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Key variants via Alzheimer’s Disease Sequencing Project whole genome sequence data
INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer’s disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491364/ https://www.ncbi.nlm.nih.gov/pubmed/37693453 http://dx.doi.org/10.1101/2023.08.28.23294631 |
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author | Wang, Yanbing Sarnowski, Chloé Lin, Honghuang Pitsillides, Achilleas N Heard-Costa, Nancy L Choi, Seung Hoan Wang, Dongyu Bis, Joshua C Blue, Elizabeth E Boerwinkle, Eric De Jager, Philip L Fornage, Myriam Wijsman, Ellen M Seshadri, Sudha Dupuis, Josée Peloso, Gina M DeStefano, Anita L |
author_facet | Wang, Yanbing Sarnowski, Chloé Lin, Honghuang Pitsillides, Achilleas N Heard-Costa, Nancy L Choi, Seung Hoan Wang, Dongyu Bis, Joshua C Blue, Elizabeth E Boerwinkle, Eric De Jager, Philip L Fornage, Myriam Wijsman, Ellen M Seshadri, Sudha Dupuis, Josée Peloso, Gina M DeStefano, Anita L |
author_sort | Wang, Yanbing |
collection | PubMed |
description | INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer’s disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer’s Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS. |
format | Online Article Text |
id | pubmed-10491364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-104913642023-09-09 Key variants via Alzheimer’s Disease Sequencing Project whole genome sequence data Wang, Yanbing Sarnowski, Chloé Lin, Honghuang Pitsillides, Achilleas N Heard-Costa, Nancy L Choi, Seung Hoan Wang, Dongyu Bis, Joshua C Blue, Elizabeth E Boerwinkle, Eric De Jager, Philip L Fornage, Myriam Wijsman, Ellen M Seshadri, Sudha Dupuis, Josée Peloso, Gina M DeStefano, Anita L medRxiv Article INTRODUCTION: Genome-wide association studies (GWAS) have identified loci associated with Alzheimer’s disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS: We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer’s Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS: Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION: This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS. Cold Spring Harbor Laboratory 2023-08-29 /pmc/articles/PMC10491364/ /pubmed/37693453 http://dx.doi.org/10.1101/2023.08.28.23294631 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Wang, Yanbing Sarnowski, Chloé Lin, Honghuang Pitsillides, Achilleas N Heard-Costa, Nancy L Choi, Seung Hoan Wang, Dongyu Bis, Joshua C Blue, Elizabeth E Boerwinkle, Eric De Jager, Philip L Fornage, Myriam Wijsman, Ellen M Seshadri, Sudha Dupuis, Josée Peloso, Gina M DeStefano, Anita L Key variants via Alzheimer’s Disease Sequencing Project whole genome sequence data |
title | Key variants via Alzheimer’s Disease Sequencing Project whole genome sequence data |
title_full | Key variants via Alzheimer’s Disease Sequencing Project whole genome sequence data |
title_fullStr | Key variants via Alzheimer’s Disease Sequencing Project whole genome sequence data |
title_full_unstemmed | Key variants via Alzheimer’s Disease Sequencing Project whole genome sequence data |
title_short | Key variants via Alzheimer’s Disease Sequencing Project whole genome sequence data |
title_sort | key variants via alzheimer’s disease sequencing project whole genome sequence data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491364/ https://www.ncbi.nlm.nih.gov/pubmed/37693453 http://dx.doi.org/10.1101/2023.08.28.23294631 |
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