Population genomics of human gene expression

By Barbara E. Stranger, Alexandra C. Nica, M. S. Forrest, A. Dimas, C. Bird, C. Beazley, Catherine E. Ingle, M. Dunning, Paul Flicek, Daphne Koller and 4 more. Nature Genetics, 2007

1,231 citations recorded by the source corpus.

Genetic variation influences gene expression, and this variation in gene expression can be efficiently mapped to specific genomic regions and variants. Here we have used gene expression profiling of Epstein-Barr virus–transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with gene expression identified at least 1,348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of gene expression variation.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2fc98a13d0065b39f5943908c64970ee07d630e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Candidate Causal Regulatory Effects by Integration of Expression QTLs with Complex Trait Genetic Associations (2010, methodology): “This method [14] captures the vast majority of associations discovered with standard linear regression (LR) models, with the additional advantage that it’s not affected by outliers and hence has more power and allows direct comparison of nominal P-values.”
Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics (2011, background): “Recently, GWASs have been conducted on mRNA levels, themselves quantitative traits, in expression QTL (eQTL) mapping studies (see Table 1) (e.g., Dixon et al. 2007; Goring et al. 2007; Stranger et al. 2007a,b; Dimas et al. 2009).”
CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription. (2015, background): “Separate expression quantitative trait loci (eQTL) data also suggested that this allele alters the expression level of GSDMB and ORMDL3 (Montgomery et al., 2010; Stranger et al., 2007).”
Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses (2012, neutral): “Our understanding of the genetic basis of gene expression has been developed by studying species such as yeast 3–5 , worms 6 , mice 7,8 and humans 9–12 .”
Estimating the size of the human interactome (2008, background): “In addition to alternative splice variants (4), posttranslational processes (5), and other (e.g., genetic) factors influencing gene expression (6, 7), the structure of interactome is one of the crucial factors underlying the complexity of biological organisms.”
Protein networks in disease. (2008, background): “data have been generated in humans, including a number of eQTL studies (Dixon et al. 2007; Goring et al. 2007; Stranger et al. 2007).”