Genetic analysis of hematological parameters in incipient lines of the collaborative cross

• Published in: G3 : genes - genomes - genetics Vol. 2; no. 2; pp. 157 - 165
• Main Authors: Kelada, Samir N P, Aylor, David L, Peck, Bailey C E, Ryan, Joseph F, Tavarez, Urraca, Buus, Ryan J, Miller, Darla R, Chesler, Elissa J, Threadgill, David W, Churchill, Gary A, Pardo-Manuel de Villena, Fernando, Collins, Francis S
• Format: Journal Article
• Language: English
• Published: United States Genetics Society of America 01.02.2012
• Subjects: BASIC BIOLOGICAL SCIENCES; complex traits; hematology; hemoglobin b; mean red cell volume; mouse collaborative cross; mouse genetic resource; Mouse Genetic Resources; mouse genetics; QTL; shared ancestry; Mouse Genetic Resource; Mouse Collaborative Cross; hematology; QTL; complex traits; mouse genetics; mean red cell volume; shared ancestry; hemoglobin β
• ISSN: 2160-1836; 2160-1836
• DOI: 10.1534/g3.111.001776

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, including mean red cell volume (Chr 7 and Chr 14), white blood cell count (Chr 18), percent neutrophils/lymphocytes (Chr 11), and monocyte number (Chr 1). We used evolutionary principles and unique bioinformatics resources to reduce the size of candidate intervals and to view functional variation in the context of phylogeny. Many quantitative trait loci regions could be narrowed sufficiently to identify a small number of promising candidate genes. This approach not only expands our knowledge about hematological traits but also demonstrates the unique ability of the CC to elucidate the genetic architecture of complex traits.