Large-scale in silico mapping of complex quantitative traits in inbred mice. PLoS One 2007 Jul 25;2(7):e651
Date
07/27/2007Pubmed ID
17653278Pubmed Central ID
PMC1920557DOI
10.1371/journal.pone.0000651Scopus ID
2-s2.0-38549083432 (requires institutional sign-in at Scopus site) 32 CitationsAbstract
Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with approximately 40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community.
Author List
Liu P, Vikis H, Lu Y, Wang D, You MMESH terms used to index this publication - Major topics in bold
ADAM ProteinsADAM12 Protein
Animals
Atherosclerosis
Cardiovascular Diseases
Chromosome Mapping
Disease Models, Animal
Genetic Association Studies
Genetic Linkage
Genetic Variation
Genome-Wide Association Study
Humans
Membrane Proteins
Mice
Mice, Inbred Strains
Models, Genetic
Neoplasms
Obesity
Phenotype
Quantitative Trait Loci
