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A growing number of studies examine the risk of breast cancer (BC) conferred by pathogenic variants of these genes. A meta\u2010analysis combining the reported risk estimates can provide an overall estimate of age\u2010specific risk of developing BC, that is, penetrance for a gene. However, estimates reported by case\u2013control studies often suffer from ascertainment bias. Currently, there is no method available to adjust for such bias in this setting. We consider a Bayesian random effect meta\u2010analysis method that can synthesize different types of risk measures and extend it to incorporate studies with ascertainment bias. This is achieved by introducing a bias term in the model and assigning appropriate priors. We validate the method through a simulation study and apply it to estimate BC penetrance for carriers of pathogenic variants in the ATM and PALB2 genes. Our simulations show that the proposed method results in more accurate and precise penetrance estimates compared to when no adjustment is made for ascertainment bias or when such biased studies are discarded from the analysis. The overall estimated BC risk for individuals with pathogenic variants are (1) 5.77% (3.22%\u20139.67%) by age 50 and 26.13% (20.31%\u201332.94%) by age 80 for ATM; (2) 12.99% (6.48%\u201322.23%) by age 50, and 44.69% (34.40%\u201355.80%) by age 80 for PALB2. The proposed method allows meta\u2010analyses to include studies with ascertainment bias, resulting in inclusion of more studies and thereby more accurate estimates.<\/jats:p>","DOI":"10.1002\/sim.10323","type":"journal-article","created":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T08:50:56Z","timestamp":1737795056000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adjusting for Ascertainment Bias in Meta\u2010Analysis of Penetrance for Cancer Risk"],"prefix":"10.1002","volume":"44","author":[{"given":"Thanthirige Lakshika M.","family":"Ruberu","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences University of Texas at Dallas  Richardson Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danielle","family":"Braun","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Harvard T.H. Chan School of Public Health  Boston Massachusetts USA"},{"name":"Department of Data Science Dana Farber Cancer Institute  Boston Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni","family":"Parmigiani","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Harvard T.H. Chan School of Public Health  Boston Massachusetts USA"},{"name":"Department of Data Science Dana Farber Cancer Institute  Boston Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7758-4460","authenticated-orcid":false,"given":"Swati","family":"Biswas","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences University of Texas at Dallas  Richardson Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/2381468318821103"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1007983107"},{"issue":"9","key":"e_1_2_10_4_1","first-page":"787","article-title":"What's New in Genetic Testing for Cancer Susceptibility?","volume":"30","author":"Plichta J. 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