Bioinformatics strategy to advance the interpretation of Alzheimer's disease GWAS discoveries: The roads from association to causation

Abstract

Introduction: Genome-wide association studies (GWAS) discovered multiple late-onset Alzheimer's disease (LOAD)-associated SNPs and inferred the genes based on proximity; however, the actual causal genes are yet to be identified. Methods: We defined LOAD-GWAS regions by the most significantly associated SNP 60.5 Mb and developed a bioinformatics pipeline that uses and integrates chromatin state segmentation track to map active enhancers and virtual 4C software to visualize interactions between active enhancers and gene promoters. We augmented our pipeline with biomedical and functional information. Results: We applied the bioinformatics pipeline using three w1 Mb LOAD-GWAS loci: BIN1, PIC-ALM, CELF1. These loci contain 10-24 genes, an average of 106 active enhancers and 80 CTCF sites. Our strategy identified all genes corresponding to the promoters that interact with the active enhancer that is closest to the LOAD-GWAS-SNP and generated a shorter list of prioritized candidate LOAD genes (5-14/loci), feasible for post-GWAS investigations of causality. Discussion: Interpretation of LOAD-GWAS discoveries requires the integration of brain-specific functional genomic data sets and information related to regulatory activity.

Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions

“… 47 , 48 , 49 , 50 Several bioinformatics approaches have been described to study the functional role of GWAS‐enhancer elements and variants on gene expression and in turn, development or progression of neurodegenerative diseases including LOAD. These approaches have included fine mapping DNA methylation sites in prefrontal cortex neurons from brains with different degrees of Alzheimer's disease pathology, 51 cataloguing enhancers in LOAD regions and mapping promoter‐enhancer interaction using Circular Chromosomal Conformation Capture (4C) data to prioritize genes for experimental follow‐up, 28 and integrating datasets of enhancer activity, TF binding sites, and eQTL 10 , 52 , 53 to characterize the effects of non‐coding genetic variation associated with LOAD risk. A recent study reported non‐coding LOAD SNPs that affect the function of enhancers and in turn impact the expression of distal genes via chromatin loops.…”

“…The approach for identifying active enhancers in LOAD GWAS regions is described in detail in Lutz et al. 28 In brief, the region tagged by each LOAD‐SNP was initially defined by anchoring the center of the region on the GWAS SNP and extending 500 kb in each direction to cover a 1Mb locus. Genes on the boundary of the 1Mb region were examined and the locus extended to cover the full length of the gene if the boundary intersects within a gene.…”

Translational animal models for Alzheimer's disease: An Alzheimer's Association Business Consortium Think Tank

“…As described at the meeting, Dr. Lutz detailed novel unpublished data and rigorous statistical methods that can be used to provide verification of pathologic pathway communality, which is defined as the extent that pathways between human disease and the animal models correlate. 67 Generating these modified animal models is now feasible because of recent advances in genome editing, particularly CRISPR/Cas technology, which has dramatically reduced the time and cost of gene targeting and modification 68 and enabled the creation of novel in vivo models of human disease, 69 potentially making them more predictive of aspects of AD in humans.…”

“…50,89,90 These types of deep phenotyping efforts not only validate the characteristics of each animal model, but provide data for comparative analysis with human AD patients. In this regard, Lutz et al 67 presented a novel bioinformatics strategy to compare genomics, transcriptomics, and proteomics data from human AD patients with an AD animal model to help decipher how strongly different AD models align with the human disease. These types of approaches will clearly guide model development, biomarker development, and drug development in the Alzheimer's arena.…”

“…like pathologic, disease-based change. Confirmation of model reliability will depend on comprehensive statistical analyses and the application of single nucleus transcriptomic studies and new statistical platforms to compare human and mouse data sets 67. These systems may represent the tip of the iceberg, especially as more genes are identified in subpopulations of AD patients, and cell-type specific changes in gene expression related to disease progression and resistance are better defined to permit comparisons between model systems and human subjects.In addition to ongoing efforts in individual laboratoriesthroughout the world, the NIA established the Model Organism Development & Evaluation for Late-Onset Alzheimer's Disease (MODEL-AD) consortium in 2016 with Indiana University, The Jackson Laboratory (JAX), The University of Pittsburgh (PITT), Sage Bionetworks, and the University of California, Irvine, to develop better in vivo models that may accelerate the discovery and development of new therapies.…”