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In Proceedings of the National Academy of Sciences of the United States of America

Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the identification of mutations causing immune phenotypes in mice by creating Candidate Explorer (CE), a machine-learning software program that integrates 67 features of genetic mapping data into a single numeric score, mathematically convertible to the probability of verification of any putative mutation-phenotype association. At this time, CE has evaluated putative mutation-phenotype associations arising from screening damaging mutations in ∼55% of mouse genes for effects on flow cytometry measurements of immune cells in the blood. CE has therefore identified more than half of genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.

Xu Darui, Lyon Stephen, Bu Chun Hui, Hildebrand Sara, Choi Jin Huk, Zhong Xue, Liu Aijie, Turer Emre E, Zhang Zhao, Russell Jamie, Ludwig Sara, Mahrt Elena, Nair-Gill Evan, Shi Hexin, Wang Ying, Zhang Duanwu, Yue Tao, Wang Kuan-Wen, SoRelle Jeffrey A, Su Lijing, Misawa Takuma, McAlpine William, Sun Lei, Wang Jianhui, Zhan Xiaoming, Choi Mihwa, Farokhnia Roxana, Sakla Andrew, Schneider Sara, Coco Hannah, Coolbaugh Gabrielle, Hayse Braden, Mazal Sara, Medler Dawson, Nguyen Brandon, Rodriguez Edward, Wadley Andrew, Tang Miao, Li Xiaohong, Anderton Priscilla, Keller Katie, Press Amanda, Scott Lindsay, Quan Jiexia, Cooper Sydney, Collie Tiffany, Qin Baifang, Cardin Jennifer, Simpson Rochelle, Tadesse Meron, Sun Qihua, Wise Carol A, Rios Jonathan J, Moresco Eva Marie Y, Beutler Bruce


ENU mutagenesis, automated meiotic mapping, flow cytometry, immune cells, machine learning