2010年11月23日，由深圳华大基因研究院、中南大学湘雅医院等单位合作研究的成果“利用全外显子组测序技术发现小脑共济失调新的致病基因TGM6”在国际知名杂志Brain上在线发表，这是我国科学家应用外显子组测序技术进行单基因病研究的一项突破，对促进国内单基因病研究的发展具有重要意义。
该研究对患有小脑共济失调的同一家系4个患者进行了全外显子组测序，在每个患者的外显子区域平均检测到约5800个潜在影响基因功能的变异，包括非同义突变（NS）、剪切位点突变（SS）和插入缺失突变（Indel），其中包含了大量的罕见变异。研究人员通过生物信息学分析，将候选致病基因突变锁定为TGM6基因第10外显子保守区域的一个错义突变。进一步研究发现，在另一个患有该病的家系里，TGM6基因同样存在错义突变，从而证实TGM6基因是小脑共济失调家族的新致病基因，属于SCA23亚型。该研究还对测序的家系同步进行了定位克隆，通过连锁分析将致病位点定位在20号染色体短臂的8.4Mb的区域，该区域包含了91个基因，TGM6基因正是其中之一。
小脑共济失调是一种常染色体显性遗传的神经系统疾病，疾病临床常表现为运动失调。应用传统的定位克隆方法已经在人类基因组上定位了对应不同亚型的31个位点，并确定了其中19个亚型的致病基因。但是，定位克隆方法只能将候选致病基因定位到基因组上的一段区域，并不能确定致病基因，因此具有很大的局限性。而外显子组测序技术不仅可以直接检测到包括罕见突变在内的大量突变，还能进一步通过生物信息分析确定候选致病基因。TGM6基因的发现，对今后阐明该病发病机制、遗传诊断和新药研发具有重要的研究和应用价值。
新一代测序技术的产生和发展为疾病研究带来了新的机遇。作为一种高效、快速和高性价比的研究方法，全外显子组测序技术已经开始应用于遗传病的研究。自2009年以来，国际顶级杂志上相继报道了十多篇将外显子组测序技术应用于单基因病的研究成果。华大基因的科学家采用外显子测序技术也取得多项重要研究成果。他们对50个藏族人的进行外显子测序和分析找到与藏族人高原适应性密切相关的基因，文章发表在《科学》杂志上；通过与丹麦的科研机构合作，华大基因对200个丹麦人的外显子进行了测序研究，证实了人群中存在大量低频率非同义突变，文章发表在《自然—遗传学》上。外显子组测序技术已经得到国际单基因病领域科学家的广泛认可，也将越来越多的应用于单基因疾病的研究中。这将对确定单基因病的致病基因的发现产生巨大推动作用。利用测序技术替代传统的定位克隆研究方法，或将二者相结合，已成为研究学者们进行人类遗传疾病研究的又一新途径。
华大基因于2010年5月启动了“千种单基因病计划”，希望通过与国内外单基因病研究领域的科学家合作，充分利用华大基因先进的基因组测序技术和强大的生物信息分析能力，将丰富的单基因病遗传资源转化为新的科学发现，促进单基因病研究的发展。(生物谷Bioon.com)
生物谷推荐英文摘要：
Brain (2010) doi: 10.1093/brain/awq323
TGM6 identified as a novel causative gene of spinocerebellar ataxias using exome sequencing
Jun Ling Wang1,2,*, Xu Yang3,*, Kun Xia2,*, Zheng Mao Hu2, Ling Weng1, Xin Jin3,4, Hong Jiang1,5, Peng Zhang3, Lu Shen1,5, Ji Feng Guo1,5, Nan li1, Ying Rui Li3, Li Fang Lei1, Jie Zhou1, Juan Du1, Ya Fang Zhou1, Qian Pan2, Jian Wang3, Jun Wang3,6, Rui Qiang Li3,6 and Bei Sha Tang1,2,5
1 Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan province 410008, China
2 National Key Lab of Medical Genetics of China, Changsha, Hunan province 410008, China
3 BGI-Shenzhen, Shenzhen, Guangdong province 518083, China
4 Innovative Program for Undergraduate Students, School of Bioscience and Biotechnology, South China University of Technology, Guangzhou 510641, China
5 Neurodegenerative Disorders Research Center, Central South University, Changsha, Hunan province 410008, China
6 Department of Biology, University of Copenhagen, Copenhagen DK-2200, Denmark
Autosomal-dominant spinocerebellar ataxias constitute a large, heterogeneous group of progressive neurodegenerative diseases with multiple types. To date, classical genetic studies have revealed 31 distinct genetic forms of spinocerebellar ataxias and identified 19 causative genes. Traditional positional cloning strategies, however, have limitations for finding causative genes of rare Mendelian disorders. Here, we used a combined strategy of exome sequencing and linkage analysis to identify a novel spinocerebellar ataxia causative gene, TGM6. We sequenced the whole exome of four patients in a Chinese four-generation spinocerebellar ataxia family and identified a missense mutation, c.1550T–G transition (L517W), in exon 10 of TGM6. This change is at a highly conserved position, is predicted to have a functional impact, and completely cosegregated with the phenotype. The exome results were validated using linkage analysis. The mutation we identified using exome sequencing was located in the same region (20p13–12.2) as that identified by linkage analysis, which cross-validated TGM6 as the causative spinocerebellar ataxia gene in this family. We also showed that the causative gene could be mapped by a combined method of linkage analysis and sequencing of one sample from the family. We further confirmed our finding by identifying another missense mutation c.980A–G transition (D327G) in exon seven of TGM6 in an additional spinocerebellar ataxia family, which also cosegregated with the phenotype. Both mutations were absent in 500 normal unaffected individuals of matched geographical ancestry. The finding of TGM6 as a novel causative gene of spinocerebellar ataxia illustrates whole-exome sequencing of affected individuals from one family as an effective and cost efficient method for mapping genes of rare Mendelian disorders and the use of linkage analysis and exome sequencing for further improving efficiency.
