{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:37:24Z","timestamp":1787567844922,"version":"build-2736575974"},"reference-count":75,"publisher":"eLife Sciences Publications, Ltd","license":[{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["R01HG011138"],"award-info":[{"award-number":["R01HG011138"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["R35HG010718"],"award-info":[{"award-number":["R35HG010718"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elifesciences.org"],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>\n                    Root causal gene expression levels \u2013 or\n                    <jats:italic>root causal genes<\/jats:italic>\n                    for short \u2013 correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.\n                  <\/jats:p>","DOI":"10.7554\/elife.100949","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T09:25:44Z","timestamp":1728984344000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.100949","source":"Crossref","is-referenced-by-count":0,"title":["Discovering root causal genes with high-throughput perturbations"],"prefix":"10.7554","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9894-9694","authenticated-orcid":true,"given":"Eric V","family":"Strobl","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01an3r305","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Pittsburgh","place":["Pittsburgh, United 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