RNA-seq analysis for detecting quantitative trait-associated genes

Abstract

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 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. Given the current trend in RNA-seq pricing, we expect our methods to be successfully applied in various RNA-seq studies with numerous biological replicates that handle continuous response traits.

SCGN Administration prevents Insulin Resistance and Diabetic Complications in High-Fat Diet Fed Animals

“…Based on the available information and its logical acquaintance with SCGN function, we predicted the involvement of SCGN in the regulation of obesity and insulin resistance. Association of SCGN with body-weight QTC (14), downregulation of SCGN in HFD fed animals (7) and reduced CSF SCGN concentration in the insulin resistant subjects (15) offer confidence to our rationalized assumption. The development of glucose intolerance at an early life-stage and age associated progressive hyperglycemia in SCGN knockout mice further suggest a confounding role of SCGN in preserving euglycemia (16).…”

BALLI: Bartlett-Adjusted Likelihood-based LInear Model Approach for Identifying Differentially Expressed Gene with RNA-seq Data

“…Holstein milk data, consisting of 21 Holstein cows, were generated to detect genes related to the productivity of daily milk. High and low milk yields were considered the primary exposure variables, and parity and lactation period were included as covariates (Seo, et al, 2016). In this study, twelve tentative DEGs were chosen, and technically validated using quantitative real time polymerase chain reaction (qRT-PCR).…”

“…Among the twelve genes, nine ( TOX4, HNRNPL, SPTSSB, NOS3, C25H16orf88, KALRN, SLC4A1, NLN, and PMCH ) were significantly validated. According to Seo et al (2016), however, no DEGs including the nine genes were found at FDR 0.1 significance level by DESeq2 and voom as well as their methods due to the lack of statistical power (Seo, et al, 2016). Our proposed methods and existing methods (DESeq2, edgeR, and voom) were applied to the data analysis.…”

“…The read counts were downloaded from the ReCount website (Frazee, et al, 2011). Holstein data were obtained to identify genes associated with milk yield and consisted of high and low milk yielding groups whose number of subjects is 9 and 12, respectively (Seo, et al, 2016). Furthermore, parity and lactation periods were available and were considered as covariates.…”

Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes

“…This type of analysis is ubiquitous across many elds, including: evolutionary developmental biology [2], cancer biology [3], agriculture [4,5], ecological physiology [6,7], and biological oceanography [8]. In recent years, substantial investments have been made in data generation, primary data analysis, and development of downstream applications, such as biomarkers and diagnostic tools [9,10,11,12,13,14,15,16] Methods for de novo RNAseq assembly of the most common short read Illumina sequencing data continue to evolve rapidly, especially for non-model species [17]. At this time, there are several major de novo transcriptome assembly software tools available to choose from, including Trinity [18], SOAPdenovo-Trans [19], Trans-ABySS [20], Oases [21], SPAdes [22], IDBAtran [23], and Shannon [24].…”