Role of ABCA7 loss-of-function variant in Alzheimer's disease: a replication study in European–Americans
- Jorge L. Del-Aguila†1, 2,
- Maria Victoria Fernández†1, 2,
- Jessica Jimenez1, 2,
- Kathleen Black1, 2,
- Shengmei Ma1, 2,
- Yuetiva Deming1, 2,
- David Carrell1,
- Ben Saef1, 2,
- Alzheimer’s Disease Neuroimaging Initiative,
- Bill Howells1, 2,
- John Budde1, 2 and
- Carlos Cruchaga1, 2Email author
© Del-Aguila et al. 2015
Received: 7 August 2015
Accepted: 12 October 2015
Published: 10 December 2015
A recent study found a significant increase of ABCA7 loss-of-function variants in Alzheimer’s disease (AD) cases compared to controls. Some variants were located on noncoding regions, but it was demonstrated that they affect splicing. Here, we try to replicate the association between AD risk and ABCA7 loss-of-function variants at both the single-variant and gene level in a large and well-characterized European American dataset.
We genotyped the GWAS common variant and four rare variants previously reported for ABCA7 in 3476 European–Americans.
We were not able to replicate the association at the single-variant level, likely due to a lower effect size on the European American population which led to limited statistical power. However, we did replicate the association at the gene level; we found a significant enrichment of ABCA7 loss-of-function variants in AD cases compared to controls (P = 0.0388; odds ratio =1.54). We also confirmed that the association of the loss-of-function variants is independent of the previously reported genome-wide association study signal.
Although the effect size for the association of ABCA7 loss-of-function variants with AD risk is lower in our study (odds ratio = 1.54) compared to the original report (odds ratio = 2.2), the replication of the findings of the original report provides a stronger foundation for future functional applications. The data indicate that different independent signals that modify risk for complex traits may exist on the same locus. Additionally, our results suggest that replication of rare-variant studies should be performed at the gene level rather than focusing on a single variant.
A recent study found that loss-of-function variants in ABCA7 (ATP-binding cassette transporter A7) confer greater risk for Alzheimer’s disease (AD) . Steinberg et al.  analyzed sequence, genome-wide association study (GWAS), and linkage data from 3419 individuals with AD and 151,805 controls from Iceland. Gene-based analyses, including nonsense, missense, frameshift splice-site variants and canonical splice-site variants (‘loss-of-function’), identified ABCA7 as the most significant gene (odds ratio (OR) = 2.12, P = 2.2 × 10–13) for AD. This association was mainly driven by a single splice-site variant, rs200538373 (OR = 4.47, P = 3.4 × 10–7), although other coding variants and splice variants were also found. This association was replicated at the gene level by genotyping the loss-of-function variants in more than 6500 AD cases and controls from four independent datasets (OR = 1.73, P = 0.0056). Interestingly, the OR for the variant (rs200538373) that led the association on the discovery series was in the opposite direction in the replication dataset (OR = 0.93). Additionally, none of these loss-of-function variants were in linkage disequilibrium (LD) with the ABCA7 common variant identified by GWAS , suggesting that there are multiple and independent mechanisms throughout the ABCA7 region that increase risk for AD. Based on these results, we tried to replicate the association of the ABCA7 loss-of-function variants in a large cohort of European–Americans.
A total of 1776 AD cases and 1700 controls were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Charles F. and Joanne Knight Alzheimer's Disease Research Center (Knight-ADRC) and the National Institute on Aging Genetics Initiative for Late-Onset Alzheimer’s Disease (NIALOAD) [3, 4]. The Institutional Review Board at the Washington University School of Medicine in Saint Louis approved the study. Research was carried out in accordance with the approved protocol. Written informed consent was obtained from participants and their family members by the Clinical Core of the Knight-ADRC. The approval number for the Knight-ADRC Genetics Core family studies is 93-0006.
In the original study, Steinberg et al.  genotyped the ABCA7 variants on 6681 European non-Icelandic individuals and only four variants were polymorphic (Supplementary Table 5 from Steinberg et al. ). Therefore, we decided to genotype the variants that were found to be polymorphic on the non-Icelandic population together with the GWAS common variant. The ABCA7 GWAS common variant (rs4147929) as well as the loss-of-function variants (p.Glu709Alafs*86, p.Leu1403Argfs*7, rs200538373, and rs113809142) reported by Steinberg et al.  were genotyped in our dataset using competitive allele-specific polymerase chain reaction KASPar genotyping technologies as described previously [3, 4].
Allelic frequencies, Hardy-Weinberg equilibrium and single-variant association tests were performed with PLINK-1.9 . For gene level analysis, burden analyses were performed using a combined multivariate and collapsing (CMC) test . Age, gender, and principal component factors (PCs) were included in all association tests as covariates.
