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

Optimization of dairy cattle breeding plans with increased female reproductive rates

IntroductionNicholas and Smith (1983) proposed Multiple Ovulation and Embryo Transfer (MOET) nucleus breeding schemes to increase reponse rates in dairy cattle breeding. Predicted genetic gains were up to twice as high as those of conventional progeny testing schemes. In the MOET nucleus breeding schemes, selection was within a closed nucleus herd using short generation intervals and mainly sib information. Juga and Maki-Tanila (1987) simulated MOET nucleus schemes and found that predicted rates of gain were 124 % higher than simulated. From this two questions arise: i) how to predict response rates correctly; and ii) how to make optimal use of MOET in dairy cattle breeding.Prediction of response ratesMajor factors decreasing response rates are:1. Reduction of variances due to selection. This consists of reduction of variances of information sources, which were previously selected for, and reduction of genetic variance due to linkage disequilibrium between genes as described by Bulmer (1971). The effect of linkage disequilibrium could be accounted for by correcting the genetic variance for all selection on ancestral sources of information. This factor reduced response rates by 20-30%.2. Reduction of selection differentials due to small numbers of selection candidates and small numbers of families (Hill, 1976). Selection differentials are often predicted assuming that breeding value estimates of selection candidates are uncorrelated. However, family relations between selection candidates cause correlations between breeding value estimates. Especially in schemes with short generation intervals, where breeding value estimates are mainly based on information of sibs, correlations between breeeding value estimates of sibs are high, since these are based on the same sources of information. An approximation for the reduced selection differentials in a nested full-half sib family structure was derived. Predicted response rates were reduced by up to another 30%.3. Variance reduction due to inbreeding. Also, this factor reduces genetic variance and thus genetic gain substantially, when the inbreeding coefficient is large. Even with an inbreeding rate of 5% per generation, i.e. effective population size is 10 animals per generation, it takes about 5 generations before the inbreeding coefficient is large enough to be of importance. Therefore, average selection response during the first five generations is not much reduced (6%). For 10 generations this figure is 13%. Thus, the impact of this factor depends on the time horizon (here: the time period during which the breeding population is expected to be closed to foreign breeding stocks). In view of the large difference in effective population size, i.e. 10 animals per generation vs. infinite (no inbreeding), it is concluded that up to 10 generations the impact of variance reduction due to inbreeding on the ranking of breeding schemes is not large. The first and second factor were accounted for in this study. Variance reduction due to inbreeding was neglected.Breeding schemesIn nucleus breeding schemes, nucleus dams are selected from the female nucleus population, which has the same genetic level as the bull stud. In progeny testing schemes, bull dams are selected from commercial herds. However, some cows born in commercial herds are daughters of bull sires and bull dams and are thus of equal genetic level as the bulls in the stud. These cows are comparable with the nucleus females in nucleus schemes and have an higher probability of being selected than 'normal' commercial cows. Thus, progeny testing schemes are open nucleus schemes, where daughters of bull sires and bull dams form the nucleus females and where 'normal' cows are the base population.It was assumed here, that milk production records were not biased by housing of nucleus animals, i.e. in special nucleus herds or dispersed across commercial herds. There are only three differences between the nucleus schemes proposed by Nicholas and Smith (1983) and progeny testing schemes, that use MOET to increase reproductive rates of bull dams:1. In the closed nucleus schemes of Nicholas and Smith, selection of dams is within the nucleus herd, whereas in open nucleus / progeny testing schemes nucleus and base population females serve as selection candidates. When, relative to the nucleus size, many bull dams have to be selected, this is advantageous for the open nucleus/progeny testing scheme. When the number of selected dams is small due to the use of MOET, relatively many dams will be selected from the genetically superior nucleus population. This implies that the open and closed nucleus schemes become more similar, when female reproductive rates increase. With on average 8 offspring per donor cow per year, differences in genetic gain between open and closed nucleus breeding schemes were small.2. Generation intervals are much longer in progeny testing schemes than in nucleus breeding schemes, which is partly due to progeny testing of bulls. James (1987) shows that generation intervals could be optimised in any schemes by selecting for BLUP breeding values across all age classes. The ad hoc nature of this optimization makes predefining generation intervals of breeding schemes redundant. Consequently, this difference between nucleus breeding and progeny testing schemes disappears. However, in practical progeny testing schemes, generation intervals are not optimised: only proven bulls (at least 5 years old) are considered for selection and usually cows without a milk production record are not considered for selection of bull dams. Selection response increases by about 15%, when these restrictions are abolished. Biasedness of breeding value estimates of young animals will reduce this improvement and selection response might be even reduced.3. Progeny testing of young bulls in the base population. In open nucleus breeding schemes with optimised generation intervals, progeny testing reduces genetic gains by up to 10% depending on the number of sires used.Variances of selection responsesVariance of the selection response is a measure for risk of the breeding plan. Further, variance of the selection response and inbreeding are positively related. Reduction of generation intervals due to the optimization procedure increased the standard deviation of the selection response by a factor 2 to 3. Utility theory was used to weigh selection response against its variance. A quadratic approximation of the utility function and maximum risk aversion were assumed. Schemes with optimised (short) generation intervals had the highest utility.Closed nucleus schemes had a lower utility than open nucleus schemes, both with optimized generation intervals. This was due to the 80 % higher standard deviation of the selection response in closed nucleus schemes. Differences in selection response were small: closed nucleus schemes had 3% more selection response than open nucleus schemes (8 offspring per donor cow). In these open nucleus schemes, selection of nucleus dams from the base was very intense, which resulted in less genetic variance in the nucleus offspring. This caused the small difference in genetic gain.Main conclusions- Variance reduction due to selection reduces predicted genetic gain by 20 - 30 %.- Correlations between breeding value estimates of relatives reduce predicted selection differentials in breeding schemes by up to another 30%.- As reproduction rates of females increase, optimised open nucleus schemes (or progeny testing schemes) become more closed. With an average of 8 offspring per selected cow, open and closed nucleus schemes have almost euqual genetic gains.- Variances of selection responses increase substantially, when generation intervals are reduced. However, when selection response and its variance were weighted, shorter generation intervals were still prefered.- Variance of the selection response of closed nucleus schemes is higher than that of open nucleus schemes (both having optimised generation intervals). Therefore, under the assumption that field and nucleus herd milk production records both are unbiassed, open nucleus schemes were prefered.</TT

