Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimer's Disease

PLOS ONE, Jun 2014

Background Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls. Principal Findings In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10−6) and 14 (IGHV1-67 p = 7.9×10−8) which indexed novel susceptibility loci. Significance The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease.

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Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimer's Disease

Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimer's Disease - 109 Department of Medicine (Geriatrics), University of Mississippi Medical Center, Jackson, Mississippi, United States of America, 110 Rush Alzheimers Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America, 111 Laboratory of Epidemiology, Demography, and Biometry, National Institute of Health, Bethesda, Maryland, United States of America, 112 Aging Research Center, Department Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden, 113 Department Geriatric Medicine, Genetics Unit, Karolinska University Hospital Huddinge, Stockholm, Sweden, 114 Division of Clinical Neurosciences, School of Medicine, University of Southampton, Southampton, United Kingdom, 115 Departments of Neurology and Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands, 116 Department of Pathology, University of Washington, Seattle, Washington, United States of America, 117 Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America, 118 INSERM UMR_S975-CNRS UMR 7225, Universite Pierre et Marie Curie, Centre de recherche de lInstitut du Cerveau et de la Moe lle e pinie` re-CRICM, Ho pital de la Salpetrie` re, Paris France, 119 AP-HP, H opital de la Pitie-Salpetrie` re, Paris, France, 120 Department of Epidemiology, University of Washington, Seattle, Washington, United States of America, 121 Laboratory of Neurogenetics, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, United States of America, 122 Imperial College, London, United Kingdom, 123 Department of Biology, Brigham Young University, Provo, Utah, United States of America, 124 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America, 125 Human Genetics Center and Div. of Epidemiology, University of Texas Health Sciences Center at Houston, Houston, Texas, United States of America, 126 Hospital Universitari Vall dHebron - Institut de Recerca, Universitat Auto` noma de Barcelona. (VHIR-UAB), Barcelona, Spain, 127 Department of Neurology, Medical University Graz, Graz, Austria, 128 Centre de Memoire de Ressources et de Recherche de Bordeaux, CHU de Bordeaux, Bordeaux, France, 129 Inserm U708, Victor Segalen University, Bordeaux, France, 130 Institute of Human Genetics, Department of Genomics, Life and Brain Center, University of Bonn, and German Center for Neurodegenerative Diseases (DZNE, Bonn), Bonn, Germany, 131 Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, KIADRC, Stockholm, Sweden, 132 Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America, 133 Vanderbilt Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, United States of America, 134 Department of Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America, 135 McGill University and Ge nome Quebec Innovation Centre, Montreal, Canada, 136 Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America, 137 Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, United States of America, 138 Center for Medical Systems Biology, Leiden, The Netherlands, 139 Department of Psychiatry and Psychotherapy and Institute of Human Genetics, University of Bonn, Bonn, Germany, 140 The Framingham Heart Study, Framingham, Massachusetts, United States of America, 141 Centre Hospitalier Re gional Universitaire de Lille, Lille, France Background: Alzheimers disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimers Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimers cases and 48,466 controls. Principal Findings: In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.461026) and 14 (IGHV1-67 p = 7.961028) which indexed novel susceptibility loci. Significance: The additional genes identified in this study, have an array of functions previously implicated in Alzheimers disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimers disease. Citation: Escott-Price V, Bellenguez C, Wang L-S, Choi S-H, Harold D, et al. (2014) Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimers Disease. PLoS ONE 9(6): e94661. doi:10.1371/journal.pone.0094661 Editor: Yong-Gang Yao, Kunming Institute of Zoology, Chinese Academy of Sciences, China Received December 3, 2013; Accepted March 17, 2014; Published June 12, 2014 This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Funding: The i-Select chips was funded