Prediction of gene expression with cis-SNPs using mixed models and regularization methods

BMC Genomics

Research Article

Xiang Zhou¹  Shuiping Huang²  Ping Zeng³ 

[1] Department of Biostatistics, University of Michigan, 1415 Washington Heights, 48104, Ann Arbor, MI, USA;Department of Epidemiology and Biostatistics, Xuzhou Medical University, 209 Tongshan Rd, 221004, Xuzhou, Jiangsu, China;Department of Epidemiology and Biostatistics, Xuzhou Medical University, 209 Tongshan Rd, 221004, Xuzhou, Jiangsu, China;Department of Biostatistics, University of Michigan, 1415 Washington Heights, 48104, Ann Arbor, MI, USA;

关键词: Gene expression;    Cis-SNPs;    Prediction model;    Linear mixed model;    Lasso;    Elastic net;    Bayesian sparse linear mixed model;   

DOI  :  10.1186/s12864-017-3759-6

 received in 2016-10-20, accepted in 2017-05-03,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundIt has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases.MethodsWe compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data.ResultsThe simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R2 ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R2 ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks.ConclusionsGene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

Fig. 6

Fig. 1