Dynamic selection for maximizing response with constrained inbreeding in schemes with overlapping generations

-Minimum coancestry mating with a maximum of one offspring per mating pair (MC1) is compared with random mating schemes for populations with overlapping generations. Optimum contribution selection is used, whereby ∆F is restricted. For schemes with ∆F restricted to 0.25% per year, 256 animals born per year and heritability of 0.25, genetic gain increased with 18% compared with random mating. The effect of MC1 on genetic gain decreased for larger schemes and schemes with a less stringent restriction on inbreeding. Breeding schemes hardly changed when omitting the iteration on the generation interval to find an optimum distribution of parents over age-classes, which saves computer time, but inbreeding and genetic merit fluctuated more before the schemes had reached a steady-state. When bulls were progeny tested, these progeny tested bulls were selected instead of the young bulls, which led to increased generation intervals, increased selection intensity of bulls and increased genetic gain (35% compared to a scheme without progeny testing for random mating). The effect of MC1 decreased for schemes with progeny testing. MC1 mating increased genetic gain from 11-18% for overlapping and 1-4% for discrete generations, when comparing schemes with similar genetic gain and size. mating / overlapping generations / selection / rate of inbreeding / genetic response / optimum contribution

Section: Practical Schemesmentioning

confidence: 67%

“…it adapts to the current situation and makes the contribution of families more equal. This makes optimum contribution selection more efficient on maximizing genetic gain with a restriction on inbreeding than a static selection tool [7,12]. For practical schemes, the actual contribution of each animal can only be aimed at.…”

Section: Practical Schemesmentioning

confidence: 99%

Non-random mating for selection with restricted rates of inbreeding and overlapping generations

Sonesson¹,

Meuwissen²

2002

“…[10,11,18], we assumed no total control of the selection scheme. Males were sampled, as in previous studies, according to their optimum contribution, but all females were selected.…”

Section: Genetic Evaluation Methodsmentioning

confidence: 99%

“…Increased inbreeding could for instance result from direct selection for a nondisease allele, detected by DNA genotyping, when the non-disease alleles come from a limited number of ancestral families. We will use a selection method that maximises genetic response with a restriction on the rate of inbreeding [10,11,18,20]. The optimum contributions, which are translated to the optimum number of progeny will be calculated for each male selection candidate, assuming that female selection is at random.…”

Section: Introductionmentioning

confidence: 99%

Selection against genetic defects in conservation schemes while controlling inbreeding

Sonesson

Janss²,

Meuwissen³

2003

-We studied different genetic models and evaluation systems to select against a genetic disease with additive, recessive or polygenic inheritance in genetic conservation schemes. When using optimum contribution selection with a restriction on the rate of inbreeding (∆F) to select against a disease allele, selection directly on DNA-genotypes is, as expected, the most efficient strategy. Selection for BLUP or segregation analysis breeding value estimates both need 1-2 generations more to halve the frequency of the disease allele, while these methods do not require knowledge of the disease mutation at the DNA level. BLUP and segregation analysis methods were equally efficient when selecting against a disease with single gene or complex polygene inheritance, i.e. knowledge about the mode of inheritance of the disease was not needed for efficient selection against the disease. Smaller schemes or schemes with a more stringent restriction on ∆F needed more generations to halve the frequency of the disease alleles or the fraction of diseased animals. Optimum contribution selection maintained ∆F at its predefined level, even when selection of females was at random. It is argued that in the investigated small conservation schemes with selection against a genetic defect, control of ∆F is very important. genetic defects / selection / inbreeding / conservation

“…These schemes were, however, not compared at the same rate of inbreeding. Optimum contribution selection is a group selection method that maximises genetic gain with a restriction on ∆F for schemes with both discrete [9,17] and overlapping [10,18] generations. It is dynamic, such that it adapts to current selection candidates, and can therefore correct skewnesses in contribution of families over generations.…”

Section: Introductionmentioning

confidence: 99%

A combination of walk-back and optimum contribution selection in fish: a simulation study

Sonesson

2005

-The aim of this paper was to study the performance of a novel fish breeding scheme, which is a combination of walk-back and optimum contribution selection using stochastic simulation. In this walk-back selection scheme, batches of different sizes (50, 100, 1000, 5000 and 10 000) with the phenotypically superior fish from one tank with mixed families were genotyped to set up the pedigree. BLUP estimated breeding values were calculated. The optimum contribution selection method was used with the rate of inbreeding (∆F) constrained to 0.005 or 0.01 per generation. If the constraint on ∆F could not be held, a second batch of fish was genotyped etc. Compared with the genotyping of all selection candidates (1000, 5000 or 10 000), the use of batches saves genotyping costs. The results show that two batches of 50 fish were often necessary. With a batch size of 100, genetic level was 76-92% of the genetic level achieved for schemes with all fish being genotyped and thus candidates for the optimum contribution selection step. More parents were selected for schemes with larger batches, resulting in a higher genetic gain, especially when all selection candidates were genotyped. There was little extra genetic gain in genotyping of 1000 fish instead of 100 for the larger schemes of 5000 and 10 000 candidates. The accuracy of breeding values was similar for all batch sizes (∼0.30), but higher (∼0.5) when all candidates were included. Since only the phenotypically most superior fish were genotyped, BLUP-EBV were biased. Compared with genotyping of all selection candidates, the use of batches saves genotyping costs, while simultaneously maintaining high genetic gains. fish breeding / selection / parentage testing / walk-back selection / genetic markers