Many recent RNA-seq studies were focused mainly on detecting the differentially expressed genes (DEGs) between two or more conditions. In contrast, only a few attempts have been made to detect genes associated with quantitative traits, such as obesity index and milk yield, on RNA-seq experiment with large number of biological replicates.
This study by researchers at the Seoul National University illustrates the linear model application on trait associated genes (TAGs) detection in two real RNA-seq datasets: 89 replicated human obesity related data and 21 replicated Holsteins’ milk production related RNA-seq data. Based on these two datasets, the performance between suggesting methods, such as ordinary regression and robust regression, and existing methods: DESeq2 and Voom, were compared. The results indicate that suggesting methods have much lower false discoveries compared to the precedent two group comparisons based approaches in our simulation study and qRT-PCR experiment. In particular, the robust regression outperforms existing DEG finding method as well as ordinary regression in terms of precision.
Ten of the most significantly associated genes from the simple linear regression with two types of RNA-seq data, respectively.
The X-axis represents log2 TMM normalized gene expressions. Y-axis represents quantitative traits such as BMI and breeding value of the milk yield for human and bovine RNA-seq data, respectively. The blue-lines and grey-area represent estimated fit-line and standard errors estimated from the ordinary lease squared estimator, respectively. (a) Significantly detected BMI-associated genes in human RNA-seq data analysis (FDR adjusted P-value < 0.1). RAD9B gene shows that the result is strongly affected by outlier points. (b) Significantly detected milk yield-associated genes in bovine RNA-seq analysis. In the result of bovine analysis, relatively higher standard errors were estimated than human analysis, which may be due to the sample size difference.
Given the current trend in RNA-seq pricing, the researchers expect their methods to be successfully applied in various RNA-seq studies with numerous biological replicates that handle continuous response traits.