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      Role of ABCA7 loss-of-function variant in Alzheimer's disease: a replication study in European–Americans

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          Abstract

          Introduction

          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.

          Methods

          We genotyped the GWAS common variant and four rare variants previously reported for ABCA7 in 3476 European–Americans.

          Results

          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.

          Conclusions

          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.

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          Most cited references4

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          Rare-variant association analysis: study designs and statistical tests.

          Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions. Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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            Differential confounding of rare and common variants in spatially structured populations

            Well-powered genome-wide association studies, now possible through advances in technology and large-scale collaborative projects, promise to reveal the contribution of rare variants to complex traits and disease. However, while population structure is a known confounder of association studies, it is unknown whether methods developed to control stratification are equally effective for rare variants. Here we demonstrate that rare variants can show a systematically different and typically stronger stratification than common variants, and that this is not necessarily corrected by existing methods. We show that the same process leads to inflation for load-based tests and can obscure signals at truly associated variants. We show that populations can display spatial structure in rare variants even when FST is low, but that allele-frequency dependent metrics of allele sharing can reveal localized stratification. These results underscore the importance of collecting and integrating spatial information in the genetic analysis of complex traits.
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              Missense variant in TREML2 protects against Alzheimer's disease

              TREM and TREM-like receptors are a structurally similar protein family encoded by genes clustered on chromosome 6p21.11. Recent studies have identified a rare coding variant (p.R47H) in TREM2 that confers a high risk for Alzheimer's disease (AD). In addition, common single nucleotide polymorphisms in this genomic region are associated with cerebrospinal fluid biomarkers for AD and a common intergenic variant found near the TREML2 gene has been identified to be protective for AD. However, little is known about the functional variant underlying the latter association or its relationship with the p.R47H. Here, we report comprehensive analyses using whole-exome sequencing data, cerebrospinal fluid biomarker analyses, meta-analyses (16,254 cases and 20,052 controls) and cell-based functional studies to support the role of the TREML2 coding missense variant p.S144G (rs3747742) as a potential driver of the meta-analysis AD-associated genome-wide association studies signal. Additionally, we demonstrate that the protective role of TREML2 in AD is independent of the role of TREM2 gene as a risk factor for AD.
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                Author and article information

                Contributors
                314-286-0546 , ccruchaga@wustl.edu
                Journal
                Alzheimers Res Ther
                Alzheimers Res Ther
                Alzheimer's Research & Therapy
                BioMed Central (London )
                1758-9193
                10 December 2015
                10 December 2015
                2015
                : 7
                : 73
                Affiliations
                [ ]Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue B8134, St. Louis, MO 63110 USA
                [ ]The Hope Center for Neurological Disorders, BJC Institute of Health, 425 S. Euclid Ave, St. Louis, MO 63110 USA
                Article
                154
                10.1186/s13195-015-0154-x
                4675010
                26654793
                ac71f949-bd97-430e-a304-8c83153fc259
                © Del-Aguila et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 7 August 2015
                : 12 October 2015
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000065, National Institute of Neurological Disorders and Stroke (US);
                Award ID: R01-NS085419
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging (US);
                Award ID: R01-AG044546
                Award ID: P01-AG003991
                Award ID: R01-AG035083
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000957, Alzheimer's Association (US);
                Award ID: NIRG-11-200110
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006312, BrightFocus Foundation (US);
                Award ID: A2013359S
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Neurology
                Neurology

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