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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Psychiatr Genet. Author manuscript; available in PMC 2011 December 1.
Published in final edited form as:
PMCID: PMC2964434
NIHMSID: NIHMS213331

PCDH11X variation is not associated with late-onset Alzheimer disease susceptibility

SUMMARY

A recent genome-wide association study and follow-up shows significant association with the protocadherin 11 X-linked (PCDH11X) gene. Carrasquillo et al. (2009) show statistical association with four PCDH11X polymorphisms (rs5984894, rs2573905, rs5941047, rs4568761) in five of seven cohorts. The combined analysis of 2,356 cases and 2,384 controls showed the strongest association with a p-value of 2.2 × 10-7 with an allele specific odds ratio of 1.30 (95% CI, 1.18–1.43) at the rs5984894 polymorphism. We tested for association at these four SNPs in two independent datasets and then performed a joint analysis. Though we had adequate power to detect effects sizes with the reported odds ratios, we did not detect association between LOAD and the PCDH11X polymorphisms in our dataset of 889 cases and 850 controls, indicating that the PCDH11X association, if not a false positive, is not as strong or generalized as previously hypothesized.

Keywords: Alzheimer disease, genetic association study, PCDH11X

INTRODUCTION

Late onset Alzheimer disease (LOAD [MIM 104300]) is the leading cause of dementia in the elderly and has a complex etiology, with a strong genetic risk component. A recent genome-wide association study implicated the protocadherin 11 X-linked (PCDH11X) gene as a risk locus for LOAD Carrasquillo et al. (2009). Their combined dataset contained 2,356 cases and 2,384 controls, and detected association at the rs5984894 SNP (p-value of 2.2 × 10-7, odds ratio = 1.3 [C.I., 1.18–1.43]). To validate this result, we examined the rs5984894 SNP and three others (rs2573905, rs5941047, and rs4568761) in two independent Alzheimer datasets and tested for the association between the four SNPs and LOAD.

MATERIALS AND METHODS

Sample Ascertainment

The Beecham sample set is derived from the Collaborative Alzheimer Project (CAP, the John P. Hussman Institute for Human Genomics at the Miller School of Medicine University of Miami and the Center for Human Genetics Research at Vanderbilt University Medical Center) as described in Beecham et al. (2009). The Naj sample set is derived from additional samples from CAP, samples from the National Cell Repository for Alzheimer’s Disease (NCRAD), and an autopsy series from Mount Sinai Medical (Naj et al., 2009). The autopsy series consisted of 306 cases and 81 controls recently deceased who had affection status verified through both clinical review and autopsy. Written informed consent was obtained for all participants, in agreement with protocols approved by the institutional review board at each contributing center. Each LOAD affected individual met the NINCDS-ADRDA criteria for possible, probable or definite AD and had an age at onset (AAO) greater than 60 years of age McKhann et al. (1984). All cognitive controls were examined; none showed signs of dementia in clinical history or upon interview and each had a Mini-Mental State Exam (MMSE) score > 27 or a Modified Mini-Mental State (3MS) Exam score > 87 Teng and Chui (1987). No cases and only 81 controls were obtained through NCRAD, so overlap with the Carrasquillo dataset, if any, is minimal.

After quality control filters the Beecham sample set contains a total of 988 individuals (492 LOAD cases 496 cognitive controls), and the Naj sample set contains a total of 755 samples (399 cases and 356 controls). Both sample sets are described in Table 1.

TABLE 1
Description of Sample sets

Genotyping

For both Beecham and Naj sample sets, DNA was extracted using Puregene chemistry (QIAGEN, Germantown, MD, USA). For the Beecham samples, we performed genotyping using the Illumina Beadstation and the Illumina HumanHap 550 beadchip, following the recommended conditions. For the Naj samples, we used the Illumina HumanHap 1M beadchip. Genotyping efficiency was greater than 99%, and quality assurance was achieved by the inclusion of two CEPH controls that were genotyped multiple times. The lab was blinded to affection status and quality-control samples. Sample and SNP quality control measures are discussed in Beecham et al. (2009). All samples were subject to a 98% efficiency threshold and were tested for gender consistency and relatedness. All SNPs were subject to efficiency, MAF, and Hardy-Weinberg thresholds. Heterozygous genotypes for males were set to missing. The four SNPs that were part of the primary analysis (rs5984894, rs2573905, rs5941047, and rs4568761) each had over 99% genotyping efficiency, though the rs2573905 SNP was only genotyped on the 1M chip in the Naj sample set.

