Increased genetic gains in sheep,beef and dairy breeding programs from using female reproductive technologies combined with optimal contribution selection and genomic breeding values

Background

Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro embryo production and embryo transfer (JIVET) can boost rates of genetic gain but they can also increase rates of inbreeding. Inbreeding can be managed using the principles of optimal contribution selection (OCS), which maximizes genetic gain while placing a penalty on the rate of inbreeding. We evaluated the potential benefits and synergies that exist between genomic selection (GS) and reproductive technologies under OCS for sheep and cattle breeding programs.

Methods

Various breeding program scenarios were simulated stochastically including: (1) a sheep breeding program for the selection of a single trait that could be measured either early or late in life; (2) a beef breeding program with an early or late trait; and (3) a dairy breeding program with a sex limited trait. OCS was applied using a range of penalties (severe to no penalty) on co-ancestry of selection candidates, with the possibility of using multiple ovulation and embryo transfer (MOET) and/or juvenile in vitro embryo production and embryo transfer (JIVET) for females. Each breeding program was simulated with and without genomic selection.

Results

All breeding programs could be penalized to result in an inbreeding rate of 1 % increase per generation. The addition of MOET to artificial insemination or natural breeding (AI/N), without the use of GS yielded an extra 25 to 60 % genetic gain. The further addition of JIVET did not yield an extra genetic gain. When GS was used, MOET and MOET + JIVET programs increased rates of genetic gain by 38 to 76 % and 51 to 81 % compared to AI/N, respectively.

Conclusions

Large increases in genetic gain were found across species when female reproductive technologies combined with genomic selection were applied and inbreeding was managed, especially for breeding programs that focus on the selection of traits measured late in life or that are sex-limited. Optimal contribution selection was an effective tool to optimally allocate different combinations of reproductive technologies. Applying a range of penalties to co-ancestry of selection candidates allows a comprehensive exploration of the inbreeding vs. genetic gain space.     

Plant breeding: Past achievements and expectations for the future

Plant breeding improvements have been responsible for 50 percent or more of the gains in yield per unit area of major crop plants in the United States over the past 50 yr. Rates of gain attributable to genetic improvements have averaged 1% per year, have generally been linear, and show no sign of slackening. Extrapolations indicate that varieties and hybrids of the year 2000 will yield, on average, 15% more than those of 1985 Improvements in tolerance to environmental stress, in grain-to-straw ratios, and in standability, as well as maintenance of required levels of resistance to disease, insect and nematode pests, have been the major genetic causes of higher achieved yields and will continue to be the foundation for further gains in productivity and stability. Broadened genetic diversity is also an increasingly important goal to promote stability and increase productivity potentials. Proportionately large research inputs are now needed to maintain desired rates of improvement, compared to earlier years. It seems likely that contributions from biotechnology will become increasingly important in years to come if improvement rates are to be maintained.     

Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle (Open Access publication)

The efficiency of the French marker-assisted selection (MAS) was estimated by a simulation study. The data files of two different time periods were used: April 2004 and 2006. The simulation method used the structure of the existing French MAS: same pedigree, same marker genotypes and same animals with records. The program simulated breeding values and new records based on this existing structure and knowledge on the QTL used in MAS (variance and frequency). Reliabilities of genetic values of young animals (less than one year old) obtained with and without marker information were compared to assess the efficiency of MAS for evaluation of milk, fat and protein yields and fat and protein contents. Mean gains of reliability ranged from 0.015 to 0.094 and from 0.038 to 0.114 in 2004 and 2006, respectively. The larger number of animals genotyped and the use of a new set of genetic markers can explain the improvement of MAS reliability from 2004 to 2006. This improvement was also observed by analysis of information content for young candidates. The gain of MAS reliability with respect to classical selection was larger for sons of sires with genotyped progeny daughters with records. Finally, it was shown that when superiority of MAS over classical selection was estimated with daughter yield deviations obtained after progeny test instead of true breeding values, the gain was underestimated.     

