Dataset Information

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

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 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.

SUBMITTER: Xu D

PROVIDER: S-EPMC8285956 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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

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

Proceedings of the National Academy of Sciences of the United States of America 20210701 28

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-learn ...[more]

PMID: 34260399

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Project description:Primary hyperparathyroidism (PHPT) is characterized by overproduction of parathyroid hormone and subsequent hypercalcemia. Approximately 10% of PHPT cases are hereditary, and several genes, such as MEN1, RET, CASR, and CDC73, are responsible for the familial forms of PHPT. However, other genetic mutations involved in the etiology of PHPT are largely unknown. In this study, we identified genetic variants that might be responsible for PHPT, including familial PHPT, benign sporadic PHPT, and sporadic parathyroid cancer, using next-generation sequencing (NGS). A total of 107 patients with PHPT who underwent NGS from 2017 to 2021 at Severance Hospital were enrolled. We reviewed the pathogenic variants, likely pathogenic variants, and variants of uncertain significance (VUS) according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology criteria. Of the 107 patients (mean age: 47.6 ± 16.1 years, women 73.8%), 12 patients were diagnosed with familial PHPT, 13 with parathyroid cancer, and 82 with benign sporadic PHPT. Using NGS, we identified three pathogenic variants in two genes (CDC73 and MEN1), 10 likely pathogenic variants in six genes (CASR, CDC73, LRP5, MEN1, SDHA, and VHL), and 39 non-synonymous VUS variants that could be related to parathyroid disease. Interestingly, we identified one GCM2 variant (c.1162A>G [p.Lys388Glu]) and five APC variants that were previously reported in familial isolated hyperparathyroidism, benign sporadic PHPT, and parathyroid cancer. We also analyzed the characteristics of subjects with positive genetic test results (pathogenic or likely pathogenic variants), and 76.9% of them had at least one of the following features: 1) age < 40 years, 2) family history of PHPT, 3) multiglandular PHPT, or 4) recurrent PHPT. In this study, we analyzed the NGS data of patients with PHPT and observed variants that could possibly be related to PHPT pathogenesis. NGS screening for selected patients with PHPT might help in the diagnosis and management of the disease.

Dataset Information

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

Publications

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

Similar Datasets

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