{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T12:59:20Z","timestamp":1769086760461,"version":"3.49.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Seed Fund for PI Research\u2014Basic Research"},{"DOI":"10.13039\/501100003803","name":"University of Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003803","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Research Grants Council General Research Fund","award":["17307324"],"award-info":[{"award-number":["17307324"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Transcriptome-wide association study (TWAS) aims to identify trait-associated genes regulated by significant variants to explore the underlying biological mechanisms at a tissue-specific level. Despite the advancement of current TWAS methods to cover diverse traits, traditional approaches still face two main challenges: (i) the lack of methods that can guarantee finite-sample false discovery rate (FDR) control in identifying trait-associated genes; and (ii) the requirement for individual-level data, which is often inaccessible.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address this challenge, we propose a powerful knockoff inference method termed TWAS-GKF to identify candidate trait-associated genes with a guaranteed finite-sample FDR control. TWAS-GKF introduces the main idea of Ghostknockoff inference to generate knockoff variables using only summary statistics instead of individual-level data. In extensive studies, we demonstrate that TWAS-GKF successfully controls the finite-sample FDR under a pre-specified FDR level across all settings. We further apply TWAS-GKF to identify genes in brain cerebellum tissue from the Genotype-Tissue Expression (GTEx) v8 project associated with schizophrenia (SCZ) from the Psychiatric Genomics Consortium (PGC), and genes in liver tissue related to low-density lipoprotein cholesterol (LDL-C) from the UK Biobank, respectively. The results reveal that the majority of the identified genes are validated by Open Targets Validation Platform.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package TWAS.GKF is publicly available at https:\/\/github.com\/AnqiWang2021\/TWAS.GKF.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae502","type":"journal-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T01:26:32Z","timestamp":1724549192000},"source":"Crossref","is-referenced-by-count":4,"title":["TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference"],"prefix":"10.1093","volume":"40","author":[{"given":"Anqi","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial Science, The University of Hong Kong , Hong Kong SAR, 999077, 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