Evaluation of Sex-Aware PrediXcan Models for Predicting Gene Expression
Evaluation of Sex-Aware PrediXcan Models for Predicting Gene Expression
复制标题
用于预测基因表达的性别感知 PrediXcan 模型的评估
DOI：
10.1142/9789811250477_0033
复制
发表时间：
2021
影响因子：
--
通讯作者：
L. Dumitrescu
中科院分区：
文献类型：
--
作者：
Emily R. Mahoney;Vaibhav A. Janve;Timothy J. Hohman;L. Dumitrescu
基于基因的方法，如PrediX可以使用表达数量性状基因座，建立组织特异性基因表达模型时，只有遗传数据是可用的。组织特异性基因表达和基因表达的遗传结构存在已知的性别差异，但迄今为止尚未将这种差异纳入预测基因表达模型。我们使用来自基因型组织表达（GTEx）项目（195名女性和371名男性）的全血转录组数据建立了性别感知PrediXcan模型，并在独立数据集中评估了它们的性能。具体而言，按照Gamazon et al. 2015中描述的方法构建PrediXcan模型，但我们包括全样本和性别特异性模型。利用来自1000个基因组计划的EUR队列（178名女性和171名男性）的淋巴母细胞RNA测序数据评价了验证。在5，283个常染色体基因中评价了观察到的表达与预测表达之间的相关性（R2），以确定模型的性能。总之，我们成功预测了男性中的1，149个基因和女性中的623个基因，而3，511个基因似乎不是性别特异性的。在性别特异性基因中，15%（男性189个基因，女性73个基因）在性别特异性模型中表现出比全样本模型更高的R2，尽管预测能力的总体增益通常很小，并且在测量误差范围内。然而，两个女性特异性基因和六个男性特异性基因显示出显着更好的预测时，使用性别特异性的重量与全样本的重量;此外，这些基因中的几个在线粒体代谢中发挥作用，这是已知的性激素的影响。两者合计，这些结果支持以前的报告的遗传结构的性别特异性表达的小贡献。尽管如此，性别感知PrediXcan模型仍然能够提供强大的性别特异性预测信号。未来的研究探索X染色体和组织特异性对性别特异性遗传调控表达的贡献将阐明这种方法的实用性。
Gene-based methods such as PrediXcan use expression quantitative trait loci to build tissue-specific gene expression models when only genetic data is available. There are known sex differences in tissue-specific gene expression and in the genetic architecture of gene expression, but such differences have not been incorporated into predicted gene expression models to date. We built sex-aware PrediXcan models using whole blood transcriptomic data from the Genotype-Tissue Expression (GTEx) project (195 females and 371 males) and evaluated their performance in an independent dataset. Specifically, PrediXcan models were built following the method described in Gamazon et al. 2015, but we included both whole-sample and sex-specific models. Validation was evaluated leveraging lymphoblast RNA sequencing data from the EUR cohort of the 1000 Genomes Project (178 females and 171 males). Correlations (R2) between observed and predicted expression were evaluated in 5,283 autosomal genes to determine performance of models. In sum, we successfully predicted 1,149 genes in males and 623 in females, while 3,511 genes appeared to be not sex-specific. Of the sex-specific genes, 15% (189 genes in males and 73 genes in females) exhibited higher R2 in sex-specific models compared to whole-sample models, although the overall gain in predictive power was generally minimal and well within measurement error. Nevertheless, two female-specific genes and six male-specific genes showed significantly better prediction when using the sex-specific weights versus the whole-sample weights; furthermore, several of these genes play a role in mitochondrial metabolism, which is known to be influenced by sex hormones. Taken together, these results support previous reports of the small contribution of genetic architecture to sex-specific expression. Still, sex-aware PrediXcan models were able to provide robust sex-specific prediction signals. Future studies exploring the contribution of the X chromosome and tissue specificity on sex-specific genetically regulated expression will clarify the utility of this method.
影响因子：
64.8
作者：
通讯作者：
--