Evidence map›Paper›PMID 39822590›Full record

ArticleAlzheimer's & dementia (New York, N. Y.)

LD-informed deep learning for Alzheimer's gene loci detection using WGS data.

Taeho Jo, Paula Bice, Kwangsik Nho, Andrew J Saykin, Alzheimer's Disease Sequencing Project

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's & dementia (New York, N. Y.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. New Genetic Associations Between Alzheimer's Disease and Its Key Risk Factors.Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society · 2026
    Article
  2. DuAL-Net: A Dual-Network Approach for Alzheimer's Disease Risk Prediction UsingComputational and structural biotechnology journal · 2026
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Taeho JoIndiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis Indiana USA.ORCID https://orcid.org/0000-0003-1765-5735
Paula BiceIndiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis Indiana USA.
Kwangsik NhoIndiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis Indiana USA.
Andrew J SaykinIndiana Alzheimer Disease Research Center and Center for Neuroimaging, Department of Radiology and Imaging Sciences Indiana University School of Medicine Indianapolis Indiana USA.ORCID https://orcid.org/0000-0002-1376-8532
Alzheimer's Disease Sequencing Project

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 ARTHUR W TOGA · 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
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
Translational pharmacoepidemiology: neuroprotection and neurotoxicity of antihypertensives and strong anticholinergicsU19AG066567 · NIA · KAISER FOUNDATION RESEARCH INSTITUTE · PI Christine L MacDonald · 2021 to 2026
$80.4M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
ARIC Neurocognitive Study (ARIC-NCS) Renewal 2023-2028U01HL096812 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI JOSEF CORESH, THOMAS H MOSLEY · 2010 to 2026
$65.7M
Alzheimer's Disease Genetics ConsortiumU01AG032984 · NIA · UNIVERSITY OF PENNSYLVANIA · PI SCHELLENBERG, GERARD DAVID · 2009 to 2024
$60.4M
THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI GRABOWSKI, THOMAS J. · 1985 to 2019
$57.2M
NHGRI NIH HHS RC2 HG005605NHGRI NIH HHS U54 HG003067NHGRI NIH HHS U54 HG003079NHGRI NIH HHS U54 HG003273NHLBI NIH HHS HHSN268200800007CNHLBI NIH HHS HHSN268201100005CNHLBI NIH HHS HHSN268201100005GNHLBI NIH HHS HHSN268201100005INHLBI NIH HHS HHSN268201100006CNHLBI NIH HHS HHSN268201100007CNHLBI NIH HHS HHSN268201100007INHLBI NIH HHS HHSN268201100008CNHLBI NIH HHS HHSN268201100008INHLBI NIH HHS HHSN268201100009CNHLBI NIH HHS HHSN268201100009INHLBI NIH HHS HHSN268201100010CNHLBI NIH HHS HHSN268201100011CNHLBI NIH HHS HHSN268201100011INHLBI NIH HHS HHSN268201100012CNHLBI NIH HHS HHSN268201200036CNHLBI NIH HHS HHSN268201500001CNHLBI NIH HHS HHSN268201500001INHLBI NIH HHS N01 HC055222NHLBI NIH HHS N01 HC085079NHLBI NIH HHS N01 HC085080NHLBI NIH HHS N01 HC085081NHLBI NIH HHS N01 HC085082NHLBI NIH HHS N01 HC085083NHLBI NIH HHS N01 HC085086NHLBI NIH HHS R01 HL070825NHLBI NIH HHS R01 HL105756NHLBI NIH HHS R01 HL172803NHLBI NIH HHS RC2 HL102419NHLBI NIH HHS U01 HL080295NHLBI NIH HHS U01 HL096812NHLBI NIH HHS U01 HL096814NHLBI NIH HHS U01 HL096899NHLBI NIH HHS U01 HL096902NHLBI NIH HHS U01 HL096917NHLBI NIH HHS U01 HL130114NIA NIH HHS K23 AG030944NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P20 AG068024NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG010133NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG038072NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062677NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066462NIA NIH HHS P30 AG066468NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066508NIA NIH HHS P30 AG066509NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG066518NIA NIH HHS P30 AG066519NIA NIH HHS P30 