Bioquant, Heidelberg University, Germany.
Heidelberg University Biochemistry Center (BZH), Germany.
FEBS Lett. 2018 Feb;592(4):463-474. doi: 10.1002/1873-3468.12988. Epub 2018 Feb 8.
Increasingly available genomic sequencing data are exploited to identify genes and variants contributing to diseases, particularly cancer. Traditionally, methods to find such variants have relied heavily on allele frequency and/or familial history, often neglecting to consider any mechanistic understanding of their functional consequences. Thus, while the set of known cancer-related genes has increased, for many, their mechanistic role in the disease is not completely understood. This issue highlights a wide gap between the disciplines of genetics, which largely aims to correlate genetic events with phenotype, and molecular biology, which ultimately aims at a mechanistic understanding of biological processes. Fortunately, new methods and several systematic studies have proved illuminating for many disease genes and variants by integrating sequencing with mechanistic data, including biomolecular structures and interactions. These have provided new interpretations for known mutations and suggested new disease-relevant variants and genes. Here, we review these approaches and discuss particular examples where these have had a profound impact on the understanding of human cancers.
越来越多的基因组测序数据被用于识别导致疾病（尤其是癌症）的基因和变体。传统上，寻找此类变体的方法严重依赖于等位基因频率和/或家族史，往往忽略了对其功能后果的任何机制理解。因此，虽然已知的癌症相关基因数量有所增加，但对于许多基因，其在疾病中的机制作用仍不完全清楚。这一问题突显了遗传学和分子生物学之间的巨大差距，遗传学主要旨在将遗传事件与表型相关联，而分子生物学最终旨在对生物过程进行机制理解。幸运的是，新方法和几项系统研究通过将测序与包括生物分子结构和相互作用在内的机制数据相结合，为许多疾病基因和变体提供了启示。这些为已知突变提供了新的解释，并提出了新的与疾病相关的变体和基因。在这里，我们回顾这些方法，并讨论这些方法在理解人类癌症方面产生深远影响的特定例子。