Breeding for Biomass Yield in Switchgrass Using Surrogate Measures of Yield

Development of switchgrass (Panicum virgatum L.) as a dedicated biomass crop for conversion to energy requires substantial increases in biomass yield. Most efforts to breed for increased biomass yield are based on some form of indirect selection. The objective of this paper is to evaluate and compare the expected efficiency of several indirect measures of breeding value for improving sward-plot biomass yield of switchgrass. Sward-plot biomass yield, row-plot biomass, and spaced-plant biomass were measured on 144 half-sib families or their maternal parents from the WS4U-C2 breeding population of upland switchgrass. Heading date was also scored on row plots and anthesis date was scored on spaced plants. Use of any of these indirect selection criteria was expected to be less efficient than direct selection for biomass yield measured on sward plots, when expressed as genetic gain per year. Combining any of these indirect selection criteria with half-sib family selection for biomass yield resulted in increases in efficiency of 14 to 36%, but this could only be achieved at a very large cost of measuring phenotype on literally thousands of plants that would eventually have no chance of being selected because they were derived from inferior families. Genomic prediction methods offered the best solution to increase breeding efficiency by reducing average cycle time, increasing selection intensity, and placing selection pressure on all additive genetic variance within the population. Use of genomic selection methods is expected to double or triple genetic gains over field-based half-sib family selection.     

Genotype-assisted optimum contribution selection to maximize selection response over a specified time period

Genotype-assisted selection (GAS), i.e. selection for an identified quantitative trait locus (QTL) and polygenic background genes, has been shown to increase short-term genetic gain but may reduce long-term genetic gains. In order to avoid this reduction of long-term gain, multi-generation optimization of truncation selection schemes is needed. This paper presents a multi-generation optimization of optimum contribution (OC) selection with selection on an identified QTL. This genotype-assisted optimum contribution (GAOC) selection method assumes that the optimum selection differential at the QTL is constant over the time horizon, and achieves this by controlling the increase of the frequency of the positive QTL allele. Implementation was straightforward by an additional linear restriction in the OC algorithm. GAOC achieved 35.2%, 2.3% and 1.1%, respectively, more cumulative genetic gain than OC selection (ignoring the QTL) using time horizons of 5, 10 and 15 generations. When one-generation optimization of GAS was used instead of multi-generation optimization, these figures were 2.8%, 3.1% and 3.2%, respectively. Simulated annealing was used to optimize the increases of the frequency of the positive QTL allele in order to test the optimality of GAOC. This latter resulted in genetic gains that were always within 0.4% of those of GAOC. In practice, short-term genetic gains are also important, which makes one-generation optimization of genetic gain closer to optimal.     

Bias in the prediction of genetic gain due to mass and half-sib selection in random mating populations

The prediction of gains from selection allows the comparison of breeding methods and selection strategies, although these estimates may be biased. The objective of this study was to investigate the extent of such bias in predicting genetic gain. For this, we simulated 10 cycles of a hypothetical breeding program that involved seven traits, three population classes, three experimental conditions and two breeding methods (mass and half-sib selection). Each combination of trait, population, heritability, method and cycle was repeated 10 times. The predicted gains were biased, even when the genetic parameters were estimated without error. Gain from selection in both genders is twice the gain from selection in a single gender only in the absence of dominance. The use of genotypic variance or broad sense heritability in the predictions represented an additional source of bias. Predictions based on additive variance and narrow sense heritability were equivalent, as were predictions based on genotypic variance and broad sense heritability. The predictions based on mass and family selection were suitable for comparing selection strategies, whereas those based on selection within progenies showed the largest bias and lower association with the realized gain.     

Optimization of selection contribution and mate allocations in monoecious tree breeding populations

Background

The combination of optimized contribution dynamic selection and various mating schemes was investigated over seven generations for a typical tree breeding scenario. The allocation of mates was optimized using a simulated annealing algorithm for various object functions including random mating (RM), positive assortative mating (PAM) and minimization of pair-wise coancestry between mates (MCM) all combined with minimization of variance in family size and coancestry. The present study considered two levels of heritability (0.05 and 0.25), two restrictions on relatedness (group coancestry; 1 and 2%) and two maximum permissible numbers of crosses in each generation (100 and 400). The infinitesimal genetic model was used to simulate the genetic architecture of the trait that was the subject of selection. A framework of the long term genetic contribution of ancestors was used to examine the impacts of the mating schemes on population parameters.

Results

MCM schemes produced on average, an increased rate of genetic gain in the breeding population, although the difference between schemes was small but significant after seven generations (up to 7.1% more than obtained with RM). In addition, MCM reduced the level of inbreeding by as much as 37% compared with RM, although the rate of inbreeding was similar after three generations of selection. PAM schemes yielded levels of genetic gain similar to those produced by RM, but the increase in the level of inbreeding was substantial (up to 43%).

Conclusion

The main reason why MCM schemes yielded higher genetic gains was the improvement in managing the long term genetic contribution of founders in the population; this was achieved by connecting unrelated families. In addition, the accumulation of inbreeding was reduced by MCM schemes since the variance in long term genetic contributions of founders was smaller than in the other schemes. Consequently, by combining an MCM scheme with an algorithm that optimizes contributions of the selected individuals, a higher long term response is obtained while reducing the risk within the breeding program.     

