权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

A Resource for the Genetic Dissection of Complex Traits

复杂性状遗传解析的资源

基本信息

批准号：
10564298
负责人：
ANTHONY Douglas LONG
金额：
$ 62.56万
依托单位：
UNIVERSITY OF KANSAS LAWRENCE
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2027-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10564298
关键词：
Alleles Animals Architecture Biological Models Biology Cell Differentiation process Cell physiology Collection Communities Complement Complex Computer software Consensus Control Animal DNA Transposable Elements Data Data Set Diagnostic Disease Dissection Drosophila genus Equilibrium Event Experimental Designs Fat Body Frequencies Future Gene Expression Gene Frequency Generations Genes Genetic Genetic Diseases Genetic Models Genome Genomic Segment Genotype Goals Haplotypes Health Heterogeneity Human Immune response Immunologics Inbreeding Individual Infection Learning Link Maps Mediating Memory Mendelian disorder Mental Depression Methods Modeling Molecular Movement Mutation Nature Pharmaceutical Preparations Phenotype Population Process Quantitative Trait Loci Reaction Recombinants Recurrence Regulation Regulator Genes Research Research Personnel Resolution Resources Sampling Site Structure System Techniques Testing Therapeutic Time Tissues Toxic effect Update Validation Variant Vial device Xenobiotics analytical method causal variant cohort data visualization design empowerment experimental study flexibility fly genetic resource genetic variant genome resource genome sequencing genome wide association study human disease improved insight life history model organism mosaic multidimensional data novel novel strategies pathogen predictive modeling rare variant response segregation tool trait transcriptome sequencing usability user-friendly whole genome

项目摘要

PROJECT SUMMARY A large, diverse set of common human diseases, and most of the biomedically-relevant traits that model organism biologists routinely target, are complex and polygenic. Population variation in these traits has a sizeable genetic component, and dissecting this variation can yield improved diagnostics and therapeutics, and enable detailed descriptions of the molecular and cellular processes underlying disease and trait variation. Genomewide association studies (GWAS) are largely responsible for dramatic progress in the analysis of human complex traits in recent years. GWAS have linked many genes to variation in human health, and imply a significant fraction of trait variation is controlled by thousands of tiny-effect, primarily regulatory sites spread along the genome. Despite this critical insight, there remain many gaps in our understanding of the nature of causative loci. One notable caveat of the GWAS approach is its inherent bias towards intermediate- frequency alleles; GWAS are ill-suited to uncovering rare alleles of large effect and genes harboring several individually-rare mutations, events that mutation-selection balance models predict contribute to trait variation. The dissection of complex traits in model organisms offers great experimental flexibility, and the opportunity to deploy methods synergistic with the dominant GWAS paradigm, yielding a more complete picture of the genetic basis of trait variation. With this in mind we established the DSPR (Drosophila Synthetic Population Resource) as a shareable toolkit for complex trait analysis in flies. The set of 1600 DSPR strains were derived from 15 highly-characterized founder genotypes, and represent the most extensive multiparental population (MPP) available in animals. The DSPR is used by many research groups, has enabled the identification of thousands of QTL, and has associated rare alleles with trait variation. The DSPR complements GWAS approaches for uncovering the full spectrum of allelic variation contributing to complex traits. In Aim 1 we will validate and refine the genotypes of all DSPR strains, strengthening the living resource we will continue to share with the Drosophila community, and enhance the usability of the collection by integrating analytical routines into the powerful R/qtl2 software platform. In Aim 2 we will extend the DSPR, and enable extreme QTL (or X-QTL) mapping. For many traits, an efficient and user-friendly strategy to identify QTL is to compare allele frequencies in phenotypically-extreme recombinant populations to those in control cohorts. We will derive such mixed DSPR populations, experimentally refine our approach, and distribute populations and software to facilitate novel, investigator-driven research. In Aim 3 we will broaden the utility of the DSPR to explore the nature of expression regulation. Using a host-pathogen model, we will exploit the rapid regulatory change that occurs during the immune response to develop dynamic eQTL mapping methods, finding loci that contribute to variation in the trajectory of the regulatory reaction. These general resources will allow researchers employing any MPP to move beyond static pictures of gene expression in populations.

项目总结