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

从种群中大量不相关个体中取样的基因组序列之间的差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。最广为人知的问题涉及导致疾病发病率和经济上重要性状变异的基因和突变的位置。自然和历史已经在个体和群体中创造了各种各样的马赛克基因组，这些基因组今天可以被研究。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）中发现模式，这些模式与感兴趣的重要特征（表型）有显著的和生物学上有意义的关联。随着基因组水平技术的发展，关于种群水平变异的数据越来越多，但在数据分析中仍然存在一些具有挑战性的问题。这一建议的重点是新的，关键的计算问题，出现在人口规模的基因组数据采集和分析。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是至关重要的。这项研究将由一个由计算机科学家、数学家和遗传学家组成的跨学科小组进行。主要的基于生物学的算法问题涉及历史重组、基因转换和横向基因转移的频率、位置和全时间结构的计算推导。主要的技术问题涉及数据缺失或数据容易出错的重要问题，以及从基因型数据中推断单倍型数据的问题。通过使用优化技术来解决数据丢失或容易出错的问题。单倍型推导问题使用了各种各样的技术，基于开发更完整和现实的生物学模型，以了解潜在的单倍型是如何进化的。其中一个因素是将重组纳入模型，将先前构建重组历史的工作与从基因型推断单倍型的工作联系起来。这些算法和软件将使生物学家更好地了解重组、基因转换和横向基因转移的历史和作用，并处理数据中的问题。仅举两个例子，这些工具可以促进通过关联定位发现基因的任务，以及了解横向基因转移如何帮助细菌迅速产生抗生素耐药性。我们的相关教育和外联工作将加强其影响。

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.