Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK.
Department of Genetics.
Curr Opin Neurol. 2021 Oct 1;34(5):756-764. doi: 10.1097/WCO.0000000000000986.
Amyotrophic lateral sclerosis (ALS) is an archetypal complex disease wherein 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 summarize 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.
肌萎缩侧索硬化症(ALS)是一种典型的复杂疾病,对于大多数患者来说,疾病风险和严重程度是多种遗传和环境因素相互作用的结果。我们正处于一个前所未有的发现阶段,新的大规模全基因组关联研究(GWAS)和风险基因的发现速度正在加快。然而,ALS 观察到的大部分遗传率尚未被发现,我们还远未揭示出总遗传结构,这对于全面的疾病分类是必要的。
我们总结了最近的进展并进行了讨论。新的机器学习模型将有助于解决非线性遗传相互作用。通过使用细胞特异性表观遗传谱减少搜索空间并将范围扩大到包括遗传相关表型,可能会提高遗传发现的统计能力。结构变异、体细胞异质性以及考虑环境修饰因子是重大挑战,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。
完全外显的孟德尔风险基因的出现需要新的实验设计和验证标准。挑战是巨大的,但成功的疾病分类的潜在回报是大规模和有效的个体化医疗。