Reverse Engineering Quantitative Genetic Variation

项目摘要 大多数人类疾病的风险可归因于许多相互作用基因的等位基因分离， 环境敏感影响。个性化精准医疗的未来发展需要 预测理解DNA序列变异如何通过调节引起表型变异 调控基因网络的一部分。这在人群中是具有挑战性的，因为与 复杂的性状嵌入在相对较大的局部连锁不平衡（LD）块中，其中 分离分子多态性不是独立的。因此，这些变体不一定是因果关系， 但可能与同一LD块内的真正常见或罕见的致病变体处于LD中。而且 大多数与复杂性状相关的变异位于基因间区域，编码的上游或下游 区域或内含子中。这些变体可能是调节性的，影响基因表达的变化。 正式证明了分子遗传变异、基因遗传变异 表达和其他中间分子表型，以及数量性状表型的遗传变异 在人类中是不可能的。黑腹果蝇遗传参考组（DGRP）是 在我们的实验室中产生，由205个自交系组成，来自单次授精的测序系 从罗利，NC农贸市场收集的雌性。我们已经使用DGRP进行了全基因组范围的 关联（GWA）作图用于许多生物体数量性状以及全基因组基因表达， 它已经产生了关于基因型-表型图的可检验的假设，包括性别，遗传 特定背景和环境的影响。DGRP中的GWA映射精度很高 因为LD随着物理距离的增加而迅速局部下降。在这里，我们建议使用以下方法来测试这些假设： CRISPR/Cas9介导的精确等位基因置换以在功能上验证（1）加性、上位性和 影响寒战昏迷恢复时间的常见变异体的环境特异性效应;（2）多效性，上位性 和稀有变异的环境特异性效应;和（3）新的转录区（NTR）和顺-反 转录网络，并评估其对全基因组表达和数量性状的影响。这些 拟议的研究将使我们能够评估常见和罕见变异的直接和多效性作用， 基因和基因间区域，这是男性和女性之间共享和不同的，无论是在尊重 生物体数量性状表型以及全基因组基因表达。我们将能够 明确评估生物体表型和基因的上位相互作用的存在和程度 表达性状和创造“设计师”基因型之间的上位性相互作用的等位基因在定义的遗传 背景这些研究将极大地推进我们对自然发生的分子 变异影响基因表达和生物体表型。

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.