Statistical Analysis

We tested for population substructure using Eigenstrat software Price et al. (2006) under the default settings (including five iterations of outlier removal). Hardy-Weinberg equilibrium was tested using an exact test in PLINK Purcell et al. (2007). We tested for association using logistic regression in PLINK. For the rs5984894 SNP we used both an additive model (Table 2) and a genotypic model (Table 3). For the genotypic models, males and females were tested separately (male hemizygotes for the major allele were the referent in the male-only analysis; female homozygotes for the major allele were the referent in the female-only analysis). For rs2573905, rs5941047, and rs4568761 we used the additive model (Table 4). Sex was included as a covariate in the additive model and the top three principle components were included to correct for any population substructure. Power was calculated using the Genetic Power Calculator Purcell et al. (2003).

TABLE 2
Descriptive statistics and additive association results for SNP rs5984894.
TABLE 3
Logistic regression results for rs5984894 genotypic model.
TABLE 4
Descriptive and association statistics for rs2573905, rs5941047, rs4568761.

RESULTS & DISCUSSION

The PCDH11X polymorphisms were not associated with late-onset Alzheimer disease in our datasets. The rs5984894 SNP was not associated with LOAD in either dataset and had a p-value=0.80 in the combined analysis, with an odds ratio of 1.02 (Table 1; 95% CI: 0.88–1.18). With this sample size, we have over 93% power to detect the reported odds ratio of 1.30 at the alpha=0.10 level. To test for sex or genotype specific effects we used logistic regression and a genotypic model on males and females separately (Table 2). There was no association detected in either the hemizygous males (p=0.59), female heterozygotes (p=0.90), or the female homozygotes (p=0.95). Genotypes were obtained on 1,739 of 1,743 samples (efficiency = 99.8%), there was no Hardy-Weinberg disequilibrium (case p-value = 0.25, control p-value = 0.29), and there were very few heterozygous males (1.3%). Minor allele frequency of the rs5984894 SNP for controls was comparable to that found by Carrasquillo et al, implying there were no major differences in genotyping between the different platforms. There were three additional SNPs highlighted in the Carrasquillo manuscript: rs2573905, rs5941047, and rs4568761. None of these SNPs were associated with LOAD (Table 3; p-values=0.96, 0.35, 0.41 respectively), though the rs2573905 SNP was not genotyped in the Beecham et al dataset. Finally, we performed association analysis across the gene (Figure 1). None of the 80 SNPs tested in the Naj dataset were associated with LOAD. Three of the 23 SNPs tested in the Beecham dataset showed nominal association (rs370928, rs453810, rs117393; p-value = 0.03, 0.04, 0.03), though none would survive a multiple testing correction. These SNPs were not in strong LD with those reported in Carrasquillo et al (r2 < 0.30).

FIGURE 1
Plot of the PCDH11X region; open circles are from the Beecham et al dataset, filled-in circles are from the Naj dataset. The dashed line represents the p-value=0.05 line.

We do not replicate the PCDH11X association signal. We have 93% power to detect the reported odds ratio (1.30) at an alpha=0.10 level and high quality genotyping, yet do not have statistical association. This suggests that any effect of PCDH11X on Alzheimer risk in our population is less than OR=1.30. Given that we have 80% power to detect an effect as small as an OR of 1.22, any effect of the PCDH11X polymorphisms is likely smaller than this. It is of note that we do not have adequate power to detect effects in the lower range of the reported OR confidence interval (1.18 to 1.43). Follow-up studies often fail to detect association because initial association results tend to over-estimate genetic effects sizes. This “Winner’s curse” effect can make replication difficult Goring et al. (2001); Kraft (2008).

It is also possible that loci other than those tested confer risk (allelic heterogeneity) or that polymorphisms may confer risk only in individuals with particular genetic backgrounds or environments (gene-gene or gene-environment interaction). Although our datasets seem quite comparable, if the Carrasquillo datasets were enriched for the susceptibility backgrounds or environments and our cohorts were not, then they would detect association and we would not. Though PCDH11X is an intriguing candidate gene, it must be further studied to determine its role, if any, in the etiology of Alzheimer disease.

Acknowledgments

We thank the AD patients and their families for participating in our study. This work was supported by grants from the National Institutes of Health: National Institute on Aging (AG20135, AG19757, AG010491, AG0005138, AG002219), National Institute of Neurological Disorders and Stroke (NS31153), the Alzheimer Association, and the Louis D. Scientific Award of the Institut de France. A subset of the participants was ascertained while Margaret A. Pericak-Vance was a faculty member at Duke University.

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