants in APOE
(rs429358/rs7412 a.k.a. ε
2/3/4) and in a more newly identified gene, TOMM40
(rs2075650), were strongly associated with bilateral hippocampus and amygdala volumes. Four additional SNPs were associated at the P
level with regional gray matter density. In the EPHA4
gene, rs10932886 was correlated with gray matter density in the left precuneus and bilateral frontal regions—regions in which atrophy occurs in late AD [105
codes for the EPH receptor A4—a receptor tyrosine kinase that regulates dendritic spine morphology in pyramidal cells of the adult hippocampus. EPHA4
also helps to control glial glutamate transport resulting in regulation of hippocampal function [106
]. Its association with hippocampal structure and function makes this gene an intriguing target for future study. Likewise, rs6463843 in the NXPH1
gene was associated with gray matter density in the left middle orbital frontal gyrus. NXPH1
encodes the neurexophilin 1 protein, which is a physical ligand for α
-neurexins—proteins that may participate in synaptic function [107
]. Finally, rs4692256 (LOC391642) was associated with gray matter density in the right hippocampus, but the function of the genetic material containing that SNP is unknown. The authors also reported a number of other associations at the more liberal P
Two other recent ADNI-based GWA studies focused their searches on temporal lobe structures; temporal lobe volume is highly heritable and is also a relatively good predictor of developing AD. Potkin et al. (2009) used a genome-wide search for polymorphisms affecting hippocampal gray matter density, and identified novel AD susceptibility genes in 381 subjects who had AD or were normal controls [108
]. AD cases differed in genotype from controls at rs429358 (one of the two SNPs comprising the APOE
2/3/4 genotype), and at rs2075650 in the TOMM40
gene. Using a significance threshold of P
and covarying for age, sex, and the number of APOE
4 alleles, four SNPs were associated with right or left hippocampal gray matter density [108
]. Two of these, rs10074258 and rs12654281, were in or near the EFNA5
], which encodes the ephrin-A5 protein implicated in nervous system development including in the hippocampus [109
]. The gene function and association with hippocampal structure across multiple SNPs makes it an alluring target for future study. Two other SNPs associated with hippocampal gray matter density at the P
level were rs10781380 in the PRUNE2
gene and rs1888414 near the FDPSP
]. These two SNPs have a less clear tie to AD-related symptoms compared with those in EFNA5.
At the P
level, the authors also identified correlations of right or left hippocampal gray matter density with genotypes at an additional 11 SNPs.
In a larger study also using the ADNI dataset, Stein and colleagues (2010) used MRI and GWA to identify SNPs associated with temporal lobe and hippocampal volumes in 742 AD and MCI patients and healthy elderly adults, controlling for age and sex () [81
]. The authors also evaluated the relationship between temporal lobe volume and the APOE2/3/
4 genotype, which was not part of the Illumina gene chip used in the GWA. As expected, APOE
4 was associated with lower temporal lobe volume. Additionally, at a significance level of P
< 5 × 10−7
, the authors identified two SNPs that were associated with bilateral temporal lobe volume across diagnoses: rs10845840 in the GRIN2B
gene (independent of an APOE
4 effect), and rs2456930, located in an intergenic region of chromosome 15 [81
]. The GRIN2B
gene codes for a regulatory subunit 2B (NR2B) of the NMDA (N-methyl D-aspartate) glutamate receptor. NR2B is implicated in learning, memory, and structural plasticity, and cognitive deficits in Alzheimer's disease [110
]. The same glutamate receptor is also the target of memantine [112
], a drug designed to slow the progression of AD. This makes GRIN2B
an attractive target for future AD investigations generally, and also specifically with respect to how it may modulate memantine drug effects.
Finally, in the first voxelwise GWA (vGWA) study, Stein and colleagues (2010) examined the effects of genetic variation on brain structure as determined using tensor-based morphometry, while controlling for age and sex [113
]. Rather than testing for genetic associations with one or a small number of structural measures, associations were tested at each of hundreds of thousands of voxels in the image—leading to a whole-brain, whole-genome search. The authors evaluated 740 subjects from the ADNI study who had AD or MCI, or were normal controls, and identified only the most significant SNP association at each voxel. Top SNPs identified within known genes in this GWA search were rs476463 in the CSMD2
gene and rs2429582 in the CADPS2
(CUB and Sushi multiple domains 2) maps to a chromosomal region that may contain a suppressor of oligodendrogliomas [114
], although little is yet known about the protein function. CADPS2
codes for Ca++
-dependent secretion activator 2, a protein that regulates synaptic vesicle and large dense core vesicle priming in neurons, and promotes monoamine uptake and storage in neurons [115
]. Although no SNP survived a false discovery rate correction at P
< .05 [113
], this method remains promising when larger sample sizes become available. Stringent corrections are needed when searching an entire image for genomic effects, but the size of the search space can be greatly reduced by carrying forward promising voxels to later analyses. Because of this, the sample sizes needed to replicate a GWA finding, when searching an entire image, are typically much smaller than the discovery sample size (as low as 300-400 rather than 700 subjects [113
]) as the voxels with no effects can be discarded in the replication analyses.
