HIGH-THROUGHPUT IDENTIFICATION OF NONCODING FUNCTIONAL SNPS

Abstract

Genome wide association studies (GWAS) have provided significant insight into complex diseases by identifying numerous disease-associated non-coding single nucleotide polymorphisms (SNPs). However, GWAS cannot distinguish functional SNPs (fSNPs) from other disease-associated SNPs that are in linkage disequilibrium (LD). To address this challenge, we developed a novel approach to bridge the gap between GWAS and biological mechanisms with a set of high throughput methodologies. We use SNP-seq (Single Nucleotide Polymorphism-Sequencing) and Reel-seq (Regulatory Element-Sequencing) to identify disease-associated fSNPs and FREP-MS (Flanking Restriction Enhanced DNA Pulldown-Mass Spectrometry) to characterize these fSNPs by identifying regulatory proteins that specifically bind to the fSNPs. To prove the feasibility, we applied this approach to a couple of disease-associated loci. For this study, we applied the same approach to a library that contains SNPs in LD on 27 loci that are associated with late onset Alzheimer's disease (LOAD). We confirmed 27 fSNPs on 12 LOAD risk loci by allele-imbalanced gel shifting including 5 on the HLA-DQA/ HLA DRB1 locus. We identified multiple proteins that specifically bind to each of these fSNPs. The regulation of the risk gene expression on these 12 loci will be explored.