We have identified here two common polymorphisms that are associated with temporal lobe volume with genome-wide support in a large cohort of elderly subjects, assessed with brain imaging and genome-wide scanning. We also identified several potential candidate genes associated with both temporal lobe and hippocampal volume. We identified one SNP within an intergenic region on chromosome 15 which is strongly associated with temporal lobe volume. The most strongly associated polymorphism was within the GRIN2B
gene, which encodes the NR2B subunit of the NMDA receptor, and is a promising functional candidate considering the prior evidence of its involvement in learning and memory, structural plasticity of the brain, and in characteristic features of AD and neurodegeneration, including as a therapeutic target receptor. NMDA receptors have long been implicated in long-term potentiation, a key process in learning and memory, and over-expression of the GRIN2B
glutamate receptor gene enhances learning and memory in mice (Tang et al., 1999
). Synaptic plasticity mediated through NMDA receptors also causes structural remodeling of neurons, which reinforces these connections (Lamprecht and LeDoux, 2004
). Pharmaceutical blockade of NMDA receptor channels can limit cell death induced by excitotoxicity (Kemp and McKernan, 2002
; Parsons et al., 2007
). In addition, the relative prevalence and location of the NR2B subunit within the synapse is age-dependent. In early postnatal development, there is greater prevalence of the NR2B subunit, and its distribution shifts toward extrasynaptic locations with aging (Yashiro and Philpot, 2008
In addition, we performed fine-scale voxel-by-voxel mapping of associations between this genetic polymorphism and brain structure. The genes identified here were found based on gross summaries of anatomy, and stringent genome-wide evidence. However, we subsequently used a voxel-based mapping method to assess, at each point in the brain, the statistical association between rs10845840 and variations in brain structure. This clarified the anatomical specificity and localization of the gene effects, revealing strong effects in the bilateral temporal poles and bilateral medial temporal lobes. In situ
hybridization in post-mortem human brain has revealed high expression of GRIN2B mRNA within pyramidal cells of the temporal cortex and hippocampus (Schito et al., 1997
; Allen Institute for Brain Science, 2009
), consistent with this SNP having effects in these regions.
These findings may add another piece to the multifactorial genetic puzzle of late onset AD. Late-onset AD is hypothesized to be influenced by many genes, each with a relatively small effect (Tanzi, 1999
; Waring and Rosenberg, 2008
). Difficulties in finding these genes may arise from the heterogeneous nature of the disease, which can lead to groups of subjects with the same diagnosis but with different genetic architectures. Here, we use a different approach by studying a phenotype that is biologically based and is strongly associated with the disease. We note that the risk allele identified in the GRIN2B
gene is over-represented in patients with AD and MCI. It passes the genome-wide support threshold for association with temporal lobe volume deficits, which are a known risk factor for AD. The polymorphisms identified here also have relatively small effect: rs2456930 decreases temporal lobe volume, on average, by 1.473% per risk allele, and rs10845840 decreases temporal lobe volume by 1.457% per risk allele. Each of these genetic variations may contribute somewhat to the as yet unmodeled sources of heritability of Alzheimer’s disease beyond the currently accepted risk alleles, such as APOE
ε4 (Maher, 2008
; McCarthy et al., 2008
). A combined approach of studying genetic risk for AD through diagnosis, neuroimaging and structural endophenotypes may result in progress in discovering genetic contributors to late-onset AD.
The image pre-processing conducted here used a 9 parameter linear registration step that matches the position and scales the size of each brain to the MDT. In general, we use 9 parameter registration in our cross-sectional studies of Alzheimer’s disease because head size and brain size vary so widely across subjects; the temporal lobe tends to be more vulnerable to atrophy than the rest of the brain, so there is still substantial residual atrophy in AD versus controls even after adjusting for brain size. Because of this, temporal lobe atrophy is typically easier to detect after controlling for overall brain volume, because the effects of wide variations in head size have been largely removed. In addition, work by Paling et al. (Paling et al., 2004
) has advocated the use of 9 parameter linear registration, especially in multi-site imaging studies, as it can correct for scanner voxel size variations in large studies involving multiple sites, scanners, and acquisition sequences, such as this one (these are typically mild and may result in variations of 1–3% in brain volume, but they add to measurement error).
