Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA.
Trends Genet. 2023 Jun;39(6):442-450. doi: 10.1016/j.tig.2023.02.002. Epub 2023 Feb 28.
Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.
人类疾病的基因组研究通常由不同的研究群体进行（即，专注于罕见疾病、常见疾病或癌症）。尽管不同疾病类别的发病机制起源存在差异，但这些研究都有一个共同的目标，即确定对疾病表型临床表现至关重要的因果基因组事件。此外，这些研究还面临着一些共同的挑战，包括理解疾病的复杂遗传结构、解析变异在多个尺度上的影响，以及解释非编码突变。在这里，我们深入探讨了这些挑战，并认为要妥善解决这些挑战，需要在疾病群体之间使用更统一的词汇和方法。为此，我们提出了一种统一的观点，将变异的影响与各种基因组疾病联系起来。
