Genome-wide linkage analysis of 723 affected relative pairs with late-onset Alzheimer's disease

Biostatistics and Bioinformatics Unit, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.
Human Molecular Genetics (Impact Factor: 6.39). 11/2007; 16(22):2703-12. DOI: 10.1093/hmg/ddm224
Source: PubMed


Previous attempts to identify genetic loci conferring risk for late-onset Alzheimer's disease (LOAD) through linkage analysis have observed some regions of linkage in common. However, due to the sometimes-considerable overlap between the samples, some of these reports cannot be considered to be independent replications. In order to assess the strength of the evidence for linkage and to obtain the best indication of the location of susceptibility genes, we have amalgamated three large samples to give a total of 723 affected relative pairs (ARPs). Multipoint, model-free ARP linkage analysis was performed. Genome-wide significant evidence for linkage was observed on 10q21.2 (LOD=3.3) and genome-wide suggestive evidence was observed on 9q22.33 (LOD=2.5) and 19q13.32 (LOD=2.0). One further region on 9p21.3 was identified with an LOD score>1. We observe no evidence to suggest that more than one locus is responsible for the linkage to 10q21.2, although this linked region may harbour more than one susceptibility gene. Evidence of allele-sharing heterogeneity between the original collection sites was observed on chromosome 9 but not on chromosome 10 or 19. Evidence for an interaction was observed between loci on chromosomes 10 and 19. Where samples overlapped, the genotyping consistency was high, estimated to average at 97.3%. Our large-scale linkage analysis consolidates clear evidence for a susceptibility locus for LOAD on 10q21.2.

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    • "In this study, we aimed to develop a system biology approach based on genomic convergence of genetic data from multiple high-dimensional genome-wide studies and network modelling of protein-protein interactions to prioritize candidate genes linked to AD. We identified 108 common overlapping genes from integrated analysis of three datasets - GWL [8,32,33], GWA [34] and GWE [[35,36]; GSE5281] and ranked them using our ranked based scoring method. We identified direct protein interactors of 108 candidate genes and then created a layered PPI network comprising of 640 nodes based on subcellular localization of proteins. "
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    ABSTRACT: Alzheimer's disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling. Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes. With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers then candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.
    BMC Genomics 03/2014; 15(1):199. DOI:10.1186/1471-2164-15-199 · 3.99 Impact Factor
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    • "In particular, Pericak-Vance and colleagues identified the LOAD locus on 9p21.3 in a genomewide microsatellite-based linkage screen on 466 AD families [14] and confirmed in a genetic study of a consanguineous Israeli-Arab community [15]. In 2007 Hamshere and colleagues analyzing 723 affected relative pairs with genomewide linkage analysis showed evidence for disease locus for LOAD on chromosome 9p [8]. In 2008 Züchner identified a chromosomal area under the linkage peak containing several potential AD candidate genes and analyzed CDKN2A and CDKN2B genes. "
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    ABSTRACT: Alzheimer's disease (AD) is the most common form of dementia clinically characterized by progressive impairment of memory and other cognitive functions. Many genetic researches in AD identified one common genetic variant (ε4) in Apolipoprotein E (APOE) gene as a risk factor for the disease. Two independent genome-wide studies demonstrated a new locus on chromosome 9p21.3 implicated in Late-Onset Alzheimer's Disease (LOAD) susceptibility in Caucasians. In the present study, we investigated the role of three SNP's in the CDKN2A gene (rs15515, rs3731246, and rs3731211) and one in the CDKN2B gene (rs598664) located in 9p21.3 using an association case-control study carried out in a group of Caucasian subjects including 238 LOAD cases and 250 controls. The role of CDKN2A and CDKN2B genetic variants in AD is not confirmed in our LOAD patients, and further studies are needed to elucidate the role of these genes in the susceptibility of AD.
    International Journal of Alzheimer's Disease 04/2011; 2011(7):374631. DOI:10.4061/2011/374631
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    • "The gene encoding TrkB, NTRK2, is located on chromosome 9, specifically 9q22. This region has been genetically linked to AD [24] [25]. Despite the experimental evidence functionally linking TrkB signaling to APP metabolism and synaptic function, case-control and genomewide association studies of NTRK2 single nucleotide polymorphisms (SNPs) International Journal of Alzheimer's Disease found no significant association with AD [25] [26] [27] [28] [29] [30]. "
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    ABSTRACT: We report that NTRK2, the gene encoding for the TrkB receptor, can regulate APP metabolism, specifically AICD levels. Using the human neuroblastoma cell line SH-SY5Y, we characterized the effect of three TrkB isoforms (FL, SHC, T) on APP metabolism by knockdown and overexpression. We found that TrkB FL increases AICD-mediated transcription and APP levels while it decreases sAPP levels. These effects were mainly mediated by the tyrosine kinase activity of the receptor and partially by the PLC-γ- and SHC-binding sites. The TrkB T truncated isoform did not have significant effects on APP metabolism when transfected by itself, while the TrkB SHC decreased AICD-mediated transcription. TrkB T abolished TrkB FL effects on APP metabolism when cotransfected with it while TrkB SHC cotransfected with TrkB FL still showed increased APP levels. In conclusion, we demonstrated that TrkB isoforms have differential effects on APP metabolism.
    International Journal of Alzheimer's Disease 02/2011; 2011:729382. DOI:10.4061/2011/729382
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