EAGER: Developing a High Resolution Method for Mapping Regulatory QTLs

许多研究小组的一个主要重点是描述控制数量性状变异的特定基因组元素（由许多基因及其与环境的相互作用决定的性状）。一些性状变异可以很容易地定位到特定的基因，因为它们与编码蛋白的变异有明确的联系。然而，许多性状变异很难精确地描述，因为它是由控制基因表达的DNA序列的差异引起的。由于技术原因，这些所谓的调控元件的变异传统上比蛋白质编码区域的变异更难表征。该项目的目的是开发新的方法和计算资源，使生物学家能够识别控制自然和新兴模型系统中定量变化的调节因素。这些方法的发展将改变生物学家将表型与基因型联系起来的方式，从而改变他们如何研究进化变化的功能基础。这些方法将被广泛使用，并可能影响到农业和生物工程等完全不同的领域。核苷酸多态性关联研究，有时结合转录丰度数据，已成为鉴定性状变异的基因组位点的首选方法。然而，这种普遍采用的方法往往无法描述特定的因果基因组元素。相反，它通常会选择中性标记，表明在跨越数万到数十万个核苷酸的区域进行选择。在某些情况下，这可能足以确定致病基因，特别是当关联间隔包含蛋白质编码区域的明显变化时。然而，在许多情况下，很难提高分辨率，因为很难确定监管要素的重要功能变化。在这个项目中，研究人员正在努力开发一种新的、高分辨率的方法来精确绘制顺式调控数量性状位点（creqtl）。具体来说，他们的目标是通过将标准基因分型分析与染色质结构和活性转录分析相结合来表征creqtl。这种方法将允许关联研究同时考虑调控活性和核苷酸序列，并可能导致定位到调控元件的qtl的分辨率达到150bp。该项目的总体目标是优化该方法，以用于新兴的模型系统，并开发用于creqtl注释和统计推断的生物信息学工具，重点是利用现成的草稿质量基因组。

A major of focus of many research groups is to characterize specific genomic elements that control variation in quantitative traits (traits that are determined by many genes and their interactions with the environment). Some trait variation can be easily mapped to particular genes due to clear associations with variation in encoded proteins. Much trait variation, however, is difficult to characterize with precision because it is caused by differences in DNA sequences that control gene expression. Variation in these so-called regulatory elements has traditionally been much more difficult to characterize than variation in protein-coding regions for technical reasons. The purpose of this project is to develop new methods and computational resources to allow biologists to identify regulatory elements that control quantitative variation in natural and emerging model systems. The development of these methods will transform the manner in which biologists link phenotype with genotype and thus how they study the functional basis of evolutionary change. The methods will be broadly used and likely impact fields as disparate as agriculture and bioengineering. Nucleotide polymorphism association studies, sometimes coupled with transcript abundance data, have become a favorite approach for identifying genomic loci underlying trait variation. This commonly adopted approach, however, often fails to characterize specific causal genomic elements. Instead, it often settles for neutral markers indicative of selection across regions spanning tens to hundreds of thousands of nucleotides. In some cases this may be sufficient to identify causal genes, especially when association intervals contain obvious changes in protein coding regions. In many cases, however, it is difficult to improve resolution because important functional changes in regulatory elements are difficult to identify. For this project the investigators are working to develop a novel, high-resolution approach to precisely map cis-regulatory quantitative trait loci (creQTLs). Specifically, they aim to characterize creQTLs by combining standard genotyping analysis with assays for chromatin structure and active transcription. This approach would allow association studies to consider both regulatory activity and nucleotide sequences, and could lead to resolution of 150bp for QTLs mapping to regulatory elements. The overarching goal of this project is to optimize this approach for use in emerging model systems and to develop bioinformatic tools for annotation and statistical inference of creQTLs, with an emphasis on utilizing readily available draft-quality genomes.