The Experts below are selected from a list of 12 Experts worldwide ranked by ideXlab platform
Belen Jimenezmena - One of the best experts on this subject based on the ideXlab platform.
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heterogeneity in effective Population size and its implications in conservation genetics and animal breeding
Conservation Genetics Resources, 2016Co-Authors: Belen Jimenezmena, Thomas BataillonAbstract:Effective Population size (Ne) is defined as the size of an Idealized Population undergoing the same rate of genetic drift as the Population under study. It is a central concept in Population genetics and used extensively in the design and monitoring of conservation and breeding programs. It is most often assumed that genetic drift effects are homogeneous across the genome and thus, so is Ne. However, theory predicts that various processes can modify rates of genetic drift experienced by a locus in the genome. In particular, selection affecting linked neutral sites and rates of recombination can modulate the intensity of genetic drift throughout the genome. The increasing availability of sequence data has made it possible to test whether a single Ne can account for the evolution of the genome. In this article, we discuss reasons why a heterogeneous Ne can be expected theoretically and review what evidence is available from empirical studies. We finish by discussing potential implications for conservation and breeding practices. The evidence suggests that heterogeneity in Ne can be just as important as heterogeneity in mutation rates in determining levels of genetic diversity throughout the genome.
F Goyache - One of the best experts on this subject based on the ideXlab platform.
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improving the estimation of realized effective Population sizes in farm animals
Journal of Animal Breeding and Genetics, 2009Co-Authors: J P Gutierrez, I Cervantes, F GoyacheAbstract:Computation of inbreeding rate (DeltaF) must consider that inbreeding is delayed with one generation with respect to the Idealized Population when addressed using individual inbreeding coefficients. The expression relating inbreeding in generation t with inbreeding rate F(t) = 1 - (1-DeltaF)(t) should be more correctly written in real animal Populations as F(t) = 1 - (1-DeltaF)(t-1), as changes in allele frequencies occur in the equivalent co-ancestries in the previous generation. This simple approach is tested on simulated and real pedigrees thus demonstrating that: (i) the adjusted individual increase in inbreeding becomes stable in Populations under random mating while the unadjusted parameter does not; (ii) regression of the unadjusted parameter over generations in pedigrees under random mating is highly significant while after correction it is not significant; and (iii) the variance of the adjusted parameter is reduced with the generations.
J P Gutierrez - One of the best experts on this subject based on the ideXlab platform.
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improving the estimation of realized effective Population sizes in farm animals
Journal of Animal Breeding and Genetics, 2009Co-Authors: J P Gutierrez, I Cervantes, F GoyacheAbstract:Computation of inbreeding rate (DeltaF) must consider that inbreeding is delayed with one generation with respect to the Idealized Population when addressed using individual inbreeding coefficients. The expression relating inbreeding in generation t with inbreeding rate F(t) = 1 - (1-DeltaF)(t) should be more correctly written in real animal Populations as F(t) = 1 - (1-DeltaF)(t-1), as changes in allele frequencies occur in the equivalent co-ancestries in the previous generation. This simple approach is tested on simulated and real pedigrees thus demonstrating that: (i) the adjusted individual increase in inbreeding becomes stable in Populations under random mating while the unadjusted parameter does not; (ii) regression of the unadjusted parameter over generations in pedigrees under random mating is highly significant while after correction it is not significant; and (iii) the variance of the adjusted parameter is reduced with the generations.
Thomas Bataillon - One of the best experts on this subject based on the ideXlab platform.
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heterogeneity in effective Population size and its implications in conservation genetics and animal breeding
Conservation Genetics Resources, 2016Co-Authors: Belen Jimenezmena, Thomas BataillonAbstract:Effective Population size (Ne) is defined as the size of an Idealized Population undergoing the same rate of genetic drift as the Population under study. It is a central concept in Population genetics and used extensively in the design and monitoring of conservation and breeding programs. It is most often assumed that genetic drift effects are homogeneous across the genome and thus, so is Ne. However, theory predicts that various processes can modify rates of genetic drift experienced by a locus in the genome. In particular, selection affecting linked neutral sites and rates of recombination can modulate the intensity of genetic drift throughout the genome. The increasing availability of sequence data has made it possible to test whether a single Ne can account for the evolution of the genome. In this article, we discuss reasons why a heterogeneous Ne can be expected theoretically and review what evidence is available from empirical studies. We finish by discussing potential implications for conservation and breeding practices. The evidence suggests that heterogeneity in Ne can be just as important as heterogeneity in mutation rates in determining levels of genetic diversity throughout the genome.
I Cervantes - One of the best experts on this subject based on the ideXlab platform.
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improving the estimation of realized effective Population sizes in farm animals
Journal of Animal Breeding and Genetics, 2009Co-Authors: J P Gutierrez, I Cervantes, F GoyacheAbstract:Computation of inbreeding rate (DeltaF) must consider that inbreeding is delayed with one generation with respect to the Idealized Population when addressed using individual inbreeding coefficients. The expression relating inbreeding in generation t with inbreeding rate F(t) = 1 - (1-DeltaF)(t) should be more correctly written in real animal Populations as F(t) = 1 - (1-DeltaF)(t-1), as changes in allele frequencies occur in the equivalent co-ancestries in the previous generation. This simple approach is tested on simulated and real pedigrees thus demonstrating that: (i) the adjusted individual increase in inbreeding becomes stable in Populations under random mating while the unadjusted parameter does not; (ii) regression of the unadjusted parameter over generations in pedigrees under random mating is highly significant while after correction it is not significant; and (iii) the variance of the adjusted parameter is reduced with the generations.
