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Neurobiol Aging. Author manuscript; available in PMC Mar 1, 2013.
Published in final edited form as:
PMCID: PMC3010357
NIHMSID: NIHMS200191
Association of CLU and PICALM variants with Alzheimer's disease
M.I. Kamboh,a R. L. Minster,a F.Y. Demirci,a M. Ganguli,b S.T. DeKosky,c O.L. Lopez,d and M.M. Barmadaa
aDepartment of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
bDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
dDepartment of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
cOffice of the Dean and Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, USA
Correspondence author at: M. Ilyas Kamboh, PhD, Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA Tel: 412-624-3066; Fax: 412-383-7844; kamboh/at/pitt.edu
Two recent large genome-wide association studies have reported significant associations in the CLU (APOJ), CR1 and PICALM genes. In order to replicate these findings, we examined 7 single nucleotide polymorphisms (SNPs) most significantly implicated by these studies in a large case-control sample comprising of 2,707 individuals. Principle components analysis revealed no population substructure in our sample. While no association was observed with CR1 SNPs (P=0.30–0.457), a trend of association was seen with the PICALM (P=0.071–0.086) and CLU (P=0.148–0.258) SNPs. A meta-analysis of three studies revealed significant associations with all three genes. Our data from an independent and large case-control sample suggest that these gene regions should be followed up by comprehensive resequencing to find functional variants.
Keywords: Alzheimer's disease, Genetics, Association
The genetics of late-onset Alzheimer's disease (LOAD) is complex with the possible involvement of several genes. Although APOE has emerged as the strongest susceptibility marker for LOAD, it accounts for <30% of the disease risk (Slooter et al., 1998). In order to identify the remaining genes for LOAD, efforts have been focused on conducting genome-wide association (GWA) studies (Coon et al., 2007; Grupe et al., 2007; Li et al., 2008; Reinman et al., 2007; Abraham et al., 2008; Beecham et al., 2009; Bertram et al., 2008; Carrasquillo et al., 2009). However, with the exception of the APOE region, no other significant associations were replicated across these GWA studies. This highlights the difficulties in identifying the remaining LOAD genes which are thought to make a relatively small contribution to the overall risk of disease and they can only be discovered using well powered case-control samples. Two recent large GWA studies have identified significant association of LOAD with single nucleotide polymorphisms (SNPs) in the clusterin (CLU, a.k.a. APOJ), complement component receptor 1 (CR1) and phosphatidylinositol-binding clathrin assembly protein (PICALM) genes (Harold et al., 2009; Lambert et al., 2009). We set out to replicate these findings in a relatively large case-control sample of 2,707 individuals from a single geographical location in Western Pennsylvania.
Late-onset AD cases were Caucasian Americans (n=1,348; mean age-at-onset [AAO] 72.6 ± 6.4 [S.D.] years; 65.6% women; 22.8% autopsy confirmed) recruited by the University of Pittsburgh Alzheimer's Disease Research Center, all of whom met NINCDS-ADRDA criteria for probable or definite AD. Controls were non-demented Caucasian Americans, age 60 or above (n=1,359; mean age 74.7 ± 6.5 years; 60.8% women). The study was approved by the University of Pittsburgh Institutional Review Board.
Genotyping was performed using TaqMan SNP genotyping assays (Applied Biosystems, Foster City, CA). Each 384-well plate used for genotyping comprised both cases and controls. The E*2/E*3/E*4APOE polymorphism was determined either as described previously (Kamboh et al., 1995) or using TaqMan genotyping assays. Allele and genotype frequencies were calculated by the direct counting method. Odds ratios (ORs) were adjusted for the effects of age (AAO in cases), gender and APOE genotype. Association with AAO was tested with ANOVA in the LOAD sample using gender and APOE genotype as covariates. A meta-analysis was performed by combining our genotyping data for the 7 SNPs examined with the available genotype data for the corresponding SNPs from the two published GWA studies (Harold et al., 2009; Lambert et al., 2009). A fixed-effects (inverse variance) meta-analysis (Kazeem and Farrall, 2005) was performed using the catmap 1.6 (Nicodemus, 2008) package in R 2.7.2 (R Development Core Team, 2007). Odds ratios and confidence intervals and their P-values were calculated as well as the P-values from a formal test of the homogeneity of the various studies' ORs.
