Motivation

Improved DNA technology has made it practical to estimate single nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth and development related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty.

Results

We introduce a completely tuning-free Bayesian Gaussian process (GP) based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo (MCMC) method which allows full uncertainty quantification. Several data sets are analysed and our results clearly illustrate that the 95 % credible intervals of the proposed joint estimation method (which "borrows strength" from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 softwares and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals.

Availability

The C++ implementation dynBGP and simulated data are available in GitHub (https://github.com/aarjas/dynBGP). The programs can be run in R. Real datasets are available in QTL archive (https://phenome.jax.org/centers/QTLA).

Supplementary information

Supplementary data are available at Bioinformatics online.

中文翻译：

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用贝叶斯高斯过程模型估计动态SNP遗传力。

动机

改进的DNA技术使估算未知关系的远亲个体中的单核苷酸多态性（SNP）遗传力变得可行。对于与生长和发育相关的特征，由于过程的时间依赖性，将SNP遗传力估计值基于纵向数据是有意义的。但是，到目前为止，仅开发了很少的统计方法来估计动态SNP遗传力并量化其完全不确定性。

结果

我们引入了一种完全不需调整的贝叶斯高斯过程（GP）为基础的方法，用于估算动态方差分量和遗传力。对于参数估计，我们使用了现代的马尔可夫链蒙特卡洛（MCMC）方法，该方法可实现完全不确定性量化。分析了几个数据集，我们的结果清楚地表明，所提议的联合估计方法（从相邻时间点“借入强度”）的95％可信区间比首先估计方差分量的两阶段基线方法要窄得多在每个时间点独立执行，然后进行平滑处理。我们将该方法与使用MTG2和BLUPF90软件的随机回归模型进行了比较，定量方法表明了我们方法的优越性能。给出了多达1000个时间点的模拟和真实数据的结果。最后，我们证明了该方法可用于具有成千上万个人的模拟数据的可扩展性。

可用性

GitHub（https://github.com/aarjas/dynBGP）中提供了C ++实现dynBGP和模拟数据。这些程序可以在R中运行。Real数据集在QTL存档（https://phenome.jax.org/centers/QTLA）中可用。

补充资料

补充数据可从生物信息学在线获得。