Systems Genetics of Complex Diseases Using RNA-Sequencing Methods

Abstract

Next generation sequencing technologies have enabled the generation of huge quantities of biological data, and nowadays extensive datasets at different 'omics levels have been generated. Systems genetics is a powerful approach that allows to integrate different 'omics level and understand the biological mechanisms behind complex diseases or traits. In the recent, transcriptomic studies with microarrays have been replaced with the new powerful RNA-seq technologies. This has led to detection of novel gene transcripts, novel regulatory mechanisms, allele specific gene expression and numerous non-coding RNAs (ncRNAs). The integration of transcriptomics data with genomic data in a systems genetics context represents a valuable possibility to go deep into the causal and regulatory mechanisms that generate complex traits and diseases. However RNA-Seq data have to be treated carefully and the choice of the right methodology could have a great impact on the final results. Furthermore the integration of different level is not trivial. Here we give a comprehensive systems genetics overview of the methods and tools for analysis and the integration of RNA-Seq data including ncRNAs. We focused principally on merits and demerits of tools for post mapping quality control, normalization, differential expression analysis, gene network analysis, and integration of different omics data in order to generate a comprehensive guideline to systems genetics analysis using RNA-Seq data.

dearseq: a variance component score test for RNA-seq differential analysis that effectively controls the false discovery rate

“…When comparing DEA methods, the evaluation of their empirical FDR with respect to the targeted (nominal) level is often overlooked ( 5–10 ). Nonetheless, some issues with inflated FDR in DEA have been previously reported in the literature ( 11–15 ), but those warnings have made little apparent impact on DEA practices.…”

Gene coexpression network analysis reveals perirenal adipose tissue as an important target of prenatal malnutrition in sheep

“…First, hub genes were selected from each module based on MM, representing the degree to which a gene is correlated with the module’s eigengene. The MM is strongly correlated with intramodular connectivity and can therefore be used to reveal master regulatory genes or hub genes, as regulating the expression of other genes in the module can prioritize potential candidate genes ( 53 , 62 ). Genes with the highest MM (MM ≥0.8 and P < 0.05) in each selected module that significantly correlated to at least one trait of interest were prioritized for hub gene(s) selection ( 63 ).…”

dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate

“…When comparing DEA methods, the evaluation of their empirical FDR with respect to the targeted (nominal) level is often overlooked [5,6,7,8,9,10]. Nonetheless, some issues with inflated FDR in DEA have been previously reported in the literature [11,12,13,14,15], but those warnings have made little apparent impact on DEA practices.…”