A Fast and Robust Strategy to Remove Variant-Level Artifacts in Alzheimer Disease Sequencing Project Data.

Journal

Neurology. Genetics
ISSN: 2376-7839
Titre abrégé: Neurol Genet
Pays: United States
ID NLM: 101671068

Informations de publication

Date de publication:
Oct 2022
Historique:
received: 02 02 2022
accepted: 31 05 2022
entrez: 15 8 2022
pubmed: 16 8 2022
medline: 16 8 2022
Statut: epublish

Résumé

Exome sequencing (ES) and genome sequencing (GS) are expected to be critical to further elucidate the missing genetic heritability of Alzheimer disease (AD) risk by identifying rare coding and/or noncoding variants that contribute to AD pathogenesis. In the United States, the Alzheimer Disease Sequencing Project (ADSP) has taken a leading role in sequencing AD-related samples at scale, with the resultant data being made publicly available to researchers to generate new insights into the genetic etiology of AD. To achieve sufficient power, the ADSP has adapted a study design where subsets of larger AD cohorts are collected and sequenced across multiple centers, using a variety of sequencing platforms. This approach may lead to variable variant quality across sequencing centers and/or platforms. In this study, we sought to implement and evaluate filters that can be applied fast to robustly remove variant-level artifacts in the ADSP data. We implemented a robust quality control procedure to handle ADSP data. We evaluated this procedure while performing exome-wide and genome-wide association analyses on AD risk using the latest ADSP whole ES (WES) and whole GS (WGS) data releases (NG00067.v5). We observed that many variants displayed large variation in allele frequencies across sequencing centers/platforms and contributed to spurious association signals with AD risk. We also observed that sequencing platform/center adjustment in association models could not fully account for these spurious signals. To address this issue, we designed and implemented variant filters that could capture and remove these center-specific/platform-specific artifactual variants. We derived a fast and robust approach to filter variants that represent sequencing center-related or platform-related artifacts underlying spurious associations with AD risk in ADSP WES and WGS data. This approach will be important to support future robust genetic association studies on ADSP data, as well as other studies with similar designs.

Sections du résumé

Background and Objectives UNASSIGNED
Exome sequencing (ES) and genome sequencing (GS) are expected to be critical to further elucidate the missing genetic heritability of Alzheimer disease (AD) risk by identifying rare coding and/or noncoding variants that contribute to AD pathogenesis. In the United States, the Alzheimer Disease Sequencing Project (ADSP) has taken a leading role in sequencing AD-related samples at scale, with the resultant data being made publicly available to researchers to generate new insights into the genetic etiology of AD. To achieve sufficient power, the ADSP has adapted a study design where subsets of larger AD cohorts are collected and sequenced across multiple centers, using a variety of sequencing platforms. This approach may lead to variable variant quality across sequencing centers and/or platforms. In this study, we sought to implement and evaluate filters that can be applied fast to robustly remove variant-level artifacts in the ADSP data.
Methods UNASSIGNED
We implemented a robust quality control procedure to handle ADSP data. We evaluated this procedure while performing exome-wide and genome-wide association analyses on AD risk using the latest ADSP whole ES (WES) and whole GS (WGS) data releases (NG00067.v5).
Results UNASSIGNED
We observed that many variants displayed large variation in allele frequencies across sequencing centers/platforms and contributed to spurious association signals with AD risk. We also observed that sequencing platform/center adjustment in association models could not fully account for these spurious signals. To address this issue, we designed and implemented variant filters that could capture and remove these center-specific/platform-specific artifactual variants.
Discussion UNASSIGNED
We derived a fast and robust approach to filter variants that represent sequencing center-related or platform-related artifacts underlying spurious associations with AD risk in ADSP WES and WGS data. This approach will be important to support future robust genetic association studies on ADSP data, as well as other studies with similar designs.

Identifiants

DOI: 10.1212/NXG.0000000000200012 PMID: 35966919 PMC: PMC9372872
pubmed: 35966919
doi: 10.1212/NXG.0000000000200012
pii: NNG-2022-200015
pmc: PMC9372872
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e200012

Subventions

Organisme : NIA NIH HHS
ID : P30 AG066509
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG066515
Pays : United States

Informations de copyright

Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

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Auteurs

Michael E Belloy (ME)

Department of Neurology and Neurological Sciences (M.E.B., Y.L.G., S.J.E., M.D.G., Z.H.), Stanford University, CA; Institut du Cerveau-Paris Brain Institute-ICM (Y.L.G.), France; School of Biosciences and Veterinary Medicine (V.N.), University of Camerino, Italy; and Quantitative Sciences Unit (Z.H.), Department of Medicine, Stanford University, CA.
ORCID: 0000-0001-7748-9033

Yann Le Guen (Y)

Department of Neurology and Neurological Sciences (M.E.B., Y.L.G., S.J.E., M.D.G., Z.H.), Stanford University, CA; Institut du Cerveau-Paris Brain Institute-ICM (Y.L.G.), France; School of Biosciences and Veterinary Medicine (V.N.), University of Camerino, Italy; and Quantitative Sciences Unit (Z.H.), Department of Medicine, Stanford University, CA.
ORCID: 0000-0001-6649-8364

Sarah J Eger (SJ)

Department of Neurology and Neurological Sciences (M.E.B., Y.L.G., S.J.E., M.D.G., Z.H.), Stanford University, CA; Institut du Cerveau-Paris Brain Institute-ICM (Y.L.G.), France; School of Biosciences and Veterinary Medicine (V.N.), University of Camerino, Italy; and Quantitative Sciences Unit (Z.H.), Department of Medicine, Stanford University, CA.
ORCID: 0000-0002-7407-9626

Valerio Napolioni (V)

Department of Neurology and Neurological Sciences (M.E.B., Y.L.G., S.J.E., M.D.G., Z.H.), Stanford University, CA; Institut du Cerveau-Paris Brain Institute-ICM (Y.L.G.), France; School of Biosciences and Veterinary Medicine (V.N.), University of Camerino, Italy; and Quantitative Sciences Unit (Z.H.), Department of Medicine, Stanford University, CA.
ORCID: 0000-0002-4378-6838

Michael D Greicius (MD)

Department of Neurology and Neurological Sciences (M.E.B., Y.L.G., S.J.E., M.D.G., Z.H.), Stanford University, CA; Institut du Cerveau-Paris Brain Institute-ICM (Y.L.G.), France; School of Biosciences and Veterinary Medicine (V.N.), University of Camerino, Italy; and Quantitative Sciences Unit (Z.H.), Department of Medicine, Stanford University, CA.

Zihuai He (Z)

Department of Neurology and Neurological Sciences (M.E.B., Y.L.G., S.J.E., M.D.G., Z.H.), Stanford University, CA; Institut du Cerveau-Paris Brain Institute-ICM (Y.L.G.), France; School of Biosciences and Veterinary Medicine (V.N.), University of Camerino, Italy; and Quantitative Sciences Unit (Z.H.), Department of Medicine, Stanford University, CA.

Classifications MeSH