Felipe Vivian P S, Okut Hayrettin, Gianola Daniel, Silva Martinho A, Rosa Guilherme J M
Department of Animal Sciences, University of Wisconsin, Madison, 53706, USA.
Department of Animal Sciences, Biometry and Genetics Branch, University of Yuzuncu Yil, Van, 65080, Turkey.
BMC Genet. 2014 Dec 29;15:149. doi: 10.1186/s12863-014-0149-9.
Genotype imputation is an important tool for whole-genome prediction as it allows cost reduction of individual genotyping. However, benefits of genotype imputation have been evaluated mostly for linear additive genetic models. In this study we investigated the impact of employing imputed genotypes when using more elaborated models of phenotype prediction. Our hypothesis was that such models would be able to track genetic signals using the observed genotypes only, with no additional information to be gained from imputed genotypes.
For the present study, an outbred mice population containing 1,904 individuals and genotypes for 1,809 pre-selected markers was used. The effect of imputation was evaluated for a linear model (the Bayesian LASSO - BL) and for semi and non-parametric models (Reproducing Kernel Hilbert spaces regressions - RKHS, and Bayesian Regularized Artificial Neural Networks - BRANN, respectively). The RKHS method had the best predictive accuracy. Genotype imputation had a similar impact on the effectiveness of BL and RKHS. BRANN predictions were, apparently, more sensitive to imputation errors. In scenarios where the masking rates were 75% and 50%, the genotype imputation was not beneficial. However, genotype imputation incorporated information about important markers and improved predictive ability, especially for body mass index (BMI), when genotype information was sparse (90% masking), and for body weight (BW) when the reference sample for imputation was weakly related to the target population.
In conclusion, genotype imputation is not always helpful for phenotype prediction, and so it should be considered in a case-by-case basis. In summary, factors that can affect the usefulness of genotype imputation for prediction of yet-to-be observed traits are: the imputation accuracy itself, the structure of the population, the genetic architecture of the target trait and also the model used for phenotype prediction.
基因型填充是全基因组预测的一项重要工具，因为它能够降低个体基因分型的成本。然而，基因型填充的益处大多是针对线性加性遗传模型进行评估的。在本研究中，我们调查了在使用更精细的表型预测模型时采用填充基因型的影响。我们的假设是，此类模型仅使用观察到的基因型就能追踪遗传信号，无法从填充基因型中获得额外信息。
在本研究中，使用了一个包含1904个个体以及1809个预先选择标记的基因型的远交小鼠群体。针对线性模型（贝叶斯最小绝对收缩和选择算子 - BL）以及半参数和非参数模型（分别为再生核希尔伯特空间回归 - RKHS和贝叶斯正则化人工神经网络 - BRANN）评估了填充的效果。RKHS方法具有最佳的预测准确性。基因型填充对BL和RKHS的有效性有类似影响。显然，BRANN预测对填充误差更敏感。在掩码率为75%和50%的情况下，基因型填充并无益处。然而，当基因型信息稀疏（90%掩码）时，基因型填充纳入了关于重要标记的信息并提高了预测能力，尤其是对于体重指数（BMI），而当填充的参考样本与目标群体相关性较弱时，对于体重（BW）也是如此。
总之，基因型填充并非总是有助于表型预测，因此应逐案考虑。概括而言，可影响基因型填充对尚未观察到的性状预测有用性的因素包括：填充准确性本身、群体结构、目标性状的遗传结构以及用于表型预测的模型。
