{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:09:57Z","timestamp":1790150997546,"version":"4.0.1"},"reference-count":80,"publisher":"Wiley","issue":"20-22","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"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":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Genome\u2010wide association studies (GWAS) have shown that pleiotropy, whereby a single genetic variant or gene influences multiple traits, is common in complex human diseases. Detecting cross\u2010phenotype associations from GWAS summary statistics remains challenging because of small effect sizes, extensive multiple testing, heterogeneous effects, and possible differences in effect direction across traits. Methods that jointly analyze multiple traits can improve the ability to detect pleiotropic signals while retaining the practical advantages of summary statistic\u2010based analyses. Although a range of statistical approaches has been developed for this purpose, practical guidance on their application, assumptions, and interpretation remains limited. This tutorial reviews several widely used methods for pleiotropy detection from GWAS summary statistics, including ASSET, PLACO, GPA, CPBayes, and GCPBayes, and demonstrates their application using breast and thyroid cancer datasets. We also highlight the importance of accounting for effect heterogeneity, correlation, and biological group structure at the gene and pathway levels in the detection and interpretation of pleiotropic association signals.<\/jats:p>","DOI":"10.1002\/sim.70717","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:54:58Z","timestamp":1787910898000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Guide for Exploring Pleiotropic Associations in Genome\u2010Wide Association Studies Using Summary Statistics"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8020-9689","authenticated-orcid":false,"given":"Christina Y.","family":"Feng","sequence":"first","affiliation":[{"name":"School of Mathematical and Physical Sciences Macquarie University  Sydney New South Wales 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