AG066530NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072946NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072973NIA NIH HHS P30 AG072975NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072977NIA NIH HHS P30 AG072978NIA NIH HHS P30 AG072979NIA NIH HHS P50 AG005136NIA NIH HHS P50 AG008012NIA NIH HHS P50 AG016574NIA NIH HHS R01 AG009029NIA NIH HHS R01 AG009956NIA NIH HHS R01 AG011101NIA NIH HHS R01 AG011380NIA NIH HHS R01 AG015819NIA NIH HHS R01 AG015928NIA NIH HHS R01 AG017917NIA NIH HHS R01 AG018016NIA NIH HHS R01 AG019085NIA NIH HHS R01 AG019757NIA NIH HHS R01 AG019771NIA NIH HHS R01 AG020098NIA NIH HHS R01 AG021136NIA NIH HHS R01 AG021547NIA NIH HHS R01 AG022018NIA NIH HHS R01 AG023629NIA NIH HHS R01 AG025259NIA NIH HHS R01 AG027161NIA NIH HHS R01 AG027944NIA NIH HHS R01 AG028786NIA NIH HHS R01 AG031272NIA NIH HHS R01 AG032289NIA NIH HHS R01 AG032990NIA NIH HHS R01 AG033040NIA NIH HHS R01 AG033193NIA NIH HHS R01 AG041797NIA NIH HHS R01 AG042188NIA NIH HHS R01 AG042210NIA NIH HHS R01 AG044546NIA NIH HHS R01 AG044829NIA NIH HHS R01 AG046949NIA NIH HHS R01 AG048234NIA NIH HHS R01 AG048927NIA NIH HHS R01 AG049607NIA NIH HHS R01 AG054047NIA NIH HHS R01 AG054076NIA NIH HHS R01 AG057739NIA NIH HHS R01 AG057909NIA NIH HHS R01 AG058066NIA NIH HHS R01 AG059716NIA NIH HHS R01 AG060747NIA NIH HHS R01 AG061155NIA NIH HHS R01 AG064614NIA NIH HHS R01 AG064877NIA NIH HHS R01 AG068193NIA NIH HHS R01 AG079280NIA NIH HHS R03 AG063250NIA NIH HHS R37 AG015473NIA NIH HHS R56 AG051876NIA NIH HHS RC4 AG039085NIA NIH HHS RF1 AG015473NIA NIH HHS RF1 AG051504NIA NIH HHS RF1 AG053303NIA NIH HHS RF1 AG054023NIA NIH HHS RF1 AG054047NIA NIH HHS RF1 AG054052NIA NIH HHS RF1 AG054074NIA NIH HHS RF1 AG054080NIA NIH HHS RF1 AG057519NIA NIH HHS RF1 AG058066NIA NIH HHS RF1 AG058267NIA NIH HHS RF1 AG058501NIA NIH HHS T32 AG071444NIA NIH HHS U01 AG006781NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG032984NIA NIH HHS U01 AG046139NIA NIH HHS U01 AG049505NIA NIH HHS U01 AG049506NIA NIH HHS U01 AG049507NIA NIH HHS U01 AG049508NIA NIH HHS U01 AG052409NIA NIH HHS U01 AG052410NIA NIH HHS U01 AG052411NIA NIH HHS U01 AG057659NIA NIH HHS U01 AG058589NIA NIH HHS U01 AG058635NIA NIH HHS U01 AG058654NIA NIH HHS U01 AG058922NIA NIH HHS U01 AG062602NIA NIH HHS U01 AG062943NIA NIH HHS U01 AG068057NIA NIH HHS U01 AG072177NIA NIH HHS U19 AG024904NIA NIH HHS U19 AG066567NIA NIH HHS U19 AG074879NIA NIH HHS U24 AG021886NIA NIH HHS U24 AG041689NIA NIH HHS U24 AG056270NIA NIH HHS U24 AG072122NIA NIH HHS U24 AG074855NIA NIH HHS U54 AG052427NIA NIH HHS UF1 AG047133NINDS NIH HHS P01 NS026630NINDS NIH HHS P50 NS039764NINDS NIH HHS P50 NS071674NINDS NIH HHS R01 NS017950NINDS NIH HHS R01 NS029993NINDS NIH HHS R01 NS069719NINDS NIH HHS R01 NS080820NINDS NIH HHS U01 NS041588NLM NIH HHS R01 LM012535NLM NIH HHS R01 LM013463Wellcome Trust
6 · The paper itself

Abstract

introductionThe exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.

methodsThe Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7416 non-Hispanic white (NHW) participants (3150 cognitively normal older adults (CN), 4266 AD).

results30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1500 LD blocks and confirmed previously known variants, including DISCUSSION: The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue-specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep-Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large-scale sequencing studies. Highlights: Growing genomic datasets require advanced tools to identify genetic loci in sequencing.Deep-Block, a novel AI framework, was used to process large-scale ADSP WGS data.Deep-Block identified both known and novel AD-associated genetic loci.rs429358 (

Indexed as

Alzheimer's diseasedeep learninggenetic lociimputation methodslinkage disequilibriumwhole‐genome sequencing

Identifiers

PMID39822590
PMCPMC11736638

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.