title: Genotype-assisted optimum contribution selection to maximize selection response over a specified time period.
abstract: Genotype-assisted selection (GAS), i.e. selection for an identified quantitative trait locus (QTL) and polygenic background genes, has been shown to increase short-term genetic gain but may reduce long-term genetic gains. In order to avoid this reduction of long-term gain, multi-generation optimization of truncation selection schemes is needed. This paper presents a multi-generation optimization of optimum contribution (OC) selection with selection on an identified QTL. This genotype-assisted optimum contribution (GAOC) selection method assumes that the optimum selection differential at the QTL is constant over the time horizon, and achieves this by controlling the increase of the frequency of the positive QTL allele. Implementation was straightforward by an additional linear restriction in the OC algorithm. GAOC achieved 35.2%, 2.3% and 1.1%, respectively, more cumulative genetic gain than OC selection (ignoring the QTL) using time horizons of 5, 10 and 15 generations. When one-generation optimization of GAS was used instead of multi-generation optimization, these figures were 2.8%, 3.1% and 3.2%, respectively. Simulated annealing was used to optimize the increases of the frequency of the positive QTL allele in order to test the optimality of GAOC. This latter resulted in genetic gains that were always within 0.4% of those of GAOC. In practice, short-term genetic gains are also important, which makes one-generation optimization of genetic gain closer to optimal.
authors.affiliation: Centre for Integrative Genetics, Institute of Animal Science, Agricultural University of Norway. theo.meuwissen@iha.nlh.no
authors.forename: Theo H E
authors.initials: TH
authors.lastname: Meuwisseni
authors.forename: Anna K
authors.initials: AK
authors.lastname: Sonesson
mesh_terms: D003627:Data Interpretation, Statistical; D005828:Genetics, Population; D005838:Genotype; D008957:Models, Genetic; D009010:Monte Carlo Method; D040641:Quantitative Trait Loci; D012641:Selection, Genetic