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Determining the source of missing heritability

确定缺失遗传力的来源

基本信息

批准号：
9536842
负责人：
Michael Springer
金额：
$ 33.22万
依托单位：
HARVARD MEDICAL SCHOOL
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：
2016-09-01 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9536842
关键词：
Address Affect Alleles Animal Model Biological Assay Biological Models Biological Process Cell physiology Cells Clustered Regularly Interspaced Short Palindromic Repeats Complex Disease Drug Targeting Eukaryota Flow Cytometry Fluorescence Frequencies Galactose Gene Order Genes Glucose Heritability Human Human Genome Individual Knowledge Laboratories Libraries Link Measures Medical Metabolic Methodology Methods Nature Partner in relationship Pathway interactions Phenotype Population Probability Reporter Research Rest Role Saccharomyces cerevisiae Saccharomycetales Series Signal Transduction Source System Testing Variant Work Yeasts deletion library disorder risk dosage drug testing experimental study falls gene interaction genetic variant genome wide association study human disease inorganic phosphate mutant rare variant response trait yeast genome

项目摘要

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.

项目总结