Ober Ulrike, Huang Wen, Magwire Michael, Schlather Martin, Simianer Henner, Mackay Trudy F C
Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-Universität Göttingen, Göttingen, Germany.
Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, United States of America.
PLoS One. 2015 May 7;10(5):e0126880. doi: 10.1371/journal.pone.0126880. eCollection 2015.
The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17%) of the genetic variance among lines in females (males), the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.
从分子多态性数据预测数量性状表型的能力将给进化生物学、医学、人类生物学以及动植物育种带来变革。绘制数量性状基因座的努力为数量性状生物学带来了新的见解，但单个显著的数量性状基因座组合起来通常预测能力较低。在动植物育种群体中利用所有分离变异可获得良好的预测能力，但对性状生物学的了解却很少。在这里，我们使用果蝇遗传参考面板对与适应性相关的性状冷昏迷恢复时间进行全基因组关联分析和基因组预测。我们发现冷昏迷恢复时间存在大量的总遗传变异，其遗传结构在雄性和雌性之间有所不同，有少数具有大的主效应的分子变异，并且存在上位性的证据。尽管顶级加性变异解释了雌性（雄性）品系间36%（17%）的遗传方差，但使用基因组最佳线性无偏预测和基于所有常见分离变异的亲缘关系矩阵时，雌性的预测能力非常低，而雄性为零。我们推测低预测能力是由于基因组最佳线性无偏预测模型假设的微效多基因遗传结构与冷昏迷恢复时间的真实遗传结构不匹配所致。事实上，我们发现当我们将数量性状基因座定位与基因组预测相结合，仅在亲缘关系矩阵中纳入与主效应和上位效应相关的顶级变异时，基因组最佳线性无偏预测模型的预测能力会显著提高。这种与性状相关的预测方法的优势在于它能产生具有生物学可解释性的预测模型。
