The Experts below are selected from a list of 10233 Experts worldwide ranked by ideXlab platform
M P L Calus - One of the best experts on this subject based on the ideXlab platform.
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breeding top genotypes and accelerating response to recurrent selection by selecting parents with greater gametic variance
Genetics, 2020Co-Authors: P Bijma, Yvonne C J Wientjes, M P L CalusAbstract:Because of variation in linkage phase and heterozygosity among individuals, some individuals produce genetically more variable gametes than others. With the availability of genomic EBVs (GEBVs) or estimates of SNP-effects together with phased genotypes, differences in gametic variability can be quantified by simulating a set of virtual gametes of each selection candidate. Previous results in dairy cattle show that gametic variance can be large. Here, we show that breeders can increase the probability of breeding a top-ranking genotype and response to recurrent selection by selecting parents that produce more variable gametes, using the index I = G E B V + 2 x p S D g G E B V , where x p is the standardized normal Truncation Point belonging to selected proportion p, and SDgGEBV is the SD of the GEBV of an individual’s gametes. Benefits of the index were considerably larger in an ongoing selection program with equilibrium genetic parameters than in an initially unselected population. Superiority of the index over selection on GEBV increased strongly with the magnitude of the S D g G E B V , indicating that benefits of the index may vary considerably among populations. Compared to selection on ordinary GEBV, the probability of breeding a top-ranking individual can be increased by ∼36%, and response to selection by ∼3.6% when selection is strong (P = 0.001) based on values for the Holstein-Friesian dairy cattle population. Two-stage selection, with a preselection on GEBV and a final selection on the index, considerably reduced computational requirements with little loss of benefits. Response to multiple generations of selection and inheritance of the SDgEBV require further study.
P Bijma - One of the best experts on this subject based on the ideXlab platform.
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breeding top genotypes and accelerating response to recurrent selection by selecting parents with greater gametic variance
Genetics, 2020Co-Authors: P Bijma, Yvonne C J Wientjes, M P L CalusAbstract:Because of variation in linkage phase and heterozygosity among individuals, some individuals produce genetically more variable gametes than others. With the availability of genomic EBVs (GEBVs) or estimates of SNP-effects together with phased genotypes, differences in gametic variability can be quantified by simulating a set of virtual gametes of each selection candidate. Previous results in dairy cattle show that gametic variance can be large. Here, we show that breeders can increase the probability of breeding a top-ranking genotype and response to recurrent selection by selecting parents that produce more variable gametes, using the index I = G E B V + 2 x p S D g G E B V , where x p is the standardized normal Truncation Point belonging to selected proportion p, and SDgGEBV is the SD of the GEBV of an individual’s gametes. Benefits of the index were considerably larger in an ongoing selection program with equilibrium genetic parameters than in an initially unselected population. Superiority of the index over selection on GEBV increased strongly with the magnitude of the S D g G E B V , indicating that benefits of the index may vary considerably among populations. Compared to selection on ordinary GEBV, the probability of breeding a top-ranking individual can be increased by ∼36%, and response to selection by ∼3.6% when selection is strong (P = 0.001) based on values for the Holstein-Friesian dairy cattle population. Two-stage selection, with a preselection on GEBV and a final selection on the index, considerably reduced computational requirements with little loss of benefits. Response to multiple generations of selection and inheritance of the SDgEBV require further study.
Yvonne C J Wientjes - One of the best experts on this subject based on the ideXlab platform.
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breeding top genotypes and accelerating response to recurrent selection by selecting parents with greater gametic variance
Genetics, 2020Co-Authors: P Bijma, Yvonne C J Wientjes, M P L CalusAbstract:Because of variation in linkage phase and heterozygosity among individuals, some individuals produce genetically more variable gametes than others. With the availability of genomic EBVs (GEBVs) or estimates of SNP-effects together with phased genotypes, differences in gametic variability can be quantified by simulating a set of virtual gametes of each selection candidate. Previous results in dairy cattle show that gametic variance can be large. Here, we show that breeders can increase the probability of breeding a top-ranking genotype and response to recurrent selection by selecting parents that produce more variable gametes, using the index I = G E B V + 2 x p S D g G E B V , where x p is the standardized normal Truncation Point belonging to selected proportion p, and SDgGEBV is the SD of the GEBV of an individual’s gametes. Benefits of the index were considerably larger in an ongoing selection program with equilibrium genetic parameters than in an initially unselected population. Superiority of the index over selection on GEBV increased strongly with the magnitude of the S D g G E B V , indicating that benefits of the index may vary considerably among populations. Compared to selection on ordinary GEBV, the probability of breeding a top-ranking individual can be increased by ∼36%, and response to selection by ∼3.6% when selection is strong (P = 0.001) based on values for the Holstein-Friesian dairy cattle population. Two-stage selection, with a preselection on GEBV and a final selection on the index, considerably reduced computational requirements with little loss of benefits. Response to multiple generations of selection and inheritance of the SDgEBV require further study.
