Advances in the genetic classification of amyotrophic lateral sclerosis.
Advances in the genetic classification of amyotrophic lateral sclerosis.
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DOI：
10.1097/wco.0000000000000986
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发表时间：
2021-10-01
影响因子：
4.8
通讯作者：
Veldink JH
中科院分区：
文献类型：
--
作者：
Cooper-Knock J;Harvey C;Zhang S;Moll T;Timpanaro IS;Kenna KP;Iacoangeli A;Veldink JH
肌萎缩侧索硬化症（ALS）是一种典型的复杂疾病，对于大多数患者而言，疾病的风险和严重程度是多种遗传和环境因素相互作用的产物。我们正处于一个前所未有的发现时期，新的大规模全基因组关联研究（GWAS）和加速发现风险基因。然而，许多观察到的遗传性ALS是未被发现的，我们还没有接近阐明的总遗传结构，这将是必要的综合疾病亚分类。我们总结了最近的发展，并讨论了未来。新的机器学习模型将有助于解决非线性遗传相互作用。遗传发现的统计能力可以通过使用细胞特异性表观遗传特征来减少搜索空间并将我们的范围扩大到包括遗传相关的表型来提高。结构变异、体细胞异质性和环境修饰剂的考虑代表了重大挑战，这将需要整合多种技术和多学科方法，包括临床医生、遗传学家和病理学家。从完全渗透孟德尔风险基因的转移需要新的实验设计和新的验证标准。挑战是巨大的，但成功的疾病亚分类的潜在回报是大规模和有效的个性化医疗。
Amyotrophic lateral sclerosis (ALS) is an archetypal complex disease where disease risk and severity are, for the majority of patients, the product of interaction between multiple genetic and environmental factors. We are in a period of unprecedented discovery with new large-scale genome-wide association study (GWAS) and accelerating discovery of risk genes. However, much of the observed heritability of ALS is undiscovered and we are not yet approaching elucidation of the total genetic architecture which will be necessary for comprehensive disease subclassification. We summarise recent developments and discuss the future. New machine learning models will help to address nonlinear genetic interactions. Statistical power for genetic discovery may be boosted by reducing the search-space using cell-specific epigenetic profiles and expanding our scope to include genetically correlated phenotypes. Structural variation, somatic heterogeneity and consideration of environmental modifiers represent significant challenges which will require integration of multiple technologies and a multidisciplinary approach including clinicians, geneticists and pathologists. The move away from fully penetrant Mendelian risk genes necessitates new experimental designs and new standards for validation. The challenges are significant but the potential reward for successful disease subclassification is large-scale and effective personalized medicine.
体细胞性超突变区域的肿瘤异质性的超紧致分析。
影响因子：
12.3
作者：
Spence JM;Spence JP;Abumoussa A;Burack WR
通讯作者：
Burack WR