2008
DOI: 10.1038/nature06758
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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 … Show more

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Cited by 1,202 publications
(1,156 citation statements)
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References 39 publications
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“…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.…”
Section: Rna-seq In Human Complex Diseases V Costa Et Almentioning
confidence: 99%
“…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.…”
Section: Rna-seq In Human Complex Diseases V Costa Et Almentioning
confidence: 99%
“…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.…”
Section: Associating Variation With Regulationmentioning
confidence: 99%
“…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.…”
Section: Growth Factorsmentioning
confidence: 99%