Mapping the Genetic Architecture of Gene Expression in Human Liver
Author Information
Author(s): Schadt Eric E, Molony Cliona, Chudin Eugene, Hao Ke, Yang Xia, Lum Pek Y, Kasarskis Andrew, Zhang Bin, Wang Susanna, Suver Christine, Zhu Jun, Millstein Joshua, Sieberts Solveig, Lamb John, GuhaThakurta Debraj, Derry Jonathan, Storey John D, Avila-Campillo Iliana, Kruger Mark J, Johnson Jason M, Rohl Carol A, van Nas Atila, Mehrabian Margarete, Drake Thomas A, Lusis Aldons J, Smith Ryan C, Guengerich F. Peter, Strom Stephen C, Schuetz Erin, Rushmore Thomas H, Ulrich Roger
Primary Institution: Rosetta Inpharmatics, Seattle, Washington, United States of America
Hypothesis
Identifying molecular phenotypes that vary in response to changes in DNA that also associate with changes in disease traits can provide functional information to identify and validate susceptibility genes.
Conclusion
The study identified over 6,000 associations between SNP genotypes and liver gene expression traits, providing insights into the genetic architecture of gene expression and its implications for common human diseases.
Supporting Evidence
- The study profiled more than 39,000 transcripts in human liver samples.
- Identified over 6,000 associations between SNPs and gene expression traits.
- Highlighted RPS26 as a candidate susceptibility gene for type 1 diabetes.
- Identified SORT1 and CELSR2 as candidate susceptibility genes for coronary artery disease.
- Demonstrated the utility of integrating genotypic and expression data.
- Provided functional support for candidate susceptibility genes identified in GWAS.
- Showed significant overlap of eQTLs between liver, blood, and adipose tissues.
- Utilized a large sample size to enhance statistical power in genetic association testing.
Takeaway
Scientists looked at how genes in the liver change when DNA changes, helping to find out which genes might cause diseases like diabetes and heart problems.
Methodology
The study profiled gene expression in 427 human liver samples and genotyped 782,476 SNPs to assess the relationship between genetic variants and gene expression.
Potential Biases
Potential biases due to missing demographic data and the reliance on specific tissue types.
Limitations
Demographic data varied across centers and was missing in many cases, which may affect the generalizability of the findings.
Participant Demographics
The cohort comprised 427 Caucasian subjects.
Statistical Information
P-Value
p<10−65
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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