Gene Discovery in Muscular Dystrophy

描述（由申请人提供）：肢带状肌营养不良症（LGMD）是一类肌肉营养不良症，其特征是进行性近端骨骼肌无力，肌肉活检显示明显的异常模式。然而，lgmd有许多遗传原因和一系列不同的临床结果。主要由于支持性治疗，受影响个体的生活质量得到了显著改善，但目前尚无可用的治疗方法。很大一部分LGMD患者的已知基因没有突变，这表明LGMD的几个致病基因尚未被发现。直到最近，寻找这些基因一直很困难，因为许多受影响的个体代表零星病例，因此潜在的LOD评分很低，连锁扫描将产生许多候选基因。下一代测序技术的快速发展正在降低这些障碍，并将改变研究和临床领域对基因诊断的许多假设。下一代测序将使研究人员和临床医生能够识别散发病例中的致病突变，以及产生广泛、低、

DESCRIPTION (provided by applicant): Limb-girdle muscular dystrophy (LGMD) is a class of muscular dystrophies characterized by progressive proximal skeletal muscle weakness and a distinct pattern of abnormalities on muscle biopsy. However, the LGMDs have a number of genetic causes and an array of different clinical outcomes. The quality of life has improved significantly for affected individuals, due primarily to supportive treatment, but currently no cur is available. A significant proportion of individuals affected by LGMD do not have mutations in the known genes, indicating that several causative genes for LGMD have yet to be discovered. The search for these genes has until recently been difficult, as many affected individuals represent sporadic cases, and thus potential LOD scores would be low and linkage scans would yield many candidates genes. The rapid advances in next generation sequencing technology is lowering these barriers, and will change many assumptions about genetic diagnosis in the research and clinical realms. Next generation sequencing will enable researchers and clinicians to identify causative mutations in sporadic cases as well as larger families that yield broad, low, or multiple linkage peaks with an abundance of candidate genes. The investigator along with his laboratory and his collaborators, have enrolled many kindreds affected by LGMD. Several of these families have a high likelihood of yielding novel causative genes. This combined approach, using both linkage analysis and next generation sequencing, is a powerful one that will broaden our knowledge of the muscular dystrophies. Next generation sequencing is already being applied in clinical settings, and a better understanding of the strengths and limitations of this technology will be important to grasp to make the greatest use of its potential.