Prediction of IBD based on population history for fine gene mapping.
A novel multiple regression method (RM) is developed to predict identity-by-descent probabilities at a locus L (IBDL), among individuals without pedigree, given information on surrounding markers and population history. These IBDL probabilities are a function of the increase in linkage disequilibrium (LD) generated by drift in a homogeneous population over generations. Three parameters are sufficient to describe population history: effective population size (Ne), number of generations since foundation (T), and marker allele frequencies among founders (p). IBDL are used in a simulation study to map a quantitative trait locus (QTL) via variance component estimation. RM is compared to a coalescent method (CM) in terms of power and robustness of QTL detection. Differences between RM and CM are small but significant. For example, RM is more powerful than CM in dioecious populations, but not in monoecious populations. Moreover, RM is more robust than CM when marker phases are unknown or when there is complete LD among founders or Ne is wrong, and less robust when p is wrong. CM utilises all marker haplotype information, whereas RM utilises information contained in each individual marker and all possible marker pairs but not in higher order interactions. RM consists of a family of models encompassing four different population structures, and two ways of using marker information, which contrasts with the single model that must cater for all possible evolutionary scenarios in CM.
Hernández-Sánchez
Jules
J
Roslin Institute, Midlothian, EH25 9PS, Scotland, UK. Jules.Hernandez@bbsrc.ac.uk
Haley
Chris S
CS
Woolliams
John A
JA
D000483:Alleles; D002875:Chromosomes; D003198:Computer Simulation; D005787:Gene Frequency; D040961:Genetic Drift; D005819:Genetic Markers; D008957:Models, Genetic; D015233:Models, Statistical; D020161:Physical Chromosome Mapping; D011153:Population / Q000235:genetics*; D011336:Probability; D040641:Quantitative Trait Loci; D012044:Regression Analysis
D005819:Genetic Markers