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This article relates to breeding programs that seek to manage genetic diversity. The method maximises a multicomponent objective function, applicable across breeding scenarios. However, this paper focuses on breeding decisions following immigration of ten unrelated individuals into a highly inbred simulated population (F≈0.34). We use Optimal Contribution Selection to maximise retention of genetic diversity. However, some treatments add Coancestry Assortative Mating (CAM). This helps to avoid early dilution of immigrant genetic material, maximising its ability to contribute to genetic diversity in the longer term. After 20 generations, this resulted in considerably increased genetic diversity, with mean coancestries 59% of what random pairing gave. To manage progeny inbreeding, common practice is to reject matings above an upper limit. As a sub-optimal rules-based approach, this resulted in 26% decreased genetic diversity and 8% increased inbreeding in the long term, compared to random pairing. In contrast, including mean progeny inbreeding as a continuous variable in the overall objective function decreased final inbreeding by 37% compared to random pairing. Adding some emphasis on selection for a single trait resulted in a similar pattern of effects on coancestry and inbreeding, with 12% higher trait response under CAM. Results indicate the properties of alternative methods, but we encourage users to do their own investigations of particular scenarios, such as including inbreeding depression. Practical implementation of these methods is discussed: they have been widely adopted in domestic animal breeding and are highly flexible to accommodate a wide range of technical and logistical objectives and constraints.

International audience; In the past three decades, in vitro systematic evolution of ligands by exponential enrichment (SELEX) has yielded many aptamers for translational applications in both research and clinical settings. Despite their promise as an alternative to antibodies, the low success rate of SELEX (∼30 %) has been a major bottleneck that hampers the further development of aptamers. One hurdle is the lack of chemical diversity in nucleic acids. To address this, the aptamer chemical repertoire has been extended by introducing exotic chemical groups, which provide novel binding functionalities. This review will focus on how modified aptamers can be selected and evolved, with illustration of some successful examples. In particular, unique chemistries are exemplified. Various strategies of incorporating modified building blocks into the standard SELEX protocol are highlighted, with a comparison of the differences between pre-SELEX and post-SELEX modifications. Nucleic acid aptamers with extended functionality evolved from non-natural chemistries will open up new vistas for function and application of nucleic acids.

Common bean (Phaseolus vulgaris L.) is a major component of agricultural systems and diets of the urban and rural populations of East and Central Africa, providing Fe and Zn essential to the health and well-being of African women and children, and protein essential for the entire household. However, bean consumption is limited by constraints such as long cooking time (CT). Cooking demands large amounts of water, fuel and time. It has negative effects on the environment, livelihoods, security and health. Genetic variability in cooking time is documented. Recent development of new breeding methods based on pedigree and genomic selection together with optimal contribution selection (OCS) offers an opportunity to accelerate breeding for rapid CT and higher Fe and Zn grain content. Genotypic and phenotypic data of an African diversity pool, representing key bean market classes, were used to generate genomic estimated breeding values (GEBVs) for grain yield, CT, Fe and Zn. GEBV’s were weighted to maximise the desired outcome in an economic index. From 161 candidate bean genotypes with GEBVs, 67 were chosen for 80 matings within six major grain market classes. An additional 22 breeder nominated matings were included. The predicted outcomes in the first cycle showed a major improvement in population mean for index (+286.77 US$/ha), 6.2% increase in GY and 7.3% reduction in CT, with an achieved increase in population co-ancestry of 0.0753. A 30% reduction in the mean population CT and improved Fe (15%) and Zn (10%), is expected after 5 cycles of annual recurrent selection.

International audience; AbstractBackgroundRecessive loss-of-function (LOF) alleles at genes which are essential for life, can result in early embryonic mortality. Cattle producers can use the LOF carrier status of individual animals to make selection and mate allocation decisions.MethodsTwo beef cattle breeding strategies i.e. (1) selection against LOF carriers as parents and (2) simultaneous selection and mate allocation to avoid the occurrence of homozygous offspring in three scenarios, which differed in number and frequency of LOF alleles were evaluated using the mate selection program, MateSel. Scenarios included (a) seven loci with high-frequency LOF alleles, (b) 76 loci with low-frequency LOF alleles, and (c) 50 loci with random high- and low-frequency LOF alleles. In addition, any savings resulting from the information obtained by varying the percentage (0–100%) of the herd genotyped, together with segregation analysis to cover ungenotyped animals, were calculated to determine (1) which percentage optimized net profit for a fixed cost of genotyping ($30/test), and (2) the breakeven cost for genotyping.ResultsWith full knowledge of the LOF alleles carried by selection candidates, the most profitable breeding strategy was always simultaneous selection and mate allocation to avoid homozygous affected offspring (aa) as compared to indiscriminate selection against carrier parents (Aa). The breakeven value of genotyping depended on the number of loci modeled, the LOF allele frequencies, and the mating/selection strategies used. Genotyping was most valuable when it was used to avoid otherwise high levels of embryonic mortalities. As the number of essential loci with LOF alleles increased, especially when some were present at relatively high minor allele frequencies, embryonic losses increased, and profit was maximized by genotyping 10 to 20% of a herd and using that information to reduce these losses.ConclusionsGenotyping 100% of the herd was never the most profitable outcome in any scenario; however, genotyping some proportion of the herd, together with segregation analysis to cover ungenotyped animals, maximized overall profit in scenarios with large numbers of loci with LOF alleles. As more LOF alleles are identified, such a mate selection software will likely be required to optimally select and allocate matings to balance the rate of genetic gain, embryonic losses, and inbreeding.

Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro fertilization and embryo transfer (JIVET) have been shown to accelerate genetic gain by increasing selection intensity and decreasing generation interval. Genomic selection (GS) increases the accuracy of selection of young candidates which can further accelerate genetic gain. Optimal contribution selection (OCS) is an effective method of keeping the rate of inbreeding at a sustainable level while increasing genetic merit. OCS could also be used to selectively and optimally allocate reproductive technologies in mate selection while accounting for their cost. This study uses stochastic simulation to simulate breeding programmes that use a combination of artificial insemination (AI) or natural mating (N), MOET and JIVET with GS. OCS was used to restrict inbreeding to 1.0% increase per generation and also to optimize use of reproductive technologies, considering their effect on genetic gain as well as their cost. Two Australian sheep breeding objectives were used as an example to illustrate the methodology-a terminal sire breeding objective (A) and a dual-purpose self-replacing breeding objective (B). The objective function used for optimization considered genetic merit, constrained inbreeding and cost of technologies where costs were offset by a premium paid to the seedstock breeder investing in female reproductive technologies. The premium was based on the cumulative discounted expression of genetic merit in the progeny of a commercial tier in the breeding programme multiplied by the proportion of that benefit received by the breeder. With breeding objective B, the highest premium of 64% paid to the breeder resulted in the highest allocation of reproductive technologies (4%-10% for MOET and 19%-54% for JIVET) and hence the highest annual genetic gain. Conversely, breeding objective A, which had a lower dollar value of the breeding objective and a maximum of 5% mating types for JIVET and zero for MOET were optimal, even when highest premiums were paid. This study highlights that the level of investment in breeding technologies to accelerate genetic gain depends on the investment of genetic improvement returned to the breeder per index point gain achieved. It also demonstrates that breeding programmes can be optimized including allocation of reproductive technologies at the individual animal level. Accounting for revenue to the breeder and cost of the technologies can facilitate more practical decision support for beef and sheep breeders.

The real world contains many sources of natural variation. Useful simulation models of real-world problems, such as the optimal allocation of beef cattle into feedlot pens, need to take this into account. Monte Carlo simulation is the usual method of achieving this, using multiple model runs, each with a different set of randomly generated variables. However, when the goal is system optimisation, this approach can make the computation extremely intensive, and difficult for the optimisation of even moderately-sized models. This problem was addressed in an industry model of feedlot operations, where animals are selected and drafted into separate pens, and grown to the optimal weight and fat ranges in the pricing grid. For each pen, it is planned that the majority of the animals achieve this targeted region of the price grid, so that the number of discounted carcasses will be minimal. The main problem is that animals which appear reasonably similar at intake then display variable growth and fattening rates. Hence deterministic predictions are unrealistic, and stochasticity needs to be factored in. However this interferes with the efficient optimisation of this feedlot operation, which ultimately needs to be done 'real-time' (at animal induction), and on-site at the feedlot. Our solution to this dilemma was to transfer all of the modelled stochastic processes onto the evaluation side of this problem, by integrating over Normal distributions applied to the expected prices for each animal. This allows the optimisation runs to directly estimate the expected economic outcome using just one model evaluation for each trial management strategy. This process is illustrated with two case-studies, based on separate past intakes of animals into a commercial feedlot. For both intakes, the simulated results as reflected by the carcass values for the actual times on feed matched well with the observed values. Simulated results indicate that increases in profitability can be obtained both by altering the days on feed, and by better allocation of the animals to the pens. The overall predicted increase in profit per animal was $34 for the first intake, where the actual allocation of animals to the pens was approximately random (the order they were offloaded), and $20 for the second example where the animals had been allocated to pens on a more structured basis (by weights). This optimised allocation scheme appears to offer obvious improvements for feedlot operations, and is currently being trialed by a number of industry collaborators.

Milk recording systems for dairy buffaloes in the Philippines suffer from missing or irregular fat and protein test intervals; hence a suitable method to predict lactation yield is needed. The test interval method was compared with methods that incorporate information on standard lactation curves using Wilmink's (Wil) or Wood's (Wood) function. The latter two methods were also evaluated using a multiple trait procedure with covariances between milk, fat and protein yields, MTP-Wil and MTP-Wood, for 305D yield prediction. For this purpose, 1700 lactation records of dairy buffaloes from seven herds located in the north and south of the Philippines were used. All lactations have daily milk yields and most also have monthly fat and protein test day yields. Accuracy of predicting 305D milk, fat and protein yields were evaluated using ≥9 test day yields (≥9TD) of complete lactations, part lactation of varying lengths and lactations with missing tests with varying intervals between tests. The five methods were compared based on mean squared error and correlation between the predicted and true 305D yield. Prediction of 305D milk and protein yields was equally precise among the five methods when ≥9 test day yields were used. In general, MTP-Wil had slightly better precision compared with TIM and Wil methods in extending part lactations to 305D yields for all 3 traits. The use of Wood's function to model the lactation curve resulted in large prediction error when extending yields to 305D from short lactations. The methods, MTP-Wil and MTP-Wood, which utilize information on covariances between traits had slightly better performance and are able to handle missing tests and longer intervals between tests better than other methods. Fat and protein 305D yields can be predicted using a single test with almost similar accuracy using ≥9TD yields provided that complete milk test day information is available. Overall, MTP-Wil performed better than the other methods and is more applicable in situations where missing tests or irregular test intervals are frequently encountered in milk recording system.

Prediction of heterosis has a long history with mixed success, partly due to low numbers of genetic markers and/or small data sets. We investigated the prediction of heterosis for egg number, egg weight and survival days in domestic white Leghorns, using ∼400 000 individuals from 47 crosses and allele frequencies on ∼53 000 genome-wide single nucleotide polymorphisms (SNPs). When heterosis is due to dominance, and dominance effects are independent of allele frequencies, heterosis is proportional to the squared difference in allele frequency (SDAF) between parental pure lines (not necessarily homozygous). Under these assumptions, a linear model including regression on SDAF partitions crossbred phenotypes into pure-line values and heterosis, even without pure-line phenotypes. We therefore used models where phenotypes of crossbreds were regressed on the SDAF between parental lines. Accuracy of prediction was determined using leave-one-out cross-validation. SDAF predicted heterosis for egg number and weight with an accuracy of ∼0.5, but did not predict heterosis for survival days. Heterosis predictions allowed preselection of pure lines before field-testing, saving ∼50% of field-testing cost with only 4% loss in heterosis. Accuracies from cross-validation were lower than from the model-fit, suggesting that accuracies previously reported in literature are overestimated. Cross-validation also indicated that dominance cannot fully explain heterosis. Nevertheless, the dominance model had considerable accuracy, clearly greater than that of a general/specific combining ability model. This work also showed that heterosis can be modelled even when pure-line phenotypes are unavailable. We concluded that SDAF is a useful predictor of heterosis in commercial layer breeding.

Long-range phasing and haplotype library imputation methodologies are accurate and efficient methods to provide haplotype information that could be used in prediction of breeding value or phenotype. Modelling long haplotypes as independent effects in genomic prediction would be inefficient due to the many effects that need to be estimated and phasing errors, even if relatively low in frequency, exacerbate this problem. One approach to overcome this is to use similarity between haplotypes to model covariance of genomic effects by region or of animal breeding values. We developed a simple method to do this and tested impact on genomic prediction by simulation. Results show that the diagonal and off-diagonal elements of a genomic relationship matrix constructed using the haplotype similarity method had higher correlations with the true relationship between pairs of individuals than genomic relationship matrices built using unphased genotypes or assumed unrelated haplotypes. However, the prediction accuracy of such haplotype-based prediction methods was not higher than those based on unphased genotype information.

Deterministic sampling was used to numerically evaluate the expected log-likelihood surfaces of QTL-marker linkage models in large pedigrees with simple structures. By calculating the expected values of likelihoods, questions of power of experimental designs, bias in parameter estimates, approximate lower-bound standard errors of estimates and correlations among estimates, and suitability of statistical models were addressed. Examples illustrated that bracket markers around the QTL approximately halved the standard error of the recombination fraction between the QTL and the marker, although they did not affect the standard error of the QTL's effect, that overestimation of the distance between the markers caused overestimation of the distance between the QTL and marker, that more parameters in the model did not affect the accuracy of parameter estimates, that there was a moderate positive correlation between the estimates of the QTL effect and its recombination distance from the marker, and that selective genotyping did not introduce bias into the estimates of the parameters. The method is suggested as a useful tool for exploring the power and accuracy of QTL linkage experiments, and the value of alternative statistical models, whenever the likelihood of the model can be written explictly.

Selective harvesting in wild deer (Odocoileus spp.) populations is a common practice that may influence antler size. However, in free-ranging populations, response due to selection is unknown or difficult to quantify because antlers are influenced by nutrition and population demographics. We used quantitative genetic models to predict how white-tailed deer (O. virginianus) antlers would respond to selection and what variables (i.e., population size, age structure, mating ratio, and heritability) most affected antler size. We validated our quantitative genetics program by comparing model results with a population of deer used for controlled breeding experiments; modeled antler points (AP) and score increased (2.2–4.3 AP and 48.5–97.7 cm, respectively) after 8 years of selection, similar to observed increases in AP (3.2) and score (92.3 cm) from the controlled population. In modeled free-ranging populations, mating ratio, age structure, and heritability were more important in influencing antler size than size of the population. However, response to selection in free-ranging populations was lower (0.1–0.9 AP) than controlled breeding populations even after 20 years of selection. These results show that selective harvesting of free-ranging white-tailed deer may be inefficient to change population-level genetic characteristics related to antler size. Response of antlers in free-ranging deer will be less than controlled populations, and possibly modeled free-ranging simulations, because individual reproductive success of males is lower, breeding is done by a large group of males, and reproductive and survival rates are lower. These factors, and others, reduce the amount of improvement that can be made to antlers due to selection. Therefore, selective harvesting in free-ranging populations should be justified for managing population demographics and dynamics, but not for changing the genetic characteristics of populations. © 2011 The Wildlife Society.

Livestock plays an important role in the livelihoods of millions of poor smallholders within developing countries. Although it has been recognized that smallholders could benefit from genetic improvement strategies (breeding programmes), the challenges associated with establishing genetic improvement strategies for smallholder systems are considerable, resulting in limited successes to date. This paper considers the potential role of marker-based selection to genetic improvement strategies for smallholder production systems, taking into account the socio-economic, infrastructural and institutional issues. The paper stresses that no genetic improvement strategy, with or without the use of markers, is likely to succeed unless a systems approach is applied and other system constraints are met. The marker-based selection technologies discussed in this paper include marker- and gene-assisted selection, marker-assisted introgression and genomic selection. The issues addressed include the priorities of marker-based selection against other genetic improvement strategies, the potential benefits of these technologies to smallholder producers, and conditions under which marker-based selection will most likely succeed in smallholder production systems. We suggest that, of these marker-based selection technologies, marker-assisted introgression and the emerging technology of genomic selection appear to offer the most promise to smallholders, although this is yet to be clearly demonstrated. In relation to genomic selection, developing countries should maintain a ‘watching brief’ on this technology, but simultaneously act now so that they can capitalize on this technology if and when it becomes applicable in the future.