Single and multitrait estimates of breeding values for survival using sire and animal models

Survival data were simulated under the Weibull model in a half-sib family design, and about 50% of the records were censored. The data were analysed using the proportional hazard model (PHM) and, after transformation to survival scores, using a linear and a binary (logit) model (LIN and BIN, respectively), where the survival scores are indicators of survival during time period t given survival up to period t-1. Correlations between estimated and true breeding values of sires (accuracies of selection) were very similar for all three models (differences were smaller than 0•3%). Daughter effects were however less accurately predicted by the LIN model, i.e. taking proper account of the distribution of the survival data yields more accurate predictions of daughter effects. The estimated variance components and regressions of true on estimated breeding values were difficult to compare for the LIN models, because estimated breeding values were expressed as additive effects on survival scores while the simulated true breeding values were expressed on the underlying scale. Also the differences in accuracy of selection between sire and animal model breeding value estimates were small, probably due to the half-sib family design of the data. To estimate breeding values for functional survival, i.e. the component of survival that is genetically independent of production (here milk yield), two methods were compared: (i) breeding values were predicted by a single-trait linear model with a phenotypic regression on milk yield; and (ii) breeding values were predicted by a two-trait linear model for survival and milk yield, and breeding values for survival corrected for milk yield were obtained by a genetic regression on the milk yield breeding value estimates. Both methods yielded very similar accuracies of selection for functional survival, and are expected to be equivalent

Development and characterization of transgenic dominant male sterile rice toward an outcross-based breeding system

Genomic selection is attracting attention in the field of crop breeding. To apply genomic selection effectively for autogamous (self-pollinating) crops, an efficient outcross system is desired. Since dominant male sterility is a powerful tool for easy and successive outcross of autogamous crops, we developed transgenic dominant male sterile rice (Oryza sativa L.) using the barnase gene that is expressed by the tapetum-specific promoter BoA9. Barnase-induced male sterile rice No. 10 (BMS10) was selected for its stable male sterility and normal growth characteristics. The BMS10 flowering habits, including heading date, flowering date, and daily flowering time of BMS10 tended to be delayed compared to wild type. When BMS10 and wild type were placed side-by-side and crossed under an open-pollinating condition, the seed-setting rate was <1.5%. When the clipping method was used to avoid the influence of late flowering habits, the seed-setting rate of BMS10 increased to a maximum of 86.4%. Although flowering synchronicity should be improved to increase the seed-setting rate, our results showed that this system can produce stable transgenic male sterility with normal female fertility in rice. The transgenic male sterile rice would promote a genomic selection-based breeding system in rice

Establishment and optimization of genomic selection to accelerate the domestication and improvement of intermediate wheatgrass

Citation: Zhang, X., Sallam, A., Gao, L., Kantarski, T., Poland, J., DeHaan, L. R., . . . Anderson, J. A. (2016). Establishment and optimization of genomic selection to accelerate the domestication and improvement of intermediate wheatgrass. Plant Genome, 9(1). doi:10.3835/plantgenome2015.07.0059Intermediate wheatgrass (IWG) is a perennial species and has edible and nutritious grain and desirable agronomic traits, including large seed size, high grain yield, and biomass. It also has the potential to provide ecosystem services and an economic return to farmers. However, because of its allohexaploidy and self-incompatibility, developing molecular markers for genetic analysis and molecular breeding has been challenging. In the present study, using genotyping-by-sequencing (GBS) technology, 3436 genomewide markers discovered in a biparental population with 178 genets, were mapped to 21 linkage groups (LG) corresponding to 21 chromosomes of IWG. Genomic prediction models were developed using 3883 markers discovered in a breeding population containing 1126 representative genets from 58 half-sib families. High predictive ability was observed for seven agronomic traits using cross-validation, ranging from 0.46 for biomass to 0.67 for seed weight. Optimization results indicated that 8 to 10 genets from each half-sib family can form a good training population to predict the breeding value of their siblings, and 1600 genomewide markers are adequate to capture the genetic variation in the current breeding population for genomic selection. Thus, with the advances in sequencing-based marker technologies, it was practical to perform molecular genetic analysis and molecular breeding on a new and challenging species like IWG, and genomic selection could increase the efficiency of recurrent selection and accelerate the domestication and improvement of IWG.A. © Crop Science Society of America