SEI(BIO): Computational Population Genomics: Using Variation to Connect Genotypes to Phenotypes

SEI(BIO)：计算群体基因组学：利用变异将基因型与表型联系起来

• 批准号: 0513910
• 负责人: Daniel Gusfield
• 金额: $ 70万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0513910&HistoricalAwards=false
• 关键词: SEI; BIO; Computational; Population; Genomics

The computational comparison of variations among genomic sequences sampled from a large number of unrelated individuals in a population is a very powerful way to address both fundamental and applied biological questions. The best known questions concern the location of genes and mutations that contribute to disease incidence and to variation in economically important traits. Nature and history have created a large variety of mosaic genomes among individuals and populations who can be studied today. The grand challenge is to exploit these natural experiments by finding patterns in and among the different mosaic genomes (the genotypes) that have significant and biologically meaningful associations with important traits (the phenotypes) of interest. With genomic level technologies, the needed data on population level variation is becoming available but challenging problems remain in the analysis of the data.This proposal focuses on novel, critical computational problems that arise in population-scale genomic data acquisition and analysis. The algorithmic problems of concern are divided into biology-based problems and technology-based problems, but the interplay of technology and biology is critical. This research will be conducted by an interdisciplinary group of computer scientists, mathematicians and geneticists. The main biology-based algorithmic problems concern the computational deduction of the frequency, location, and the full temporal structure of historical recombination, gene-conversion and lateral gene- transfer. The main technology-based problems are concerned with important problems of missing data or error-prone data, and with the deduction of haplotype data from genotype data. The problems of missing or error-prone data are approached through the use of optimization techniques. The haplotype deduction problem uses a variety of techniques, based on exploiting more complete and realistic biological models of how the underlying haplotypes have evolved. One element is the incorporation of recombination into the models, connecting previous work on constructing histories of recombinations with work on deducing haplotypes from genotypes.The algorithms and software will allow biologists to better understand the historyand role of recombination, gene-conversion and lateral gene transfer, and to cope with problems in the data. As just two examples, the tools could facilitate the tasks of gene finding by association mapping, and in understanding how lateral gene-transfer helps bacteria to rapidly develop antibiotic resistance. The impact will be enhanced by our associated educational and outreach efforts.

从一个群体中大量不相关个体中取样的基因组序列之间的变异的计算比较是解决基础和应用生物学问题的一种非常有效的方法。最为人所知的问题涉及基因和突变的位置，这些基因和突变会导致疾病的发生和重要经济性状的变异。自然和历史已经在今天可以研究的个体和种群中创造了大量多样的嵌合基因组。最大的挑战是利用这些自然实验，在不同的镶嵌基因组（基因型）中找到与重要性状（表型）有显著和生物学意义的关联的模式。随着基因组水平技术的发展，人们越来越需要群体水平变异的数据，但在数据分析中仍然存在一些具有挑战性的问题，该建议侧重于群体规模基因组数据采集和分析中出现的新的关键计算问题。人们关注的算法问题分为基于生物学的问题和基于技术的问题，但技术和生物学的相互作用至关重要。这项研究将由计算机科学家、数学家和遗传学家组成的跨学科小组进行。基于生物学的主要算法问题涉及历史重组、基因转换和横向基因转移的频率、位置和完整时间结构的计算推导。 基于技术的主要问题涉及缺失数据或易出错数据的重要问题，以及从基因型数据推导单体型数据。通过使用最优化技术来处理数据丢失或易出错的问题。单倍型推导问题使用了多种技术，基于利用更完整和更现实的生物模型来研究潜在的单倍型是如何进化的。一个要素是将重组纳入模型，将以前构建重组历史的工作与从基因型推断单体型的工作联系起来。算法和软件将使生物学家更好地理解重组、基因转换和横向基因转移的历史和作用，并科普数据中的问题。仅举两个例子，这些工具可以通过关联映射促进基因发现的任务，以及了解横向基因转移如何帮助细菌快速产生抗生素耐药性。我们的相关教育和外联工作将加强这种影响。