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

项目摘要/摘要 拟议研究的广泛目标是全面剖析糖尿病的遗传基础。 酵母中的许多复杂表型，可以说是最强大的真核模型系统 它的基因组很小，遗传操作很容易，而且能够产生非常大的样本量。 进化保守也确保了许多酵母特性与生物医学有直接的相似之处。 重要的人类表型。我们试图全面地鉴定导致一种 各种性状，研究它们的效应大小和它们在种群中的频率的分布，并建立规则 用于预测未知意义的变体的功能效应。成功地回答了这些问题 将为人类和其他生物的基因-表型研究的设计提供重要的指导。 医学、生物学和农业方面的兴趣，并能够提高基于基因组的诊断准确性 对病人进行排序。具体地说，将使用我们构建的资源，该资源由近15,000个基因分型和 来自16个不同酵母菌株杂交的表型分离物，以识别致病基因并优先排序 个别假定的因果遗传变异。然后，我们将通过直接工程来确定特定的因果变体 数以千计的候选变体批量生产。我们将使用我们为大规模并行开发的方法 由CRISPR/Cas9进行有针对性的编辑，以设计酵母细胞池，每个酵母细胞池携带2000个自然变体中的一个。 我们将对编辑后的酵母细胞池进行选择性条件选择，并跟踪 随着时间的推移，通过对识别每个编辑的DNA条形码进行短读测序，引入了变体。到时候我们会的 扩展大规模并行定向编辑以生成在16种不同酵母小组中发现的所有变体 菌株。我们将分析单核苷酸多态(SNPs)、小规模插入或 紧密连锁变异的缺失(Indels)和单倍型效应。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和 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”来允许 产生特定类别的突变。最终，我们将编辑和测量表型结果 成千上万的自然遗传变异，这将提供对基因的深刻理解 基于许多特征，并使我们能够开发准确的算法来预测任何 基因变异。

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.