Alzheimer Disease Susceptibility Loci: Evidence for a Protein Network under Natural Selection

Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences Department of Neurology, Brigham and Women's Hospital, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.
The American Journal of Human Genetics (Impact Factor: 10.93). 04/2012; 90(4):720-6. DOI: 10.1016/j.ajhg.2012.02.022
Source: PubMed


Recent genome-wide association studies have identified a number of susceptibility loci for Alzheimer disease (AD). To understand the functional consequences and potential interactions of the associated loci, we explored large-scale data sets interrogating the human genome for evidence of positive natural selection. Our findings provide significant evidence for signatures of recent positive selection acting on several haplotypes carrying AD susceptibility alleles; interestingly, the genes found in these selected haplotypes can be assembled, independently, into a molecular complex via a protein-protein interaction (PPI) network approach. These results suggest a possible coevolution of genes encoding physically-interacting proteins that underlie AD susceptibility and are coexpressed in different tissues. In particular, PICALM, BIN1, CD2AP, and EPHA1 are interconnected through multiple interacting proteins and appear to have coordinated evidence of selection in the same human population, suggesting that they may be involved in the execution of a shared molecular function. This observation may be AD-specific, as the 12 loci associated with Parkinson disease do not demonstrate excess evidence of natural selection. The context for selection is probably unrelated to AD itself; it is likely that these genes interact in another context, such as in immune cells, where we observe cis-regulatory effects at several of the selected AD loci.

Download full-text


Available from: Barbara E Stranger
  • Source
    • "the gene coding region but not in the coding region , have been identified to influence AD risk ( Harold et al . , 2009 ; Lambert et al . , 2009 ; Carrasquillo et al . , 2010 , 2015 ; Chen et al . , 2012 ; Tanzi , 2012 ; Liu et al . , 2013 ; Morgen et al . , 2014 ) . It has been reported that some AD - associated SNPs influence PICALM expression ( Raj et al . , 2012 ) . We recently studied the highly validated rs3851179 PICALM variants whose rs3851179 A allele is associated with a lower AD risk than the rs3851179 G allele ( Lambert et al . , 2009 , 2013 ) using inducible pluripotent stem cell ( iPSC ) - derived endothelial cells . These studies revealed that the protective rs3851179 A allele signif"
    [Show abstract] [Hide abstract]
    ABSTRACT: Amyloid beta (Aβ) homeostasis in the brain is governed by its production and clearance mechanisms. An imbalance in this homeostasis results in pathological accumulations of cerebral Aβ, a characteristic of Alzheimer’s disease (AD). While Aβ may be cleared by several physiological mechanisms, a major route of Aβ clearance is the vascular-mediated removal of Aβ from the brain across the blood-brain barrier (BBB). Here, we discuss the role of the predominant Aβ clearance protein—low-density lipoprotein receptor-related protein 1 (LRP1)—in the efflux of Aβ from the brain. We also outline the multiple factors that influence the function of LRP1-mediated Aβ clearance, such as its expression, shedding, structural modification and transcriptional regulation by other genes. Finally, we summarize approaches aimed at restoring LRP1-mediated Aβ clearance from the brain
    Full-text · Article · Jul 2015 · Frontiers in Aging Neuroscience
  • Source
    • "In recent years, integrative approaches combining multiple data sources have been widely used to identify susceptible genes in complex disorders such as AD [12,13], epilepsy [14], type 2 diabetes [15,16], prostate cancer [17], depression [18], schizophrenia [19] and Parkinson’s disease (PD) [20]. Such approaches may help imbibe disease specific biological knowledge that may not be available from one dimensional approaches. "
    [Show abstract] [Hide abstract]
    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.
    Full-text · Article · Mar 2014 · BMC Genomics
  • Source
    • "However, the interactions in terms of the functional consequences and evolutionary history of these loci remain largely unknown. Recently, several studies have proposed natural positive selection based on haplotype analysis in various populations [27]–[28]. Candidate regions that are experiencing positive selection could be identified by a number of sophisticated statistical methods. In this study, we applied the integrated haplotype score (iHs), which was developed in 2006 [23], [29]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Prostate cancer (PCa) is a global disease causing large numbers of deaths every year. Recent studies have indicated the RTK/ERK pathway might be a key pathway in the development of PCa. However, the exact association and evolution-based mechanism remain unclear. This study was conducted by combining genotypic and phenotypic data from the Chinese Consortium for Prostate Cancer Genetics (ChinaPCa) with related databases such as the HapMap Project and Genevar. In this analysis, expression of quantitative trait loci (eQTLs) analysis, natural selection and gene-based pathway analysis were involved. The pathway analysis confirmed the positive relationship between PCa risk and several key genes. In addition, combined with the natural selection, it seems that 4 genes (EGFR, ERBB2, PTK2, and RAF1) with five SNPs (rs11238349, rs17172438, rs984654, rs11773818, and rs17172432) especially rs17172432, might be pivotal factors in the development of PCa. The results indicate that the RTK/ERK pathway under natural selection is a key link in PCa risk. The joint effect of the genes and loci with positive selection might be one reason for the development of PCa. Dealing with all the factors simultaneously might give insight into prevention and aid in predicting the success of potential therapies for PCa.
    Full-text · Article · Nov 2013 · PLoS ONE
Show more