by the French National Foundation on Alzheimers disease and related disorders. The French National Fondation on Alzheimers disease and related disorders supported several I-GAP meetings and communications. Data management involved the Centre National de Ge notypage,and was supported by the Institut Pasteur de Lille, Inserm, FRC (fondation pour la recherche sur le cerveau) and Rotary. This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimers disease) and by the LABEX GENMED grant (Medical Genomics). The French National Foundation on Alzheimers disease and related disorders and the Alzheimers Association (Chicago, Illinois) grant supported IGAP in-person meetings, communication and the Alzheimers Association (Chicago, Illinois) grant provided some funds to each consortium for analyses. EADI The authors thank Dr. Anne Boland (CNG) for her technical help in preparing the DNA samples for analyses. This work was supported by the National Foundation for Alzheimers disease and related disorders, the Institut Pasteur de Lille and the Centre National de Ge notypage. The Three-City Study was performed as part of a collaboration between the Institut National de la Sante et de la Recherche Medicale (Inserm), the Victor Segalen Bordeaux II University and Sanofi-Synthe labo. The Fondation pour la Recherche Me dicale funded the preparation and initiation of the study. The 3C Study was also funded by the Caisse Nationale Maladie des Travailleurs Salaries, Direction Ge nerale de la Sante, MGEN, Institut de la Longevite , Agence Francaise de Securite Sanitaire des Produits de Sante , the Aquitaine and Bourgogne Regional Councils, Agence Nationale de la Recherche, ANR supported the COGINUT and COVADIS projects. Fondation de France and the joint French Ministry of Research/INSERM Cohortes et collections de donnees biologiques programme. Lille Ge nopo le received an unconditional grant from Eisai. The Three-city biological bank was developed and maintained by the laboratory for genomic analysis LAG-BRC - Institut Pasteur de Lille. Belgium sample collection: The patients were clinically and pathological characterized by the neurologists Sebastiaan Engelborghs, Rik Vandenberghe and Peter P. De Deyn, and in part genetically by Caroline Van Cauwenberghe, Karolien Bettens and Kristel Sleegers. Research at the Antwerp site is funded in part by the Belgian Science Policy Office Interuniversity Attraction Poles program, the Foundation Alzheimer Research (SAO-FRA), the Flemish Government initiated Methusalem Excellence Program, the Research Foundation Flanders (FWO) and the University of Antwerp Research Fund, Belgium. Karolien Bettens is a postdoctoral fellow of the FWO. The Antwerp site authors thank the personnel of the VIB Genetic Service Facility, the Biobank of the Institute Born-Bunge and the Departments of Neurology and Memory Clinics at the Hospital Network Antwerp and the University Hospitals Leuven. Finish sample collection: Financial support for this project was provided by the Health Research Council of the Academy of Finland, EVO grant 5772708 of Kuopio University Hospital, and the Nordic Centre of Excellence in Neurodegeneration. Italian sample collections: the Bologna site (FL) obtained funds from the Italian Ministry of research and University as well as Carimonte Foundation. The Florence site was supported by grant RF-2010-2319722, grant from the the Cassa di Risparmio di Pistoia e Pescia (Grant 2012) and the Cassa di Risparmio di Firenze (Grant 2012). The Milan site was supported by a grant from the (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona. Marcelle MorrisonBogorad, PhD., Tony Phelps, PhD and Walter Kukull PhD are thanked for helping to co-ordinate this collection. ADNI Funding for ADNI is through the Northern California Institute for Research and Education by grants from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, Glaxo-SmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., Alzheimers Association, Alzheimers Drug Discovery Foundation, the Dana Foundation, and by the National Institute of Biomedical Imaging and Bioengineering and NIA grants U01 AG024904, RC2 AG036535, K01 AG030514. