Research article
Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data
1 Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Tjele, DK-8830, Denmark
2 School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7264, USA
3 Trans Ova Genetics, Sioux Center, Sioux, IA, 51250, USA
BMC Genomics 2012, 13:456 doi:10.1186/1471-2164-13-456
Published: 5 September 2012Additional files
Additional file 1:
Figure S3. Distribution of phenotypes of traits Body Weight including 440 animals, Feed Intake and Feed Efficiency including 337 animals each.
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Additional file 2:
Figure S1. Decomposition of the proportion of variance explained by SNPs at the level of chromosomes and individual SNPs in two models: the independent model SNP and the conditional model SNP+GEX for Feed Intake. (a) explained variances from SNPs in SNP model (black) and SNP+GEX model (white) in each chromosome. (b) explained variance by individual SNPs in SNP model and (c) SNP+GEX model.
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Additional file 3:
Figure S2. Decomposition of the proportion of variance explained by SNPs at the level of chromosomes and individual SNPs in two models: the independent model SNP and the conditional model SNP+GEX for Feed Efficiency. (a) explained variances from SNPs in SNP model (black) and SNP+GEX model (white) in each chromosome. (b) explained variance by individual SNPs in SNP model and (c) SNP+GEX model.
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Additional file 4:
Figure S4. Comparison of predicted breeding values versus phenotypes in the models using pedigree information only (PED), SNPs information only (SNP) and gene expression information only (GEX) for three traits Body Weight, Feed Intake and Feed Efficiency according to correlation shown in Table 3.
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