Computational analysis of complex genetic interactions

项目总结/摘要 生物体的DNA序列（基因型）如何决定其形式和功能（表型）？ 大规模平行报告基因分析（MPRA）、深度突变扫描和 组合CRISPR筛选有可能在一个特定的水平上揭示基因型-表型关系。 通过测量表型为数万至数百万的基因型在一个前所未有的详细程度， 单一实验然而，解释这些实验的结果是困难的，因为 基因型本质上是高维的，并且突变的组合通常以复杂的方式相互作用。 我的研究计划的重点是开发新的计算工具，以分析数据，从这些高- 通量实验，其目标是（1）鉴定基因型的主要定性特征- 在特定的生物系统中的表型关系，（2）解释这些定性特征如何产生， 潜在的发育，细胞生物学和生物物理学机制，（3）能够准确预测 未测量的基因型的表型，和（4）量化这些预测中的不确定性。 我在未来五年的主要研究目标是开发新的计算和统计方法， 技术能够捕捉高阶上位性，即发生在三个或三个以上的基因之间的遗传相互作用。 更多的突变虽然当代高通量诱变实验表明，这些更高- 订单交互非常普遍，我们目前缺乏通用的，有原则的统计模型， 模拟这样的互动。我的研究小组目前正在开发两种不同但相关的方法， 模拟这些互动。虽然这两种方法都显示出最先进的预测性能， 尽管这些方法具有数万到数十万个基因型的数据集，但仍有大量工作要做，以使这些方法适应 最大可用数据集的规模，其中包含数百万基因型的测量。在 未来几年，我们计划将这些方法构建成一个综合框架，用于分析复杂的遗传 相互作用，包括不确定性的量化、生物学解释工具和探索性数据 分析，和实用的软件，可以使用和解释计算生物学家和 实验主义者 高通量诱变实验具有通过以下方式改变分子生物学的潜力： 提供了一个通用的工具，用于询问任意遗传的基因型-表型关系， 元素重要的应用包括映射自适应路径免疫逃逸和耐药性 感染性疾病的变异，设计改进的抗体和酶，以及基因组变异的解释。 这里提出的计算工具的发展将通过提供一个原则性的和 这些实验揭示了理解复杂遗传相互作用的功能框架。

Project Summary / Abstract How does the DNA sequence of an organism (genotype) determine its form and function (phenotype)? New technologies such as massively parallel reporter assays (MPRAs), deep mutational scanning, and combinatorial CRISPR screens have the potential to expose the genotype-phenotype relationship at an unprecedented level of detail by measuring phenotypes for tens of thousands to millions of genotypes in a single experiment. However, interpreting the results of these experiments is difficult because the space of genotypes is intrinsically high-dimensional and combinations of mutations often interact in complicated ways. My research program is focused on developing new computational tools to analyze data from these high- throughput experiments, with the goals of (1) identifying the major qualitative features of the genotype- phenotype relationship in specific biological systems, (2) explaining how these qualitative features arise from underlying developmental, cell biological and biophysical mechanisms, (3) being able to accurately predict the phenotypes of unmeasured genotypes, and (4) quantifying the uncertainty in these predictions. My primary research objective over the next five years is to develop new computational and statistical techniques capable of capturing higher-order epistasis, that is, genetic interactions that occur between three or more mutations. Although contemporary high-throughput mutagenesis experiments reveal that these higher- order interactions are extremely prevalent, we currently lack general, principled statistical models capable of modeling such interactions. My research group is currently developing two different, but related, methods for modeling these interactions. While both methods display state-of-the-art predictive performance on smaller datasets with tens to hundreds of thousands of genotypes, substantial work remains to adapt these methods to the scale of the largest available datasets, which contain measurements for millions of genotypes. In the coming years, we plan to build these methods into an integrated framework for analyzing complex genetic interactions, complete with quantification of uncertainty, tools for biological interpretation and exploratory data analysis, and practical software that can be used and interpreted by both computational biologists and experimentalists. High-throughput mutagenesis experiments have the potential to transform molecular biology by providing a general-purpose tool for interrogating the genotype-phenotype relationship of an arbitrary genetic element. Important applications include mapping adaptive paths to immune escape and drug resistance variants in infectious disease, designing improved antibodies and enzymes, and genomic variant interpretation. Development of the computational tools proposed here will further these goals by providing a principled and functional framework for understanding the complex genetic interactions revealed in these experiments.