Abstract
Late-onset Alzheimer's disease (LOAD) is a multifactorial disorder with over twenty loci associated with disease risk. Given the number of genome-wide significant variants that fall outside of coding regions, it is possible that some of these variants alter some function of gene expression rather than tagging coding variants that alter protein structure and/or function. RegulomeDB is a database that annotates regulatory functions of genetic variants. In this study, we utilized RegulomeDB to investigate potential regulatory functions of lead single nucleotide polymorphisms (SNPs) identified in five genome-wide association studies (GWAS) of risk and age-at onset (AAO) of LOAD, as well as SNPs in LD (r2≥0.80) with the lead GWAS SNPs. Of a total 614 SNPs examined, 394 returned RegulomeDB scores of 1–6. Of those 394 variants, 34 showed strong evidence of regulatory function (RegulomeDB score <3), and only 3 of them were genome-wide significant SNPs (ZCWPW1/rs1476679, CLU/rs1532278 and ABCA7/rs3764650). This study further supports the assumption that some of the non-coding GWAS SNPs are true associations rather than tagged associations and demonstrates the application of RegulomeDB to GWAS data.
Highlights
Over 1200 genome-wide association studies (GWAS) have been published since 2005 [1]
We have demonstrated the utility of two publicly available bioinformatics tools, Broad Institute’s single nucleotide polymorphisms (SNPs) Annotation and Proxy search (SNAP) tool [12] and RegulomeDB [2], to investigate potential regulatory functions of recently identified, non-APOE variants for known and suggestive loci associated with risk and age-at onset (AAO) of Late-onset Alzheimer’s disease (LOAD)
Included among these SNPs were the 28 genome-wide significant SNPs from 21 non-APOE LOAD risk loci (PICALM, BIN1, CD33, CD2AP, MS4A4A/MS4A6E, ABCA7, EPHA1, CLU, CR1, HLA-DRB5/HLA-DRB1, PTK2B, SORL1, SLC24A4/RIN3, DSG2, INPP5D, MEF2C, NME8, ZCWPW1, CELF1, FERMT2, and CASS4) [5,6,7] and 16 SNPs from novel suggestive loci identified in two GWAS of risk and AAO of LOAD (DCHS, HRK/RNFT2, ADAMTS9, KCNV2/VLDLR, LEMD2/ MLN/MIR1275, LOC390958/Sec11C, ZNF592/ALPK3/SLC28A1, PSMD1/HTR2B/ARMC9, NRXN3, PPP1R3B, MMP3/MMP12, FLJ37543, PCDH7, LOC440390, MAPRE1P2 pseudogene, and PP1R2P5 pseudogene) [8], [9]
Summary
Over 1200 genome-wide association studies (GWAS) have been published since 2005 [1]. While some of these studies have been crucial for determining genes responsible for disease phenotypes, including determination of genes involved in inflammatory bowel disease and age-related macular degeneration, the majority of variants identified show modest effect size at best. 88% of significant variants are located in either intronic or intergenic regions that do not encode proteins, suggesting their association with disease may occur for reasons other than changes in protein structure and/or function [2]. Given these findings, researchers recently have begun to deliberate implications of these non-coding variants. RegulomeDB is a database developed to capture these data, and subsequently, assess the likelihood that a particular variant affects transcription factor binding The advent of such databases is advantageous for studying gene associations of complex diseases [2]
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