High throughput S. cerevisiae HAM, GWA & QT/QTL architecture

描述(由申请人提供)： 广泛的长期目标：在具体目标1中，我们将对另外88个遗传多样性的酿酒酵母菌株的基因组进行测序和注释，使这些菌株的总数达到96个，这将作为特定目标2-4的基础。在具体目标2中，我们将使用单倍体关联图谱来确定候选的药物遗传学QTGs，这些QTGs控制着临床上使用的抗癌和/或抗真菌药物5-氟嘧啶类药物的耐药性，以及膜应激源，其中许多是临床使用的抗真菌药物。在具体目标3中，我们将在4,560个F1成员群体中使用全基因组关联来识别候选的药物遗传学QTG。同时，我们将在500和600个F2成员群体中定位药物遗传QTL。单倍体关联作图和全基因组关联将在多个QTL中识别候选QTL。同样，QTL定位将识别许多单倍体关联作图和全基因组关联真假关联和假阴性。最后，在具体目标4中，我们将使用高通量F1 RHA来测试候选QTL，评估上位性与遗传异质性，并确定F1群体范围的QT/QTL架构。 公共卫生相关性： 我们对数量性状的理解很差，包括人类药物疗效和副作用的药物遗传变异。提高我们对数量性状和药物遗传学的理解是由易于处理的模型系统帮助的，例如酿酒酵母。在这项研究中，我们开发了一个新的酿酒酵母遗传资源群体，它允许高通量的单倍体关联作图和全基因组关联。这个项目的健康相关性在于我们在酿酒酵母中对一系列抗癌和抗真菌药物的定量药物遗传学的研究。因此，我们对酿酒酵母定量药物遗传学的研究将有助于我们理解一般的数量性状，特别是药物遗传学。

DESCRIPTION (provided by applicant): Broad long-term objectives: In Specific Aim 1, we will sequence and annotate the genomes of an additional 88 genetically diverse S. cerevisiae strains, bringing the total of such strains to 96, which will serve as the bases for Specific Aims 2-4. In Specific Aim 2, we will use haploid association mapping to identify candidate pharmacogenetic QTGs controlling resistance to 5- fluoropyrimidines, which are clinically used anti-cancer and/or antifungal agents, and membrane stressors, many of which are clinically used antifungal agents. In Specific Aim 3, we will use genome wide association, in a 4,560 member F1 population, to identify candidate pharmacogenetic QTGs. In parallel, we will QTL map, in 500 and 600 member F2 populations, pharmacogenetic QTLs. The haploid association mapping and genome wide association will identify candidate QTGs in many QTLs. Similarly, the QTL mapping will identify many haploid association mapping and genome wide association true/false associations and false negatives. Finally, in Specific Aim 4, we will use high throughput F1 RHA to test candidate QTGs, evaluate epistasis vs. genetic heterogeneity and determine F1 population-wide QT/QTL architecture. PUBLIC HEALTH RELEVANCE: Our understanding of quantitative traits, which includes pharmacogenetic variations in human drug efficacy and side effects, is poor. Improving our understanding of quantitative traits and of pharmacogenetics is aided by tractable model systems, such as Saccharomyces cerevisiae. In this study, we develop a novel S. cerevisiae genetic resource population that allows for high throughput haploid association mapping and genome wide association. The health relatedness of this project lies in our study in S. cerevisiae of quantitative pharmacogenetics of a set of anti-cancer and anti-fungal drugs. Therefore, our study in S. cerevisiae of quantitative pharmacogenetics will aid our understanding of quantitative traits in general and of pharmacogenetics in particular.