UK Dementia Research Institute at Cardiff University, Division of Psychological Medicine and Clinical Neuroscience, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK.
Dis Model Mech. 2022 Jun 1;15(6). doi: 10.1242/dmm.049376.
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.
医学基因组学的一个主要挑战是理解为什么具有相同疾病的个体具有不同的临床症状，以及为什么具有相同突变的个体可能受到不同疾病的影响。在每种复杂疾病中，确定不同遗传和非遗传风险因素的贡献是理解疾病机制的关键障碍。遗传研究依赖于精确的表型，并且当表型不精确时，无法发现疾病的遗传贡献。为了解决这一挑战，已经开发出了深度表型队列，这些队列收集了详细的、精细的数据集。这些队列帮助我们研究潜在的生物学途径和风险因素，以确定治疗靶点，从而推进精准医学。神经退行性疾病帕金森病的表型表现多样，遗传度适中，其潜在的疾病机制仍存在争议。因此，为这种疾病开发了深度表型队列。在这里，我们专注于帕金森病，并探讨深度表型如何帮助解决遗传和表型异质性带来的挑战。我们还讨论了最近的数据收集和计算方法，以及必须克服的方法学挑战。
