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Genetic variants influencing human aging from late-onset Alzheimer's disease (LOAD) genome-wide association studies (GWAS).

ABSTRACT: Genetics plays a crucial role in human aging with up to 30% of those living to the mid-80s being determined by genetic variation. Survival to older ages likely entails an even greater genetic contribution. There is increasing evidence that genes implicated in age-related diseases, such as cancer and neuronal disease, play a role in affecting human life span. We have selected the 10 most promising late-onset Alzheimer's disease (LOAD) susceptibility genes identified through several recent large genome-wide association studies (GWAS). These 10 LOAD genes (APOE, CLU, PICALM, CR1, BIN1, ABCA7, MS4A6A, CD33, CD2AP, and EPHA1) have been tested for association with human aging in our dataset (1385 samples with documented age at death [AAD], age range: 58-108 years; mean age at death: 80.2) using the most significant single nucleotide polymorphisms (SNPs) found in the previous studies. Apart from the APOE locus (rs2075650) which showed compelling evidence of association with risk on human life span (p = 5.27 × 10(-4)), none of the other LOAD gene loci demonstrated significant evidence of association. In addition to examining the known LOAD genes, we carried out analyses using age at death as a quantitative trait. No genome-wide significant SNPs were discovered. Increasing sample size and statistical power will be imperative to detect genuine aging-associated variants in the future. In this report, we also discuss issues relating to the analysis of genome-wide association studies data from different centers and the bioinformatic approach required to distinguish spurious genome-wide significant signals from real SNP associations.

SUBMITTER: Shi H

PROVIDER: S-EPMC4120742 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Genetic variants influencing human aging from late-onset Alzheimer's disease (LOAD) genome-wide association studies (GWAS).

Shi Hui H Belbin Olivia O Belbin Olivia O Medway Christopher C Brown Kristelle K Kalsheker Noor N Carrasquillo Minerva M Proitsi Petroula P Powell John J Lovestone Simon S Goate Alison A Younkin Steven S Passmore Peter P Morgan Kevin K

Neurobiology of aging 20120323 8

Genetics plays a crucial role in human aging with up to 30% of those living to the mid-80s being determined by genetic variation. Survival to older ages likely entails an even greater genetic contribution. There is increasing evidence that genes implicated in age-related diseases, such as cancer and neuronal disease, play a role in affecting human life span. We have selected the 10 most promising late-onset Alzheimer's disease (LOAD) susceptibility genes identified through several recent large g ...[more]

PMID: 22445811

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Project description:Abstract Introduction As new late‐onset Alzheimer's disease (LOAD) genetic risk loci are identified and brain cell–type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single‐cell molecular profiling experiments and validation using disease models and gene editing technologies. Prior work has characterized and prioritized active enhancers located in LOAD‐genome‐wide association study (GWAS) regions and their potential interactions with candidate genes. The current study extends this work by focusing on single nucleotide polymorphisms (SNPs) within these LOAD enhancers and their impact on altering transcription factor (TF) binding. The proposed bioinformatics pipeline progresses from SNPs located in LOAD‐GWAS regions to a filtered set of candidate regulatory SNPs that have a predicted strong effect on TF binding. Methods Active enhancers within LOAD‐associated regions were identified and SNPs located in the enhancers were catalogued. SNPs that disrupt TF binding sites were prioritized and the respective TFs were filtered to include only those that were expressed in brain tissues relevant to LOAD. The TFs binding to the corresponding sequence was further confirmed by ChIP‐seq signals. Finally, the high‐priority candidate SNPs were evaluated as expression quantitative trait loci (eQTLs) in disease‐relevant tissues. Results We catalogued 61 strong enhancers in LOAD‐GWAS regions encompassing 326 SNPs and 104 TF binding sites. Seventy‐seven and 78 of the TFs were expressed in brain and monocytes, respectively, out of which 19 TF‐binding sites showed ChIP‐seq signals. Eleven SNPs were found to interrupt with TF binding out of which three SNPs were also significant eQTL. Discussion This study provides a framework to catalogue noncoding variations in enhancers located in LOAD‐GWAS loci and characterize their likelihood to perturb TF binding. The approach integrates multiple data types to characterize and prioritize SNPs for putative regulatory function using single‐cell multi‐omics assays and gene editing.

Dataset Information

Genetic variants influencing human aging from late-onset Alzheimer's disease (LOAD) genome-wide association studies (GWAS).

Publications

Genetic variants influencing human aging from late-onset Alzheimer's disease (LOAD) genome-wide association studies (GWAS).

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