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimers Association; Alzheimers Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; 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; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of 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 Alzheimers Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514. The authors thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-o_cio ADGC members. Support was also from the Alzheimers Association (LAF, IIRG-08-89720; MP-V, IIRG-05-14147) and the United States Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program. Peter St George-Hyslop is supported by Wellcome Trust, Howard Hughes Medical Institute, and the Canadian Institute of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: Bruce M. Psaty serves on the DSMB for a clinical trial of a device funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Data used in preparation of this article were obtained from the Alzheimers Disease Neuroimaging Initiative (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. This does not alter the authors adherence to PLOS ONE policies on sharing data and materials. . These authors contributed equally to this work. " Membership of the UK Brain Expression consortium is provided in Materials S1. The prevalence of Alzheimers disease (AD) is increasing as more people live into old age. Hope for finding preventative and clinical therapies lies in the ability to gain a better understanding of the underlying biology of the disease, and genetics will provide a valuable starting point for advancement. Rare monogenic forms of AD, the majority of which are attributable to mutations in one of three genes, APP, PSEN1 and PSEN2, exist, but common, lateonset AD is genetically complex with heritability estimated to be between 5679%[1,2]. Along with the APOE polymorphism[3], 20 common susceptibility loci have been identified associated with AD[49]. (This figure does not include CD33 as it did not show genome-wide significance in the original report[9].) Recently, a moderately rare variant in TREM2 has also shown evidence for association[10]. However, new variants remain to be found. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach, which focuses on the pattern of association within gene regions. Genome-wide association (GWA) studies to date have focused on single nucleotide polymorphisms (SNPs) as the unit of analysis. Single locus tests are the simplest to generate and to interpret, but have limitations. For example, if susceptibility is conferred by multiple variants within a locus[11,12], this gives rise to complex patterns of association that might not be reflected by association to the same SNPs in different samples, despite apparently reasonably powered tests[13,14]. In addition, rare risk-increasing variants may not be tagged by single SNPs, as is e.g. the case for CLU in which significant enrichment of rare variants in patients was observed independent of the single locus GWA signal[15]. It is therefore likely that the power to detect association might be enhanced by exploiting information from multiple signals within genes encompassed by gene-wide statistical approaches[12]. Disease risk may reflect the co-action of several loci but the number of loci involved at the individual or the population levels are unknown, as is the spectrum of allele frequencies and effect sizes[16]. The observations of multiple genome-wide significant or suggestive linkage signals for disorders, that do not readily replicate between studies but which are not randomly distributed across the genome[17,18] is compatible with the existence of multiple risk alleles of moderate effect that would implicate a locus in disease risk, when analysed together. Thus the first aim of this study is to test for gene-wide association with AD, using a powerful mega-meta analysis of genome-wide datasets as part of the International Genomics of Alzheimers Project (IGAP) Consortium comprising four AD genetic consortia (see the full list of consortia members in Materials S1): Genetic and Environmental Risk in Alzheimers Disease (GERAD), European Alzheimers Disease Initiative (EADI), Cohorts for Heart and Aging in Genomic Epidemiology (CHARGE) and Alzheimers Disease Genetics Consortium (ADGC) (see full IGAP datasets description in Materials S2). A two stage study was undertaken. In Stage 1 the combined sample included 17,008 AD cases and 37,154 controls. In Stage 2 loci with p-values (combined over all SNPs at the locus) less than 1024 were selected for replication for 8,572 AD cases and 11,312 controls of European ancestry. We observed evidence for gene-wide association at loci which implicate genes which already show genome-wide significant association from single SNP analysis (CR1, BIN1, HLA-DRB5/HLA-DRB1, CD2AP, EPHA1, PTK2B, CLU, MS4A6A, PICALM, SORL1, SLC24A4, ABCA7, APOE), three new genes in the vicinity of lately reported single SNP hits[9] (ZNF3, NDUFS3, MTCH2) and two novel loci (TP53INP1, combined p = 1.461026 and IGHV1-67 combined p = 7.961028). Initially, we tested for excess genetic signal revealed by the Stage 1 IGAP SNP GWAS study. We observed more SNPs at all significance intervals, and more genes at multiple significance thresholds, than expected by chance (Table S1). This is unlikely to be due to uncorrected stratification, since each of the individual GWAS samples in the IGAP Stage 1 analysis was corrected for Over-representation p-values were calculated with chi-square/Fishers exact tests counting the genes within 0.5 Mb as one locus. doi:10.1371/journal.pone.0094661.t001 ethnic variation. Thus it is likely that the sample contains novel genetic signals, in addition to those detected by the primary analysis[9,19]. Next, we looked at overrepresentation of significant genes in the Stage 1 data. Table 1 gives the observed and expected numbers of significant genes at significance levels 1024, 1025, 1026 when all genes are counted in the analyses and when the known genes (Table S1) and genes within 500kb of them are excluded, the observed numbers of genes are much larger than expected at all significance levels (all p#0.001). Thus there are more loci associated with AD to find. Furthermore, the number of independent nominally significant loci at Stage 2 (N = 60, (13.5%)) was significantly greater than expected by chance (p = 4.6610212). The percentage of replicated loci increased with the decrease of the gene-wise significance threshold at Stage 1 (see Table 2 for details). Combining the gene-wide p-values in both stages 1 and 2, using Fishers method revealed two new gene-based genome-wide significant (p,2.561026) loci TP53INP1 and IGHV1-67. The TP53INP1 gene is located on chromosome 8:95,938,200 95,961,615 and its combined gene-based p-value = 1.461026 (Table 3). Table S3 provides details for each SNP contributing to the gene-based result. Out of 45 SNPs in the gene, three SNPs (rs4735333, rs1713669, rs896855) have p-value#1024. Figure 1 shows the LD plot of this gene and suggests that there are at least two partially independent signals in the TP53INP1 gene (r2 between the pairs of most significant SNPs rs4735333-rs1713669 and rs1713669- rs896855 are 0.65 and 0.6 respectively). The IGHV1-67 gene on chromosome 14:107,136,620 107,137,059 has combined p-value = 7.961028 (Tables 3). This gene is covered by two SNPs (rs2011167, rs1961901), both are significant at 1024 level. LD plot in Figure 2 and Table S4 indicate that the two most significant SNPs in IGHV1-67 gene represent almost the same signal (r2 = 0.92, calculated with SNAP software[20], 1000 genomes Pilot 1 dataset, CEU population panel, (http://www.broadinstitute.org/mpg/snap)). To look at the gene expression patterns in these novel genes, we used the Webster-Myers expression dataset[21], available at http://labs.med.miami.edu/myers/LFuN/data%20ajhg.html. Comparing 137 AD vs 176 controls with temporal or frontal cortex expression values by t-test, t showed significantly higher TP53INP1 expression in cases compared to controls (p = 0.0128). Further examination in the BRAINEAC database[22] (www. braineac.org) from the UK Brain Expression Consortium showed TP53INP1 to have a best cis-eQTL p-value of 6.861026 (for rs4582532 SNP, which is about 7.6 kb upstream of the gene). The three SNPs with association p#1024 mentioned above (rs4735333, rs1713669, rs896855) had significant cis-eQTL pvalues of 8.261026, 7.861025 and 1.161025 respectively in BRAINEAC brain expression data. The r2 between the cis-eQTL and the three associated SNPs were 0.80, 0.65, and 0.81, respectively). Further analysis of additional independent brain expression and methylation datasets (see Methods S1) indicated significant cis eQTLs and meQTLs for TP53INP1 (Tables S10 and S11). The probe for the meQTL is in a CpG island region that corresponds well with ENCODE DNAse/ChIP-seq/Histone marks and is located upstream (,1.5 kb) of the TP53INP1 Table 2. Overrepresentation of significant loci, excluding regions of 0.5 Mb around previously reported[48] and Stage 1 IGAP genes[9,19] containing genome-wide significant SNPs. Numbers of loci (genes) The observed number of genes is calculated by combining significant loci within 0.5 Mb into one signal. The APOE region is excluded (CHR19; 44,411,940 46,411,945bp). The total number of genes after exclusions is 24,849. doi:10.1371/journal.pone.0094661.t002 Figure 1. Linkage disequilibrium structure of TP53INP1 gene. The SNPs which are significant at 1024 level are circled in red. doi:10.1371/journal.pone.0094661.g001 transcription start site. In combination these results suggest a possible epigenetic mechanism whereby the associated variants in the region influence TP53INP1 expression in several brain regions. These expression data provide further evidence supporting the functional relevance of TP53INP1 to AD susceptibility. The IGHV1-67 gene was not found in those databases. In addition we detected two genome-wide significant loci 1) ZNF3 (chr7: 99,661,65399,679,371; p = 8.661027) and 2) two closely located genes on chromosome 11 MTCH2 (47,638,858 47,664,206, combined p = 2.561026) and NDUFS3 (47,600,632 47,606,114, combined p = 4.861027) (Table 4). None of these genes harbour genome-wide significant SNPs in the SNP GWAS analysis on its own (see Tables S5-S7). Figures S1-S3 show LD plots of these additional genes. ZNF3 and NDUFS3, MTCH2 genes on chromosomes 7 and 11, respectively, lie close to rs1476679 (chr7:100,004,446; ZCWPW1) and rs1083872 (chr11:47,557,871; CELF1) SNPs, which are shown to be genome-wide significant in the IGAP study, when combining Stage 1 and Stage 2 data. Figures S1-S3 show LD structure of these genes in relation to the IGAP singe genome-wide significant hits. (Note that the NDUFS3 gene on chromosome 11 was genebased genome-wide significant already at Stage 1.) Although none of these SNPs actually lie within the genes mentioned above, it is possible that they may account for the gene-based signals through linkage disequilibrium. In order to test whether the gene-based signals are independent of these strongly-associated SNPs, we performed single-SNP association for each SNP annotated to these genes by regression, adjusting for the significant SNPs mentioned above, along with the other study covariates. The resulting pvalues were combined into gene-based tests, as described previously. Under this conditional analysis ZNF3 gene does not show significant association, however NDUFS3 still shows a trend towards significance (p = 0.081) (see Table S8 for details). Furthermore, five genes in chr11:47,593,74947,615,961 (KBTBD4, NDUFS3, LOC100287127, FAM180B, C1QTNF4) all have p,0.05 with gene-based analysis 610 kb, when conditioning by the genome-wide significant hit rs10838725 in this region. This may partially be explained by the SNP rs10838731 (p = 1.261023 after conditioning by rs10838725) which is shared by all latter five genes. Gene-based analysis with 610 kb around genes did not reveal additional genome-wide significant loci in the Stage 1 data set. Moreover, the significance of the genes identified above did not improve in general, indicating that adding 10 kb flanking regions to genes introduces more noise to the gene-based signal. The combined Stage 1 and Stage 2 gene-based analysis provided further evidence for significant signals in the loci on chr 11 with 8 genes (SPI1, SLC39A13, LOC100287086, PTPMT1, KBTBD4, NDUFS3, LOC100287127, FAM180B) and on chr 7 with 6 genes (LOC100128334, MCM7, PILRB, PILRA, LOC100289298, C7orf51), all reaching genome-wide significance. This is likely to be due to the fact that including genes flanking regions captures a greater number of the same SNPs or SNPs in high LD showing significant association. The Manhattan plot of the gene-based p-values (Figure 3) gives a general overview of the gene-based results and shows the new loci in relation to previously reported genes (see also QQ-plots in Figure S4). The results of gene-wide analysis for the genes, which were previously reported as associated with AD[4-8] and those which are GWAS significant in the Stage 1 analysis are presented in Table S9. Out of 16 reported susceptibility genes, 15 are nominally significant with gene-wide analysis (almost all p-values are smaller than 1024), however not all of them reach the genebased genome-wide significance level (2.561026) when the number of SNPs per gene and LD structure of the gene is taken into account. We did not observe genome-wide significance for CD33 gene. This gene was genome-wide significant in Stage 1 (p = 1.961026), but the association was attenuated when combining Stage 1 and Stage 2 data (p = 1.7961025), similar to the single SNP association result in the SNP GWAS study[9,19]. Discussion In this study we show that there are more signals in the GWAS imputed data at SNP- and gene-based levels than revealed by single SNP analysis. A gene-based analysis is a next logical step after the single SNP analyses in any attempt to combine possible several signals in genes and thus enhance the power of the association analyses. The first new gene TP53INP1 (chromosome 8) encodes a protein that is involved in mediating autophagy-dependent cell death via apoptosis through altering the phosphorylation state of p53[23] and in modulating cell-extracellular matrix adhesion and cell migration[24]. TP53INP1 encodes a pro-apoptotic tumor suppressor and its antisense oligonucleotide has been used as potential treatment for castration-resistant prostate cancer[25]. This association is notable, given the potential inverse association between cancer and AD that has previously been reported [26,27]. The second new gene IGHV1-67 (chromosome 14) is a