High resolution genetic dissection of complex and quantitative traits in yeast

酵母复杂和数量性状的高分辨率遗传解析

基本信息

批准号：
9005198
负责人：
Joshua Michael Akey
金额：
$ 54.08万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2017-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9005198
关键词：
Address Alleles Animal Model Antifungal Agents Architecture Basic Science Bioinformatics Biological Models Biomedical Research Characteristics Chromosome Mapping Communities Complement Complex Computing Methodologies Data Data Set Data Sources Development Disease Dissection Fungal Drug Resistance Genes Genetic Genetic Determinism Genetic Variation Genomic Segment Genomics Genotype Goals Haplotypes Human Learning Maps Meiosis Methods Microbial Biofilms Mus Nucleotides Organism Pathogenesis Phenotype Plants Population Population Genetics Prevalence Resistance Resolution Resources Saccharomyces Saccharomyces cerevisiae Saccharomycetales Statistical Methods Variant Yeasts base design fly gene environment interaction gene interaction genome sequencing insight interest novel offspring pre-clinical public health relevance rare variant response therapeutic target tool trait whole genome

项目摘要

DESCRIPTION (provided by applicant): The genetic dissection of complex and quantitative traits remains a formidable challenge in basic and biomedical research. Although the yeast Saccharomyces cerevisiae is a potentially powerful model system to address fundamental questions about genetic architecture, its promise has not been fully realized. In particular, there is a need to develop new tools to reveal insights into the fundamental characteristics of genetic architecture. To this end, In Aim 1, we will develop a powerful mapping population in yeast for the high-resolution genetic dissection of complex and quantitative traits. Specifically, we will create 10,000 progeny from a funnel cross among eight intelligently selected parental strains that captures a substantial proportion of genetic variation segregating in natural isolates of S. cerevisiae. Preliminary analyses demonstrate the power to map variants of weak effect and context dependent effects, such as gene-gene interactions, will be extremely high. Importantly, the large number of meioses will allow extraordinarily high mapping resolution, often at the scale of a single gene or smaller. All 10,000 progeny will be densely genotyped, allowing whole- genome sequence data to be accurately imputed. In Aim 2, we will develop new statistical methods for leveraging the inherent power of this experimental cross. In particular, we will develop new methods for detecting gene-gene interactions and predicting causal variants from heterogeneous sources of data. Finally, in Aim 3 we will use the experimental cross to comprehensively delineate the genetic architecture of a suite of biomedically important phenotypes such as antifungal resistance and biofilm formation. Overall, the mapping population and statistical tools that we develop will enable powerful and comprehensive insights into the genetic architecture of complex and quantitative traits, complement the development of complex crosses in other model organisms, provide new methods for the interpretation of whole-genome sequence data, and yield novel insights into potential therapeutic targets relevant to fungal pathogenesis.

描述（由申请人提供）：在基础和生物医学研究中，复杂和定量性状的遗传解剖仍然是一个巨大的挑战。尽管酿酒酵母的酵母菌是解决有关遗传结构基本问题的潜在强大模型系统，但其承诺尚未得到充分实现。特别是，有必要开发新的工具来揭示对遗传结构的基本特征的见解。为此，在AIM 1中，我们将在酵母中开发出强大的地图种群，用于复杂和定量性状的高分辨率遗传解剖。具体而言，我们将从八个智能选择的父母菌株中的漏斗杂交中创造10,000个进展，这些菌株捕获了酿酒酵母自然分离株中大部分遗传变异的比例。初步分析表明，绘制弱效应和上下文依赖性效应的变体（例如基因 - 基因相互作用）的能力将非常高。重要的是，大量的MEIO将允许在单个基因或更小的规模上允许非常高的映射分辨率。所有10,000个进度将不完全基因分型，从而可以准确估算全基因组序列数据。在AIM 2中，我们将开发新的统计方法来利用该实验十字的继承能力。特别是，我们将开发用于检测基因 - 基因相互作用并预测来自异质数据来源的因果变异的新方法。最后，在AIM 3中，我们将使用实验十字架来全面地描绘出一组生物医学重要的表型（例如抗真菌抗性和生物膜形成）的遗传结构。总体而言，我们开发的映射种群和统计工具将使对复杂和定量性状的遗传结构有强大而全面的见解，完成其他模型生物体中复杂杂交的发展，为全基因组序列数据的解释提供了新的方法，并对与Fungal Penogenase的潜在治疗目标产生新颖的见解。