Determining the source of missing heritability

项目摘要 人类的大多数特征都是复杂的/定量的。同样，许多常见的人类疾病都是 复杂；它们通常不是由少数基因引起的，而是受到影响的 如果不是数千个基因的话也有数百个。人们对数量性状知之甚少，因为 概念、实验和分析方面的限制。这项提案旨在解决以下几个关键问题 问题：1)可以驱动数量性状的基因是什么？它们是如何相互关联的？ 2)在自然群体中，驱动数量性状变异的基因是什么；3) 每个个体数量基因的表型如何组合在一起来决定整体 性状的表型，即基因-基因交互作用是否重要。半乳糖和乳糖的诱导 芽殖酵母中的磷代谢基因是经典的 探测信号的真核模型系统。本提案所述的初步结果 表明这些反应也是复杂的特征。我们的实验室已经发展出了高度- 吞吐量流式细胞术方法是准确确定作用的关键 自然变异株和突变株数量性状上的基因。建立在我们的 实验优势，我们将结合荧光报告菌株与一系列缺失或 剂量微扰库。我们将生成最全面的量化清单 基因，并评估这些数量基因在和 在特征之间。使用等位基因互换结合批量分离分析和经典连锁 我们将确定数量基因的等位基因在自然界中的差异程度。通过组合 在零到四个等位基因或数量基因缺失之间，我们将能够直接检测 基因-基因相互作用的重要性。这种方法的组合应该会大大增强 我们对复杂特征的理解与人类疾病有直接关系。

Project Summary Most human traits are complex/quantitative. Similarly, many common human diseases are complex; they typically are not caused by a small number of genes, but instead are influenced by hundreds if not thousands of genes. Little is known about quantitative traits due to conceptual, experimental, and analytical limitations. This proposal aims to address several key questions: 1) what are the genes that can drive a quantitative trait and how are they interrelated, 2) what are the genes that drive variation in a quantitative trait in natural populations, and 3) how do the phenotypes of each individual quantitative gene combine to determine the overall phenotype of the trait, i.e. are gene-gene interactions important. The induction of galactose and phosphate metabolic genes in the budding yeast Saccharomyces cerevisiae are classical Eukaryotic model systems for probing signaling. Preliminary results described in this proposal show that these responses are also complex traits. Our laboratory has developed high- throughput flow cytometry methods that are essential for accurately determining the effects of genes on quantitative traits both among natural variants and mutant strains. Building on our experimental strengths, we will combine fluorescence reporter strains with a series of deletion or dosage perturbation libraries. We will generate the most comprehensive list of quantitative genes yet in each of these traits, and assess the interplay of these quantitative genes within and between traits. Using allele swaps combined with bulk segregant analysis and classical linkage we will determine the extent to which alleles of quantitative genes vary in nature. By combining between zero to four alleles or deletion of quantitative genes, we will be able to directly test the importance of gene-gene interactions. This combination of approaches should greatly enhance our understanding of complex traits and have direct relevance for human disease.