Biomedical subjects

Anna K Sonesson

Publications and source records attributed to Anna K Sonesson.

5 recordsLinked to original sources

PubMed2004

Genotype-assisted optimum contribution selection to maximize selection response over a specified time period.

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.

Data Interpretation, Statistical↗

PubMed2002

The use of frozen semen to minimize inbreeding in small populations.

In this study, we compared the average coancestry and inbreeding levels for two genetic conservation schemes in which frozen semen from a gene bank is used to reduce the inbreeding in a live population. For a simple scheme in which only semen of generation-0 (G0) sires is used, the level of inbreeding asymptotes to 1/(2N), where N is the number of newborn sires in the base generation and rate of inbreeding goes to zero. However, when only sires of G0 are selected, all genes will eventually descend from the founder sires and all genes from the founder dams are lost. We propose an alternative scheme in which N sires from generation 1 (G1), as well as the N sires from G0, have semen conserved, and the semen of G0 and G1 sires is used for dams of odd and even generation numbers, respectively. With this scheme, the level of inbreeding asymptotes to 1/(3N) and the genes of founder dams are also conserved, because 50% of the genes of sires of G1 are derived from the founder dams. A computer simulation study shows that this is the optimum design to minimize inbreeding, even if semen from later generations is available.

Animals↗

PubMedDate not supplied

Non-random mating for selection with restricted rates of inbreeding and 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 DeltaF is restricted. For schemes with DeltaF 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.

Age Factors↗

PubMedDate not supplied

Selection against genetic defects in conservation schemes while controlling inbreeding.

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 (DeltaF) 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 DeltaF needed more generations to halve the frequency of the disease alleles or the fraction of diseased animals. Optimum contribution selection maintained DeltaF 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 DeltaF is very important.

Animals↗

PubMedDate not supplied

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

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, 1,000, 5,000 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 (DeltaF) constrained to 0.005 or 0.01 per generation. If the constraint on DeltaF could not be held, a second batch of fish was genotyped etc. Compared with the genotyping of all selection candidates (1000, 5,000 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 1,000 fish instead of 100 for the larger schemes of 5,000 and 10,000 candidates. The accuracy of breeding values was similar for all batch sizes (approximately 0.30), but higher (approximately 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.

Animals↗