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通过阿尔茨海默病测序项目全基因组序列数据得到的关键变异体。

Key variants via Alzheimer's Disease Sequencing Project whole genome sequence data.

作者信息

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

机构信息

Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA.

Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.

出版信息

medRxiv. 2023 Aug 29:2023.08.28.23294631. doi: 10.1101/2023.08.28.23294631.

DOI:10.1101/2023.08.28.23294631
PMID:37693453
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10491364/
Abstract

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.

摘要

引言

全基因组关联研究(GWAS)已确定了与阿尔茨海默病(AD)相关的基因座,但未确定这些基因座内的特定致病基因或变异。全基因组序列(WGS)数据分析可对整个基因组进行检测并捕捉罕见变异,可能会在GWAS基因座内识别出致病变异。

方法

我们利用阿尔茨海默病测序项目(ADSP)的WGS数据,在合并人群(N病例 = 2184,N对照 = 2383)中进行了单常见变异关联分析和罕见变异汇总分析,并在亚组人群中进行了靶向分析。分析仅限于83个先前确定的GWAS先导变异100 kb范围内的变异。

结果

在涉及OARD1/NFYA/TREML1、JAZF1、FERMT2和SLC24A4基因的五个基因组区域内,有17个变异与AD显著相关。单变异和罕见变异汇总分析均表明KAT8与AD有关。

讨论

本研究证明了利用WGS深入了解通过GWAS确定的AD基因座的实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12c7/10491364/59b4910cf6a2/nihpp-2023.08.28.23294631v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12c7/10491364/fe2c09b56a5a/nihpp-2023.08.28.23294631v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12c7/10491364/59b4910cf6a2/nihpp-2023.08.28.23294631v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12c7/10491364/fe2c09b56a5a/nihpp-2023.08.28.23294631v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12c7/10491364/59b4910cf6a2/nihpp-2023.08.28.23294631v1-f0002.jpg

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