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Unravelling complex trait architecture using DNA sequence data

使用 DNA 序列数据揭示复杂的性状结构

基本信息

批准号：
317460274
负责人：
Professor Dr. Martin Schlather
金额：
--
依托单位：
Lehrstuhl für Angewandte Stochastik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/317460274?language=en
关键词：
Unravelling complex trait architecture using

项目摘要

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.

分子生物学的进步已经导致了人类、动物和植物的大量基因组数据的可用性。这些数据，加上大量个体的表型和系谱信息，有望为控制复杂性状的遗传机制提供新的生物学见解。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的样本大小（n << p设置）。在全基因组遗传值预测的背景下，提出了各种具有适当先验（产生系数收缩）的贝叶斯线性回归模型，当协变量或预测因子的数量大于观测值的数量时，可以应用这些模型。然而，这些方法在模型参数推断和序列数据中功能突变识别方面的性能在很大程度上是未知的。本研究的总体目标是确定最佳的贝叶斯回归方法的推断标记的影响，在高维数据。我们将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，包括模拟和实验数据中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。实施问题，如相关MCMC算法的计算效率和行为将进行探讨，并提供评估推理的不确定性和贝叶斯灵敏度相对于分层模型中使用的不同先验的指导方针。通过研究超过1000个测序的拟南芥种质，其中高质量的表型数据是可用的，全基因组回归模型的能力，以了解统计性状架构（例如，涉及的基因组区域、效应大小、对方差的贡献）将被评估并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统计方法，以利用许多植物物种已经可用或即将到来的大量基因组数据。