The sample size needed to detect statistical relationships between genetic risk factors and specific brain measures depends upon the measure being studied. Beckett and colleagues (2010) recently compared the ability of various MRI- and PET-derived attributes to track the progression of MCI and AD [116
]. Regions of interest derived from specific brain voxels showing significant relationships to cognitive impairment in previous studies gave greater power to detect a slowing of the disease than measures related to whole structures such as the hippocampus. The increased power of statistical voxel selection was later reinforced by studies using both MRI [117
] and FDG-PET [118
]. Such statistically predefined regions of interest may be promising targets of genetic studies in which gene effects can be mapped using statistical mapping approaches. By focusing on regions with greatest statistical effects, the power to detect or replicate genetic effects in follow-up studies is vastly increased [119
]. In that regard, imaging studies can avoid a general problem in large-scale genetics; by focusing on promising voxels, replication samples may in fact be smaller than the discovery samples, if the effects of the genes in the brain are somewhat localized. The selection of sets of voxels showing significant genetic associations is helpful to boost power, above and beyond focusing solely on regions that are clinically important to the disease of interest (which is also important). Such an approach has been advocated by Chen et al. (2010) and Wu et al. (2010) [118
]. There are at least three advantages in focusing on specific voxels over predefined anatomical regions of interest. First, although a given gene variant may affect a region that shows dramatic effects in a given disease, that whole region may not be equally affected. Using a voxelwise approach may help to identify subregions that would provide a more concentrated focus for future replication efforts. An example is a recent study of the brain derived neurotrophic factor (BDNF
) genes, in which common variants were associated with brain fiber integrity on DTI, in 455 subjects [119
]. When the sample was split into two, the same regions of the white matter showed associations in each subsample, but there would have been no a priori
reason to select those regions as implicated. Limiting a search to significant voxels in follow-up studies boosts power by avoiding image wide corrections for statistical tests at voxels less likely to show an effect. Secondly, although the focus of a study may be AD, pathways altered by a specific gene variant may be relevant to multiple complex diseases and disorders. Data collection and analysis are costly in genetic neuroimaging studies. Therefore, reporting all significant results can provide information that may not otherwise be easily obtained but may be useful to researchers at large. Thirdly, image based tests for replication, such as conjunction tests, can be devised that allow specific sets of brain regions, not just specific genes, to be replicated as showing associations (see, e.g., Ho et al. 2010 [76
In GWA studies, it is conventional to enforce a significance cut-off of P
. This represents a Bonferroni-type correction for the false positives that could occur when 500,000 SNPs are searched for statistical effects. As adjacent SNPs are somewhat correlated (due to linkage disequilibrium effects), the effective number of tests is slightly fewer than the number of SNPs tested, but even SNPs falling below P
are considered to show “genome-wide evidence” requiring replication in subsequent studies or in meta-analyses of multiple independent datasets. So far, there is no universal agreement as to what statistical threshold for GWA studies is the best. The above ROI-based GWA studies reviewed here all used a threshold of at least 10−7
to report their top findings [81
], which controls for multiple comparisons in the tests performed. Dudbridge and Gusnanto (2008) suggested that a genome-wide significance threshold should not account only for markers that have been tested in a study, but also for all possible genomic variation. This leads to a more conservative threshold of P
< 7.2 × 10−8
]. Because of the required time and cost of collecting and analyzing neuroimaging data, the sample sizes here, although large for imaging studies, remain small for genetic studies. These smaller sample sizes may produce false positives unless independent replication is performed. Still, functionally promising SNPs have been identified in these studies, highlighting numerous replication targets for future work.
All four of the above GWA studies were performed using scans from the ADNI dataset with a high degree of overlap of subjects. Even so, the top SNPs were not replicated across studies. This may be due to a number of methodological factors. First, the sample sizes needed to detect a genetic association depend on the minor allele frequency and effect size, and are typically between a few hundred and several thousand subjects. With this limitation, measures that show association in one study may be missing in another. Even different software used to measure the same structure do not give perfectly correlated measures. Also, many associations will be missed due to imprecision in the measures—single gene effects are typically only detectable for measures with the highest precision and reproducibility. Additionally, across studies, the initial genetic searches did not adjust for the same covariates in addition to age and sex. For instance, Potkin and colleagues covaried for APOE
], but Shen and colleagues covaried for education, handedness, and baseline intracranial volume [104
], and the Stein et al. studies did not use additional covariates [81
]. Finally, the choices of ROI and methods of delineating those regions varied across studies. The ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) project (http://enigma.loni.ucla.edu/
] is one of several multicenter initiatives to standardize genetic and imaging methods. Its goal is to empower future replication efforts and make it easier to perform meta-analyses. Because different SNP sets are genotyped in different studies, imputation methods are employed to allow the same set of genomic variations to be queried across every dataset.
