Dataset Information

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 ±0.5 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 ∼1 Mb LOAD-GWAS loci: BIN1, PICALM, 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.

SUBMITTER: Lutz MW

PROVIDER: S-EPMC6699885 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Lutz Michael W MW Sprague Daniel D Chiba-Falek Ornit O

Alzheimer's & dementia : the journal of the Alzheimer's Association 20190628 8

<h4>Introduction</h4>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.<h4>Methods</h4>We defined LOAD-GWAS regions by the most significantly associated SNP ±0.5 Mb and developed a bioinformatics pipeline that uses and integrates chromatin state segmentation track to map active enhancers and virtual 4C software to visualize interactio ...[more]

PMID: 31262699

Similar Datasets

Project description:BackgroundAlzheimer's disease is a debilitating and highly heritable neurological condition. As such, genetic studies have sought to understand the genetic architecture of Alzheimer's disease since the 1990s, with successively larger genome-wide association studies (GWAS) and meta-analyses. These studies started with a small sample size of 1086 individuals in 2007, which was able to identify only the APOE locus. In 2013, the International Genomics of Alzheimer's Project (IGAP) did a meta-analysis of all existing GWAS using data from 74 046 individuals, which stood as the largest Alzheimer's disease GWAS until 2018. This meta-analysis discovered 19 susceptibility loci for Alzheimer's disease in populations of European ancestry.Recent developmentsThree new Alzheimer's disease GWAS published in 2018 and 2019, which used larger sample sizes and proxy phenotypes from biobanks, have substantially increased the number of known susceptibility loci in Alzheimer's disease to 40. The first, an updated GWAS from IGAP, included 94 437 individuals and discovered 24 susceptibility loci. Although IGAP sought to increase sample size by recruiting additional clinical cases and controls, the two other studies used parental family history of Alzheimer's disease to define proxy cases and controls in the UK Biobank for a genome-wide association by proxy, which was meta-analysed with data from GWAS of clinical Alzheimer's disease to attain sample sizes of 388 324 and 534 403 individuals. These two studies identified 27 and 29 susceptibility loci, respectively. However, the three studies were not independent because of the large overlap in their participants, and interpretation can be challenging because different variants and genes were highlighted by each study, even in the same locus. Furthermore, neither the variant with the strongest Alzheimer's disease association nor the nearest gene are necessarily causal. This situation presents difficulties for experimental studies, drug development, and other future research. WHERE NEXT?: The ultimate goal of understanding the genetic architecture of Alzheimer's disease is to characterise novel biological pathways that underly Alzheimer's disease pathogenesis and to identify novel drug targets. GWAS have successfully contributed to the characterisation of the genetic architecture of Alzheimer's disease, with the identification of 40 susceptibility loci; however, this does not equate to the discovery of 40 Alzheimer's disease genes. To identify Alzheimer's disease genes, these loci need to be mapped to variants and genes through functional genomics studies that combine annotation of variants, gene expression, and gene-based or pathway-based analyses. Such studies are ongoing and have validated several genes at Alzheimer's disease loci, but greater sample sizes and cell-type specific data are needed to map all GWAS loci.

Dataset Information

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

Introduction

Methods

Results

Discussion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets