{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T13:50:30Z","timestamp":1791467430872,"version":"4.3.4"},"reference-count":35,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The transcriptome\u2010wide association study (TWAS) is a powerful approach to identifying novel genes associated with complex phenotypes. Standard TWAS approaches build a prediction model for the genetic component of expression based on reference transcriptome data. Next, an outcome is regressed on the predicted expression in separate GWAS data. The traditional TWAS approach disregards the uncertainty of predicted expression, which can lead to unreliable inference on gene\u2010phenotype associations. We propose a novel approach that adjusts for the uncertainty of predicted expression in TWAS. We adapt techniques from measurement error theory and implement bootstrapping algorithms for penalized regression to explicitly obtain an adjustment factor to be incorporated into the unadjusted TWAS. We base the framework on adaptive Lasso. Using extensive simulations, we show that our approach produces more accurate estimates of the gene's effect size than a traditional TWAS approach. Traditional TWAS marginally inflates the type I error rate, whereas the adjusted TWAS adequately controls it. At the expense of a modestly inflated false positive rate, an unadjusted TWAS offers a limited increase in power compared to the adjusted TWAS. Simulations show that some other unadjusted and unified TWAS approaches can also inflate the type I error rate, particularly when using summary\u2010level data. We demonstrate the merits of the proposed adjusted approach by conducting TWAS for height and lipid phenotypes, LDL and HDL cholesterol, and triglycerides while integrating the Geuvadis transcriptome and UK Biobank GWAS data.<\/jats:p>","DOI":"10.1002\/gepi.70060","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:54:07Z","timestamp":1791464047000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A TWAS Method Calibrated for the Uncertainty of Predicted Expression"],"prefix":"10.1002","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5609-075X","authenticated-orcid":false,"given":"Arunabha","family":"Majumdar","sequence":"first","affiliation":[{"name":"Department of Mathematics Indian Institute of Technology Hyderabad Sangareddy Telangana India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5425-1230","authenticated-orcid":false,"given":"Tanushree","family":"Haldar","sequence":"additional","affiliation":[{"name":"Institute for Human Genetics University of California San Francisco San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"issue":"1","key":"e_1_2_8_2_1","doi-asserted-by":"crossref","first-page":"6152","DOI":"10.1038\/s41467-024-50454-2","article-title":"Glutathione Synthesis in the Mouse Liver Supports Lipid Abundance Through nrf2 Repression","volume":"15","author":"Asantewaa G.","year":"2024","journal-title":"Nature Communications"},{"issue":"1","key":"e_1_2_8_3_1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Benjamini Y.","year":"1995","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"7726","key":"e_1_2_8_4_1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK Biobank Resource With Deep Phenotyping and Genomic Data","volume":"562","author":"Bycroft C.","year":"2018","journal-title":"Nature"},{"issue":"12","key":"e_1_2_8_5_1","doi-asserted-by":"crossref","first-page":"4497","DOI":"10.1090\/S0002-9939-2010-10474-4","article-title":"Asymptotic Properties of the Residual Bootstrap for Lasso Estimators","volume":"138","author":"Chatterjee A.","year":"2010","journal-title":"Proceedings of the American Mathematical Society"},{"issue":"494","key":"e_1_2_8_6_1","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1198\/jasa.2011.tm10159","article-title":"Bootstrapping Lasso Estimators","volume":"106","author":"Chatterjee A.","year":"2011","journal-title":"Journal of the American Statistical Association"},{"issue":"4","key":"e_1_2_8_7_1","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1093\/biomet\/ass044","article-title":"Two\u2010Stage Testing Procedures With Independent Filtering for Genome\u2010Wide Gene\u2010Environment Interaction","volume":"99","author":"Dai J. 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