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

摘要 阐明自然界中观察到的令人敬畏的表型多样性的潜在遗传原因 种群数量是生物学中的一个重大挑战。尽管了解遗传基础的重要性， 复杂的性状，我们目前缺乏有关遗传成分的完整知识。遗传变异 影响染色体片段的结构和拷贝数是一个未被充分认识的组成部分， 遗传结构。在这个建议中，我们试图获得结构变异（SV）的物种范围内的观点， 拷贝数变异（CNV）以及这些变异如何影响自然种群的表型景观。在 为了实现这一目标，我们将利用酿酒酵母的遗传优势， 我们最近完成了1,011个自然分离株的全基因组重测序， 规模表型分析的努力。CNV在我们的菌株收集中很常见，并且在细胞中具有超大的表型影响。 到目前为止，我们已经完成了测试。我们建议：目标1。将长读序应用于更好的 表征这些菌株中存在的结构变异并产生高质量的从头组装。在 目的2，我们将收集大量环境条件下的定量菌株表型， 特别强调我们和其他人发现对CNVs特别敏感的条件。我们将 还包括分子性状如mRNA和蛋白质丰度。然后我们将执行关联测试 目的1中发现的变异，以确定在整个物种的程度， CNVs有助于性状变异。由于关联方法对罕见的遗传变异表现不佳，我们将 在目标3中，使用一个控制杂交的杂交组合，使我们能够更好地调查所有存在的变异。最后在 目标4：我们将通过构建工程化的基因库来补充我们在自然发生的变异方面的工作。 变体。我们将创建大剂量系列的片段扩增和缺失池，以更好地 了解它们在多种遗传背景下的后果。我们的工作将导致一个更完整的 理解基因型-表型联系的复杂性， 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.