Article in Neurology. Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 12% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
6 authors at 1 institution in 2 countries.
Michael E BelloyDepartment 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 https://orcid.org/0000-0001-7748-9033
Yann Le GuenDepartment 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 https://orcid.org/0000-0001-6649-8364
Sarah J EgerDepartment 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 https://orcid.org/0000-0002-7407-9626
Valerio NapolioniDepartment 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 https://orcid.org/0000-0002-4378-6838
Michael D GreiciusDepartment 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 HeDepartment 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.
Università di Camerino · IT
Funding
Large Scale Sequencing and Analysis of GenomesU54HG003067 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GABRIEL, STACEY, LANDER, ERIC S · 2004 to 2015
$568.6M
Large Scale Genome SequencingU54HG003079 · NHGRI · WASHINGTON UNIVERSITY · PI DUTCHER, SUSAN K · 2004 to 2016
$445.7M
The Human Genome Sequencing CenterU54HG003273 · NHGRI · BAYLOR COLLEGE OF MEDICINE · PI GIBBS, RICHARD A · 2004 to 2015
$341.3M
Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Duygu Tosun-Turgut · 2016 to 2026
$226.7M
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD)U24AG021886 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI TATIANA M. FOROUD · 2002 to 2026
$119.8M
TRIAL OF VALPROATE TO ATTENUATE THE PROGRESSION OF ADU01AG010483 · NIA · UNIVERSITY OF ROCHESTER · PI AISEN, PAUL S. · 1991 to 2012
$116.2M
VCID and Stroke in a Bi-racial National CohortU01NS041588 · NINDS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI CUSHMAN, MARY, HOWARD, GEORGE · 2002 to 2022
$96.0M
NATIONAL ALZHEIMERS COORDINATING CENTER (NACC)U01AG016976 · NIA · UNIVERSITY OF WASHINGTON · PI KUKULL, WALTER ANTHONY · 1999 to 2020
$72.8M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
TDP-43 Loss-of-Function: Biology to BiomarkersP01AG019724 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Jennifer Merrilees · 2002 to 2026
$67.2M
ARIC Neurocognitive Study (ARIC-NCS) Renewal 2023-2028U01HL096812 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI JOSEF CORESH, THOMAS H MOSLEY · 2010 to 2026
Background and Objectives: 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: 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: 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: 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.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
A Fast and Robust Strategy to Remove Variant-Level Artifacts in Alzheimer Disease Sequencing Project Data. · full record | OpenQuestion