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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.