Genetic basis of stress tolerance in natural populations of yeast

描述(由申请人提供)：复杂的性状是通过群体中数量性状基因座分离的综合效应而遗传的。这些遗传决定因素可能有大有小的影响，也可能以无数种方式结合在一起。尽管通过全基因组关联研究和其他方法对复杂性状的遗传决定因素进行了十年的研究，但该领域迄今尚未确定从基因推断表型的最佳方法。我们建议使用酵母遗传学来学习一系列数量性状的潜在遗传结构。酵母系统拥有卓越的实验工具，真核生物中保守的生物学，以及越来越多的可供借鉴的不同基因组序列。我们将首先关注强影响的基因座，即“孟德尔式”特征。这些基因便于通过等位基因交换实验进行遗传作图和实验验证。我们的假设是，在一个群体中，具有强效等位基因的基因也将具有更多数量效应的等位基因。这一观点类似于“稀有变异”假说，在该假说中，低频率的强效等位基因会导致高患病风险。我们试图发现，这些相同的基因是否也可能对整个群体具有重要意义，因为影响较低的等位基因可能更难识别。我们将以三个具体目标推进这一项目，每个目标都由一名专家调查。在目标1中，我们将从酿酒酵母和矛盾链霉菌两个物种中探索天然酵母种群的表型多样性。这些物种有大量的表型和基因多样性，我们可以利用。特别是，我们将观察到酵母在其自然历史中可能经历的环境压力下生长能力的分离。第二个目标是确定那些分离为少量大效应基因座的表型的遗传基础。为了定位致病基因座，我们将使用批量分离定位和深度测序相结合的方法，然后通过等位基因交换实验来证明因果关系。第三个目标是测试这些相同的基因座是否对一大批菌株的表型变异很重要。我们将从数百个遗传多样性的分离株中扩增等位基因，将它们移植到等基因背景中，并通过基于池的、定量的、竞争性的分析来分析基因功能。等位基因频率将使用深度测序进行测量。这种方法的结合将使我们能够识别耐逆境性状以一种简单的基因方式分离的杂交组合，绘制致病基因的图谱，并确定这些基因中其他变异在整个群体中的重要性。我们的结果将有助于理解复杂性状的遗传基础。 与公共卫生相关：大多数人类疾病具有复杂的潜在遗传基础；然而，有一小部分疾病分离为简单的孟德尔特征。我们将利用真核生物发芽酵母的模型来确定其变异与遗传简单性状相关的基因是否也存在跨种群的数量变异。我们的结果可能会影响对人类这类特征的研究。

DESCRIPTION (provided by applicant): Complex traits are inherited via the combined effects of quantitative trait loci segregating in a population. These genetic determinants may have large or small effects, and may combine in a myriad of ways. Despite a decade of work on mapping the genetic determinants of complex traits via genome-wide association studies and other methods, the field has as yet not determined an optimal approach to inferring phenotype from genotype. We propose to use yeast genetics to learn the underlying genetic architecture of a series of quantitative traits. The yeast system has superior experimental tools, conserved biology across eukaryotes, and a growing collection of diverse genome sequences on which to draw. We will first focus on loci of strong effect, i.e. "Mendelian" traits. These lend themselves o easy genetic mapping and experimental confirmation via allele swap experiments. Our hypothesis is that genes with strong effect alleles will also harbor alleles of more quantitative effect across a population. This idea is akin to the "rare variant" hypothesis in which low frequency strong effect alleles contribute to high disease risk. We seek to discover whether these same genes may also be of importance across the population due to lower effect alleles that may be more difficult to identify. We will pursue this project in three specific aims, each le by an expert investigator. In Aim 1, we will explore the phenotypic diversity in natural yeast populations from the species S. cerevisiae and S. paradoxus. These species have a great deal of phenotypic and genotypic diversity that we can utilize. In particular, we will observe segregation of growth ability in the face of environmental stresses that yeasts may have experienced during their natural history. The second aim is to determine the genetic basis of those phenotypes that segregate as a small number of large effect loci. To map the causative loci, we will use a bulk segregant mapping method coupled with deep sequencing, followed by allele swap experiments to prove causation. The third aim is to test whether these same loci are important for phenotypic variation across a large panel of strains. We will amplify alleles from hundreds of genetically diverse isolates, transplant them into an isogenic background, and assay gene function via a pool-based, quantitative, competitive assay. Allele frequency will be measured using deep sequencing. This combination of methods will allow us to identify crosses in which stress tolerance traits segregate in a genetically simple manner, map the causative genes, and determine the importance of additional variants in these genes across a population. Our results will inform the understanding of the genetic basis of complex traits. PUBLIC HEALTH RELEVANCE: Most human diseases have a complex underlying genetic basis; however, a subset segregates as simple Mendelian traits. We will utilize the model eukaryote budding yeast to determine whether the genes whose variants are associated with genetically simple traits also harbor quantitative variation across populations. Our results could influence the study of such traits in humans.