Even so, as we have noted in our prior studies (Brun et al., 2009
), there is however some evidence for non-proportional scaling of brain subregions relative to the overall size of the brain (Toro et al., 2008
). In all stereotaxic studies (e.g., those producing voxel-wise maps), this may confound the interpretation of apparently localized brain differences between groups. Put another way, the fraction of the brain that a specific brain substructure is expected to occupy may be larger (or smaller) in a smaller brain. Such an effect can be modeled by including brain volume as a regressor in the scaled Jacobian maps, perhaps after logarithmic transformation of both variables. For a full analysis of this effect, please see (Jancke et al., 1997
; Thompson et al., 2002
; Brun et al., 2009
). This power law effect is relevant to all morphometric studies as regional brain volume is always somewhat affected by the overall size of the brain, and it cannot be ruled out that SNPs influencing subregional volumes do so because they influence the overall size of the brain, if the relative volumes of the brain substructures follow a (nonlinear) power law.
Hippocampal volumes proved to be a less informative phenotype than temporal lobe volume. Hippocampal volumes, though widely studied in a genetic context (Seshadri et al., 2007
), are only moderately heritable (Peper et al., 2007
) most likely due to the large environmental influence as the hippocampus is a highly plastic structure – responsive to individual experiences. Additionally, though we have used new and reliable delineation methods for automatically delineating the hippocampus in the MRI scans (Morra et al., 2009
), it remains one of the most difficult structures to accurately model due to the resolution of the MRI scans and the small intensity differences between the structure and surrounding tissue.
One previous genome-wide association study of brain structure (Seshadri et al., 2007
) found SNPs with associations with temporal brain volume and hippocampal volume, but its power was limited as it examined related individuals, had few hippocampal volumes, and low genomic coverage. Those temporal lobe SNPs identified in the Seshadri study were either not identified or not replicated here. rs5028798 was not directly genotyped in our sample and no good proxy in HapMap was identified; rs2143881 was neither directly genotyped in our sample nor in HapMap; rs2793772 was not directly genotyped in our sample but was genotyped in HapMap with a good proxy rs1104973 (r2
= 1) but was not replicated (P
= 0.6964); rs10497352 was directly genotyped in our sample but was not replicated (P
= 0.6476); rs1433527 was directly genotyped in our sample but was not replicated (P
= 0.9804). Those hippocampal SNPs identified in the Seshadri study were not identified here. rs9293140 and rs1963442 were neither directly genotyped in our sample or in HapMap.
The sample sizes examined here are extremely large for an imaging study (this is one of the largest brain imaging studies to date), but are smaller than other genome-wide association studies that have not used brain scanning (Wellcome Trust Case Control Consortium, 2007
). Several factors empower the design. Scans of 742 healthy elderly control, MCI, and AD subjects allowed accurate structural measurements across a broad phenotypic range. The genome-wide analyses were not split within diagnostic groups as the goal was to present as broad a phenotypic continuum (Petersen, 2000
) as possible. Though it is possible that diagnostic groups represent distinct genetic backgrounds and may therefore confound the interpretation of our results, here we operate under the hypothesis that associations are evident regardless of diagnostic group, but may be more pronounced in disease (Gottesman and Gould, 2003
; Cannon and Keller, 2006
). In interpreting findings in this mixed cohort, it cannot be ruled out that the SNP effects are influencing the normal aging process independently of AD pathology. In fact, the SNP effects may even be present in young adults, prior to substantial brain aging. Conversely, it cannot be ruled out that such associations are driven by the presence of different diagnostic categories, and might not be found if only normal subjects were examined. In the future, when the sample sizes are greatly increased as more imaging and genetic data are collected, it should be possible to further stratify the image database to understand (1) which specific sub-populations show a detectable SNP effect, and (2) which processes (AD, aging, early development, or all of them) are influenced by the SNPs of interest. At present we have a more restricted goal of finding SNPs that influence brain structure in a mixed cohort of healthy and ill subjects, including those with AD and those who are healthy. Treating this cohort as a continuum is arguably more defensible than (for example) studying a mixed cohort of subjects with a Mendelian genetic illness (such as Fragile X) and controls. This is because for Alzheimer’s disease, a continuum is arguably evident in that some of cellular processes characteristic of AD (e.g., increased cerebral amyloid load) are typically present to some degree in those not yet diagnosed (Braskie et al., 2008
; Frisoni et al., in press
). For example, healthy elderly subjects often show some hallmarks of AD pathology at a subclinical level (amyloid plaques and tau neurofibrillary tangles) that can be detected on imaging and negatively correlate with cognitive status (Braskie et al., 2008
; Small et al., 2009
