Unravelling complex trait architecture using DNA sequence data

分子生物学的进步导致了人类、动物和植物基因组数据的大量收集。这些数据，加上大量个体的表型和系谱信息，为控制复杂特征的遗传机制提供了新的生物学见解。尽管实验数据集的规模不断增加，但基因组数据分析最突出的问题之一是，统计模型中未知参数的数量超过了可用样本量(n&lt；&lt；p设置)。各种具有适当先验(产生系数收缩)的贝叶斯线性回归模型可以在协变量或预报器的数量大于观测数量时应用于全基因组遗传值预测。然而，这些方法在推断模型参数和识别序列数据中的功能突变方面的性能在很大程度上是未知的。这项研究的总体目标是确定最优的贝叶斯回归方法来推断高维数据中的标记效应。我们将在模拟和实验数据中研究具有不同先验分布设置的新一代模型的灵敏度和贝叶斯学习特性，包括不同类型的混合分布。全基因组回归模型将在模型拟合之前通过生物驱动的标记分组来增强。将探讨相关MCMC算法的实施问题，如计算效率和行为，并将提供用于评估推理的不确定性和针对分层模型中使用的不同先验的贝叶斯敏感性的指导方针。通过研究1000多个已有高质量表型数据的已测序拟南芥种质，将评估全基因组回归模型了解统计性状结构(例如，所涉及的基因组区域、效应大小、对变异的贡献)的能力，并将其与生物学先验知识进行比较。该项目的结果将允许确定最佳的统计方法，以利用许多植物物种已经获得或即将获得的大量基因组数据。

Advances in molecular biology have led to the availability of massive collections of genomic data in human, animals, and plants. These data, together with phenotypic and genealogical information on a large number of individuals promise new biological insights on genetic mechanisms controlling complex traits. Despite the constantly increasing size of experimental data sets, one of the most prominent problems of genomic data analysis is that the number of unknown parameters in the statistical models exceeds - often by far - the available sample size (n << p setting). Various Bayesian linear regression models with proper priors (producing shrinkage of coefficients) that can be applied when the number of covariates or predictors is larger than the number of observations have been suggested in the context of genome-wide prediction of genetic values. However, the performance of these methods with respect to inference on model parameters and identification of functional mutations in sequence data is largely unknown. The overall objective of this study is to identify optimal Bayesian regression methods for inference on marker effects in high-dimensional data. We will investigate the sensitivity and Bayesian learning properties of a new generation of models with different prior distribution settings, including different types of mixture distributions, in simulated and experimental data. Whole-genome regression models will be enhanced by biologically driven grouping of markers prior to model fitting. Implementation issues, such as computational effectiveness and behavior of the associated MCMC algorithms will be explored and guidelines for assessing the uncertainty of inferences and the Bayesian sensitivity with respect to different priors used in hierarchical models will be provided. By studying more than 1000 sequenced Arabidopsis thaliana accessions for which high quality phenotypic data are available, the ability of whole-genome regression models to learn about statistical trait architecture (e.g., genomic regions involved, effect sizes, contributions to variance) will be assessed and compared to biological prior knowledge. The results from the project will allow identifying optimal statistical methods to harness the large amount of genomic data which is already available or upcoming for many plant species.