Mobile Element Discovery and Distribution in Human Populations and Diseases.

移动元素在人群和疾病中的发现和分布。

• 批准号: 9190983
• 负责人: Eugene James Gardner
• 金额: $ 2.38万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-08 至 2017-05-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9190983
• 关键词: Affect; Algorithm Design; Algorithms; Animal Model; Base Sequence; Biological Assay; Biological Models; Biological Sciences; Biology; Canis familiaris; Communities; DNA Insertion Elements; Data; Development; Disease; Elements; Evolution; Family; Genetic Variation; Genome; Goals; Hereditary Disease; Human; Human Genetics; Human Genome; Indium; Individual; Inheritance Patterns; Inherited; Jumping Genes; L1 Elements; Left; Length; Malignant Neoplasms; Mammals; Mediating; Minisatellite Repeats; Mobile Genetic Elements; Modeling; Mutagenesis; Mutation; Nuclear; Open Reading Frames; Organism; Pattern; Phase; Phenotype; Population; Population Analysis; RNA-Binding Proteins; RNA-Directed DNA Polymerase; Resources; Short Interspersed Nucleotide Elements; Site; Source; Surveys; Testing; Time; United States National Institutes of Health; Work; abstracting; base; cancer genome; computerized tools; design; disease phenotype; dog genome; follow-up; genome sequencing; human disease; improved; next generation sequencing; preference; tool; trait; whole genome

Abstract. Approximately 45% of the human genome is occupied by Mobile genetic Elements (MEs). Small subsets of these are still active and belong to three different families: L1, Alu, and SVA. These active families can generate new copies known as Mobile Element Insertions (MEIs), which can be polymorphic in humans. To find MEIs, large consortia have performed Whole Genome Sequencing (WGS) on a wide range of human populations and diseases, and designed computational tools to analyze the resulting data. One such effort, the 1000 Genomes Project (1KGP), performed WGS on 2,504 individuals from 26 different world populations to discover both MEIs and other human genetic variation. As part of the 1KGP, I designed an algorithm to find MEIs, the Mobile Element Locator Tool (MELT), and performed discovery for all active human ME families. While I found over 16,000 polymorphic MEIs, the 1KGP left several remaining open-ended questions about how these sites could affect human genetic diversity and impact human traits and diseases. For this proposal, I will work to answer some of these questions by designing new computational and sequencing tools. I will then use these tools to test the hypothesis that specific classes of MEs generate the majority of ME-derived genetic diversity among human populations and produce diseases such as cancer. I will approach this question using two aims. In Aim 1, I will develop computational tools to study how MEs have propagated in the human species. Then, I will adapt these tools to several non-human organisms to develop a model for ME mutagenesis and as a resource for the scientific community. Finally, I will use these new tools and one such model organism, canines, to understand ME distribution and propagation. In aim 2, I will develop sequencing tools to analyze a particularly active version of the L1 ME. Using this assay I will characterize a large number of active L1s, and subsequently associate these active L1s with ME activity in cancer. Overall, these studies will provide a better understanding of the relationship between MEs and human phenotypes and disease.

抽象的。 大约45％的人类基因组被移动遗传因素（MES）占据。小的子集 这些仍然活跃，属于三个不同的家庭：L1，Alu和SVA。这些活跃的家庭可以 生成称为移动元件插入（MEI）的新副本，在人类中可能是多态的。到 查找Meis，大财团已经在广泛的人类上进行了整个基因组测序（WGS） 种群和疾病，并设计了计算工具来分析所得数据。一项努力， 1000个基因组项目（1kgp），对来自26个不同世界人口的2,504个人进行了WGS 发现Meis和其他人类遗传变异。作为1kgp的一部分，我设计了一种算法来查找 Meis是移动元素定位器工具（融化），并为所有活跃的人类ME家庭进行了发现。 当我发现超过16,000个多态性梅斯时，1公斤 这些站点如何影响人类的遗传多样性并影响人类的特征和疾病。对于这个建议，我 将通过设计新的计算和测序工具来回答其中一些问题。然后我会 使用这些工具来测试特定类别的MES产生大多数ME衍生遗传的假设 人群之间的多样性和产生癌症等疾病。我将使用 两个目标。在AIM 1中，我将开发计算工具来研究MES如何在人类中传播 物种。然后，我将这些工具适应几种非人类生物，以为我开发模型 诱变和作为科学界的资源。最后，我将使用这些新工具等等 建模有机体，犬科，了解我的分布和传播。在AIM 2中，我将开发排序 分析L1 ME的特别活跃版本的工具。使用此测定法，我将描述一个大数 活性L1，随后将这些活性L1与我在癌症中的活性相关联。总体而言，这些研究 将更好地理解MES与人类表型与疾病之间的关系。