title: Decomposing genomic variance using information from GWA, GWE and eQTL analysis.
abstract: A commonly used procedure in genome-wide association (GWA), genome-wide expression (GWE) and expression quantitative trait locus (eQTL) analyses is based on a bottom-up experimental approach that attempts to individually associate molecular variants with complex traits. Top-down modeling of the entire set of genomic data and partitioning of the overall variance into subcomponents may provide further insight into the genetic basis of complex traits. To test this approach, we performed a whole-genome variance components analysis and partitioned the genomic variance using information from GWA, GWE and eQTL analyses of growth-related traits in a mouse F2 population. We characterized the mouse trait genetic architecture by ordering single nucleotide polymorphisms (SNPs) based on their P-values and studying the areas under the curve (AUCs). The observed traits were found to have a genomic variance profile that differed significantly from that expected of a trait under an infinitesimal model. This situation was particularly true for both body weight and body fat, for which the AUCs were much higher compared with that of glucose. In addition, SNPs with a high degree of trait-specific regulatory potential (SNPs associated with subset of transcripts that significantly associated with a specific trait) explained a larger proportion of the genomic variance than did SNPs with high overall regulatory potential (SNPs associated with transcripts using traditional eQTL analysis). We introduced AUC measures of genomic variance profiles that can be used to quantify relative importance of SNPs as well as degree of deviation of a trait's inheritance from an infinitesimal model. The shape of the curve aids global understanding of traits: The steeper the left-hand side of the curve, the fewer the number of SNPs controlling most of the phenotypic variance.
authors.affiliation: Animal Science Department, Faculty of Agriculture, Tarbiat Modares University, PO Box 14115-336, Tehran, Iran.
authors.forename: A
authors.initials: A
authors.lastname: Ehsani
authors.affiliation: Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830, Tjele, Denmark.
authors.forename: L
authors.initials: L
authors.lastname: Janss
authors.affiliation: Department of Genetics, University of North Carolina at Chapel Hill, NC, 27599-7264, USA.
authors.forename: D
authors.initials: D
authors.lastname: Pomp
authors.affiliation: Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, DK-8830, Tjele, Denmark.
authors.forename: P
authors.initials: P
authors.lastname: Sørensen
mesh_terms: D050154:Adiposity / Q000235:genetics; D000818:Animals; D019540:Area Under Curve; D001499:Bayes Theorem; D001786:Blood Glucose / Q000032:analysis; D001835:Body Weight / Q000235:genetics; D015870:Gene Expression; D056726:Genetic Association Studies; D016014:Linear Models; D051379:Mice; D008813:Mice, Inbred ICR; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D059467:Transcriptome
chemical_list: D001786:Blood Glucose
keywords: Bayesian; GWAS; SNPs; area under the curve; blood glucose; body fat; body weight; decomposition; mouse