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We analyzed 968 longitudinal whole-blood transcriptomes from 165 healthy individuals across eight independent studies spanning diverse platforms, time scales (50 minutes to 16 weeks), and common environmental exposures. Using a cross-study analytical framework integrating variance partitioning, repeatability, and time-associated differential expression, we quantified temporal stability for 6,064 RNAs and classified transcripts into \u201ctrait\u201d (stable) and \u201cstate\u201d (dynamic) categories representing the extremes of longitudinal changes in transcript abundance. We identified 1,118 trait RNAs exhibiting stable within-individual levels of abundance but substantial inter-individual variability, enriched for whole-blood eGenes (P = 6.0 \u00d7 10\u207b\u00b2\u2070), supporting a genetic basis for stability. In contrast, 1,504 state RNAs showed context-dependent temporal variation and were enriched for translation and RNA-binding pathways. Integration with genetic datasets revealed that 4,395 (72%) blood transcripts were linked to at least one whole-blood eQTL, collectively associated with 18,358 GWAS trait relationships, providing disease-relevant context for transcript stability. We developed the Blood RNA Stability Atlas to integrate these features and demonstrate both top-down (disease-to-gene) and bottom-up (gene-to-context) applications for biomarker prioritization and interpretation. These findings establish temporal stability as a defining property of the blood transcriptome and provide a practical, publicly accessible framework for distinguishing stable baseline abundance levels from context-dependent transcriptional responses, informing biomarker selection, study design, and hypothesis generation.<\/jats:p>","DOI":"10.64898\/2026.07.07.737044","type":"posted-content","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:45:22Z","timestamp":1783572322000},"source":"Crossref","is-referenced-by-count":0,"title":["The Blood RNA Stability Atlas: defining temporal structure and trait-state programs in the human whole-blood transcriptome"],"prefix":"10.64898","author":[{"given":"Will C.","family":"Baltazar","sequence":"first","affiliation":[{"name":"The Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin Dell Medical School, Austin, TX"},{"name":"Baylor College of Medicine, Houston, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert O.","family":"Messing","sequence":"additional","affiliation":[{"name":"The Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin Dell Medical School, Austin, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3274-412X","authenticated-orcid":false,"given":"Laura B.","family":"Ferguson","sequence":"additional","affiliation":[{"name":"The Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin Dell Medical School, Austin, TX"},{"name":"Bowles Center for Alcohol Studies, University of North Carolina School of Medicine, Chapel Hill, NC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026071104253595000_2026.07.07.737044v1.1","doi-asserted-by":"crossref","unstructured":"Hassan, M. , et al., Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. 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