Selection system efficiencies for computer simulated progeny test field designs in loblolly pine

Summary Six simulated progeny test field designs in combination with three within-family selection systems were tested on three loblolly pine (Pinus taeda L.) progeny test sites in southeastern Oklahoma and southwestern Arkansas, to compare genetic gains for the single trait, height. Residual deviations obtained by subtraction of family and plantation mean effects for each plantation were combined with simulated genetic effects with known family variance structure. The simulated genetic populations, arranged in the following progeny test field designs — large square or almost square plots, five- and ten-tree row plots, five-tree noncontiguous plots, two tree row plots, and single-tree plots — were superimposed on the residual data for each plantation. Within-family selection methods based on deviations from block means, deviations from neighborhood means and deviations from plot means were built into the model. Realized genetic gain attained by each design — selection system combination was compared with the genetic gain theoretically possible if selection accuracy were perfect, and with expected gain estimated using the general linear model. In general, average realized genetic gain compared well with expected gain. Differences between designs with large versus small plots were generally lower than expected, although the single-tree plot design always yielded highest realized gain. Realized gain was generally higher than expected when within-family selection was based on deviations from block or neighborhood means, but equal to or lower than expected when selection was based on deviations from plot means.     

Prospects for increasing yield in macadamia using component traits and genomics

Selection of candidate cultivars in macadamia requires extensive phenotypic measurements over many years and trials. In particular, yield traits such as nut-in-shell yield and kernel yield are economically vital characteristics and therefore guide the selection process for new cultivars. However, these traits can only be measured in mature trees, resulting in long generation intervals and slow rates of genetic gain. In addition, these traits are expensive to measure. Strategies to reduce the generation interval and increase the intensity of selection include using yield component traits, identification of markers associated with component traits, and genomic selection for yield. Yield component traits that contribute to resource availability for fruit formation include floral and nut characteristics. In this review, these traits will be investigated to estimate their relative importance in macadamia breeding and their heritability and correlations with yield. Furthermore, the usefulness of genome-wide association studies regarding yield component traits will be reviewed. Genetic-based breeding techniques could exploit this information to increase yield gains per breeding cycle and estimate the quantitative nature of yield traits. Genomic selection uses genome-wide molecular markers to predict the phenotype of individuals at an early age before maturity, thereby reducing the cycle time and increasing gain per unit time in plant breeding programmes. This review evaluates the potential for measurement of yield component traits, genome-wide association studies, and genomic selection to be employed in the Australian macadamia breeding programme to accelerate gains for nut yield.     

Gains from the clonal and the clonal seed-orchard options compared for tree breeding programs

Summary Gains expected from clonal propagation of selections for plantation from a breeding population were compared with those expected from seed propagation via clonal seed-orchards of selections from the same breeding population. Assumptions were made about numbers of clones selected, size of the breeding population, relative sizes of additive and dominance genetic variance components and time required for various operations. Even when dominance variance is zero, considerable extra gain is obtained by the clonal option over the seed-orchard option; mostly due to the shorter time between selection in the breeding population and field planting. When dominance variance equals additive variance, the advantage of the clonal option due to time saved is approximately equal to the advantage due to genetics (i.e. use of more of the additive variance, use of non-additive variance and greater precision of selection). This means that there is a substantial gain to be made simply by getting superior genotypes into plantations more quickly via the clonal option. The gains obtainable through the use of clonal forestry may also be obtained through seed orchards, but some decades later. In no case was the seed-orchard option superior to the clonal option in terms of the gains obtained. No clonal propagation program can advance without a strong sexually-based breeding program to supply it with improved genotypes. The opportunity for improvement comes from genetic recombination.     