Submitted by Guilherme Lemeszenski (guilherme@nead.unesp.br) on 2013-08-22T19:07:37Z No. of bitstreams: 1 S1516-35982010000700005.pdf: 122209 bytes, checksum: 328b9d58035a6f16c910f8bd158de33a (MD5) Made available in DSpace on 2013-08-22T19:07:37Z (GMT). No. of bitstreams: 1 S1516-35982010000700005.pdf: 122209 bytes, checksum: 328b9d58035a6f16c910f8bd158de33a (MD5) Previous issue date: 2010-07-01 Made available in DSpace on 2013-09-30T18:02:40Z (GMT). No. of bitstreams: 2 S1516-35982010000700005.pdf: 122209 bytes, checksum: 328b9d58035a6f16c910f8bd158de33a (MD5) S1516-35982010000700005.pdf.txt: 35641 bytes, checksum: 204149f5994f8a409ca2dd86bef11754 (MD5) Previous issue date: 2010-07-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:18:54Z No. of bitstreams: 2 S1516-35982010000700005.pdf: 122209 bytes, checksum: 328b9d58035a6f16c910f8bd158de33a (MD5) S1516-35982010000700005.pdf.txt: 35641 bytes, checksum: 204149f5994f8a409ca2dd86bef11754 (MD5) Made available in DSpace on 2014-05-20T13:18:54Z (GMT). No. of bitstreams: 2 S1516-35982010000700005.pdf: 122209 bytes, checksum: 328b9d58035a6f16c910f8bd158de33a (MD5) S1516-35982010000700005.pdf.txt: 35641 bytes, checksum: 204149f5994f8a409ca2dd86bef11754 (MD5) Previous issue date: 2010-07-01 Um programa baseado na técnica de evolução diferencial foi desenvolvido para a definição da contribuição genética ótima na seleção de candidatos a reprodução. A função- objetivo a ser otimizada foi composta pelo mérito genético esperado da futura progênie e pela coascendência média dos animais em reprodução. Conjuntos de dados reais e simulados de populações com gerações sobrepostas foram usados para validar e testar o desempenho do programa desenvolvido. O programa se mostrou computacionalmente eficiente e viável para ser aplicado na prática e as consequências esperadas de sua aplicação, em comparação a procedimentos empíricos de controle da endogamia e/ou com a seleção baseada apenas no valor genético esperado, seriam a melhora da resposta genética futura e limitação mais efetiva da taxa de endogamia. A program to determine optimum contribution selection using differential evolution was developed. The objective function to be optimized was composed by the expected merit of the future progeny and the coancestry among selected parents. Simulated and real datasets of populations with overlapping generations were used to validate and test the performance of the program. The program was computationally efficient and feasible for practical applications. The expected consequences of using the program, in contrast to empirical procedures to control inbreeding and/or to selection based exclusively on expected genetic merit, would be the improvement of the selection response and a more effective control of inbreeding. GenSys Consultores Associados S/S Ltda UNESP FCAV Departamento de Zootecnia University of New England UNESP FCAV Departamento de Zootecnia

Genotyping required to track family membership in aquaculture breeding programs is reduced dramatically by estimating the contributions of different families to pooled samples of tissue. This approach is relevant to widely differing scenarios involving animals, plants, and microbes. For the family membership scenario, SNP markers are genotyped for the contributing families' parents, and quantitatively genotyped to estimate allele frequencies within the mixed-family pooled tissue. Results are used to infer proportional contributions of the different families to the pool. Different computational strategies were tested for bias and sampling error. A correlation of 99% between estimated and true genetic contributions was achieved using 20 (50) randomly chosen SNPs at a standard error of allele frequency estimates of 0.01 (0.02). Optimal grouping of families and choice of markers further increases performance markedly. Trait means and distributions of families can be quite accurately estimated by tissue sampling across the range of trait values

Some individual genetic markers show strong and apparently consistent effects on trait merit and are taken as causative mutations that can be used directly as fixed effects in marker-assisted selection programs. If the effect of such a marker is seen to decrease over time, key reasons include epistasis, where the effect depends on genetic background, and recombination, where the marker is in fact not causative, and strong linkage disequilibrium between the marker and the causative QTL is breaking down. This paper presents a method to detect the latter scenario, including calculation of the probability of a recombinant haplotype for each gamete contributing to each individual in a pedigree. This method requires only pedigree, phenotypes and genotypic information on the single marker. Missing marker genotypes are handled by the method, but with diminishing power. For biallelic markers, strong QTL effects are needed for the method to be of clear value. Given suitable results, breeders may chose to eliminate certain individuals from the breeding program in order to continue using the single genetic marker under high linkage disequilibrium with the causative QTL. Alternatively, other linked markers might be sought that can be used individually or in haplotype tests to restore strong LD for marker-assisted selection.

The cDNA microarray is an important tool for generating large datasets of gene expression measurements. An efficient design is critical to ensure that the experiment will be able to address relevant biological questions. Microarray experimental design can be treated as a multicriterion optimization problem. For this class of problems evolutionary algorithms (EAs) are well suited, as they can search the solution space and evolve a design that optimizes the parameters of interest based on their relative value to the researcher under a given set of constraints. This paper introduces the use of EAs for optimization of experimental designs of spotted microarrays using a weighted objective function. The EA and the various criteria relevant to design optimization are discussed. Evolved designs are compared with designs obtained through exhaustive search with results suggesting that the EA can find just as efficient optimal or near-optimal designs within atractable timeframe.

Mechanistic animal growth models can incorporate a description of the genotype as represented by underlying biological traits that aim to specify the animal’s genetic potential for performance, independent from the environmental factors captured by the models. It can be argued that these traits may therefore be more closely associated to genetic potential, or components of genetic merit that are more robust across environments, than the environmentally dependent phenotypic traits currently used for genetic evaluation. The prediction of merit for underlying biological traits can be valuable for breeding and development of selection strategies across environments. Model inversion has been identified as a valid method for obtaining estimates of phenotypic and genetic components of the biological traits representing the genotype in the mechanistic model. The present study shows how these estimates were obtained for two existing pig breeds based on genetic and phenotypic components of existing performance trait records. Some of the resulting parameter estimates associated with each breed differ substantially, implying that the genetic differences between the breeds are represented in the underlying biological traits. The estimated heritabilities for the genetic potentials for growth, carcass composition and feed efficiency as represented by biological traits exceed the heritability estimates of related phenotypic traits that are currently used in evaluation processes for both breeds. The estimated heritabilities for maintenance energy requirements are however relatively small, suggesting that traits associated with basic survival processes have low heritability, provided that maintenance processes are appropriately represented by the model. The results of this study suggest that mechanistic animal growth models can be useful to animal breeding through the introduction of new biological traits that are less influenced by environmental factors than phenotypic traits currently used. Potential value comes from the estimation of underlying biological trait components and the explicit description of their expression across a range of environments as predicted by the model equations.

