Genetics of gene expression and its effect on disease

Abstract

Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

“…Since then, GWAS have unequivocally shown that SNPs affect gene expression. 4,5,75 A common finding of eQTL studies is that cis-acting SNPs (ie, in close proximity to a gene) have a strong influence on gene expression and a greater replicability in different populations and by independent detection methods. On the opposite, trans-acting variations 76 with subtle effects on expression are less replicable and their causal association to traits/diseases is not trivial.…”

Linking Variants from Genome-Wide Association Analysis to Function via Transcriptional Network Analysis

“…However, naive matching of associations from eQTL analysis and GWAS may not properly capture regulatory causality. Schadt 34 uses a model of causality that relates genotype to transcript and disease phenotype in one analysis, and has applied this approach in several studies [35][36][37][38] . This model allows the analysis to identify regulatory associations that also are putatively causal, which can better focus follow-up analysis and confirmatory experimentation.…”

Pharmaceutical futures: A fiendish puzzle

“…The paper was published backto-back with another that had used a systems approach to identify three obesity-related genes that had not sprung out of genomics data alone 4 . "The surprise here was to find that not just a handful of genes were causally implicated in the disease, but hundredswhole pathways were involved, " says Schadt.…”