Computational analysis of complex genetic interactions

复杂遗传相互作用的计算分析

• 批准号: 10675737
• 负责人: David Martin McCandlish
• 金额: $ 48万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10675737
• 关键词: Antibodies; Biological; Biological Assay; Biophysical Process; CRISPR screen; Cells; Communicable Diseases; Complex; Computer Analysis; Computer software; DNA Sequence; Data; Data Analyses; Data Set; Development; Drug resistance; Enzymes; Exposure to; Genetic; Genetic Epistasis; Genotype; Goals; Human Genome; Immune; Immune Evasion; Individual; Maps; Measurement; Measures; Methods; Modeling; Molecular Biology; Mutagenesis; Mutation; Organism; Pathogenicity; Performance; Phenotype; Proteins; Reporter; Research; Statistical Models; Techniques; Therapeutic; Uncertainty; Variant; Work; biological systems; combinatorial; computerized tools; design; emerging antibiotic resistance; experimental study; genetic element; genetic variant; high dimensionality; improved; mutation screening; new technology; prevent; programs; tool

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.

项目摘要/摘要 生物体的DNA序列(基因型)如何决定其形态和功能(表型)？ 新技术，如大规模平行报告分析(MPRA)、深度突变扫描和 组合CRISPR筛查有可能揭示基因-表型之间的关系 通过测量数万到数百万个基因类型的表型，获得前所未有的详细程度 单次实验。然而，解释这些实验的结果是困难的，因为 基因类型本质上是高维度的，突变组合往往以复杂的方式相互作用。 我的研究计划专注于开发新的计算工具来分析来自这些高- 吞吐量实验，目的是(1)确定基因的主要质量特征-- 特定生物系统中的表型关系，(2)解释这些质量特征是如何从 潜在的发育、细胞生物学和生物物理机制，(3)能够准确地预测 未测量的基因型的表型，以及(4)量化这些预测中的不确定性。 我未来五年的主要研究目标是开发新的计算和统计方法 能够捕捉高阶上位性的技术，即发生在三个或三个或多个个体之间的遗传交互作用 更多的突变。尽管当代的高通量突变实验表明，这些更高的- 订单相互作用非常普遍，我们目前缺乏通用的、有原则的统计模型 对这样的互动进行建模。我的研究小组目前正在开发两种不同但相关的方法来 对这些互动进行建模。虽然这两种方法在较小的 拥有数万到数十万种基因的数据集，还有大量的工作要做，以使这些方法适应 可获得的最大数据集的规模，其中包含数百万个基因类型的测量值。在 在接下来的几年里，我们计划将这些方法构建成一个分析复杂基因的综合框架 相互作用，包括不确定性的量化、生物学解释的工具和探索性数据 分析和实用软件，可由计算生物学家和 实验者。 高通量突变实验有可能通过以下方式改变分子生物学 提供了一种通用工具，用于查询任意基因的基因型-表型关系 元素。重要的应用包括映射免疫逃逸和耐药性的适应路径 传染病中的变异，设计改进的抗体和酶，以及基因组变异解释。 这里提出的计算工具的开发将通过提供一个原则性的和 了解这些实验中揭示的复杂遗传相互作用的功能框架。

项目成果

Idiosyncratic and dose-dependent epistasis drives variation in tomato fruit size.

• DOI: 10.1126/science.adi5222
• 发表时间: 2023-10-20
• 期刊: Science (New York, N.Y.)
• 作者: Aguirre L;Hendelman A;Hutton SF;McCandlish DM;Lippman ZB
• 通讯作者: Lippman ZB

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.

• DOI: 10.1186/s13059-022-02661-7
• 发表时间: 2022-04-15
• 期刊: Genome biology
• 影响因子: 12.3

Evolutionary paths that link orthogonal pairs of binding proteins.

连接正交结合蛋白对的进化路径。

• DOI: 10.21203/rs.3.rs-2836905/v1
• 发表时间: 2023
• 期刊: Research square
• 作者: Avizemer,Ziv;Martí-Gómez,Carlos;Hoch,ShlomoYakir;McCandlish,DavidM;Fleishman,SarelJ
• 通讯作者: Fleishman,SarelJ

System-specificity of genotype-phenotype map structure: Comment on "From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics" by Susanna Manrubia et al.

基因型-表型图结构的系统特异性：对 Susanna Manrubia 等人的“从基因型到生物体：进化动力学基石的最新技术和观点”的评论。

• DOI: 10.1016/j.plrev.2021.08.005
• 发表时间: 2021
• 期刊: Physics of life reviews
• 影响因子: 11.7
• 作者: McCandlish,DavidM

Designed active-site library reveals thousands of functional GFP variants.

• DOI: 10.1038/s41467-023-38099-z
• 发表时间: 2023-05-20
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Weinstein, Jonathan Yaacov;Marti-Gomez, Carlos;Lipsh-Sokolik, Rosalie;Hoch, Shlomo Yakir;Liebermann, Demian;Nevo, Reinat;Weissman, Haim;Petrovich-Kopitman, Ekaterina;Margulies, David;Ivankov, Dmitry;McCandlish, David M.;Fleishman, Sarel J.
• 通讯作者: Fleishman, Sarel J.