S Ansarimahyari - One of the best experts on this subject based on the ideXlab platform.
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across family marker assisted selection using selective genotyping strategies in dairy cattle breeding schemes
Journal of Dairy Science, 2008Co-Authors: S Ansarimahyari, Anders Christian Sorensen, M S Lund, Hauke Thomsen, Peer BergAbstract:This study investigated the potential loss expected from marker-assisted selection (MAS) when only a proportion of animals are genotyped using several selective genotyping strategies. A population resembling a commercial dairy cattle population over 25 yr was simulated, and the most informative individuals for genotyping were identified among the potential breeding candidates (young bulls and bull-dams). Two strategies were used to identify the most informative animals. The first genotyping strategy was based on selecting individuals for genotyping with predicted total genetic effect [sum of the predicted quantitative trait locus (QTL) and polygenic effects] close to the Truncation Point for selection. The second strategy used an index that extended the previous strategy to include the variance due to segregation of the QTL in the parents. The 2 strategies for selective genotyping were applied at the 2 different genotyping levels and compared with random selection of candidates for genotyping and complete genotyping of the potential candidates. All selective genotyping strategies at the same proportion of genotyping showed similar cumulative genetic level. The frequency of the favorable QTL allele increased faster with more animals genotyped. Extra response in total genetic effect (polygenic and QTL) was not significantly different between genotyping all candidates (100%), 20%, and 50% genotyping (except for yr 13), but all MAS strategies resulted in significantly higher response than BLUP until yr 18. With 50% (20%) genotyping of candidates for selection within a population, 95% (89%) of maximum cumulative QTL response was achieved in yr 13. All MAS schemes resulted in a 19% decrease in the rate of inbreeding compared with the BLUP scheme. Therefore, it is possible to use selective genotyping in practical dairy cattle breeding and decrease the genotyping costs with a minimal loss of response compared with complete genotyping of the potential candidates.
Peter Palmroos - One of the best experts on this subject based on the ideXlab platform.
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effects of unobserved defaults on correlation between probability of default and loss given default on mortgage loans
2009Co-Authors: Peter PalmroosAbstract:This paper demonstrates how the observed correlation between probability of default and loss given default depends on the fact that defaults in which collateral provides 100% recovery are not observed. Creditors see only the defaults of mortgagors who suffer from a fall in collateral value to less than the remaining loan principal. Consequently, the default data available to creditors amounts to a mere truncated sample from the underlying population of defaults. Correlation estimates based on such truncated samples are biased and differ substantially from estimates derived from representative non-truncated samples. Moreover, the observed correlation between default probability and loss given default is sensitive to the Truncation Point, which may explain the differences in correlation estimates found in the literature. This may also explain why correlation estimates seem to be specific to cycle phase.
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effects of unobserved defaults on correlation between probability of default and loss given on mortgage loans
Research Papers in Economics, 2009Co-Authors: Peter PalmroosAbstract:This paper demonstrates how the observed correlation between probability of default and loss given default depends on the fact that defaults in which collateral provides 100% recovery are not observed. Creditors see only the defaults of mortgagors who suffer from a fall in collateral value to less than the remaining loan principal. Consequently, the default data available to creditors amounts to a mere truncated sample from the underlying population of defaults. Correlation estimates based on such truncated samples are biased and differ substantially from estimates derived from representative non-truncated samples. Moreover, the observed correlation between default probability and loss given default is sensitive to the Truncation Point, which may explain the differences in correlation estimates found in the literature. This may also explain why correlation estimates seem to be specific to cycle phase.
