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High-throughput identification of causal variants underlying quantitative traits in yeast

高通量鉴定酵母数量性状背后的因果变异

基本信息

批准号：
10392960
负责人：
LEONID KRUGLYAK
金额：
$ 29.7万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10392960
关键词：
Agriculture Alleles Bar Codes Biological Biological Assay Biological Models Biomedical Research Bypass CRISPR/Cas technology Catalogs Cells Complex Cytosine deaminase DNA Sequence DNA sequencing Data Set Diagnosis Disease Disease susceptibility Dissection Engineering Ensure Enzymes Frequencies Gene Frequency Generations Genes Genetic Genetic Variation Genetic study Genome Genotype Goals Grain Growth Guide RNA Haplotypes Health Heritability Heterogeneity Human Individual Link Maps Massive Parallel Sequencing Measures Medical Methods Mutation Nonhomologous DNA End Joining Oligonucleotides Organism Parents Patients Phenotype Plasmids Population Predisposition Prevention Public Health Quantitative Trait Loci Research Resolution Resources Robin bird Saccharomyces cerevisiae Sample Size Single Nucleotide Polymorphism Site Statistical Data Interpretation Statistical Methods Testing Time Untranslated RNA Variant Yeasts adenosine deaminase base base editor causal variant design diagnostic accuracy disease diagnosis endonuclease fitness genetic linkage analysis genetic manipulation genetic variant genome sequencing genome-wide human disease improved insertion/deletion mutation interest personalized diagnostics prediction algorithm quantitative imaging repaired success trait variant of unknown significance virtual whole genome yeast genetics

项目摘要

PROJECT SUMMARY / ABSTRACT The broad objective of the proposed research is to achieve comprehensive dissection of the genetic basis of many complex phenotypes in the yeast S. cerevisiae, arguably the most powerful eukaryotic model system due to its small genome, ease of genetic manipulation, and the ability to generate very large sample sizes. Evolutionary conservation has also ensured that many yeast traits have direct parallels to biomedically important human phenotypes. We seek comprehensively identify the DNA sequence variants underlying a variety of traits, study the distribution of their effect sizes and their frequencies in a population, and build rules for predicting the functional effects of variants of unknown significance. Success in answering these questions will provide critical guidance for the design of genotype-phenotype studies in humans and other organisms of medical, biological, and agricultural interest, and enable improved diagnostic accuracy based on genome sequencing of patients. Specifically, will use a resource we built that consists of nearly 15,000 genotyped and phenotyped segregants from crosses between 16 diverse yeast strains to identify causal genes and prioritize individual putative causal genetic variants. We will then identify specific causal variants by directly engineering thousands of candidate variants in bulk. We will use methods we have developed for massively parallel targeted editing by CRISPR/Cas9 to engineer pools of yeast cells, each carrying one of 2000 natural variants. We will subject the edited pools of yeast cells to selective conditions and track the phenotypic consequences of the introduced variants over time by short read sequencing of DNA barcodes identifying each edit. We will then extend massively parallel targeted editing to generate all variants discovered in the panel of 16 diverse yeast strains. We will assay the effects of single nucleotide polymorphisms (SNPs), small scale insertions or deletions (indels), and haplotype effects of closely linked variants. We will include non-coding variants to better understand their effects on fitness. We will extend our engineering toolkit to employ versions of Cas9 and related enzymes that have different recognition sites, and by using Cas9-based “base editors” that allow generation of specific classes of mutations. Ultimately, we will edit and measure the phenotypic consequences of hundreds of thousands of natural genetic variants, which will provide a deep understanding of the genetic basis of many traits and enable us to develop accurate algorithms that predict the functional effect of any genetic variant.

项目总结/摘要拟议研究的广泛目标是全面剖析遗传基础，酵母S.酿酒酵母，可以说是最强大的真核模型系统，由于它的基因组小，易于遗传操作，并能够产生非常大的样本量。进化保守也确保了许多酵母性状与生物医学上的重要的人类表型。我们寻求全面识别DNA序列变异的基础上，各种性状，研究其效应大小的分布及其在群体中的频率，并建立规则用于预测未知重要性的变体的功能效应。成功回答这些问题将为人类和其他生物体的基因型-表型研究设计提供重要指导，医学、生物学和农业利益，并能够基于基因组提高诊断准确性患者的排序。具体来说，将使用我们建立的一个资源，包括近15,000个基因型和从16种不同的酵母菌株之间的杂交中分离出表型分离物，以鉴定致病基因并进行优先排序个别推定的致病遗传变异。然后，我们将通过直接工程来识别特定的因果变体数以千计的候选变体我们将使用我们开发的大规模并行方法，通过CRISPR/Cas9进行靶向编辑来工程酵母细胞池，每个细胞都携带2000种天然变体中的一种。我们将使编辑后的酵母细胞池经受选择性条件，并跟踪通过鉴定每个编辑的DNA条形码的短读段测序来确定随时间引入的变体。然后我们将扩展大规模平行靶向编辑，以生成在16种不同酵母中发现的所有变体菌株我们将分析单核苷酸多态性（SNP）、小规模插入或缺失（indels）和紧密连锁变体的单体型效应。我们将包括非编码变体，以更好地了解它们对健康的影响。我们将扩展我们的工程工具包，以采用Cas9的版本，具有不同识别位点的相关酶，并通过使用基于Cas9的“碱基编辑器”，产生特定类别的突变。最终，我们将编辑和测量表型后果，成千上万的自然遗传变异，这将提供一个深入了解的遗传许多性状的基础，使我们能够开发准确的算法，预测任何功能的影响，基因变异