| Literature DB >> 25236451 |
Chaozhi Zheng1, Martin P Boer2, Fred A van Eeuwijk2.
Abstract
The next generation of QTL (quantitative trait loci) mapping populations have been designed with multiple founders, where one to a number of generations of intercrossing are introduced prior to the inbreeding phase to increase accumulated recombinations and thus mapping resolution. Examples of such populations are Collaborative Cross (CC) in mice and Multiparent Advanced Generation Inter-Cross (MAGIC) lines in Arabidopsis. The genomes of the produced inbred lines are fine-grained random mosaics of the founder genomes. In this article, we present a novel framework for modeling ancestral origin processes along two homologous autosomal chromosomes from mapping populations, which is a major component in the reconstruction of the ancestral origins of each line for QTL mapping. We construct a general continuous time Markov model for ancestral origin processes, where the rate matrix is deduced from the expected densities of various types of junctions (recombination breakpoints). The model can be applied to monoecious populations with or without self-fertilizations and to dioecious populations with two separate sexes. The analytic expressions for map expansions and expected junction densities are obtained for mapping populations that have stage-wise constant mating schemes, such as CC and MAGIC. Our studies on the breeding design of MAGIC populations show that the intercross mating schemes do not matter much for large population size and that the overall expected junction density, and thus map resolution, are approximately proportional to the inverse of the number of founders.Entities:
Keywords: Arabidopsis multiparent recombinant inbred lines (AMPRIL); Collaborative Cross (CC); MPP; Multiparent Advanced Generation Inter-Cross (MAGIC); Multiparental populations; identity by descent (IBD); multiparent advanced generation intercross (MAGIC); quantitative trait loci (QTL) mapping resolution
Mesh:
Year: 2014 PMID: 25236451 PMCID: PMC4174956 DOI: 10.1534/genetics.114.163006
Source DB: PubMed Journal: Genetics ISSN: 0016-6731 Impact factor: 4.562