A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease.
Junyu Chen, Lei Wang, Philip L De Jager, David A Bennett, Aron S Buchman, Jingjing Yang
Article in HGG advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 25% of its field
1 · What the graph read from it
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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.
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3 · Its place in the literature
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3 citing papers in PubMed, 4 citations in OpenAlex.
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5 · Who and what money
Authors and funding
6 authors at 3 institutions in 1 country.
Junyu ChenDepartment of Epidemiology, Emory University School of Public Health, Atlanta, GA 30322, USA.
Lei WangCenter for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.
Philip L De JagerCenter for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USA.
David A BennettRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA.
Aron S BuchmanRush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA.
Jingjing YangCenter for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.
Emory University · USRush University Medical Center · USColumbia University · US
Funding
Vascular Structure and Function in Cognitive AgingP01AG003949 · NIA · YESHIVA UNIVERSITY · PI Richard B. LIPTON · 1985 to 2026
$73.9M
Alzheimer's Disease Genetics ConsortiumU01AG032984 · NIA · UNIVERSITY OF PENNSYLVANIA · PI SCHELLENBERG, GERARD DAVID · 2009 to 2024
$60.4M
SUPPLEMENT TO ALZHEIMERS DISEASE PATIENT REGISTRYU01AG006786 · NIA · MAYO CLINIC ROCHESTER · PI GRAFF-RADFORD, JONATHAN, JACK, CLIFFORD R. · 1986 to 2023
$49.6M
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1M
EPIDEMIOLOGY OF NEURAL RESERVE AND NEUROBIOLOGY IN AGINGR01AG017917 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2001 to 2023
$43.3M
THE PGRN/TDP-43 AXIS IN ALZHEIMER?S DISEASE AND NEURODEGENERATIONP50AG016574 · NIA · MAYO CLINIC ROCHESTER · PI PETERSEN, RONALD C · 1999 to 2018
$36.9M
Rush Alzheimer's Disease Research CenterP30AG072975 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI Lisa L Barnes, Julie A. Schneider · 2021 to 2026
$24.7M
Integrating the exposome and methylome to inform brain molecular changes in ADRD across established diverse cohorts.U01AG046139 · NIA · UNIVERSITY OF FLORIDA · PI ERTEKIN-TANER, NILUFER, FUNK, CORY · 2013 to 2022
$24.6M
RISK FACTORS, PATHOLOGY, AND CLINICAL EXPRESSIONS OF ADR01AG015819 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1998 to 2024
$21.4M
Multi-omic network-directed proteoform discovery, dissection and functional validation to prioritize novel AD therapeutic targetsU01AG061356 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2018 to 2022
$13.7M
Pathway discovery, validation and compound identification for Alzheimer's disease - SupplementU01AG046152 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2013 to 2017
$13.6M
TRANSGENIC MODELING OF TAUOPATHYP01AG017216 · NIA · MAYO CLINIC JACKSONVILLE · PI DICKSON, DENNIS WILLIAM · 1999 to 2011
Existing methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are limited to using non-overlapped categorical annotations or limited by the computation burden of modeling genome-wide variants. To overcome these limitations, we propose a scalable Bayesian functional GWAS method to account for multivariate quantitative functional annotations (BFGWAS_QUANT), accompanied by a scalable computation algorithm enabling joint modeling of genome-wide variants. Simulation studies validated the performance of BFGWAS_QUANT for accurately quantifying annotation enrichment and improving GWAS power. Applying BFGWAS_QUANT to study five Alzheimer disease (AD)-related phenotypes using individual-level GWAS data (n = ∼1,000), we found that histone modification annotations have higher enrichment than expression quantitative trait locus (eQTL) annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that
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 scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease. · full record | OpenQuestion