A simple model based on one single identified quantitative trait locus (QTL) in a two-way crossing system was used to demonstrate the power of mate selection algorithms as a natural means of opportunistic line development for optimization of crossbreeding programs over multiple generations. Mate selection automatically invokes divergent selection in two parental lines for an over-dominant QTL and increased frequency of the favorable allele toward fixation in the sire-line for a fully-dominant QTL. It was concluded that an optimal strategy of line development could be found by mate selection algorithms for a given set of parameters such as genetic model of QTL, breeding objective and initial frequency of the favorable allele in the base populations, etc. The same framework could be used in other scenarios, such as programs involving crossing to exploit breed effects and heterosis. In contrast to classical index selection, this approach to mate selection can optimize long-term responses.

Objective—To determine prevalence, level of inbreeding, heritability, and mode of inheritance for rupture of the cranial cruciate ligament (RCCL) in Newfoundlands. Design—Retrospective and recruitment study. Animals—574 client-owned Newfoundlands. Procedure—Medical records from January 1, 1996, to December 31, 2002, were evaluated for prevalence of RCCL. A pedigree was constructed by use of recruited Newfoundlands with RCCL status based on results of veterinary examination; level of inbreeding, heritability, and mode of inheritance were calculated. Results—Hospital prevalence for RCCL was 22%; dogs in the pedigree from the recruitment study had a mean level of inbreeding of 1.19 × 10−4, heritability of 0.27, and a possible recessive mode of inheritance with 51% penetrance for RCCL. Conclusions and Clinical Relevance—Identification of a genetic basis for RCCL in Newfoundlands provided evidence that investigators can now focus on developing methods to identify carriers to reduce the prevalence of RCCL.

Recently, evolutionary algorithms (encompassing genetic algorithms, evolution strategies, and genetic programming) have proven to be the best general method for the optimisation of large, difficult problems, including agricultural models. Differential evolution (DE) is one comparatively simple variant of an evolutionary algorithm. DE has only three or four operational parameters, and can be coded in about 20 lines of pseudo-code. Investigations of its performance in the optimisation of a challenging beef property model with 70 interacting management options (hence a 70-dimensional optimisation problem) indicate that DE performs better than Genial (a real-value genetic algorithm), which has been the preferred operational package thus far. Despite DE's apparent simplicity, the interacting key evolutionary operators of mutation and recombination are present and effective. In particular, DE has the advantage of incorporating a relatively simple and efficient form of self-adapting mutation. This is one of the main advantages found in evolution strategies, but these methods usually require the burdening overhead of doubling the dimensionality of the search-space to achieve this. DE's processes are illustrated, and model optimisations totaling over two years of Sun workstation computation are presented. These results show that the baseline DE parameters work effectively, but can be improved in two ways. Firstly, the population size does not need to be overly high, and smaller populations can be considerably more efficient; and second, the periodic application of extrapolative mutation may be effective in counteracting the contractive nature of DE's intermediate arithmetic recombination in the latter stages of the optimisations. This provides an escape mechanism to prevent sub-optimal convergence. With its ease of implementation and proven efficiency, DE is ideally suited to both novice and experienced users wishing to optimise their simulation models.

Summary This research compares three different indices for ranking animals for genotyping for a single biallelic locus. The indices were designed to predict which animal to genotype in each genotyping cycle in order to maximize the utility of the resulting information across the entire population. In each genotyping cycle, a single animal is genotyped, segregation analysis used to provide genotype probabilities for the loci of ungenotyped animals, and the index applied to determine which animal to genotype in the next cycle. The first index is based on a linear regression model combining seven herd and individual animal attributes, with the remaining two indices based on genotype probability index (GPI) and numerator relationship (CON). The linear model gives superior predictive performance, in terms of utility (herd average GPI) for the initial 5% of the population genotyped. After this point, the best index is (CON−GPI), which combines a high degree of relationship of the animal to all other live animals in the pedigree, with a low GPI. Zusammenfassung Zyklische Genotypisierungsstrategien I: Ein Vergleich der Rangierungskriterien Diese Untersuchung vergleicht drei verschiedene Indices zur Rangierung der Genotypisierung von Tieren fur einen einzelnen diallelen Genort. Die Indices wurden zur Voraussage, welches Tier in jedem Genotypisierungszyklus genotypisiert werden soll, entworfen, um den Nutzen der daraus resultierenden Information uber die gesamte Population zu maximieren. In jedem Genotypisierungszyklus wird ein einzelnes Tier genotypisiert und eine Segregationsanalyse zur Bereitstellung der Genotypwahrscheinlichkeiten fur die Genorte der nichttypisierten Tiere genutzt. Dieser Index wird zur Festlegung des Tieres verwendet, das im nachsten Zyklus genotypisiert werden soll. Der erste Index basiert auf einem linearen Regressionsmodell, welches sieben Herden- und Einzeltiereigenschaften kombiniert und zwei Indices verbleiben lasst, die auf dem Genotyp-Wahrscheinlichkeitsindex (GPI) und Verwandtschaft (CON) basieren. Auf der Basis der ersten 5% der genotypisierten Population liefert das lineare Modell hinsichtlich des Nutzens (Herdendurchschnitt GPI) eine uberlegene vorausgesagte Leistung. Nach diesem Punkt ist der beste Index (CON–GPI), welcher einen hohen Grad der Verwandtschaft der Tiere uber alle anderen lebenden Tier in dem Stammbaum mit einem niedrigen Genotyp-Wahrscheinlichkeitsindex kombiniert.

