Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 28; pp. 1 - 11
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 13.07.2021
• Subjects: Algorithms; Animals; Automation; Biological Sciences; Female; Flow Cytometry; Gene mapping; Genes; Genotype & phenotype; Germ-Line Mutation - genetics; Homeostasis; Homozygotes; Immune response; Immune system; Learning algorithms; Leukocytes - metabolism; Machine Learning; Male; Meiosis; Meiosis - genetics; Mice; Mice, Inbred C57BL; Mutation; Phenotype; Phenotypes; Probability; Quantitative trait loci; Reproducibility of Results; Software; automated meiotic mapping; immune cells; flow cytometry; ENU mutagenesis; machine learning
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2106786118

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.