As expected, the presence of APOE*4 was associated with LOAD risk (OR=4.41; 95% confidence interval [CI] (4.05–4.80). We examined a total of 7 SNPs (3 in CLU, 2 each in CR1 and PICALM genes) most significantly implicated by either one or both GWA studies (Table 1). All SNPs were in Hardy-Weinberg equilibrium in both cases and controls, except for rs2279590 in CLU that deviated slightly from Hardy-Weinberg equilibrium in LOAD cases (P=0.034). Population stratification analysis by principle components was undertaken in PLINK (Purcell et al., 2007) and SpectralGEM (Lee et al., 2009) software, both of which demonstrated an even distribution of cases and controls across all dimensions, suggesting no anticipated biases from population substructure.
Table 1
Table 1
Association of SNPs in the CLU (APOJ), PICALM and CR1 genes with Alzheimer's disease
We did not observe an association of either CR1 SNP individually or in two-site haplotype analysis with AD risk. On the other hand, two of the CLU SNPs (rs1136000 and rs2279590) and both PICALM SNPs, although not achieving statistical significance at the α=5% level, showed a trend of association similar to that reported by both GWA studies (Harold et al., 2009; Lambert et al., 2009). The trend of association was more evident for the PICALM SNPs (P=0.071 – 0.086) than the two CLU SNPs (P=0.148 – 0.258). Three-site CLU and two-site PICALM haplotype analyses did not provide improved significant signals (data not shown). The absence of statistically significant signals in our sample could be due to a lack of power because the reported effects sizes of these SNPs are extremely small and were detected using several thousand cases and controls. However, our sample had 80% power to detect ORs of 1.18–1.22 for the risk alleles and 0.83–0.85 for the protective alleles of the 7 SNPs examined, which is comparable to the reported ORs in the original reports (Harold et al., 2009; Lambert et al., 2009). Despite the lack of statistically significant signals, the similarity of the directional effects observed in our sample to those reported in the two GWA studies suggests that PICALM and perhaps CLU could be relevant to AD, although the effects are small. For this reason, we performed a meta-analysis by combining our genotyping data for the 7 SNPs to those reported in the two GWA studies (Table 2). For the CLU/rs11136000, PICALM/rs541458 and PICALM/rs3851179 SNPs, actual genotyping counts were available for all three studies (ours, Lambert et al. and Harold et al.). However, for the other SNPs, genotypes were available from two studies (ours and Lambert et al.). As shown in Table 2, the combined genotype data helped to improve the published associations for CLU/rs11136000 (from P = 10−9 to 10−16), CR1/rs3818361 (from P = 10−9 to 10−13) and both PICALM SNPs (from P = 10−8 to 10−9). These findings warrant comprehensive resequencing of these gene regions to identify functional SNPs (common, rare or both) because the reported significant SNPs with weak effect sizes are present either in introns (CLU and CR1) or in the 5' flanking region (PICALM) and they could be in linkage disequilibrium with functional SNPs either in these or nearby genes.
Table 2
Table 2
Meta-analysis based on combined genotyping data from Lambert et al., Harold et al., and the present study
In addition to the AD risk, we also examined the association of 7 SNPs with AAO. Although CR1 SNPs demonstrated no association with AD risk in our sample, they revealed association with AAO that was stronger for rs3818361 (P=0.0097). The homozygosity of the less common allele rs3818361 was associated with lower AAO than the other two genotypes (70.75 ± 7.5 years vs. 72.32 ± 6.6 and 72.86 ± 6.2 years). This AAO association needs to be confirmed in other independent samples, as it is not clear if this was present in the two GWA studies (Harold et al., 2009; Lambert et al., 2009).
In summary, our data from an independent and large case-control sample in conjunction with two published GWA studies suggest that the PICALM, CLU and CR1 gene regions should be followed-up by comprehensive resequencing to find functional variants. While the relatively small statistical effects suggest that these loci are not major risk factors for LOAD, identification of functional variants in these loci and their detailed characterization may help to broaden our understanding about the underlying mechanism(s) of LOAD.
Acknowledgements
The study was supported by U.S. National Institute on Aging grants AG030653, AG005133, and AG023651. We thank Dr. Jean-Charles Lambert for providing the actual genotyping counts from his study for our meta-analysis.
Footnotes
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