Beerenwinkel Niko, Knupfer Patrick, Tresch Achim
ETH Zürich.
Stat Appl Genet Mol Biol. 2011;10:Article 3. doi: 10.2202/1544-6115.1603. Epub 2011 Jan 6.
Evolutionary escape of pathogens from the selective pressure of immune responses and from medical interventions is driven by the accumulation of mutations. We introduce a statistical model for jointly estimating the dynamics and dependencies among genetic alterations and the associated phenotypic changes. The model integrates conjunctive Bayesian networks, which define a partial order on the occurrences of genetic events, with isotonic regression. The resulting genotype-phenotype map is non-decreasing in the lattice of genotypes. It describes evolutionary escape as a directed process following a phenotypic gradient, such as a monotonic fitness landscape. We present efficient algorithms for parameter estimation and model selection. The model is validated using simulated data and applied to HIV drug resistance data. We find that the effect of many resistance mutations is non-linear and depends on the genetic background in which they occur.
病原体从免疫反应的选择压力和医学干预中进化逃逸是由突变积累驱动的。我们引入了一个统计模型，用于联合估计基因改变与相关表型变化之间的动态关系和依赖性。该模型将定义基因事件发生偏序的联合贝叶斯网络与保序回归相结合。由此产生的基因型-表型图谱在基因型格中是不减的。它将进化逃逸描述为一个遵循表型梯度的定向过程，比如单调的适应度景观。我们提出了用于参数估计和模型选择的高效算法。该模型通过模拟数据进行验证，并应用于HIV耐药性数据。我们发现许多耐药突变的影响是非线性的，并且取决于它们发生的基因背景。
