From Estimation to Prediction of Genomic Variances: Allowing for Linkage Disequilibrium and Unbiasedness
From Estimation to Prediction of Genomic Variances: Allowing for Linkage Disequilibrium and Unbiasedness
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从基因组变异的估计到预测：考虑连锁不平衡和无偏性
DOI：
10.1101/282343
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Schlather
中科院分区：
文献类型：
--
作者：
Schreck;Piepho;Schlather
加性基因组方差是遗传力的主要成分，在表型-基因型回归模型中经常被低估。为了改善所谓的“遗传性缺失”，人们提出了各种补救措施，包括不同的模型和估计器。最近，关于基因组方差的估计量是否包括连锁不平衡（LD）以及如何在估计过程中明确考虑LD的争论一直在进行。到目前为止，随机效应模型（REM）中的基因组方差已被估计为边际参数，即无条件模型。我们建议，REM 中的基因组方差应根据 β 的条件分布预测为条件随机量。这标志着基因组方差从估计到预测的范式转变。这种方法在结构上完全符合贝叶斯回归模型（BRM），其中基因组方差的后验期望是基于β的后验估计的。我们为 (g)BLUP 中的条件基因组方差引入了一种新颖的、经过数学严格建立的预测器，它在结构上接近贝叶斯估计器。新预测器对数据的调节本质上与 LD 的贡献和预测效果的包含有关。除此之外，预测器对分布假设的依赖性比其他方法的估计器弱得多，例如GCTA-GREML。最后但并非最不重要的是，与无条件模型中的估计器相比，预测器能够创新地近似 LD 对数据集中基因组方差的影响。基于对 10346 个多态性标记进行基因分型的 1814 只小鼠的常用数据集的示例性模拟研究证实，新预测器的偏差在所有标准情况下都很小，即条件基因组方差的预测器显着减少了“缺失”遗传力”。
The additive genomic variance, the chief ingredient for the heritability, is often underestimated in phenotype-genotype regression models. Various remedies, including different models and estimators, have been proposed in order to improve on what has been coined the “missing heritability”. Recently, debates have been conducted whether estimators for the genomic variance include linkage disequilibrium (LD) and how to explicitly account for LD in estimation procedures.Up-to-now, the genomic variance in random effect models (REM) has been estimated as a parameter of the marginal, i.e. unconditional model. We propose that the genomic variance in REM should be predicted as a conditional random quantity based on the conditional distribution ofβ. This signifies a paradigm shift from the estimation to the prediction of the genomic variance. This approach is structurally in perfect accordance to the Bayesian regression model (BRM), where the posterior expectation of the genomic variance is estimated based on the posterior ofβ. We introduce a novel, mathematically rigorously founded predictor for the conditional genomic variance in (g)BLUP, which is structurally close to the Bayesian estimator. The conditioning of the novel predictor on the data is intrinsically tied to the inclusion of the contribution of LD and the predicted effects. In addition to that, the predictor shows much weaker dependence on distribution assumptions than estimators of other approaches, e.g. GCTA-GREML. Last but not least, the predictor, contrasted with the estimator in the unconditional model, enables an innovative approximation of the influence of LD on the genomic variance in the dataset.An exemplary simulation study based on the commonly used dataset of 1814 mice genotyped for 10346 polymorphic markers substantiates that the bias of the novel predictor is small in all standard situations, i.e. that the predictor for the conditional genomic variance remarkably reduces the “missing heritability”.
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影响因子：
11.1
作者：
Vinkhuyzen AA;Wray NR;Yang J;Goddard ME;Visscher PM
通讯作者：
Visscher PM
影响因子：
9.8
作者：
Zhu, Zhihong;Bakshi, Andrew;Yang, Jian
通讯作者：
Yang, Jian
关于经验 BLUE 和 BLUP 的无偏性
DOI：
--
发表时间：
1999
期刊：
影响因子：
--
作者：
Jiming Jiang
通讯作者：
Jiming Jiang
基因组学最新发展及其相关统计方法概述
DOI：
--
发表时间：
2009
期刊：
影响因子：
--
作者：
P. Bickel;James B. Brown;Haiyan Huang;Qunhua Li
通讯作者：
Qunhua Li
影响因子：
1.4
作者：
Legarra, Andres
通讯作者：
Legarra, Andres