The role of the theoretical covariance between SNPs in the design of experiments in genomic evaluations

SNP 之间的理论协方差在基因组评估实验设计中的作用

基本信息

批准号：
320694892
负责人：
Dr. Dörte Wittenburg
金额：
--
依托单位：
Institut für Genetik und Biometrie
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/320694892?language=en
关键词：
role theoretical covariance between SNPs

项目摘要

In animal breeding, molecular data (e.g., single nucleotide polymorphisms; SNPs) are incorporated as predictor variables in statistical models to reach an improved genomic evaluation of animals. This leads to more precisely estimated breeding values of not-yet phenotyped animals, which is important for breeding purposes, and enables the genetic architecture of some traits to be elucidated. Not only is the position on the genome a relevant parameter but so too is the effect size. Particularly, as with the high-dimensional SNP data that are available today, a causative variant can be pinpointed to a specific base pair on the genome. Due to the curse of dimensionality, the precision of effect estimates can only be approximated with time-consuming computational methods; it is not given in an analytical formula. As precision of estimates influences the outcome of testing for significance, the reliable identification of causative variants is complicated. The project aims to theoretically determine the standard error of SNP-effect estimates and the power of testing their significance, particularly for situations where the number of SNPs exceeds the number of individuals. The theory developed in this project will be validated with simulated and empirical data that are publicly available. Furthermore, when planning new experiments, it is essential to aim at a sufficiently large power to test the SNP effects. Thus, at a second stage, the project will investigate how a minimum required sample size can be determined prior to any genomic evaluation in a target population. The target population may consist of a mixture of half- and/or full-sib families. As genotypic data of target individuals are typically not available a-priori, we will employ the theoretical distribution of genotypes which can be inferred from parental genome information. Criteria for an optimal breeding design will be concluded by taking into account the trait-specific parameters, which have to be stipulated by the experimenter, and population-specific parameters, from which the theoretical distribution of SNPs is derived. The ultimate goal is to provide experimental designs useful for practical applications in which the genetic architecture of selected traits can be elucidated.

在动物育种中，将分子数据（例如，单核苷酸多态性； SNP）纳入统计模型中的预测变量，以实现对动物的基因组评估的改进。这导致更精确地估计的尚未表型动物的育种值，这对于繁殖目的很重要，并使某些特征的遗传结构得以阐明。基因组上的位置不仅是相关参数，而且效应大小也是如此。特别是，与当今可用的高维SNP数据一样，可以将病因变体限于基因组上的特定基对。由于维数的诅咒，效果估计的精度只能通过耗时的计算方法近似。它不是在分析公式中给出的。由于估计值的精度影响了测试意义的结果，因此，可靠的因果变体的可靠鉴定是复杂的。该项目的目的是从理论上确定SNP效应估计值的标准误差以及测试其重要性的功能，尤其是对于SNP数量超过个人数量的情况。该项目中开发的理论将通过公开可用的模拟和经验数据进行验证。此外，在计划新实验时，必须瞄准足够大的能力来测试SNP效应。因此，在第二阶段，该项目将在目标人群中进行任何基因组评估之前如何确定最低所需的样本量。目标人群可能由半张和/或全sib家族的混合物组成。由于目标个体的基因型数据通常不可用，因此我们将采用可以从亲本基因组信息推断的基因型的理论分布。最佳育种设计的标准将考虑到特定特定的参数，这些参数必须由实验者规定，并从中得出了SNP的理论分布。最终目标是提供实验设计，适用于可以阐明所选特征的遗传结构的实际应用。