Results and discussion
Association of ABCA7 variants with Alzheimer’s disease in studied groups based on Fisher’s association test, including age, gender, and PC as covariates. Gene-based analysis was conducted with CMC collapsing method
(95 % CI)
(All low frequency variants)
Although we had statistical power to replicate the association of rs113809142 with AD risk based on the effect size reported in the Icelandic population (OR = 4.42, power = 0.887), we did not have enough power to replicate the single-variant analyses based on the minor allele frequency (MAF) and effect sizes reported on the European non-Icelandic dataset. There is some debate concerning what is the best approach to replicate the association of rare variants with complex traits [7–11]. It is clear that the MAF for these low-frequency variants varies widely, not only among populations, but also within populations [10–12]. If the cases and controls are not very well matched for local genetic background, the study can produce false-positive or false-negative results [7, 11, 13]. Additionally, it is possible that a specific variant is not found, or found in an extremely low frequency in a specific population; as with the case of the TREM2 R47H variant in Asian [14, 15] or African–American populations. Although the association of the R47H variant with AD risk has been widely replicated in European–Americans, no significant association is found in Asian or African–American populations, because the MAF for this variant (R47H) is extremely low. However, other variants in the same gene could increase risk for diseases in these populations. Steinberg et al.  failed to replicate the association of the rs113809142 in the European non-Icelandic population, but they were able to replicate the association at the gene level. These results support the notion that different (local) populations have varying genetic make-ups, and therefore single-variant analyses may not be the best approach for replicating these studies. This hypothesis is also supported by recent studies from Jin et al. [3, 15] in which deep resequencing of TREM2 was performed on European–Americans and African–Americans; different variants were found in each population, and the variants in common presented very different MAFs and ORs. However, in both cases, the gene-based analyses supported the association of TREM2 with AD risk.
For this reason, we decided to perform a gene-based analysis for all the reported polymorphic ABCA7 loss-of-function variants. Since all variants presented the same direction of effect, we performed a CMC test. In our dataset, we found a significant enrichment of ABCA7 loss-of-function variants in AD cases compared to controls (P = 0.0388; OR = 1.54). Therefore, despite none of the individual loss-of-function variants reported a significant association with ABCA7 in this study, we were able to replicate two independent signals of the correlation of the ABCA7 gene with AD: the common variant and the aggregation of the loss-of-function variants.
The point estimate for the OR in this study for the gene-based analysis is slightly lower than the reported OR for the Icelandic population (OR = 2.12) or the European non-Icelandic replication datasets (OR = 1.73), although the 95 % confidence interval in this study (1.02–2.34) includes both the Icelandic OR and the European non-Icelandic OR published by Steinberg et al. . The current findings and those of the European non-Icelandic population support a possible "winner's curse" for the Icelandic discovery. Our sample size was smaller than both of the discovery series. Additionally, a proper gene-based replication would entail resequencing the candidate region to identify novel functional variants, and not just genotyping the reported variants; therefore, the real OR for the ABCA7 loss-of-function variants remains to be determined. Despite these limitations, we were able to replicate the original report. Our data also indicate that the gene-based association of these loss-of-function variants is independent of the GWAS variant, and that the aggregate effect of these variants is larger than that of the common variant alone.
Our study validates the role of noncoding loss-of-function ABCA7 variants in AD risk. Other population-specific independent variants with similar loss-of-function effects may contribute to AD risk or other complex traits. Supporting this hypothesis, a recent study has reported on an additional intronic low-frequency variant of ABCA7 (rs78117248; OR = 2.07, P = 0.0016) that increases risk for AD, also independently of the common variant. Together, these results suggest that different and independent variants modify risk for complex diseases by different mechanisms existing on the same locus [16, 17]. Other genes will also harbor rare variants increasing risk for AD, independently of the GWAS hits .
In summary, our study replicates the association of ABCA7 loss-of-function variants with AD risk, and highlights the necessity of performing gene-based, rather than single-variant analyses to replicate the association in this type of studies. Our study also confirms that there is high variability in the MAF of low-frequency variants within a population, so matching cases and controls for genetic background is a key step to avoiding false negatives or positives.