Summary This paper investigates the potential problems associated with assuming incorrect population frequencies for segregation analysis when animals are genotyped one by one in a cyclic fashion with segregation analysis carried out at each cycle. The base population allele frequencies of 0.1 and 0.5 studied, with the incorrect frequencies assumed for segregation analysis of 0.5 and 0.1 respectively, were investigated on the basis of their covering the range of possible frequencies and the most erroneous assumption of frequencies. An index modelled using linear regression (LR) was employed to choose the next animal to be genotyped in each cycle, based on segregation analysis at the incorrect frequency. The resultant utility was evaluated both at the correct frequency giving actual utility and at the incorrect frequency, the perceived utility. The results are compared with those in which all segregation analysis was carried out at the correct base population allele frequency. The assumption of incorrect population frequency for segregation analysis leads to a decline in the predictive performances of both indices in terms of the true utility of prediction. When utility is also computed at the incorrect frequency to give perceived utility, the assumption of incorrect population frequency of q = 0.5 for segregation analysis of populations simulated at q = 0.1 leads to a perceived decline in the predictive performance of the LR index. However, the assumption of incorrect population frequency of q = 0.1 for segregation analysis of populations simulated at q = 0.5 leads to a perceived increased utility. This latter paradox is explained in terms of genotype probabilities with reference to the method of construction of the genotype probability index. Zusammenfassung Zyklische Genotypisierungsstrategien II: Wahrer und wahrgenommener Nutzen unter Annahme fehlerhafter Allelfrequenzen Dieser Artikel untersucht die mit angenommenen fehlerhaften Populationsfrequenzen assoziierten potentiellen Probleme in der Segregationsanalyse, wenn ein Tier nach dem anderen in einer zyklischen Art und Weise mit einer bei jedem Zyklus durchgefuhrten Segregationsanalyse typisiert wird. Die Basispopulation mit den fur die Segregationsanalyse untersuchten Allelfrequenzen von 0,1 und 0,5 beziehungsweise den angenommen fehlerhaften Frequenzen von 0,5 und 0,1 wurde untersucht auf der Basis des Abdeckens des Bereichs der moglichen Frequenzen und der Annahme von hochst fehlerhaften Frequenzen. Ein mittels der linearen Regression (LR) modellierter Index wurde verwendet, der bei den fehlerhaften Frequenzen auf der Segregationsanalyse basiert, um das nachste zu typisierende Tier in jedem Zyklus auszuwahlen. Der resultierende Nutzen wurde sowohl bei den korrekten Frequenzen mit dem aktuellen Nutzen als auch bei den falschen Frequenzen mit dem wahrgenommenen Nutzen bewertet. Die Ergebnisse wurden mit denen verglichen, bei denen alle Segregationsanalysen auf der Basis der korrekten Allelfrequenzen der Population durchgefuhrt wurden. Die Annahme der fehlerhaften Populationsfrequenzen fur die Segregationsanalyse fuhrte zu einer Abnahme der vorausgesagten Leistungen in beiden Indices bezuglich des wahren Nutzens der Vorhersage. Wenn der Nutzen mittels der falschen Frequenzen berechnet wird, um den wahrgenommenen Nutzen zu ermitteln, fuhrt die Annahme der falschen Populationsfrequenz von q = 0,5 fur die bei q = 0,1 simulierte Segregationsanalyse der Populationen zu einem wahrgenommenen Abfall in der vorausgesagten Leistung des LR Index. Die Annahme der falschen Populationsfrequenz von q = 0,1 fur die bei q = 0,5 simulierte Segregationsanalyse der Populationen fuhrt jedoch zu einem wahrgenommenen zunehmenden Nutzen. Das letztere Paradoxon wird hinsichtlich der Genotypwahrscheinlichkeiten in Bezug auf die Methode zur Konstruktion des Genotyp-Wahrscheinlichkeits-Index erklart.

Summary This paper introduces a method to balance genetic diversity, genetic merit and population viability in the establishment of conservation programmes for which there can be control of breeding to an extent. These issues are included in a function that is optimized using an evolutionary algorithm, resulting in a solution to which subpopulations and numbers of individuals from each to be included in a conserved population. The method is illustrated by use of microsatellite data from swamp buffaloes, which are used to estimate genetic variation within and between races. Weighting factors for genetic variation, genetic merit and population viability in conserved populations were varied to give a range of interesting solutions. Higher levels of emphasis on genetic diversity led to preservation of pure races within the conserved population. In contrast, higher levels of emphasis on population viability can lead to rationalization of genetic resources through crossing of closely related races, particularly where few individuals of one or both sexes remain. The approach can be extended to handle tactical issues such as culling and mate allocation for individual animals, where opportunities for such levels of control exist in the establishment and ongoing maintenance of a conservation programme. Zusammenfassung Abwagung von genetischer Diversitat, genetischem Fortschritt und Uberlebensfahigkeit einer Population in Erhaltungsprogrammen Diese Veroffentlichung fuhrt eine Methode zur Abwagung von genetischer Diversitat, genetischem Fortschritt und der Uberlebensfahigkeit einer Population zur Erstellung von solchen Erhaltungsprogrammen ein, fur die eine Zuchtkontrolle moglich ist. Die genannten Punkte werden in eine Funktion eingeschlossen, die durch einen evolutionaren Algorithmus optimiert wird und zu einer Losung unter Einbeziehung der Subpopulationen und Anzahl an Individuen der zu konservierenden Population fuhrt. Die Methode wird veranschaulicht mit Mikrosatellitendaten von Sumpfbuffeln anhand deren die genetische Variation innerhalb und zwischen den Rassen geschatzt wurde. Die Gewichtungsfaktoren fur genetische Variation, genetischen Fortschritt und Uberlebensfahigkeit in den konservierten Populationen wurden variiert, um eine Auswahl an verschiedenen Losungen zu erhalten. Eine hohere Gewichtung der genetischen Diversitat fuhrte zur Erhaltung von reinen Rassen innerhalb einer konservierten Population. Im Gegensatz dazu kann eine hohere Gewichtung der Uberlebensfahigkeit zu einer Rationalisierung von genetischen Ressourcen fuhren, insbesondere durch Kreuzung von eng verwandte Rassen, wenn nur noch wenige Individuen eines oder beider Geschlechter vorhanden sind. Der Ansatz kann ausgeweitet werden, um taktisches Vorgehen wie Merzung und Anpaarungsempfehlungen fur individuelle Tiere fur den Fall zu berucksichtigen, dass solche Kontrollmechanismen zur Schaffung und fortlaufenden Erhaltung von Erhaltungsprogrammen existieren.