Evaluation of marker-assisted selection through computer simulation

Computer simulation was used to evaluate responses to marker-assisted selection (MAS) and to compare MAS responses with those typical of phenotypic recurrent selection (PRS) in an allogamous annual crop species such as maize (Zea mays L.). Relative to PRS, MAS produced rapid responses early in the selection process; however, the rate of these responses diminished greatly within three to five cycles. The gains from MAS ranged from 44.7 to 99.5% of the maximum potential, depending on the genetic model considered. Linkage distance between markers and quantitative trait loci (QTLs) was the factor which most limited the responses from MAS. When averaged across all models considered, flanking QTLs within two marker loci produced 38% more gain than did selection based on single markers if markers were loosely-linked to a QTL (20% recombination). Flanking markers were much less advantageous when markers were closely-linked to a QTL (5% recombination), producing an advantage over single markers of only 11%. Markers were most effective in fully exploiting the genetic potential when fewer QTLs controlled the trait. Large QTL numbers exacerbated the problem of marker-QTL recombination by requiring more generations for fixation. In annual crop species, MAS may offer a primary advantage of enabling two selection cycles per year versus the 2 years per cycle required by most PRS schemes for the evaluation of testcross progeny. MAS thus appears to allow very rapid gains for the first 2–3 years of recurrent selection, after which time conventional methods might replace MAS to achieve further responses.Publication number 19, 330 of the Minnesota Agricultural Experiment Station     

Relative changes in genetic variability and correlations in an early-maturing maize population during recurrent selection

Four cycles of S(1) family recurrent selection to improve grain yield and resistance to Striga hermonthica have been completed in TZE-Y Pop STR C(0.) In order to determine whether or not to continue with the recurrent scheme, it was desirable to evaluate the amount of residual genetic variance and associated parameters in the population. The objective of this study was to characterize the relative changes in the levels of the genetic variances, heritability estimates and genetic correlation coefficients, and to predict future gains from selection for grain yield, Striga resistance and other agronomic traits. Fifty S(1) families, derived from each cycle, were evaluated under Striga-infested and Striga-free conditions at Mokwa, Ikenne and Abuja, Nigeria, in 2005 and 2007. Under Striga infestation, genetic variances for grain yield, days to anthesis, plant height and Striga damage generally increased in the advanced cycles of selection. In contrast, the genetic variances for days to silk, anthesis-silking interval, ears per plant, ear aspect and number of emerged Striga plants decreased with selection. The advanced cycles of selection significantly out-yielded the original cycle in both research environments. Heritabilities for grain yield, Striga damage and number of emerged Striga plants were significantly greater than zero. The realized gains from selection for grain yield under Striga infestation (52?kg?ha(-1)?cycle(-1)) and Striga-free conditions (130?kg?ha(-1)?cycle(-1)) were remarkably lower than the predicted gains (350 and 250?kg?ha(-1?)cycle(-1), respectively). Adequate genetic variability exists in cycle 4 of the scheme to ensure future gains from selection.     

Genetic steady-state under BLUP selection for an infinite and homogeneous population with discrete generations

A matrix derivation is proposed to analytically calculate the asymptotic genetic variance-covariance matrix under BLUP selection according to the initial genetic parameters in a large population with discrete generations. The asymptotic genetic evolution of a homogeneous population with discrete generations is calculated for a selection operating on an index including all information (pedigree and records) from a non-inbred and unselected base population (BLUP selection) or on an index restricted to records of a few ancestral generations. Under the first hypothesis, the prediction error variance of the selection index is independent of selection and is calculated from the genetic parameters of the base population. Under the second hypothesis, the prediction error variance depends on selection. Furthermore, records of several generations of ancestors of the candidates for selection must be used to maintain a constant prediction error variance over time. The number of ancestral generations needed depends on the population structure and on the occurrence of fixed effects. Without fixed effects to estimate, accounting for two generations of ancestors is sufficient to estimate the asymptotic prediction error variance. The amassing of information from an unselected base population proves to be important in order not to overestimate the asymptotic genetic gains and not to underestimate the asymptotic genetic variances.     

Trait-specific long-term consequences of genomic selection in beef cattle

Simulation studies allow addressing consequences of selection schemes, helping to identify effective strategies to enable genetic gain and maintain genetic diversity. The aim of this study was to evaluate the long-term impact of genomic selection (GS) in genetic progress and genetic diversity of beef cattle. Forward-in-time simulation generated a population with pattern of linkage disequilibrium close to that previously reported for real beef cattle populations. Different scenarios of GS and traditional pedigree-based BLUP (PBLUP) selection were simulated for 15 generations, mimicking selection for female reproduction and meat quality. For GS scenarios, an alternative selection criterion was simulated (wGBLUP), intended to enhance long-term gains by attributing more weight to favorable alleles with low frequency. GS allowed genetic progress up to 40% greater than PBLUP, for female reproduction and meat quality. The alternative criterion wGBLUP did not increase long-term response, although allowed reducing inbreeding rates and loss of favorable alleles. The results suggest that GS outperforms PBLUP when the selected trait is under less polygenic background and that attributing more weight to low-frequency favorable alleles can reduce inbreeding rates and loss of favorable alleles in GS.     

