Hematological parameters, including red and white blood cell counts and hemoglobin concentration, are widely used clinical indicators of health and disease. These traits are tightly regulated in healthy individuals and are under genetic control. Mutations in key genes that affect hematological parameters have important phenotypic consequences, including multiple variants that affect susceptibility to malarial disease. However, most variation in hematological traits is continuous and is presumably influenced by multiple loci and variants with small phenotypic effects. We used a newly developed mouse resource population, the Collaborative Cross (CC), to identify genetic determinants of hematological parameters. We surveyed the eight founder strains of the CC and performed a mapping study using 131 incipient lines of the CC. Genome scans identified quantitative trait loci for several hematological parameters.
At 10 to 14 weeks of age, blood was drawn from the retro-orbital sinus and analyzed for hematological parameters using a HEMAVET Multispecies Hematology analyzer. The hematology panel included RBCs (no./ml), mean (red) cell volume (MCV, fL), jHb (g/dL), red cell distribution width (RDW, %), platelets (PLT, no./ml), mean platelet volume (fL), WBCs (no./ml), neutrophils (NE, no./ml), lymphocytes (LY, no./ml), monocytes (MO, no./ml), basophils (no./ml), and eosinophils (no./ml). The number of eosinophils and basophils counted were very small and, therefore, were not used for mapping.
We genotyped each mouse at the University of North Carolina–Chapel Hill, using one of two Affymetrix SNP arrays (A or B) that were produced in development of the Mouse Diversity array (Yang et al. 2009). After removing uninformative and poorly performing SNPs, these arrays contained 181,752 (A-array) and 180,976 (B-array) SNP assays, and the set of SNPs on each array did not overlap. Most mice (83%) were genotyped on the B-array and the remaining were genotyped on the A-array. These training arrays were annotated to NCBI Build 36 of the mouse genome, but we mapped QTL boundaries to Build 37 positions to integrate with other resources. We report Build 37 positions in our results. We estimated the most probable ancestor for each SNP in each mouse using the GAIN algorithm (Liu et al. 2010) and reconstructed founder haplotypes on the basis of these results. We then merged the nonoverlapping SNP datasets from arrays A and B by imputing unobserved genotypes on the basis of inferred founder haplotype
- Folder of genotypes: TableS2.zip
We evaluated the relationship between hemoglobin b gene (Hbb) expression and MCV using quantitative PCR. We examined gene expression data in two ways: (1) we calculated mean expression using the deltaCt method (relative to Rps29), and (2) we built a linear regression model for MCV including terms for Hbb gene expression (total or Hbb-b1) expressed as the deltaCt, and Hbb s vs. d genotype.