Enabling AI-based Mouse Genetic Discovery

实现基于人工智能的小鼠基因发现

• 批准号: 10724522
• 负责人: GARY A PELTZ
• 金额: $ 77.97万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724522
• 关键词: Acceleration; Affect; Alleles; Amino Acid Sequence; Artificial Intelligence; Candidate Disease Gene; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; DNA Sequence Alteration; Data; Data Analyses; Data Set; Data Sources; Databases; Development; Diabesity; Diabetes Mellitus; Disease; Disease model; Engineering; Evaluation; Exhibits; Exons; Foundations; Generations; Genes; Genetic; Genetic Enhancement; Genetic Models; Genetic Variation; Genome engineering; Genomics; Health; Healthcare; Hodgkin Disease; Homologous Gene; Human; Inbred Strain; Individual; Knock-in Mouse; Knock-out; Laboratory mice; Lymphoma; Machine Learning; Malignant Neoplasms; Maps; Measures; Methods; Mus; Network-based; Obesity; Paper; Pattern; Phenotype; Public Health; Publishing; Research; Tandem Repeat Sequences; Training; Validation; Variant; candidate identification; causal variant; computational pipelines; computerized tools; disease phenotype; genetic variant; genome wide association study; graph neural network; human disease; human model; improved; innovation; knockout gene; model organism; mouse genetics; mouse genome; mouse model; novel; protein protein interaction; public database; response; trait

Abstract No model organism has contributed more than the laboratory mouse to improving human health. Many genetic factors and therapies for human diseases were initially discovered or characterized in mice, before they were transitioned to human use. Large-scale efforts are underway to integrate recent advances in artificial intelligence (AI) into human healthcare, but very few AI advances have been used for analysis of the data produced using the model organism that has formed the foundation for many healthcare innovations. We recently developed an AI-based computational pipeline that could identify causative genetic factors for murine genetic models of human biomedical traits and diseases. After assessing the strength of allelic associations with the phenotypic response pattern exhibited by the inbred strains; this AI pipeline uses a machine-learning trained method to analyze 29M published papers and assess candidate gene-phenotype relationships; and the information obtained from assessment of their protein-protein interaction network and protein sequence features of the candidate genes are also incorporated into the graph neural network-based analysis. This project will produce a markedly enhanced AI pipeline (AIv2) that will greatly accelerate the pace of genetic discovery using murine genetic models. First, long read genomic sequencing (LRS) and computational tools are used to produce a more complete map of the pattern of genetic variation among the inbred strains, which also includes alleles for two major types of genetic variation (structural variants, tandem repeats), which are poorly characterized using conventional sequencing methods. Second, we develop two additional computational tools for the AI, which facilitate candidate gene prioritization through the evaluation of: (i) the phenotypes exhibited by 8200 mouse lines with individual gene knockouts (KOs); and (ii) the results of 5700 human GWAS covering many biomedical phenotypes to determine if alleles within the human homologues of candidate murine genes affect an analyzed trait. The ability of AIv2 to accelerate genetic discovery will be demonstrated by using it to identify new genetic factors through analysis of a public database with >10,307 datasets, which measure biomedical or disease-related responses in panels of inbred strains. Since it is critical to experimentally confirm some of the computational findings, genetic factors for two murine models of human diseases that are major public health problems (cancer, diabetes/obesity), which were identified by the AI pipeline, will be experimentally validated. CRISPR engineering is used to revert the causative mutation(s) to wildtype on the genetic background of the strain exhibiting the disease phenotype, and the genome engineered mice are analyzed to assess the contribution of the genetic factor to the disease phenotype.

摘要 没有任何模式生物比实验室小鼠对改善人类健康的贡献更大。许多遗传 人类疾病的因子和疗法最初是在小鼠中发现或表征的， 转变为人类使用。 大规模的努力正在进行中，以整合人工智能的最新进展， 人工智能（AI）进入人类医疗保健，但很少有AI进步用于数据分析 使用模式生物产生，该模式生物为许多医疗保健创新奠定了基础。我们 最近开发了一种基于人工智能的计算管道，可以识别导致小鼠死亡的遗传因素。 人类生物医学特征和疾病的遗传模型。在评估了等位基因关联的强度之后， 与近交系表现出的表型反应模式;这种人工智能管道使用机器学习 训练的方法来分析29 M发表的论文，并评估候选基因-表型关系; 从评估其蛋白质-蛋白质相互作用网络和蛋白质序列获得的信息 候选基因的特征也被结合到基于图形神经网络的分析中。 该项目将产生一个显着增强的人工智能管道（AIv 2），这将大大加快基因工程的步伐。 使用鼠遗传模型的发现。首先，长读基因组测序（LRS）和计算工具 用于绘制近交系间遗传变异模式的更完整图谱， 还包括两种主要类型的遗传变异（结构变异，串联重复）的等位基因， 使用常规测序方法表征较差。其次，我们开发了两个新的 用于AI的计算工具，其通过以下评估促进候选基因优先化：（i） 由具有单个基因敲除（科斯）的8200个小鼠品系表现出的表型;和（ii）5700个小鼠品系的结果。 人类GWAS涵盖了许多生物医学表型，以确定人类同源物中的等位基因是否 候选鼠基因影响所分析的性状。AIv 2加速基因发现的能力将是 通过分析公共数据库，使用它来识别新的遗传因素， 数据集，其测量近交系组中的生物医学或疾病相关反应。因为这是至关重要的 为了通过实验证实一些计算结果，两种小鼠模型的遗传因素， AI确定的主要公共卫生问题（癌症、糖尿病/肥胖症） 管道，将进行实验验证。CRISPR工程化用于将致病突变恢复为 在显示疾病表型的菌株的遗传背景上的野生型，和基因组工程化 分析小鼠以评估遗传因子对疾病表型的贡献。

项目成果

Genetic Discovery Enabled by A Large Language Model.

由大型语言模型实现的基因发现。

• DOI: 10.1101/2023.11.09.566468
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Tu,Tao;Fang,Zhouqing;Cheng,Zhuanfen;Spasic,Svetolik;Palepu,Anil;Stankovic,KonstantinaM;Natarajan,Vivek;Peltz,Gary
• 通讯作者: Peltz,Gary