Species-wide survey of the phenotypic impact of genomic structural variation in yeast

摘要 阐明在自然界观察到的令人惊叹的表型多样性的潜在遗传原因 种群是生物学中的一大挑战。尽管了解遗传病的遗传基础很重要 对于复杂的性状，我们目前缺乏对相关遗传成分的完整了解。遗传变异 影响染色体片段的结构和拷贝数是被低估的一个组成部分 遗传建筑。在这项建议中，我们寻求获得结构变异(SV)的物种范围的观点和 拷贝数变异(CNV)及其对自然种群表型景观的贡献。在……里面 为了做到这一点，我们将利用遗传主力酿酒酵母，为此 我们最近完成了1,011个自然分离株的全基因组重新测序，并伴随着大的 扩大表型工作的规模。CNV在我们的菌株收集中很常见，并且在 到目前为止，我们已经完成了关联测试。我们建议：目标1.应用长读测序来更好地 鉴定这些菌株中存在的结构变异，并产生高质量的从头组装。在……里面 目标2，我们将在一个大的环境条件小组中收集定量的菌株表型， 特别强调我们和其他人发现对CNV特别敏感的条件。我们会 还包括分子特征，如信使核糖核酸和蛋白质丰度。然后我们将执行关联测试 目标1中发现的变体，以确定在整个物种上SVS和 CNV对性状变异有贡献。由于关联方法对罕见的遗传变异的表现很差，我们将 在目标3中，使用双列杂交面板，其中受控杂交允许我们更好地调查所有存在的变异。最后，在 目标4我们将通过建立工程化的文库来补充我们在自然发生的变异方面的工作 变种。我们将创建大剂量系列库的节段性扩增和缺失，以更好地 了解它们在多个遗传背景下的后果。我们的工作将导致一个更完整的 了解与这一重要的但 未被充分研究的一类遗传变异。

ABSTRACT Elucidating the underlying genetic causes of the awesome phenotypic diversity observed in natural populations is a major challenge in biology. Despite the importance of understanding the genetic basis of complex traits, we currently lack complete knowledge of the relevant genetic components. Genetic variation affecting the structure and copy number of chromosomal segments is an underappreciated component of the genetic architecture. In this proposal, we seek to obtain a species-wide view of structural variation (SV) and copy number variation (CNV) and how these contribute to the phenotypic landscape of natural populations. In order to accomplish this, we will take advantage of the genetic workhorse Saccharomyces cerevisiae, for which we have recently completed whole genome resequencing of 1,011 natural isolates, plus accompanying large scale phenotyping efforts. CNVs are frequent in our strain collection and have outsize phenotypic affects in the association tests we have so far completed. We propose to: Aim 1. Apply long read sequencing to better characterize the structural variation present in these strains and generate high quality de novo assemblies. In Aim 2, we will collect quantitative strain phenotypes over a large panel of environmental conditions, with a particular emphasis on conditions that we and others have found to be particularly sensitive to CNVs. We will also include molecular traits such as mRNA and protein abundance. We will then perform association testing with the variants discovered in Aim 1 in order to determine over an entire species the degree to which SVs and CNVs contribute to trait variation. Since association methods perform poorly for rare genetic variants, we will in Aim 3 use a diallel panel where controlled crosses allow us to better survey all variation present. Finally, in Aim 4 we will complement our work on naturally occurring variation by building libraries of engineered variants. We will create large dosage series pools of segmental amplifications and deletions to better understand their consequences across multiple genetic backgrounds. Our work will result in a more complete understanding of the complexities of the genotype-phenotype connection with respect to this important but understudied class of genetic variants.