Using GWA to evaluate how genetic variance affects AD endophenotypes in cognitively intact younger and older adults may also aid in identifying AD genetic risk factors. Genetic variants associated with brain measures in young cognitively normal adults are less likely to be associated with molecular pathology. More likely, they support early vulnerabilities in the brain that AD pathology later exploits. The polymorphisms may, for instance, relate to health factors that increase the risk of AD, such as obesity and diabetes, or may relate to neural development in regions affected in early AD, such as the hippocampus and entorhinal cortex. Variants identified in cognitively intact older adults may relate to both AD molecular processes and vulnerabilities in the brain. Using amyloid imaging measures in these subjects may be helpful in identifying genetic risk factors for earliest AD changes.
An imaging measure may be associated with a particular polymorphism during development but may also be related to other gene polymorphisms with respect to degeneration later in life. Therefore, it is not the measurement, but rather its context and other demographic factors that determine whether gene effects relate to neurodevelopment or degeneration. This should be borne in mind when replicating gene effects across cohorts. For instance, in Stein et al. (2011), caudate volume was associated with commonly carried variants in dopamine-related genes, and the effects were found in an large elderly cohort scanned in North America, and replicated in a young adult cohort scanned in Australia [123
]. Such replications of SNPs may indicate gene effects that persist throughout life. The use of two very different samples is likely to identify genes of enduring relevance across the lifespan, but may miss or fail to replicate effects that exist or are more dominant only in late or early life. Naturally, there is a greater preponderance of apoptotic events in an elderly sample and more developmental or synaptogenic processes in the younger samples. For this reason, genome-wide meta-analyses must not regard failure to replicate as a sign that gene is not influential in a given part of the lifespan, or in a given cohort or continent.
In a study of normal brain aging, Seshadri and colleagues (2007) investigated genetic associations with measures of total cerebral brain volume, lobar, ventricular and white matter hyperintensity volumes, and scores on six cognitive tests. They identified three SNPs (located in ERBB4
, and RFX4
) that were associated both with measures of frontal or parietal brain volumes and with tests of executive function and abstract reasoning. These results did not survive testing for multiple comparisons, but they may be used to generate future hypotheses or to offer support to findings in future GWA studies [124
]. As this study was one of brain aging rather than of AD, cognitively normal adults were studied and not all measures examined were specific to AD risk. Therefore, some of the SNPs generated may relate more to brain aging or normal development than to AD risk.
Two GWA studies that we know of have examined endophenotypes in healthy young adults—a GWA study of caudate volume in 1198 young and old adults [123
] and the first voxelwise GWA study of diffusion tensor images [125
]. Further studies that focus on brain measurements specific to AD would be useful additions to the field. Since the brain differences that are likely to occur in normal adults are subtle compared to those in studies of a brain disease, very large numbers of subjects are needed to perform GWA in healthy young adults and to show that the results are reliable and reproducible across independent samples. The ENIGMA network brings together researchers in imaging and genetics, and current analyses are probing structural and functional neuroimaging and GWA data from over 10,000 subjects. This type of effort will prove invaluable in replication studies. ENIGMA also allows for the identification of “slow climbers”—genetic variants that may not be significant in all studies or in any one study alone, but may become highly significant when data is aggregated across studies.
GWA and vGWA involve huge numbers of comparisons, which may result in false positives if not properly controlled. It is therefore incumbent upon readers of such studies to critically evaluate the significance levels of the studies before basing potentially costly experiments upon their results. However, such exploratory studies may provide information that would not otherwise be easily obtained and can be extremely useful in focusing future work. For instance, one might not collect thousands of MRI scans to test the effect of one SNP previously found to be marginally significant. However, it may make sense to test the effects of that SNP in conjunction with other more established ones when GWA data has already been collected and the MRI scans have been physically analyzed. In this way, it is possible to build easily on previous results until they are strong enough to warrant independent exploration.
In addition, the large number of statistical tests involved in a genome-wide and/or image wide search requires special methods to boost power, including gene-based tests [126
], ridge regression models [127
], multilocus modeling, and meta-analysis. In the first voxelwise GWA studies of MRI and DTI [113
], no single SNP passed the conventional threshold for genome-wide significance; even so, the top SNPs can be prioritized when screening new imaging datasets for replications of these hits. Efforts such as the ENIGMA consortium have found that some SNPs identified by GWA are robustly associated with hippocampal volume and total brain volume. Although no single contributing site was able to find results that were genome-wide significant, the effects of several SNPs were robustly replicated when meta-analyzed across imaging datasets of more than 6400 subjects from 16 imaging sites [128