- ABCA7 :
ATC-binding cassette, subfamily A (ABC1), member 7
Alzheimer's Disease Neuroimaging Initiative
Alzheimer's Disease Research Center
Combined multivariate and collapsing
Genome-wide association study
Minor allele frequency
National Institute on Aging Genetics Initiative for Late-Onset Alzheimer’s Disease
Principal component factors
This work was supported by grants from the National Institutes of Health (NIH) (R01-NS085419; R01-AG044546, P01-AG003991, and R01-AG035083), and the Alzheimer Association (NIRG-11-200110). This research was conducted while CC was a recipient of a New Investigator Award in Alzheimer’s disease from the American Federation for Aging Research. CC is a recipient of a BrightFocus Foundation Alzheimer's Disease Research Grant (A2013359S). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276. Samples from the National Cell Repository for Alzheimer’s Disease (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. NIALOAD samples were collected under a cooperative agreement grant (U24 AG026395) awarded by the NIA. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (NIH Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the NIA, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec, Inc.; Bristol-Myers Squibb Company; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development, LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer, Inc.; Piramal Imaging; Servier; Synarc, Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Rev December 5, 2013 Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders and the Departments of Neurology and Psychiatry at Washington University School of Medicine. Data used in preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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- Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, et al. Loss-of-function variants in ABCA7 confer risk of Alzheimer's disease. Nat Genet. 2015;47:445–7. doi:10.1038/ng.3246.View ArticlePubMedGoogle Scholar
- Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013;45(12):1452–8. doi:10.1038/ng.2802.
- Jin SC, Benitez BA, Karch CM, Cooper B, Skorupa T, Carrell D, et al. Coding variants in TREM2 increase risk for Alzheimer's disease. Hum Mol Genet. 2014;23(21):5838–46. doi:10.1093/hmg/ddu277.
- Benitez BA, Jin SC, Guerreiro R, Graham R, Lord J, Harold D, et al. Missense variant in TREML2 protects against Alzheimer's disease. Neurobiol Aging. 2014;35:1510. doi:10.1016/j.neurobiolaging.2013.12.010. e19-26.PubMed CentralView ArticlePubMedGoogle Scholar
- Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. doi:10.1186/s13742-015-0047-8.PubMed CentralView ArticlePubMedGoogle Scholar
- Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008;83:311–21. doi:10.1016/j.ajhg.2008.06.024.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee S, Abecasis GR, Boehnke M, Lin X. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet. 2014;95:5–23. doi:10.1016/j.ajhg.2014.06.009.PubMed CentralView ArticlePubMedGoogle Scholar
- Lambert JC, Grenier-Boley B, Bellengez C, Pasquier F, Campion D, Dartigues JF, et al. PLD3 and the risk of sporadic Alzheimer’ disease. Nature. 2015;520(7545):E1. doi:10.1038/nature14036; In Press.
- Heilmann S, Drichel D, Clarimon J, Fernandez V, Lacour A, Ramirez A. PLD3 in non-familial Alzheimer’s disease. Nature. 2015;520(7545):E3–5 doi:10.1038/nature14039.
- Cruchaga C, Goate A. Reply to PLD3-variants in population studies. Nature. 2015;520(7545):E5–6. doi:10.1038/nature14037; In press.
- Van der Lee SJ, Wong TY, Jakobsdottir M, Bis JC, Chouraki V, Van Duijn C. PLD3-variants in population studies. Nature. 2015;520(7545):E2–3. doi:10.1038/nature14038 In Press.
- Benitez BA, Cooper B, Pastor P, Jin SC, Lorenzo E, Cervantes S, et al. TREM2 is associated with the risk of Alzheimer's disease in Spanish population. Neurobiol Aging. 2013;34:1711. doi:10.1016/j.neurobiolaging.2012.12.018. e15-7.PubMed CentralView ArticlePubMedGoogle Scholar
- Mathieson I, McVean G. Differential confounding of rare and common variants in spatially structured populations. Nat Genet. 2012;44:243–6. doi:10.1038/ng.1074.PubMed CentralView ArticlePubMedGoogle Scholar
- Miyashita A, Wen Y, Kitamura N, Matsubara E, Kawarabayashi T, Shoji M, et al. Lack of genetic association between TREM2 and late-onset Alzheimer's disease in a Japanese population. J Alzheimers Dis. 2014;41:1031–8. doi:10.3233/JAD-140225.PubMedGoogle Scholar
- Huang M, Wang D, Xu Z, Xu Y, Xu X, Ma Y, et al. Lack of genetic association between TREM2 and Alzheimer's disease in East Asian population: a systematic review and meta-analysis. Am J Alzheimers Dis Other Demen. 2015;30(6):541-6. doi:10.1177/1533317515577128.
- Cruchaga C, Haller G, Chakraverty S, Mayo K, Vallania FL, Mitra RD, et al. Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer's disease families. PLoS One. 2012;7, e31039. doi:10.1371/journal.pone.0031039.PubMed CentralView ArticlePubMedGoogle Scholar
- Vardarajan BN, Ghani M, Kahn A, Sheikh S, Sato C, Barral S, et al. Rare coding mutations identified by sequencing of Alzheimer disease genome-wide association studies loci. Ann Neurol. 2015;78:487–98. doi:10.1002/ana.24466.View ArticlePubMedGoogle Scholar