Identification of recombination events and which chromosomal segments contributed to an individual is useful for a number of applications in genomic analyses including haplotyping, imputation, signatures of selection, and improved estimates of relationship and probability of identity by descent. Genotypic data on half-sib family groups are widely available in livestock genomics. This structure makes it possible to identify recombination events accurately even with only a few individuals and it lends itself well to a range of applications such as parentage assignment and pedigree verification. Here we present hsphase, an R package that exploits the genetic structure found in half-sib livestock data to identify and count recombination events, impute and phase un-genotyped sires and phase its offspring. The package also allows reconstruction of family groups (pedigree inference), identification of pedigree errors and parentage assignment. Additional functions in the package allow identification of genomic mapping errors, imputation of paternal high density genotypes from low density genotypes, evaluation of phasing results either from hsphase or from other phasing programs. Various diagnostic plotting functions permit rapid visual inspection of results and evaluation of datasets. The hsphase package provides a suite of functions for analysis and visualization of genomic structures in half-sib family groups implemented in the widely used R programming environment. Low level functions were implemented in C++ and parallelized to improve performance. hsphase was primarily designed for use with high density SNP array data but it is fast enough to run directly on sequence data once they become more widely available. The package is available (GPL 3) from the Comprehensive R Archive Network (CRAN) or from http://www-personal.une.edu.au/~cgondro2/hsphase.htm .

El presente trabajo revisa algunos métodos de detección de genes mayores en poblaciones animales, usando solamente información fenotípica y de relaciones genéticas (pedigrí). Estos métodos incluyen regresión (vg. Findgene) y muestreo de Gibbs. Ambos usan análisis de segregación y un modelo lineal mixto que incluye los efectos de un gen mayor y sus probabilidades, la variación poligénica y los efectos ambientales fijos en el análisis de un carácter cuantitativo. El análisis de segregación por capas, da las probabilidades de la configuración genotípica para cada individuo, condicional en la información fenotípica y las relaciones genéticas en datos con genealogía a través de la población. Se analizan las limitaciones y ventajas de esta metodología comparada con la detección de loci con efecto en caracteres cuantitativos (QTL) usando marcadores moleculares. Se discute el uso de estos métodos en el diseño de estudios mas eficaces de revisión genómica para detectar QTL.

A genotype probability index (GPI) is proposed to indicate the information content of genotype probabilities derived from a segregation analysis. Typically, some individuals are genotyped at a marker locus or a quantitative trait locus, and segregation analysis is used to make genotype inferences about ungenotyped relatives. Genotype probabilities for a two-allele autosomal locus are plotted on a triangular surface. The GPI has a value of zero at the point corresponding to Hardy-Weinberg frequencies, and a value of 100% at the vertices of the triangle. Trigonometric functions are used to help calculate intermediate index values. It is proposed that such an index can be useful to help identify which ungenotyped individuals or loci should be genotyped to maximize the benefit/cost of genotyping operations.

Summary A computationally tractable approach to complex segregation analysis for large data sets is described. This is then used to extend the value of DNA tests for major locus genotype, by calculating probabilities of belonging to each genotype class for all animals not DNA tested. Under the conditions simulated, this approach led to an approximate doubling of the number of animals genotyped with 100% confidence, the ability to exclude many more animals from one genotype class, plus a high probability (> 90%) of belonging to a given genotype class for many other animals. Zusammenfassung Verwendung von Spaltungsanalyse fur rationelle Anwendung von DNA Tests fur Hauptgene Ein rechnermasig traktabler Ansatz zu komplexer Spaltungsanalyse groser Datenmengen wird beschrieben. Dieser wird verwendet, um den Wert der DNA Tests fur Hauptgene zu erweitern und zwar dadurch, das die Wahrscheinlichkeiten der Zugehorigkeit zu jeder Genotypenklasse fur Tiere ohne DNA Diagnose berechnet wird. Die Simulation ergab eine Verdopplung der Zahl der Tiere, die mit 100% Sicherheit genotypisiert werden konnten sowie Ausschlus von mehr Tieren aus einer gegebenen Genotypenklasse und schlieslich eine hohe Wahrscheinlichkeit (> 90%) der Zuweisung vieler Tiere zu gegebenen Genotypenklassen.

Multiple-sire mating is evaluated for a range of conditions, for its impact on regular genetic evaluation analyses in performance recording, and on selection response and inbreeding in breeding programs as a whole. Substitution of the average for the complete numerator relationship matrix (NRM) in the mixed model equations produced little bias in genetic trend estimates but substantial bias of inbreeding estimates. By recording sire group membership and dam identification under multiple-sire mating and using these and the average NRM in genetic evaluation analyses, it is shown that: 26–63% more genetic gain is achievable compared with selection on individual performance alone (no genetic analysis); and realised genetic gain is reduced by less than 6–24% compared with single-sire mating and use of complete BLUP analysis.

