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Reverse Engineering Quantitative Genetic Variation

逆向工程定量遗传变异

基本信息

批准号：
9915941
负责人：
Robert R. H Anholt
金额：
$ 45.39万
依托单位：
CLEMSON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-23 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9915941
关键词：
Affect Agriculture Alleles Animals Behavior Disorders Behavioral Biological Models Biology Breeding CRISPR/Cas technology Chills Code Coma Complex Controlled Environment DNA Sequence DNA Shuffling Development Disease Drosophila melanogaster Engineering Environment Exhibits Female Future Gene Expression Genes Genetic Genetic Polymorphism Genetic Transcription Genetic Variation Genotype Goals Human Human Biology Inbreeding Individual Intercistronic Region Introns Laboratories Linkage Disequilibrium Maps Mediating Medicine Molecular Molecular Genetics Morphology Pharmacology Phenotype Physiological Plants Population Positioning Attribute Predisposition Quantitative Genetics Quantitative Trait Loci Recovery Regulator Genes Risk Sampling Stream Stress System Technology Testing Time Variant causal variant farmers markets fitness genetic approach genetic architecture genetic variant genome wide association study genome-wide human disease life history male molecular phenotype nervous system development novel pleiotropism precision medicine rare variant response sex trait

项目摘要

PROJECT SUMMARY Risk for most human diseases is attributable to segregating alleles at many interacting genes with environmentally sensitive effects. Future developments towards personalized precision medicine require a predictive understanding of how DNA sequence variants give rise to phenotypic variation through modulation of regulatory gene networks. This is challenging in human populations because variants associated with complex traits are embedded in relatively large local linkage disequilibrium (LD) blocks, within which segregating molecular polymorphisms are not independent. Thus, these variants are not necessarily causal, but could be in LD with the true common or rare causal variant(s) within the same LD block. Furthermore, the majority of variants associated with complex traits are in intergenic regions, up- or down-stream of coding regions, or in introns. These variants are presumably regulatory and affect variation in gene expression. Formally proving the causal relationships between molecular genetic variation, genetic variation in gene expression and other intermediate molecular phenotypes, and genetic variation in quantitative trait phenotypes is not possible in human populations. The Drosophila melanogaster Genetic Reference Panel (DGRP) was generated in our laboratories and consists of 205 inbred, sequenced lines derived from single inseminated females collected from the Raleigh, NC Farmer’s Market. We have used the DGRP to perform genome wide association (GWA) mapping for many organismal quantitative traits as well as genome wide gene expression, which has generated testable hypotheses about the genotype-phenotype map, including sex-, genetic background- and environment-specific effects. The precision of GWA mapping in the DGRP is excellent because of rapid local decline of LD with physical distance. Here, we propose to test these hypotheses using CRISPR/Cas9 mediated precise allelic replacement to functionally validate (1) additive, epistatic and environment-specific effects of common variants that affect chill coma recovery time; (2) pleiotropic, epistatic and environment-specific effects of rare variants; and (3) novel transcribed regions (NTRs) and cis-trans transcriptional networks, and evaluate their effects on genome-wide expression and quantitative traits. These proposed studies will enable us to evaluate the direct and pleiotropic effects of common and rare variants, in both genic and intergenic regions, that are shared and distinct between males and females, both with respect to organismal quantitative trait phenotypes as well as genome wide gene expression. We will be able to explicitly evaluate the existence and magnitude of epistatic interactions for organismal phenotypes and gene expression traits and create “designer” genotypes between epistatically interacting alleles in defined genetic backgrounds. These studies will greatly advance our understanding of how subtle naturally occurring molecular variation impacts gene expression and organismal phenotypes.

项目总结