pseudogene in the immunoglobulin (IgG) variable heavy chain region of chromosome 14: its function is unknown but all genes in this region are most likely to be involved in IgG heavy chain VDJ recombinations that lead to the full repertoire of antigen-detecting immune cell clones[28]. The gene-based analysis in this study has shown its utility to enhance the information provided by single SNP analysis (i.e. NDUFS3 gene was genome-wide significant from Stage 1 using gene-based analysis whereas this gene was only genome-wide significant after combining the two stages of single SNP analysis). ZNF3 is a zinc-finger protein at the same locus on chromosome 7 as ZCWPW1 thus rendering it a candidate as the gene that contains the functional signal in this region. Although we can not identify which gene actually confers the risk to AD, it is interesting that ZNF3 function is unknown though it interacts with BAG3 which is involved in ubiquitin/proteasomal functions in protein degradation[29] and ZNF3 is regulated by upstream binding of BACH1 whose target genes have roles in the oxidative stress response and control of the cell cycle[30]. In the cluster of genes on chromosome 11, MTCH2 encodes one of the large family of inner mitochondrial membrane transporters[31] which is associated with mitochondrially-mediated cell death[32], adipocyte differentiation[33], insulin sensitivity[34] and has a genetic association with increased BMI[35]. NDUFS3 also has functions in the mitochondria as it encodes an iron-sulphur component of complex 1 (mitochondrial NADH:ubiquinone oxidoreductase) of the electron transport chain. A deficiency causes a form of Leigh syndrome[36] an early-onset progressive neurodegenerative disorder with a characteristic neuropathology consisting of focal lesions including areas of demyelination and gliosis[37]. In summary, we report two novel genes TP53INP1 (chr8: 95,938,20095,961,615; combined p = 1.461026) and IGHV1-67 (chr14: 107,136,620107,137,059; combined p = 7.961028), which were not reported as genome-wide significant before. We also report ZNF3 gene on chromosome 7 and a cluster of genes on chromosome 11 (SPI1-MTCH2), showing gene-based genomewide significant association with Alzheimers disease. These genes are in proximity with, but not the same as, those detected by genome-wide significant SNPs, demonstrating support for the Figure 2. Linkage disequilibrium structure of IGHV1-67 gene 5 kb. The SNPs which are significant at 1024 level are circled in red. doi:10.1371/journal.pone.0094661.g002 signals identified by IGAP[9,19]. They have an array of functions previously implicated in AD including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in AD. Materials and Methods Stage 1 data The main dataset was reported by the IGAP consortium[9,19] and consists in total of 17,008 cases and 37,154 controls. This sample of AD cases and controls comprises 4 data sets taken from genome-wide association studies performed by GERAD, EADI, CHARGE and ADGC (see primary IGAP manuscript[9,19] for more details). The full details of the samples and methods for conduct of the GWA studies are provided in the respective manuscripts[4-8]. Each of these datasets was imputed with Impute2[38] or MACH[39] software using the 1000 genomes data (release Dec2010) as a reference panel. In total 11,863,202 SNPs were included in the SNPs allelic association result file. To make our analysis as conservative as possible, we only included autosomal SNPs which passed stringent quality control criteria, i.e. we included only SNPs with minor allele frequencies (MAF) $0.01 and imputation quality score greater than or equal to 0.3 in each individual study, resulting in 7,055,881 SNPs which are present in at least 40% of the AD cases and 40% of the controls in the analysis. The summary statistics across datasets were combined using fixed-effects inverse variance-weighted meta-analysis. We corrected all individual SNPs p-values for genomic control (GC) l = 1.087. These SNPs are well imputed on a large proportion of the sample, which increases confidence in the accuracy of the association analysis upon which gene-wide analysis is based. Stage 2 data 11,632 SNPs with p-values ,1023 in the IGAP meta-analysis were successfully genotyped in a Stage 2 sample comprising 8,572 cases and 11,312 controls (see primary IGAP manuscript[9,19] for more details). An additional 771 SNPs were successfully genotyped to test all genes with gene-wide p-values ,10-4 in the IGAP Stage 1 analysis, excluding genes reported prior to IGAP[48], the four loci reaching genome-wide significance in the Stage 1 IGAP meta-analysis[9,19] and the 0.5Mb regions around them (Table S2). These SNPs cover 887 genes and correspond to 