Genetic basis of stress tolerance in natural populations of yeast

酵母自然群体胁迫耐受性的遗传基础

基本信息

批准号：
8272300
负责人：
Maitreya J Dunham
金额：
$ 28.12万
依托单位：
UNIVERSITY OF WASHINGTON
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：
2012-05-07 至 2016-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8272300
关键词：
Alleles Ammonium Animal Model Architecture Biological Biological Assay Biology Candidate Disease Gene Cells Chromosome Mapping Clinical Collection Complex Coupled Data Disease Ecology Environment Etiology Eukaryota Exhibits Flocculation Foundations Frequencies Gene Frequency Genes Genetic Genetic Determinism Genetic Polymorphism Genetic Variation Genome Genomics Genotype Goals Growth Human Inherited Learning Literature Maps Measures Methods Minority Modeling Mutation Natural History Parents Pattern Phenotype Population Quantitative Trait Loci Research Personnel Resistance Saccharomyces Saccharomyces cerevisiae Saccharomycetales Sampling Series Sister Stress Stress Tests System Techniques Testing Toxic effect Transplantation Ursidae Family Variant Work Yeasts base disorder risk exome experience gene function genome sequencing genome wide association study human disease member prototype research study segregation stress tolerance stressor tool trait yeast genetics

项目摘要

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.

描述（由申请人提供）：复杂性状是通过群体中分离的数量性状基因座的综合效应遗传的。这些遗传决定因素可能产生或大或小的影响，并且可能以多种方式结合起来。尽管通过全基因组关联研究和其他方法绘制复杂性状的遗传决定因素已有十年的工作，但该领域尚未确定从基因型推断表型的最佳方法。我们建议利用酵母遗传学来了解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特性以及越来越多的可供借鉴的不同基因组序列。我们将首先关注强效应基因座，即“孟德尔”性状。这些有助于通过等位基因交换实验轻松进行遗传图谱和实验确认。我们的假设是，具有强效应等位基因的基因也将在人群中拥有更多数量效应的等位基因。这个想法类似于“罕见变异”假说，其中低频强效应等位基因导致高疾病风险。我们试图发现这些相同的基因是否也可能在整个人群中发挥重要作用，因为效应较低的等位基因可能更难以识别。我们将致力于实现该项目的三个具体目标，每个目标均由一名专家研究员实现。在目标 1 中，我们将探索酿酒酵母和奇异酵母天然酵母种群的表型多样性。这些物种具有大量我们可以利用的表型和基因型多样性。特别是，我们将观察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。为了绘制致病基因座，我们将使用批量分离作图方法与深度测序相结合，然后进行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对于大量菌株的表型变异是否重要。我们将从数百个遗传多样性分离株中扩增等位基因，将它们移植到同基因背景中，并通过基于库的定量竞争检测来检测基因功能。等位基因频率将使用深度测序进行测量。这种方法的组合将使我们能够识别其中抗逆性状以遗传简单的方式分离的杂交，绘制致病基因图谱，并确定这些基因中其他变异在群体中的重要性。我们的结果将有助于理解复杂性状的遗传基础。公共卫生相关性：大多数人类疾病都有复杂的潜在遗传基础；然而，一个子集被分离为简单的孟德尔特征。我们将利用真核生物芽殖酵母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群体间的数量变异。我们的结果可能会影响对人类这些特征的研究。