口腔医学研究 ›› 2018, Vol. 34 ›› Issue (12): 1265-1270.DOI: 10.13701/j.cnki.kqyxyj.2018.12.001

• 专家特约述评 •    下一篇

功能基因组学在非综合征型唇腭裂遗传结果功能研究中的应用

刘欢1,2   

  1. 1. 武汉大学口腔医学院口腔基础医学省部共建国家重点实验室培训基地和口腔生物医学教育部重点实验室 湖北 武汉 430079;
    2. 武汉大学口腔医院牙周科 湖北 武汉 430079
  • 收稿日期:2018-11-07 出版日期:2018-12-28 发布日期:2018-12-27
  • 作者简介:刘欢, 口腔医学博士(八年制),武汉大学口腔医院副主任医师。兼任中华口腔医学会口腔生物专业委员会青年委员,IADR颅颌面生物研究网络会员学。《Development》、《Human Mutation》、《Human Molecular Biology》、《Oral Diseases》等期刊审稿人。主要研究方向是颅颌面发育基因调控网络,颅颌面遗传性疾病的发病机制及诊断和骨质疏松发病机制及诊断。2014~2017年美国爱荷华大学医学院博士后。在《Nature Communications》、《American Journal of Human Genetics》、《Human Molecular Genetics》、《Journal of Dental Research》等期刊发表论文20余篇。主持2017~2019年中国科协青年人才托举计划、国家自然科学基金面上项目等多项课题。

Implementation of Functional Genome in the Functional Study of Non-coding Variants Associated with Non-syndromic Cleft Lip with or without Cleft Palate.

LIU Huan1,2   

  1. 1. The State Key Laboratory Breeding Base of Basic Science of Stomatology and Key Laboratory of Oral Biomedicine Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan 430079, China;
    2. Department of Periodontics, Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
  • Received:2018-11-07 Online:2018-12-28 Published:2018-12-27

摘要: 唇腭裂是口腔常见的先天性颅颌面缺损,部分患儿的发病与遗传背景有关。全基因组关联分析(genome-wide association study,GWAS)等研究揭示了多个与该疾病相关的易感基因位点在基因组非编码DNA区域,由于我们对于这些非编码DNA在人唇腭发育过程中的功能缺乏了解,因此对有效的易感基因位点进行系统化生物学验证成为唇腭裂遗传学结果向临床转化中的难点。本文通过对人及小鼠颅颌面发育组织特异性功能基因组结果及机器学习可能的应用方式进行系统阐述,初步建立唇腭裂相关非编码DNA突变功能研究的完整体系。

关键词: 非综合征型唇腭裂, 功能基因组, 机器学习

Abstract: Orofacial clefting is the most common craniofacial birth defect and has a strong genetic underpinning.Genome-wide association studies have detected common variants associated with this disorder, most of which lie in the non-coding DNA segments.However, due to poor understanding on the connection between the sequence and function of non-coding DNA during human craniofacial development, how these associated variants increase the risk of orofacial clefting remain largely unknown, which is a major challenge to the translation of genetic results into clinical practice.This article aims to give a systematic introduction to craniofacial functional genome results and their integration with machine learning in order to propose an improved research system for orofacial-clefting-associated non-coding variants.

Key words: Non-syndromic cleft lip with or without cleft palate, Functional genome, Machine learning