Minimization of rate of inbreeding for small populations with overlapping generations

Sonesson, Anna K.; Meuwissen, Theo H. E.

doi:10.1017/s0016672301005079

Cited by 60 publications

(70 citation statements)

References 17 publications

(16 reference statements)

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“…Simianer, 2005). Therefore, it would be indispensable to apply breeding strategies such as optimum contribution selection (Jean-Jacques et al, 2004;Hinrichs et al, 2006), minimizing inbreeding and maximizing genetic diversity (Sonesson and Meuwissen, 2001;Ferná ndez et al, 2003;Haile-Mariam et al, 2007) in each Guernsey population, with an urgent attention needed to maintain genetic diversity of the GCN and GUS populations.…”

Section: Discussionmentioning

confidence: 99%

Genetic diversity of Guernsey population using pedigree data and gene-dropping simulations

Melka

Sargolzaei

Miglior

et al. 2013

Animal

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The objectives of this study were to analyze the trend of within-breed genetic diversity and identify major causes leading to loss of genetic diversity in Guernsey breed in three countries. Pedigree files of Canadian (GCN), South African (GSA) and American (GUS) Guernsey populations containing 130 927, 18 593 and 1 851 624 records, respectively, were analyzed. Several parameters derived from the in-depth pedigree analyses were used to measure trends and current levels of genetic diversity. Pedigree completeness index of GCN, GSA and GUS populations, in the most recent year (2007), was 97%, 74% and 79%, respectively, considering four generations back in the analysis. The rate of inbreeding in each population was 0.19%, 0.16% and 0.17% between 2002 and 2007, respectively. For the same period, the estimated effective population size for GCN, GSA and GUS was 46, 57 and 46, respectively. The estimated percentage of genetic diversity lost within each population over the last four decades was 8%, 3% and 5%, respectively. The relative proportion of genetic diversity lost due to random genetic drift in the three populations was 93%, 91% and 86%, respectively. In conclusion, the results suggested that GCN and GUS have lost more genetic diversity than GSA over the past four decades, and this loss is gaining momentum due to increasing rates of inbreeding. Therefore, strategies such as optimum contribution selection and migration of genetic material are advised to increase effective population size, particularly in GCN and GUS.

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Section: Discussionmentioning

confidence: 99%

Genetic diversity of Guernsey population using pedigree data and gene-dropping simulations

Melka

Sargolzaei

Miglior

et al. 2013

Animal

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“…What happens computationally is that the Lagrangian multiplier, λ 0 , becomes zero when all animals have the same EBV and the equations for the optimal contributions cannot be solved (since they require dividing by λ 0 ). If this was the case, the simulation program called the minimisation routine presented in [22], which was modified here to handle discrete generations.…”

Section: Optimum Contribution Selection Methods (Oc)mentioning

confidence: 99%

Selection against genetic defects in conservation schemes while controlling inbreeding

Sonesson

Janss²,

Meuwissen³

2003

Genet. Sel. Evol.

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-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

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“…Irrespective of the size of a breeding program, GS will have less buildup of inbreeding over time because the strategy can explain some of the variation due to Mendelian sampling hence minimize co-selection of siblings (Daetwyler et al, 2007). In addition, accumulation of inbreeding can be minimized by increasing the nucleus size and minimizing co-ancestry in the parents (Meuwissen, 1997;Sonesson and Meuwissen, 2001). Increasing the nucleus size with GS is relatively easier than with PT because an increase in nucleus size for PT must be accompanied by an increase in the size of the CRC population.…”

Section: Practical Considerationsmentioning

confidence: 99%

Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries

Kariuki

Brascamp

Komen

et al. 2017

Journal of Dairy Science

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In developing countries minimal and erratic performance and pedigree recording impede implementation of large-sized breeding programs. Small-sized nucleus programs offer an alternative but rely on their economic performance for their viability. We investigated the economic performance of 2 alternative small-sized dairy nucleus programs [i.e., progeny testing (PT) and genomic selection (GS)] over a 20-yr investment period. The nucleus was made up of 453 male and 360 female animals distributed in 8 non-overlapping age classes. Each year 10 active sires and 100 elite dams were selected. Populations of commercial recorded cows (CRC) of sizes 12,592 and 25,184 were used to produce test daughters in PT or to create a reference population in GS, respectively. Economic performance was defined as gross margins, calculated as discounted revenues minus discounted costs following a single generation of selection. Revenues were calculated as cumulative discounted expressions (CDE, kg) × 0.32 (€/kg of milk) × 100,000 (size commercial population). Genetic superiorities, deterministically simulated using pseudo-BLUP index and CDE, were determined using gene flow. Costs were for one generation of selection. Results show that GS schemes had higher cumulated genetic gain in the commercial cow population and higher gross margins compared with PT schemes. Gross margins were between 3.2-and 5.2-fold higher for GS, depending on size of the CRC population. The increase in gross margin was mostly due to a decreased generation interval and lower running costs in GS schemes. In PT schemes many bulls are culled before selection. We therefore also compared 2 schemes in which semen was stored instead of keeping live bulls. As expected, semen storage resulted in an increase in gross margins in PT schemes, but gross margins remained lower than those of GS schemes. We conclude that implementation of small-sized GS breeding schemes can be economically viable for developing countries.

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Minimization of rate of inbreeding for small populations with overlapping generations

Cited by 60 publications

References 17 publications

Genetic diversity of Guernsey population using pedigree data and gene-dropping simulations

Genetic diversity of Guernsey population using pedigree data and gene-dropping simulations

Selection against genetic defects in conservation schemes while controlling inbreeding

Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries

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