). As such, the effect of pathology on the SNP associations cannot be disentangled easily by focusing only on controls, as many harbor pathology at a subclinical level. Additionally, the boundary between MCI and AD is based on cognitive tests and observations of daily living that are easy to assess clinically, not biologically based boundaries (Petersen, 2000
). The continuum from healthy aging to mild impairment to disease gives the broadest phenotypic range and therefore the highest power to detect the genetic determinants of brain volume in old age, including variants that may have relevance to AD. Therefore splitting between diagnostic groups is likely to reduce power through both fewer subjects and a smaller phenotypic range (Cannon and Keller, 2006
). Even so, using a permutation algorithm we found that the findings exist regardless of diagnostic group. Additionally, the use of continuous traits (instead of discrete diagnostic categories) may also better reflect the underlying biology than clinical diagnosis alone (Potkin et al., 2009b
In this study we used a genome-wide evidence threshold of P
as in other genome-wide association studies (Wellcome Trust Case Control Consortium, 2007
; Sabatti et al., 2009
) querying multiple phenotypes. We refer in this paper to genome-wide evidence or support rather than genome-wide significance because there is not yet a universal consensus on how to define an appropriate significance threshold. We used permutation testing in which the imaging data is permuted across subjects and all SNPs are tested to estimate the probability that so high a P
-value for association could have occurred by chance. This is determined by keeping the same set of SNPs in each subject, but randomizing the assignment of images to subjects. After conducting associations with all the SNPs, the lowest P
-value is retained. This procedure can be used to determine a significance threshold that incorporates the fact that SNPs within the same subject are not independent (due to linkage disequilibrium). The two SNPs reported here have a permutation-corrected significance level of P
= 0.05419 for rs10845840 and P
= 0.1369 for rs2456930. The first of these results can be considered to mean that the GRIN2B
variant associates with the phenotype so strongly that only 1 in 20 times would any SNP at all be so strongly associated in completely null data. This is therefore evidence supporting that the association did not occur by chance.
More recent work has proposed an additional argument, suggesting that a genome-wide significance threshold should account for not just the markers directly measured as part of the experiment, but rather the variation of the entire genome (Dudbridge and Gusnanto, 2008
). Such an approach is more conservative; it is based on the premise that the subset of SNPs chosen for genotyping (which depends on the chip and the density of genotyping) could have been a “lucky” choice that came up with a high significance hit, and as such one should control for all genomic variants, even if they were not in fact genotyped in the current experiment. Such a line of argument suggests a genome-wide significance threshold of P <
Regardless of the genome-wide significance criterion, the gold standard for determining if hits are true positives is replication. Additional replication of this study’s findings is necessary. Work is actively ongoing through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Network (Thompson and Martin, 2010
) to find collaborations to replicate the findings presented here. Using a re-sampling approach, we estimate that fewer than 323 and <223 subjects will be needed to replicate the effect of rs10845840 and rs2456930, respectively, in a new sample at a reduced prior hypothesis significance level with 95% confidence. In addition to statistical validation, functional validation is also necessary to understand the mechanism by which these polymorphisms contribute to temporal lobe volume differences (McCarthy et al., 2008
). First, it is necessary to determine what the causative polymorphism is within the gene. rs2456930 resides in an intergenic region on the genome, so further characterization of the functional significance of this region is needed. rs10845840 lies in an intron of the GRIN2B
gene and is not in LD with more finely mapped potential causative mutations from a European sample identified in the current release of HapMap. However, more mutations within the gene do exist and detailed mapping of these variants could lead to identification of a causal mutation. Following this, the mechanism of action can be learned through knock-in animal models containing the causative mutation. Additionally, these intronic and intergenic gene variants could themselves alter biological pathways through changes in expression levels.
In summary, we identified potential quantitative trait loci associated with temporal lobe volume differences at a genome-wide evidence threshold in the elderly. These candidate genes can now serve as a target of study in future large replication samples. The polymorphisms identified here may also represent risk factors for diseases with characteristic temporal lobe atrophy such as AD and its common precursor, MCI; the NMDA/glutamate pathway is also a target for anti-dementia drugs such as memantine. These associations support the theory that endophenotypes (Gottesman and Gould, 2003
) will help to discover genes that influence brain structure. Ultimately studies combining imaging and genomic methods may help to provide a more mechanistic understanding of neurological and psychiatric illness.