Whole-Exome Sequencing of Discordant Monozygotic Twin Families for Identification of Candidate Genes for Microtia-Atresia

Xinmiao Fan; Lu Ping; Hao Sun; Yushan Chen; Pu Wang; Tao Liu; Rui Jiang; Xuegong Zhang; Xiaowei Chen

doi:10.3389/fgene.2020.568052

Whole-Exome Sequencing of Discordant Monozygotic Twin Families for Identification of Candidate Genes for Microtia-Atresia

Front Genet. 2020 Oct 22:11:568052. doi: 10.3389/fgene.2020.568052. eCollection 2020.

Authors

Xinmiao Fan¹, Lu Ping², Hao Sun³, Yushan Chen⁴, Pu Wang⁵, Tao Liu⁶, Rui Jiang³, Xuegong Zhang³, Xiaowei Chen¹

Affiliations

¹ Department of Otolaryngology, Peking Union Medical College Hospital, Beijing, China.
² Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
³ Bioinformatics Division, BNRIST and MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China.
⁴ Department of Otolaryngology, The Ohio State University, Columbus, OH, United States.
⁵ Department of Otolaryngology Head and Neck Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
⁶ Annoroad Gene Technology (Beijing) Co., Ltd, Beijing, China.

Abstract

Objective: We used data from twins and their families to probe the genetic factors contributing to microtia-atresia, in particular, early post-twinning variations that potentially account for the discordant phenotypes of monozygotic twin pairs.

Methods: Six families of monozygotic twins discordant for congenital microtia-atresia were recruited for study. The six patients shared a consistent clinical phenotype of unilateral microtia-atresia. Whole-exome sequencing (WES) was performed for all six twin pairs and their parents. Family segregation and multiple bioinformatics methods were applied to identify suspicious mutations in all families. Recurring mutations commonly detected in at least two families were highlighted. All variants were validated via Sanger sequencing. Gene Ontology (GO) analysis was performed to identify candidate gene sets and related pathways. Copy number variation (CNV), linkage analysis, association analysis and machine learning methods were additionally applied to isolate candidate mutations, and comparative genomics and structural modeling tools used to evaluate their potential roles in onset of microtia-atresia.

Results: Our analyses revealed 61 genes with suspected mutations associated with microtia-atresia. Five (HOXA4, MUC6, CHST15, TBX10, and AMER1) contained 7 de novo mutations that appeared in at least two families, which have been previously reported as pathogenic for other diseases. Among these, HOXA4 (c.920A>C, p.H307P) was determined as the most likely pathogenic variant for microtia-atresia. GO analysis revealed four gene sets involving 11 pathways potentially related to underlying pathogenesis of the disease. CNVs in three genes (UGT2B17, OVOS, and KATNAL2) were detected in at least two families. Linkage analysis disclosed 13 extra markers for the disease, of which two (FGFR1 and EYA1) were validated via machine learning analysis as plausible candidate genes for the disease.

Conclusion: Based on comprehensive genetic and bioinformatic analyses of WES data from six families of discordant monozygotic twins with microtia-atresia, we identified multiple candidate genes that may function in post-twinning onset of the disease. The collective findings provide novel insights into the pathogenesis of congenital microtia-atresia.

Keywords: bioinformatics analysis; genes; microtia-atresia; monozygotic discordant twins; whole-exome sequencing.