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

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

Research output: Contribution to journalArticlepeer-review

Abstract

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 damagingmutations 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.

Original languageEnglish (US)
Article numbere2106786118
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number28
DOIs
StatePublished - Jul 13 2021

Keywords

  • Automated meiotic mapping
  • ENU mutagenesis
  • Flow cytometry
  • Immune cells
  • Machine learning

ASJC Scopus subject areas

  • General

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