On the efficiency of composite interval mapping in an outbred population.
Composite interval mapping (CIM) has been successfully applied to the detection of QTL in experimental animals and plants. However, practical analyses based on CIM have been reported mainly for populations derived from cross between inbred lines. There are few studies on QTL analyses with CIM in outbred populations. To evaluate the applicability of CIM to outbred populations is prerequisite for the fine mapping of QTL in industrial animals such as pig and chicken. Some markers are usually not fully informative in outbred populations. In application of CIM to outbred populations, the influence of inclusion of such uninformative markers used as covariates on the efficiency of CIM should be investigated. In this paper a least-squares method for CIM was formalized in an F(2) population derived by crossing two outbred lines. The efficiencies of CIM were evaluated for outbred populations in comparison with simple interval mapping (SIM) for several cases of marker informativeness using simulations. By incorporating markers linked to a tested position as well as those unlinked, CIM showed a higher efficiency to separate two linked QTL over SIM. The efficiency of dissection was enhanced as the marker informativeness was increased. The power of CIM to detect an isolated QTL was improved by excluding markers linked to a tested position from covariates and higher than SIM regardless of marker informativeness. In conclusion, CIM is a useful procedure for the analysis of QTL in outbred populations even under low marker informativeness.
Department of Genome Research, Laboratory of Animal Genome, National Institute of Agrobiological Sciences, Tsukuba, Ibaraki 305-0901, Japan. hayatk@affrc.go.jp
Takeshi
T
Hayashi
Takashi
T
Awata
Hiroshi
H
Yasue
D000818:Animals; D021461:Animals, Outbred Strains / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods; D019295:Computational Biology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005819:Genetic Markers; D008957:Models, Genetic; D040641:Quantitative Trait Loci
D005819:Genetic Markers