Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423-428

deCODE genetics, 101 Reykjavik, Iceland.
Nature (Impact Factor: 41.46). 03/2008; 452(7186):423-428. DOI: 10.1038/nature06758


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.

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    • "The effects of genomic variation, modulated by lifestyle and environment , orchestrate the extensive phenotypic variability found in human populations. The quantification of narrow-sense heritability, that is, the proportion of phenotypic variance attributable to additive genetic effects, provides important information for basic and disease biology (Lichtenstein et al, 2000; Stranger et al, 2007; Emilsson et al, 2008; Visscher et al, 2008). Within the human population, the narrow-sense heritability of traits can be determined in twin cohort studies. "
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    ABSTRACT: The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2-7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis-SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood-based biomarker studies. © 2015 The Authors. Published under the terms of the CC BY 4.0 license.
    Molecular Systems Biology 02/2015; 11(2). DOI:10.15252/msb.20145728 · 10.87 Impact Factor
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    • "For instance, researchers can prioritize the genetic variants by utilizing evidence/information of their impact on biological processes giving rise to the desired phenotypes. One such biological process is the regulation of gene expression, which is believed to have influenced human evolution and play an important role in diseases (Emilsson et al., 2008; Kudaravalli et al., 2009). Expression of most genes is influenced by expression quantitative trait loci (eQTLs), which were hypothesized to be prime candidates for causal variants affecting various phenotypes (Gaffney et al., 2012; Gilad et al., 2008). "
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    ABSTRACT: Gene expression is influenced by variants commonly known as expression quantitative trait loci (eQTL). Based on this fact, researchers proposed to use eQTL/functional information univariately for prioritizing Single Nucleotide Polymorphisms (SNPs) signals from genome-wide association studies (GWAS). However, most genes are influenced by multiple eQTLs which, thus, jointly affect any downstream phenotype. Therefore, when compared to the univariate prioritization approach, a joint modeling of eQTL action on phenotypes has the potential to substantially increase signal detection power. Nonetheless, a joint eQTL analysis is impeded by i) not measuring all eQTLs in a gene and/or ii) lack of access to individual genotypes. We propose JEPEG, a novel software tool which uses only GWAS summary statistics to i) impute the summary statistics at unmeasured eQTLs and ii) test for the joint effect of all measured and imputed eQTLs in a gene. We illustrate the behavior/performance of the developed tool by analyzing the GWAS meta-analysis summary statistics from the Psychiatric Genomics Consortium stage 1 (PGC1) and the Genetic Consortium for Anorexia Nervosa (GCAN). Applied analyses results suggest that JEPEG complements commonly used univariate GWAS tools by: i) increasing signal detection power via uncovering a) novel genes or b) known associated genes in smaller cohorts, and ii) assisting in fine-mapping of challenging regions, e.g. Major Histocompatibility Complex (MHC) for schizophrenia. Availability and implementation: JEPEG, its associated database of eQTL SNPs and usage examples are publicly available at http://code.google.com/p/jepeg/. dlee4@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author(s) 2014. Published by Oxford University Press.
    Bioinformatics 12/2014; 31(8). DOI:10.1093/bioinformatics/btu816 · 4.98 Impact Factor
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    • "Importantly, these projects have reported a large fraction of open and functional chromatin sites to be cell-type specific. This is in contrast with previous eQTLs reports, which showed considerable sharing in functional regulatory variation across tissues (Emilsson et al, 2008; Kraft, 2008; Dimas et al, 2009; Ding et al, 2010; The ENCODE Project Consortium, 2012; Thurman et al, 2012). Despite progress in mapping functional elements, defining causal cis-rSNPs among correlated sites in high linkage disequilibrium (LD) remains a challenge. "
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    ABSTRACT: Most complex disease-associated genetic variants are located in non-coding regions and are therefore thought to be regulatory in nature. Association mapping of differential allelic expression (AE) is a powerful method to identify SNPs with direct cis-regulatory impact (cis-rSNPs). We used AE mapping to identify cis-rSNPs regulating gene expression in 55 and 63 HapMap lymphoblastoid cell lines from a Caucasian and an African population, respectively, 70 fibroblast cell lines, and 188 purified monocyte samples and found 40–60% of these cis-rSNPs to be shared across cell types. We uncover a new class of cis-rSNPs, which disrupt footprint-derived de novo motifs that are predominantly bound by repressive factors and are implicated in disease susceptibility through overlaps with GWAS SNPs. Finally, we provide the proof-of-principle for a new approach for genome-wide functional validation of transcription factor–SNP interactions. By perturbing NFκB action in lymphoblasts, we identified 489 cis-regulated transcripts with altered AE after NFκB perturbation. Altogether, we perform a comprehensive analysis of cis-variation in four cell populations and provide new tools for the identification of functional variants associated to complex diseases.
    Molecular Systems Biology 10/2014; 10(10). DOI:10.15252/msb.20145114 · 10.87 Impact Factor
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