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Fast Bayesian methods for genetic mapping applicable for high-throughput datasets.

机译：用于高通量数据集的遗传映射快速贝叶斯方法。

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摘要

QTL mapping is a statistical method for detecting possible gene locations (called Quantitative Trait Loci or QTL) and those genes' effects on the variation in a quantitative phenotype, such as the height of a corn plant, etc. QTL mapping has become an important issue in genetic analysis and has made important contributions to the fields of medicine and agriculture. Traditional QTL mapping methods scan the whole genome and calculate the profile likelihood ratios test statistic at each putative QTL location. The maxima of the test statistics for all putative QTL locations are compared with the genome-wide threshold to identify the QTL.; In this thesis, we propose several fast Bayesian methods for QTL mapping, which not only provide direct approximate QTL posterior probabilities at all putative gene locations, but also offer highly interpretable posterior densities for linkage, without the need for Bayes factors in model selection. The applications to simulated data and real data show these methods are highly efficient and more rapid than the alternatives, grid search integration, importance sampling, Markov Chain Monte Carlo (MCMC) sampling or adaptive quadrature. Our results also provide insight into the connection between the profile likelihood ratios test statistic and the posterior probability for linkage. The results of these methods are easy to interpret and have the advantage of producing posterior densities for all model parameters. We infer the presence of QTL at locations with largest posterior probabilities. Because of the high speed and high accuracy of these methods, they are highly suitable for studying high-throughput data sets, e.g. eQTL data sets. The eQTL analysis is a very important application of QTL mapping to a microarray data set, where thousands of transcripts are treated as the phenotypes and provides us insight into the natural variation in gene expression levels. The approach offers highly interpretable direct linkage posterior densities for each transcript, and opens new avenues for research in this area. Biologically attractive priors involving explicit hyperparameters for probabilities of cis-acting and trans-acting QTL are easily incorporated.; We also extend the one QTL Bayesian method to multiple QTL. The advantage of this method is the simultaneous detection of multiple QTL and appropriate modelling of their joint effects. Multiple QTL mapping can be computationally intensive, even for our efficient Bayesian approaches. Thus, a fully Bayesian multiple QTL approach for high-throughput datasets remains challenging. We investigate a heuristic for conditional search on the two-location search space that shows promise for identifying the global maximum, and offers the potential for extended approximate Bayesian approaches.
机译：QTL作图是一种统计方法，用于检测可能的基因位置（称为定量性状位点或QTL）以及这些基因对定量表型变异的影响，例如玉米植株的高度等。QTL作图已成为重要问题在基因分析领域，为医学和农业领域做出了重要贡献。传统的QTL定位方法会扫描整个基因组，并在每个假定的QTL位置上计算轮廓似然比检验统计量。将所有假定的QTL位置的测试统计最大值与全基因组阈值进行比较，以识别QTL。在本文中，我们提出了几种快速的贝叶斯QTL定位方法，它们不仅可以在所有推定的基因位置上提供直接的近似QTL后验概率，而且还提供了高度可解释的后验连接密度，而在模型选择中不需要贝叶斯因子。在模拟数据和真实数据上的应用表明，这些方法比其他方法，网格搜索集成，重要性采样，马尔可夫链蒙特卡洛（MCMC）采样或自适应正交算法更高效，更快速。我们的结果还提供了对概貌似然比检验统计量与联系的后验概率之间的联系的深入了解。这些方法的结果易于解释，并具有为所有模型参数产生后验密度的优点。我们推断在后验概率最大的位置存在QTL。由于这些方法的高速度和高精度，它们非常适合研究高通量数据集，例如eQTL数据集。 eQTL分析是QTL映射到微阵列数据集的非常重要的应用，其中成千上万的转录本被视为表型，并为我们提供了基因表达水平自然变化的见识。该方法为每个转录本提供了高度可解释的直接连锁后验密度，并为该领域的研究开辟了新途径。涉及显式超参数的，具有顺式作用和反式作用QTL概率的具有生物学吸引力的先验很容易被并入。我们还将一个QTL贝叶斯方法扩展到多个QTL。这种方法的优点是可以同时检测多个QTL并对其联合效应进行适当的建模。即使对于我们有效的贝叶斯方法，多个QTL映射也可能需要大量计算。因此，用于高通量数据集的完全贝叶斯多重QTL方法仍然具有挑战性。我们研究了在两位置搜索空间上进行条件搜索的启发式方法，该方法显示了识别全局最大值的希望，并提供了扩展近似贝叶斯方法的潜力。

著录项

  • 作者

    Chang, Yu-Ling.;

  • 作者单位

    The University of North Carolina at Chapel Hill.$bBiostatistics.;

  • 授予单位 The University of North Carolina at Chapel Hill.$bBiostatistics.;
  • 学科 Biology Biostatistics.; Biology Bioinformatics.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 128 p.
  • 总页数 128
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物数学方法;
  • 关键词

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