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一种通用的适合度景观演算方法在癌症中找到了受选择作用的基因。

A general calculus of fitness landscapes finds genes under selection in cancers.

机构信息

Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.

出版信息

Genome Res. 2022 May;32(5):916-929. doi: 10.1101/gr.275811.121. Epub 2022 Mar 17.

Abstract

Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.

摘要

遗传变异驱动着特征和疾病的进化。我们之前将这些变异建模为适应度景观中的小位移,并通过区分基因型和表型之间的进化关系来估计它们的功能影响。相反,在这里,我们整合这些导数来识别驱动特定特征的基因。在癌症队列中,整合鉴定出 460 个可能的肿瘤驱动基因。其中许多具有文献和实验支持,但之前在肿瘤中寻找阳性选择的基因组搜索都未能发现这些基因。这些结果不仅为癌症研究提供了新的见解,还引入了一种通用的进化演算方法,可以量化基因型-表型关系,并发现与复杂特征和疾病相关的基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6f5/9104707/4541fd065dde/916f01.jpg

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