444 independent loci where all genes within 0.5 Mb are counted as one locus. Assignment of SNPs to genes SNPs were assigned to genes if they were located within the genomic sequence lying between the start of the first and the end of the last exon of any transcript corresponding to that gene. The chromosome and location for all currently known human SNPs were taken from the dbSNP132 database, as was their assignment to genes (using build 37.1). In total, we retained 2,804,431 (39.7% of the total) SNPs which annotated 28,636 unique genes with 1 16,514 SNPs per gene. For the gene-wide analysis we have excluded genes which contain only one SNP in the IGAP Stage 1 analysis, leaving a total of 25,310 genes. If a SNP belongs to more than one gene, it was assigned to each of these genes. In order to account for possible signals which are correlated with those in a gene, gene-wide analysis was also performed using a 10 kb window around genes to assign SNPs to genes. Gene-wide analysis The gene-wide analysis was performed based on the summary p-values while controlling for LD and different number of markers per gene using an approximate statistical approach[40] adopted for set-based analysis of genetic data[41]. This is a method for calculating the significance of a set of SNPs in the absence of individual genotype data based on a theoretical approximation to Fishers statistic for combining p-values. Fishers statistic (-gln(pi)) combines probabilities and under the null hypothesis has a chisquare distribution with 2N degrees of freedom, where N is the number of markers, and the summation above is for i = 1,,N). If Fishers statistic combines the results of several tests when the tests are independent, the approximate method combines non-independent tests and requires only the list of p-values for each SNP and knowledge of correlations between SNPs. Then the value of Fishers statistic and the number of degrees of freedom is corrected by the coefficient which depends upon the number of SNPs and correlations (LD) between them. This approximation was applied to the Stage 1 and Stage 2 samples separately, and the resulting gene-wide p-values combined using Fishers method (since these are independent). LD between markers was computed using 1000 genomes data. The gene-based genome-wide significant level was set to 2.561026 to account for the number of tested genes[42]. Test for excess of associated SNPs/loci The effective number N of independent SNPs in the whole genome (excluding genes with SNPs that are genome-wide significant in the Stage 1 IGAP dataset 6 0.5 Mb was estimated by the method described in [43] taking LD into account, as were the observed number of independent SNPs significant at each pvalue criterion (adjusting individual SNP p-values for genomic control l = 1.087 before hand). LD was computed from the 1000 Genomes database (http://www.1000genomes.org/). In the absence of excess association, the expected number of independent SNPs significant at significance level a is a normally distributed random variable whose mean and standard deviation (SD) can be calculated as aN and !Na(1-a) (mean and SD for a binomial distribution). The number of independent SNPs (and thus statistical tests) in the whole genome were estimated as ,3.76106, ,3.66106 and ,3.56106 at significance levels below 0.1, between 0.05 and 0.1, and 0.2 and above respectively (see [43] for details on the dependence between the significance levels and the estimated number of independent tests). We then calculated mean of the expected number of significant SNPs in intervals a1 , p # a2, (a1, a2 = 0, 1026, 1025, , 0.5) as difference between the expected numbers of independent SNPs at a2 and a1 significance levels and SD as the square root of sum of the corresponding variances. We calculated the significance of the excess number of genes attaining the specified thresholds based upon the assumption that, under the null hypothesis of no association, the number of significant genes at a significance level of a in a scan is distributed as a binomial (N,a), where N is the total number of genes, assuming that genes are independent. Genes within 0.5 Mb of each other are counted as one signal when calculating the observed number of significant genes. This prevents significance being inflated by LD between genes, where a single association signal gives rise to several significantly-associated genes. The total number of genes was not corrected for LD in this way, making the estimate of significance of the excess number of genes conservative. Figure 3. Manhattan plot of gene-wide p-values in the Stage 1 dataset and combined gene-wide p-values where Stage 2 data are available. Each dot represents a gene, genes