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Direct measurement of gene-environment interactions by high-throughput precision genome editing

通过高通量精确基因组编辑直接测量基因与环境的相互作用

基本信息

批准号：
9905312
负责人：
Shi-An Anderson Chen
金额：
$ 2.95万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2022-07-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9905312
关键词：
Accounting Address Affect Alleles Biological Models Carbon Chromosome Mapping Diet Disease Disease model Environment Environmental Exposure Environmental Risk Factor Explosion Exposure to Future Gene Expression Genes Genetic Genetic study Genome Genomics Genotype Goals Growth Health Height Heritability Human Human Cell Line Hybrid Cells Hybrids Individual Knowledge Laboratories Lactase Lead Libraries Link Machine Learning Malaria Maps Measurement Measures Meiotic Recombination Methodology Methods Modeling Modernization Molecular Mutation Nucleotides Outcome Oxidative Stress Pathway interactions Pharmaceutical Preparations Pharmacotherapy Phenotype Pilot Projects Polygenic Traits Quantitative Trait Loci Research Resistance to infection Resolution Saccharomyces cerevisiae Scientist Sickle Cell Sickle Cell Anemia Sickle Hemoglobin Single Nucleotide Polymorphism Source Stress System Techniques Technology Time Training Treatment outcome Variant Work Yeasts career causal variant disorder risk experimental study fitness gene environment interaction genetic variant genome editing genome-wide improved individual variation mRNA Expression novel pathogen personalized medicine precise genome editing precision medicine promoter public health relevance response segregation skills trait transcriptome sequencing

项目摘要

Abstract Modern genetics has identified many genetic variants that affect traits such as height, but most phenotypic variation still cannot be explained by these variants alone. Importantly, differences in environment often result in individual variation of traits—including disease risk and drug response—for different genotypes. These relationships are known as genotype by environment (GxE) interactions. For example, the sickle cell allele of hemoglobin S causes sickle cell anemia, but also provides a fitness advantage in the presence of malaria by conferring resistance to infection. However, there are few examples where the exact causal variants are known. Therefore, we need to develop new methodology for identifying more of these GxE interactions, to improve prediction of disease risk and treatment outcomes. In this study, I will fill in the gap of knowledge in GxE interactions by establishing an experimental framework for identifying hundreds of causal GxE variants in parallel, providing the first comprehensive view of GxE causal variant landscape. Specifically, I will study how thousands of genetic variants between a laboratory yeast strain (BY) and a vineyard strain (RM) lead to their differences in growth upon stress and drug treatments, as one form of GxE interaction. In Aim 1, I will use a novel gene-editing technology that can detect the fitness effects of thousands of variants in one experiment, as shown in a pilot experiment. Using this method, I will be able to map hundreds of casual variants that contribute to growth differences under various conditions, such as carbon source, oxidative stress and drug treatment. In Aim 2, I will measure allele-specific mRNA expression (ASE) from BYxRM F1 hybrids in above-mentioned conditions and associate the mapped causal GxE variants, to identify GxE variants that influence growth rate through gene expression. Then, I will apply a machine learning model to predict causal GxE genes using the molecular features found in this study. By mapping causal GxE variants, linking them to gene expression and predicting causal genes through gene expression, I will establish a complete framework for accelerating the discovery of GxE interactions.

摘要