We report several novel candidate loci that may harbor putative disease variants in Caribbean Hispanic individuals with LOAD and confirmed associations between LOAD and the 4 genes that have been previously reported. These 4 novel loci (5 SNPs) include multiple genes, and further examination is necessary to verify their involvement in LOAD. We replicated the allelic association between LOAD and CUGBP2
in homozygous carriers of the APOE ε4
allele reported by Wijsman and colleagues (Wijsman et al, unpublished data [full citation on page 321]). This gene was studied because the strongest signal was observed in homozygous ε4
carriers and this region on chromosome 10p14 contains the gene CUGBP2. CUGBP2
has 1 isoform that is expressed predominantly in neurons, with experimental evidence suggesting involvement in apoptosis in the hippocampus.37
Further, it is involved in posttranscriptional RNA binding activities as well as pre–messenger RNA alternative splicing. Based on structural similarity, it is speculated that this gene may be involved in increasing COX2
messenger RNA. Although the current study does support association with LOAD, the pattern of the associated SNPs differed between the 2 cohorts. The difference in genetic architecture between non-Hispanic and Hispanic populations is the most likely explanation for the fact that the associated SNPs differed between the 2 populations.
We found that the 4 candidate loci that were strongly associated with LOAD and were replicated in the NIA-LOAD cohort are located near genes that could be biologically relevant to LOAD. HPCAL1
on 2p25.1 is a calcium-binding protein expressed in the brain and has been associated with hypertension in Japanese individuals,38
which in turn is associated with LOAD risk. The region 10q23.1 includes 3 genes that are expressed in the brain and have been reported by Grupe et al,39
(believed to be involved in the neuronal maintenance), LRIT1
, and RGR
We replicated associations between LOAD and SNPs in 3 of the 4 genes that were previously reported to be significant at the genome-wide level, namely CLU, PICALM, and BIN1. However, the associated SNPs between these candidate genes and LOAD were not necessarily identical in the Caribbean Hispanic individuals compared with a European American data set. Nonetheless, the overall support for the 3 genes is enhanced by the observation that the allelic association extends to an ethnically distinct population.
, believed to be involved in modulation of inflammation and lipid metabolism, was associated with LOAD in carriers of ε 4
=.00213). More than a decade ago, we examined CLU
(also known as APOJ
) as a risk factor for LOAD because it shares similar functional roles as APOE
, including cholesterol binding and involvement in inflammation or injury.40
Based on a small set of coding polymorphisms in APOJ
, Tycko and colleagues40
did observe a positive association in 1 homozygous polymorphism, but this association was no longer significant when all subjects with at least 1 copy of the APOE ε4
allele were excluded. Further, they observed a significant difference in allele frequencies by race, and the present study also shows different linkage disequilibrium patterns between the Caribbean Hispanic individuals and the NIA-LOAD cohorts (eFigure 3). Thus, the inconsistent findings across studies could be attributed to an interaction between APOE
small sample size, different distribution of ethnic background in the participants, or any combination of these factors. The present study observed an association between CLU
and LOAD in the presence of APOE ε4
(). This is consistent with the much larger study by Lambert and colleagues9
but not with the study by Harold et al.7
, a gene expressed in the central nervous system and reported to activate a caspase-independent apoptotic process, was also associated with LOAD in only carriers of ε4
is reported to be involved in the neurotransmitter release processes, thereby affecting memory functions.41,42
Together these 3 genes suggest that they contribute to the overall LOAD phenotype. However, the measures of association are unlikely to be consistent across data sets, since in addition to allelic differences among race groups, significant differences in the distribution of vascular and inflammation risk factors can also alter the observed genotype-phenotype relations, even after adjusting for other known risk factors including age, sex, and education.43,44
The current study has some limitations. First, this study, based on a modest sample size of Caribbean Hispanic individuals, does not have power to detect rare variants with weak effects; thus, some risk variants may have been missed. Based on the original GWAS set, the current study has 80% power, genome-wide, to detect alleles with a frequency of 0.35 or higher when the OR is 1.5. When the OR for SNPs is 1.7, this study has 80% power to detect SNPs with an allele frequency of 0.25 or higher. When we combined both Caribbean Hispanic data sets (specifically, one from our GWAS along with the Caribbean Hispanic subset that is part of the NIA-LOAD GWAS), the current study has 80% power genome-wide to detect SNPs with somewhat lower allele frequencies. For a SNP with an OR of 1.5, 80% power can be achieved for SNPs with an allele frequency of 0.3 or higher. For a SNP with an OR of 1.7, 80% power can be achieved for SNPs with an allele frequency of 0.2 or higher. Power calculation was carried out assuming an additive model with SNP minor allele frequency being comparable with the allele frequency of the putative variant (http://pngu.mgh.harvard.edu/~purcell/gpc/cc2.html
). Second, independent replication of the candidate SNPs in Caribbean Hispanic individuals who share comparable genetic architecture would have further strengthened the validity of the findings because the likelihood of replicating the same allele within the same SNP would be higher than in other ethnic groups. For this reason, we added a small set of Caribbean Hispanic individuals from the NIA-LOAD GWAS data set who were evaluated using the same diagnostic tools. However, the sample size remained relatively modest. When we evaluated the candidate SNPs in an independent sample of European American individuals with different genetic background (NIA-LOAD GWAS), often allelic associations for the same SNPs were modest, but different SNPs within the gene supported allelic association. However, genetic associations using a cohort with a different ethnic background strengthen the observed association since (1) it is not unexpected to have multiple variants within a gene associated with a disease (eg, PSEN1
) and (2) the findings may be generalizeable to a wider set of populations. These findings need to be further evaluated using functional genetics approaches to evaluate the validity of observed association.
We used a dense set of SNPs to survey the genome to identify novel loci and to assess support for allelic association with BIN1, CLU, and PICALM. The current cohort extends previous GWAS of non-Hispanic white populations by exploring allelic association in an admixed cohort with a different set of genetic and environmental risk factors. The confirmation in the present study further strengthens the associations between variants in these genes and LOAD. It also supports the role of other genetic (eg, APOE) and environment factors modulating the genetic variant, especially when each variant may only have a small effect size. We also identified novel candidate genes (eg, HPCAL1, DGKB) in a Caribbean Hispanic cohort and replicated the association in an independent ethnically different data set. These genes need to be examined further in independent data sets.