title: Marker-based estimation of the coefficient of coancestry in hybrid breeding programmes.
abstract: Molecular markers allow to estimate the pairwise relatedness between the members of a breeding pool when their selection history is no longer available or has become too complex for a classical pedigree analysis. The field of population genetics has several estimation procedures at its disposal, but when the genotyped individuals are highly selected inbred lines, their application is not warranted as the theoretical assumptions on which these estimators were built, usually linkage equilibrium between marker loci or even Hardy-Weinberg equilibrium, are not met. An alternative approach requires the availability of a genotyped reference set of inbred lines, which allows to correct the observed marker similarities for their inherent upward bias when used as a coancestry measure. However, this approach does not guarantee that the resulting coancestry matrix is at least positive semi-definite (psd), a necessary condition for its use as a covariance matrix. In this paper we present the weighted alikeness in state (WAIS) estimator. This marker-based coancestry estimator is compared to several other commonly applied relatedness estimators under realistic hybrid breeding conditions in a number of simulations. We also fit a linear mixed model to phenotypical data from a commercial maize breeding programme and compare the likelihood of the different variance structures. WAIS is shown to be psd which makes it suitable for modelling the covariance between genetic components in linear mixed models involved in breeding value estimation or association studies. Results indicate that it generally produces a low root mean squared error under different breeding circumstances and provides a fit to the data that is comparable to that of several other marker-based alternatives. Recommendations for each of the examined coancestry measures are provided.
authors.affiliation: Department of Biosciences and Landscape Architecture, University College Ghent, Voskenslaan 270, 9000, Ghent, Belgium. Steven.Maenhout@hogent.be
authors.forename: S
authors.initials: S
authors.lastname: Maenhout
authors.forename: B
authors.initials: B
authors.lastname: De Baets
authors.forename: G
authors.initials: G
authors.lastname: Haesaert
mesh_terms: D000465:Algorithms; D001947:Breeding / Q000379:methods*; D003198:Computer Simulation; D005819:Genetic Markers; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods*; D005838:Genotype; D007178:Inbreeding; D008433:Mathematics; D008957:Models, Genetic; D012641:Selection, Genetic; D003313:Zea mays / Q000235:genetics*
chemical_list: D005819:Genetic Markers