in blue lie within the previously reported[48] associated regions. doi:10.1371/journal.pone.0094661.g003 Supporting Information Table S2 List of genes that are genome-wide significant in the IGAP stage 1 dataset and the flanking regions which included SNPs either in r2$0.3 or association pvalue#10-3 whichever covers the largest region. (DOCX) Table S8 Gene-based analysis results, when single SNPs p-values, contributing to the gene-based p-value were adjusted for the best genome-wide significant SNP in the nearby location. (DOCX) Figure S1 ZNF3 gene with rs1476679 (ZCWPW1) reported by Lambert et al (2013) study. SNPs which are significant at 1e-3 level are circled in red, rs1476679 is highlighted in blue. (TIF) Figure S2 NDUFS3 gene rs10838725 (CELF1) reported by Lambert et al (2013) study. SNPs which are significant at 1e-3 level are circled in red, rs10838725 is highlighted in blue. (TIF) Figure S4 QQ-plot of gene-wide p-values for all genes (A) and excluding previously reported[4-8] GWAS significantly associated genes 0.5Mb (B) in the discovery dataset. Genomic control l = 1.08 and 1.07 respectively. (TIFF) Methods S1 Expression quantitative trait loci (eQTL) and Methylation quantitative trait loci (meQTL) analyses. 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Valentina Escott-Price, Céline Bellenguez, Li-San Wang, Seung-Hoan Choi, Denise Harold, Lesley Jones, Peter Holmans, Amy Gerrish, Alexey Vedernikov, Alexander Richards, Anita L. DeStefano, Jean-Charles Lambert, Carla A. Ibrahim-Verbaas, Adam C. Naj, Rebecca Sims, Gyungah Jun, Joshua C. Bis, Gary W. Beecham, Benjamin Grenier-Boley, Giancarlo Russo, Tricia A. Thornton-Wells, Nicola Denning, Albert V. Smith, Vincent Chouraki, Charlene Thomas, M. Arfan Ikram, Diana Zelenika, Badri N. Vardarajan, Yoichiro Kamatani, Chiao-Feng Lin, Helena Schmidt, Brian Kunkle, Melanie L. Dunstan, Maria Vronskaya, the United Kingdom Brain Expression Consortium, Andrew D. Johnson, Agustin Ruiz, Marie-Thérèse Bihoreau, Christiane Reitz, Florence Pasquier, Paul Hollingworth, Olivier Hanon, Annette L. Fitzpatrick, Joseph D. Buxbaum, Dominique Campion, Paul K. Crane, Clinton Baldwin, Tim Becker, Vilmundur Gudnason, Carlos Cruchaga, David Craig, Najaf Amin, Claudine Berr, Oscar L. Lopez, Philip L. De Jager, Vincent Deramecourt, Janet A. Johnston, Denis Evans, Simon Lovestone, Luc Letenneur, Isabel Hernández, David C. Rubinsztein, Gudny Eiriksdottir, Kristel Sleegers, Alison M. Goate, Nathalie Fiévet, Matthew J. Huentelman, Michael Gill, Kristelle Brown, M. Ilyas Kamboh, Lina Keller, Pascale Barberger-Gateau, Bernadette McGuinness, Eric B. Larson, Amanda J. Myers, Carole Dufouil, Stephen Todd, David Wallon, Seth Love, Ekaterina Rogaeva, John Gallacher, Peter St George-Hyslop, Jordi Clarimon, Alberto Lleo, Anthony Bayer, Debby W. Tsuang, Lei Yu, Magda Tsolaki, Paola Bossù, Gianfranco Spalletta, Petra Proitsi, John Collinge, Sandro Sorbi, Florentino Sanchez Garcia, Nick C. Fox, John Hardy, Maria Candida Deniz Naranjo, Paolo Bosco, Robert Clarke, Carol Brayne, Daniela Galimberti, Elio Scarpini, Ubaldo Bonuccelli, Michelangelo Mancuso, Gabriele Siciliano, Susanne Moebus, Patrizia Mecocci, Maria Del Zompo, Wolfgang Maier, Harald Hampel, Alberto Pilotto, Ana Frank-García, Francesco Panza, Vincenzo Solfrizzi, Paolo Caffarra, Benedetta Nacmias, William Perry, Manuel Mayhaus, Lars Lannfelt, Hakon Hakonarson, Sabrina Pichler, Minerva M. Carrasquillo, Martin Ingelsson, Duane Beekly, Victoria Alvarez, Fanggeng Zou, Otto Valladares, Steven G. Younkin, Eliecer Coto, Kara L. Hamilton-Nelson, Wei Gu, Cristina Razquin, Pau Pastor, Ignacio Mateo, Michael J. Owen, Kelley M. Faber, Palmi V. Jonsson, Onofre Combarros, Michael C. O'Donovan, Laura B. Cantwell, Hilkka Soininen, Deborah Blacker, Simon Mead, Thomas H. Mosley, David A. Bennett, Tamara B. Harris, Laura Fratiglioni, Clive Holmes, Renee F. A. G. de Bruijn, Peter Passmore, Thomas J. Montine, Karolien Bettens, Jerome I. Rotter, Alexis Brice, Kevin Morgan, Tatiana M. Foroud, Walter A. Kukull, Didier Hannequin, John F. Powell, Michael A. Nalls, Karen Ritchie, Kathryn L. Lunetta, John S. K. Kauwe, Eric Boerwinkle, Matthias Riemenschneider, Mercè Boada, Mikko Hiltunen, Eden R. Martin, Reinhold Schmidt, Dan Rujescu, Jean-François Dartigues, Richard Mayeux, Christophe Tzourio, Albert Hofman, Markus M. Nöthen, Caroline Graff, Bruce M. Psaty, Jonathan L. Haines, Mark Lathrop, Margaret A. Pericak-Vance, Lenore J. Launer, Christine Van Broeckhoven, Lindsay A. Farrer, Cornelia M. van Duijn, Alfredo Ramirez, Sudha Seshadri, Gerard D. Schellenberg, Philippe Amouyel, Julie Williams. Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimer's Disease, PLOS ONE, 2014, DOI: 10.1371/journal.pone.0094661