We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔG(AG) ), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii) a positive interaction exists between the use of genotypic information and a short generation interval on ΔG(AG) and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔG(AG) if genotypic information about the breeding goal trait is known. We examined four breeding schemes with or without genomic selection and with or without intensive use of young bulls using pseudo-genomic stochastic simulations. The breeding goal consisted of a milk production trait and a functional trait. The two breeding schemes with genomic selection resulted in higher ΔG(AG) , greater contributions of the functional trait to ΔG(AG) and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information on ΔG(AG) . Hence, a breeding scheme with genomic selection and with intensive use of young bulls (a turbo scheme) seems to offer the greatest potential. The third hypothesis was disproved as inclusion of genomically enhanced breeding values (GEBV) for an indicator trait in the selection index increased ΔG(AG) in the turbo scheme. Moreover, it increased the contribution of the functional trait to ΔG(AG) , and it decreased ΔF. Thus, indicator traits may still be profitable to use even when GEBV for the breeding goal traits are available.
Pedigree information of 852,443 registered Holstein cows and bulls, collected by the Animal Breeding Center of Iran from 1971 to 2007, was used to calculate inbreeding coefficients and their effect on production, reproduction, somatic cell count, calving ease, and longevity traits. The average inbreeding coefficient for the entire population was 2.90%, ranging from zero to 47.03%. The rates of inbreeding from 1989 to 2007 were 0.22 and 0.15% per year for females and males, respectively. The rates were higher after 2000, being 0.31 and 0.21% per year for females and males, respectively. Inbreeding had a deleterious effect on most traits. For the first 3 lactations, the inbreeding depression per 1% increase in inbreeding was -18.72, -16.19, and -27.38 kg for milk yield, -0.443, -0.367, and -0.690 kg for fat yield, and -0.476, -0.425, and -0.66 kg for protein yield, respectively. For all reproductive traits, the observed undesirable effect of inbreeding was not significant, except for the calving interval (0.53 d per 1% increase in inbreeding) in the third parity and age at first calving (0.45 d per 1% increase in inbreeding). Calving ease in heifers and cows was significantly influenced by the inbreeding of the dam, indicating that highly inbred cows had a higher incidence of difficult calvings. The estimate of inbreeding depression for somatic cell score was low and significant only for the third lactation. However, animals with high inbreeding coefficient tended to have higher somatic cell scores than animals with low inbreeding coefficients. For type traits, the influence of inbreeding was significant only for stature, chest width, body depth, size, rear udder height, suspensory ligament, udder depth, and front and rear teat placement. Cows with high levels of inbreeding coefficient were at higher relative risk of being culled.
Until now, genomic information has mainly been used to improve the accuracy of genomic breeding values for breeding animals at a population level. However, we hypothesize that the use of information from genotyped females also opens up the possibility of reducing genetic lag in a dairy herd, especially if genomic tests are used in combination with sexed semen or a high management level for reproductive performance, because both factors provide the opportunity for generating a reproductive surplus in the herd. In this study, sexed semen is used in combination with beef semen to produce high-value crossbred beef calves. Thus, on average there is no surplus of and selection among replacement heifers whether to go into the herd or to be sold. In this situation, the selection opportunities arise when deciding which cows to inseminate with sexed semen, conventional semen, or beef semen. We tested the hypothesis by combining the results of 2 stochastic simulation programs, SimHerd and ADAM. SimHerd estimates the economic effect of different strategies for use of sexed semen and beef semen at 3 levels of reproductive performance in a dairy herd. Besides simulating the operational return, SimHerd also simulates the parity distribution of the dams of heifer calves. The ADAM program estimates genetic merit per year in a herd under different strategies for use of sexed semen and genomic tests. The annual net return per slot was calculated as the sum of operational return and value of genetic lag minus costs of genomic tests divided by the total number of slots. Our results showed that the use of genomic tests for decision making decreases genetic lag by as much as 0.14 genetic standard deviation units of the breeding goal and that genetic lag decreases even more (up to 0.30 genetic standard deviation units) when genomic tests are used in combination with strategies for increasing and using a reproductive surplus. Thus, our hypothesis was supported. We also observed that genomic tests are used most efficiently to decrease genetic lag when the genomic information is used more than once in the lifetime of an animal and when as many selection decisions as possible are based on genomic information. However, all breakeven prices were lower than or equal to €50, which is the current price of low-density chip genotyping in Denmark, Finland, and Sweden, so in the vast majority of cases, it is not profitable to genotype routinely for management purposes under the present price assumptions.
The objective of this study was to analyze the development of inbreeding and estimate inbreeding depression in the Danish populations of 3 major meat type sheep breeds. The pedigrees contained 29,336 Texel, 22,838 Shropshire, and 11,487 Oxford Down. The rate of inbreeding was approximately 1% per generation for all breeds, but the rate of increase in co-ancestry was somewhat lower (0.45 to 0.71), indicating that more inbreeding has been accumulating than would be expected if mating was at random. Inbreeding depression for birth weight, ADG from birth until 2 mo, and litter size was estimated for all 3 breeds using a minimum of 15,000 records per trait and breed. All traits showed depression due to inbreeding of the animal itself. For most combinations of trait and breed, there was also a significant reduction of the phenotype due to inbreeding in the dam. The size of inbreeding depression was 1.2 to 2.6% of the mean, resulting in an increase in the inbreeding coefficient of the individual of 0.10, and estimates were similar for similar increases in maternal inbreeding. The rate of inbreeding in these breeds needs to be reduced in the future to avoid a further decline in birth weight, ADG, and litter size.
Background We tested the premise that optimum-contribution selection with pedigree relationships to control inbreeding (POCS) realises at least as much true genetic gain as optimum-contribution selection with genomic relationships (GOCS) at the same rate of true inbreeding. Methods We used stochastic simulation to estimate rates of true genetic gain realised by POCS and GOCS at a 0.01 rate of true inbreeding in three breeding schemes with best linear unbiased predictions of breeding values based on pedigree (PBLUP) and genomic (GBLUP) information. The three breeding schemes differed in number of matings and litter size. Selection was for a single trait with a heritability of 0.2. The trait was controlled by 7702 biallelic quantitative-trait loci (QTL) that were distributed across a 30-M genome. The genome contained 54,218 biallelic markers that were used in GOCS and GBLUP. A total of 6012 identity-by-descent loci were placed across the genome in base populations. Unique alleles at these loci were used to calculate rates of true inbreeding. Breeding schemes were run for 10 discrete generations. Selection candidates were genotyped and phenotyped before selection. Results POCS realised more true genetic gain than GOCS at a 0.01 rate of true inbreeding in all combinations of breeding scheme and prediction method. POCS realised 14 to 33% more true genetic gain than GOCS with PBLUP in the three breeding schemes. It realised 1.5 to 5.7% more true genetic gain than GOCS with GBLUP. Conclusions POCS realised more true genetic gain than GOCS because it managed expected genetic drift without restricting selection at QTL. By contrast, GOCS penalised changes in allele frequencies at markers that were generated by genetic drift and selection. Because these marker alleles were in linkage disequilibrium with QTL alleles, GOCS restricted changes in allele frequencies at QTL. This provides little incentive to use GOCS and highlights that we have more to learn before we can control inbreeding using genomic relationships in selective-breeding schemes. Until we can do so, POCS remains a worthy method of optimum-contribution selection because it realises more true genetic gain than GOCS at the same rate of true inbreeding.
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