On the relation between gene flow theory and genetic gain

In conventional gene flow theory the rate of genetic gain is calculated as the summed products of genetic selection differential and asymptotic proportion of genes deriving from sex-age groups. Recent studies have shown that asymptotic proportions of genes predicted from conventional gene flow theory may deviate considerably from true proportions. However, the rate of genetic gain predicted from conventional gene flow theory was accurate. The current note shows that the connection between asymptotic proportions of genes and rate of genetic gain that is embodied in conventional gene flow theory is invalid, even though genetic gain may be predicted correctly from it.     

Prediction of response with overlapping generations accounting for multistage selection

Summary A generalization of Hill's equations predicting response to selection is developed that accounts for multiple stage selection in either or both sexes. The method accounts for the flow of genes for animals selected at later stages. This allows for the use of genetic gains from later stages, which explains the reduction in variance due to previous selection. Genetic gains from different selection differentials in each reproductive pathway are incorporated into the equations. The asymptotic response to a single cycle of selection is shown to agree with classical selection theory.The method is applied to a dairy progeny testing scheme representative of an artificial insemination organization in the USA. Two models were compared: (1) the first model accounted for two-stage selection of males, the first stage being based on pedigree information and the second stage on both pedigree and progeny test information; and (2) the second model assumed single-stage male selection. Selection was based on milk volume, milk fat, and milk protein yields. The predicted asymptotic rates for a single cycle of selection were overestimated by 6% and the cumulative response to continuous selection over 20 years was overestimated by 8% by assuming singlestage male selection.Journal Paper No. J14146 of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa; Project No. 1053     

Are economic selection indices always superior to a desired gains index?

Conclusions The comparison of different selection indices is justified only if the indices are constrated to achieve the same profit function, even when each index is not optimized with respect to that profit function.When a profit function is known and is non-linear, the desired gains index may be more efficient than the economic index. The optimum desired gains index should be determined by iterative techniques over several generations to compare the genetic progress with the economic index, because gains by the economic index are not linear and the changes observed in the initial generations of selection are not the same rates in future generations, although those changes are linear in the case of the desired gains index.     

Prediction of genetic gain from quadratic optimisation with constrained rates of inbreeding

There are selection methods available that allow the optimisation of genetic contributions of selection candidates for maximising the rate of genetic gain while restricting the rate of inbreeding. These methods imply selection on quadratic indices as the selection merit of a particular individual is a quadratic function of its estimated breeding value. This study provides deterministic predictions of genetic gain from selection on quadratic indices for a given set of resources (the number of candidates), heritability, and target rate of inbreeding. The rate of gain was obtained as a function of the accuracy of the Mendelian sampling term at the time of convergence of long-term contributions of selected candidates and the theoretical ideal rate of gain for a given rate of inbreeding after an exact allocation of long-term contributions to Mendelian sampling terms. The expected benefits from quadratic indices over traditional linear indices (i.e. truncation selection), both using BLUP breeding values, were quantified. The results clearly indicate higher gains from quadratic optimisation than from truncation selection. With constant rate of inbreeding and number of candidates, the benefits were generally largest for intermediate heritabilities but evident over the entire range. The advantage of quadratic indices was not highly sensitive to the rate of inbreeding for the constraints considered.     

Higher intron loss rate in Arabidopsis thaliana than A. lyrata is consistent with stronger selection for a smaller genome

The number of introns varies considerably among different organisms. This can be explained by the differences in the rates of intron gain and loss. Two factors that are likely to influence these rates are selection for or against introns and the mutation rate that generates the novel intron or the intronless copy. Although it has been speculated that stronger selection for a compact genome might result in a higher rate of intron loss and a lower rate of intron gain, clear evidence is lacking, and the role of selection in determining these rates has not been established. Here, we studied the gain and loss of introns in the two closely related species Arabidopsis thaliana and A. lyrata as it was recently shown that A. thaliana has been undergoing a faster genome reduction driven by selection. We found that A. thaliana has lost six times more introns than A. lyrata since the divergence of the two species but gained very few introns. We suggest that stronger selection for genome reduction probably resulted in the much higher intron loss rate in A. thaliana, although further analysis is required as we could not find evidence that the loss rate increased in A. thaliana as opposed to having decreased in A. lyrata compared with the rate in the common ancestor. We also examined the pattern of the intron gains and losses to better understand the mechanisms by which they occur. Microsimilarity was detected between the splice sites of several gained and lost introns, suggesting that nonhomologous end joining repair of double-strand breaks might be a common pathway not only for intron gain but also for intron loss.