High resolution genetic dissection of complex and quantitative traits in yeast

 描述（由申请人提供）： 复杂数量性状的遗传解剖在基础和生物医学研究中仍然是一个巨大的挑战。虽然酵母酿酒酵母是一个潜在的强大的模型系统，以解决有关遗传结构的基本问题，其承诺尚未完全实现。特别是，有必要开发新的工具来揭示遗传结构的基本特征。为此，在目标1中，我们将在酵母中开发一个强大的作图群体，用于复杂和数量性状的高分辨率遗传解剖。具体地说，我们将从8个智能选择的亲本菌株之间的漏斗杂交中产生10，000个后代，这些亲本菌株捕获了在S的天然分离株中分离的相当大比例的遗传变异。啤酒。初步分析表明，映射弱效应和背景依赖效应（如基因-基因相互作用）的变体的能力将非常高。重要的是，大量的减数分裂将允许非常高的定位分辨率，通常在单个基因或更小的尺度上。所有10，000个后代将被密集地基因分型，允许全基因组序列数据被准确地插补。在目标2中，我们将开发新的统计方法来利用这种实验交叉的内在力量。特别是，我们将开发新的方法，用于检测基因-基因相互作用和预测来自异质数据源的因果变异。最后，在目标3中，我们将使用实验交叉来全面描绘一套生物医学重要表型的遗传结构，如抗真菌药耐药性和生物膜形成。总的来说，我们开发的映射人口和统计工具将使强大而全面的洞察复杂和数量性状的遗传结构，补充其他模式生物中复杂杂交的发展，为全基因组序列数据的解释提供新的方法，并产生新的见解与真菌发病机制相关的潜在治疗靶点。

 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.