Evidence map›Paper›PMID 40791706›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Polygenic associations with clinical and neuropathological trait heterogeneity across TDP-43 proteinopathies.

Barbara E Spencer, David J Irwin, Vivianna M Van Deerlin, EunRan Suh, Edward B Lee, Lauren Elman, Colin C Quinn, Defne Amado, Michael Baer, Murray Grossman and 2 more

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In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Barbara E Spencer
David J Irwin
Vivianna M Van Deerlin
EunRan Suh
Lauren Elman
Colin C Quinn
Michael Baer
Murray Grossman
David A Wolk

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: TDP-43 proteinopathies, including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration with TDP-43 (FTLD-TDP), and limbic-predominant age-related TDP-43 encephalopathy, encompass a spectrum of clinical and neuropathological traits. Despite mounting evidence for shared genetic risk across TDP-43 proteinopathies, the modifiers of individual-level traits are unknown. We aimed to identify polygenic contributions to trait heterogeneity across TDP-43 proteinopathies. Methods: We used weighted correlation analysis of GWAS summary statistics for ALS, FTLD-TDP, and hippocampal sclerosis of aging (HS-Aging) to identify data-driven clusters of highly correlated single nucleotide polymorphisms (SNPs). We performed gene ontology enrichment analysis for each identified cluster. We derived cluster-specific polygenic scores and evaluated their association with clinical and neuropathological traits in an independently evaluated sample of individuals who met neuropathological and/or genetic criteria for FTLD-TDP or ALS (n=260). Results: We identified 5 distinct data-driven clusters, including 3 GWAS phenotype-specific clusters (FTLD-TDP, ALS, HS-Aging) and 2 clusters representing the overlap between a pair of GWAS phenotypes (ALS-FTLD and FTLD-HS). Pathway analysis revealed biologically meaningful associations including distinct GWAS phenotype-specific processes within clusters. Cluster-specific ALS and FTLD-TDP polygenic risk each associated with individual-level clinical traits, even within the context of autosomal dominant mutation carriers, where higher ALS polygenic risk associated with neuromuscular impairment and higher FTLD-TDP polygenic risk associated with cognitive-behavioral impairment. Moreover, higher FTLD-TDP polygenic risk associated with higher TDP-43 burden within characteristic FTLD-TDP brain regions. Interpretation: We suggest that there are polygenic modifiers of clinical and neuropathological traits across TDP-43 proteinopathies that may contribute to individual-level differences, including likelihood for developing FTLD or ALS.

Identifiers

PMID40791706
PMCPMC12338895

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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.