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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Available from: Hreinn Stefansson, Oct 04, 2015
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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 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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    • "Previous eQTL studies in genetically diverse populations suggest that the most significant eQTL tend to be local (Rockman and Kruglyak 2006; Pickrell et al. 2010; Aylor et al. 2011; Lappalainen et al. 2013). Gene prioritization methods are becoming more important in the genome-wide association studies (GWAS) era (Hou and Zhao 2013), and genes with local eQTL are promising candidates for underlying disease-associated regions in human GWAS studies (Knight 2005; Chen et al. 2008; Emilsson et al. 2008; Musunuru et al. 2010; Hou and Zhao 2013; Li et al. 2013). An eQTL may arise from any of several biological mechanisms, including rate of transcription, rate of degredation , or processing of RNA intermediates. "
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    ABSTRACT: Massively parallel RNA sequencing (RNA-seq) has yielded a wealth of new insights into transcriptional regulation. A first step in the analysis of RNA-seq data is the alignment of short sequence reads to a common reference genome or transcriptome. Genetic variants that distinguish individual genomes from the reference sequence can cause reads to be misaligned, resulting in biased estimates of transcript abundance. Fine-tuning of read alignment algorithms does not correct this problem. We have developed Seqnature software to construct individualized diploid genomes and transcriptomes for multiparent populations and have implemented a complete analysis pipeline that incorporates other existing software tools. We demonstrate in simulated and real data sets that alignment to individualized transcriptomes increases read mapping accuracy, improves estimation of transcript abundance, and enables the direct estimation of allele-specific expression. Moreover, when applied to expression QTL mapping we find that our individualized alignment strategy corrects false-positive linkage signals and unmasks hidden associations. We recommend the use of individualized diploid genomes over reference sequence alignment for all applications of high-throughput sequencing technology in genetically diverse populations.
    Genetics 09/2014; 198(1):59-73. DOI:10.1534/genetics.114.165886 · 5.96 Impact Factor
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