Evidence map›Paper›PMID 43026550›Full record

ArticleGenome biology2026

Updates to the reference genome alter the detection and direction of genes differentially expressed in Alzheimer's disease.

Anina N Lund, Ryan C Thompson, Ying-Chich Wang, Hardik Shah, Panos Roussos, Robert Sebra, Girish N Nadkarni, Eric J Nestler, Alexander W Charney, Noam D Beckmann

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Article in Genome biology, 2026. 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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5 · Who and what money

Authors and funding

10 authors.

Anina N LundIcahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Ryan C ThompsonCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Ying-Chich WangDepartment of Genetics and Genomic Sciences, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Hardik ShahDepartment of Genetics and Genomic Sciences, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Panos RoussosFriedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Robert SebraIcahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Girish N NadkarniIcahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Eric J NestlerFriedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Alexander W CharneyCharles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Noam D BeckmannIcahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. noam.beckmann@mssm.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer's Disease gene expression studies often rely on the reproducibility of known disease associations to build confidence in novel discoveries. This approach assumes that true biological variation associated with Alzheimer's Disease is robust to a range of technical and biological confounders that likely differ across experiments. Here, we systematically assess the impact of an important yet frequently overlooked technical confounder in Alzheimer's Disease differential expression analysis: changes in gene quantification resulting from ongoing updates to the human genome reference and transcriptome annotations.

resultsTo investigate this, gene expression in a large brain transcriptomic dataset from Alzheimer's Disease patients and controls (ROSMAP) was quantified using five transcriptome annotations mapped across a broad range of human genome references, from NCBI36 to CHM13v2.0. Although nearly all genes exhibit significant differences in expression between reference pairs (i.e., were differentially quantified), genome-wide Alzheimer's Disease differential expression signatures remain highly concordant across references. Notably, while a core set of significantly differentially expressed Alzheimer's Disease genes is largely conserved across references, reference-specific Alzheimer's Disease-associated genes are enriched for pathways known to be involved in the disease. Genes whose direction of association with Alzheimer's Disease significantly differs across references are also identified, highlighting the potential influence of reference choice on Alzheimer's Disease-relevant genes and pathways. All results were replicated in an independent transcriptomics dataset (MSBB) derived from the same brain region.

conclusionsThese findings demonstrate that reference genome choice impacts the identification and interpretation of specific Alzheimer's Disease-associated genes, despite overall reproducibility of genome-wide differential expression signatures, underscoring the critical importance of reference selection in Alzheimer's Disease transcriptomic studies.

Indexed as

Alzheimer DiseaseGenome, HumanTranscriptomeGene Expression ProfilingHumans

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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.