Background Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation. Methods An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis. Results Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored. Conclusions The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations.

Background There is wide interest in calculating genomic breeding values (GEBVs) in livestock using dense, genome-wide SNP data. The general framework for genomic selection assumes all individuals are genotyped at high-density, which may not be true in practice. Methods to add additional genotypes for individuals not genotyped at high density have the potential to increase GEBV accuracy with little or no additional cost. In this study a long haplotype library was created using a long range phasing algorithm and used in combination with segregation analysis to impute dense genotypes for non-genotyped dams in the training dataset (S1) and for non-genotyped or low-density genotyped individuals in the prediction dataset (S2), using the 14th QTL-MAS Workshop dataset. Alternative low-density scenarios were evaluated for accuracy of imputed genotypes and prediction of GEBVs. Results In S1, females in the training population were not genotyped and prediction individuals were either not genotyped or genotyped at low-density (evenly spaced at 2, 5 or 10 Mb). The proportion of correctly imputed genotypes for training females did not change when genotypes were added for individuals in the prediction set whereas the number of correctly imputed genotypes in the prediction set increased slightly (S1). The S2 scenario assumed the complete training set was genotyped for all SNPs and the prediction set was not genotyped or genotyped at low-density. The number of correctly imputed genotypes increased with genotyping density in the prediction set. Accuracy of genomic breeding values for the prediction set in each scenario were the correlation of GEBVs with true breeding values and were used to evaluate the potential loss in accuracy with reduced genotyping. For both S1 and S2 the GEBV accuracies were similar when the prediction set was not genotyped and increased with the addition of low-density genotypes, with the increase larger for S2 than S1. Conclusions Genotype imputation using a long haplotype library and segregation analysis is promising for application in sparsely-genotyped pedigrees. The results of this study suggest that dense genotypes can be imputed for selection candidates with some loss in genomic breeding value accuracy, but with levels of accuracy higher than traditional BLUP estimated breeding values. Accurate genotype imputation would allow for a single low-density SNP panel to be used across traits.

Summary A stochastic simulation was carried out to investigate the impact of using an index of ehvs and accuracy on genetic response and utility. Two population types were considered based on the range of accuracy used (Type A: 0–1.0 and B: 0.5–1.0). Five hundred replications were carried out leading to the evaluation of the sample mean response and its variance. The index weightings defining plateaux within 1% and 5% of the maximum response were identified, these suggesting that for population Types A and B, minimal loss in response will occur if weightings on accuracy of up to ± 0.5 σ and ± 1.5σ are selected. These weightings result in relatively minor changes in the ranking of animals, particularly where only a small number of animals are selected. Utility was defined as a function of the mean and variance of the sample response. The index weightings leading to the maximal utility were identified for seven utility functions (three risk averse, three risk preferring and a risk neutral function). Placing some weighting on accuracy when making selection decisions can be done with little compromise in expected mean response. This gives opportunity to reflect risk preferring or risk averse attitudes through the use of negative or positive weightings on accuracy. However, it seems that somewhat extreme utility functions are required before this opportunity can result in significant benefit. Zusammenfassung Der Wert von Genauigkeit bei Selektionsentscheidungen Zur Untersuchung des Einflusses eines Index aus geschatzten Zuchtwerten und Genauigkeit auf genetischen Fortschritt und Nutzlichkeit wurde durch stochastische Simulation durchgefuhrt. Es wurden zwei Populationstypen untersucht, die einen Bereich von 0–1 (A) in der Genauigkeit oder 0.5–1 (B) haben. Funfhundert Wiederholungen wurden zur Bewertung des Stichprobendurchschnitts hinsichtlich Erfolg und Varianz durchgefuhrt. Die Indexgewichtung, die zu einem Plateau innerhalb 1% bzw. 5% des Maximalfortschrittes fuhrte, wurde identifiziert und lies darauf schliesen, das fur Populationen des Typus A und B minimaler Erfolgsverlust vorhanden ist, wenn Gewichtungen der Genauigkeit von bis zu ± 0,5 und ± 1,5 σ ausgewahlt wurden. Diese Gewichtungen resultierten in relativ geringfugigen Anderungen in der Rangierung von Tieren, besonders bei geringer Zahl von Kandidaten. Nutzlichkeit wurde als eine Funktion von Mittelwert und Varianz des Stichprobenerfolges betrachtet. Indexgewichtungen fur maximale Nutzlichkeit wurden fur sieben Nutzlichkeitsfunktionen (drei Risikoabwendungen, drei Risikoerwahlungen und eine risikoneutrale Funktion) identifiziert. Eine gewisse Gewichtung der Genauigkeit bei Selektionsentscheidungen kann mit wenig Verminderung des erwarteten mittleren Erfolges vorgenommen werden, so das Risikobevorzugungen oder Risikoabwendungen durch Veranderung negativer oder positiver Gewichtungen der Genauigkeit moglich und uberlegenswert sind. Allerdings scheint es, das eher extreme Nutzlichkeitsfunktionen vorhanden sein mussen, bevor diese Anderungen in signifikanten Vorteilen resultieren.

Summary Stochastic simulation was used to evaluate a range of selection strategies with respect to both additive genetic response and inbreeding. Strategies involving selection on BLUP ebvs or individual phenotype, followed by random mating, were compared with mate selection strategies which used portfolio analysis to give joint consideration to genetic merit and inbreeding. An adapted Mean Of Total Absolute Deviations (MOTAD) method was used in a mate selection model to define optimal matings with regard to aggregate genetic merit and inbreeding for a base population h2 of 0.2. Compared with random mating following selection on BLUP ebvs, inbreeding levels after 10 years of selection were able to be reduced under BLUP plus mate selection from ∼.23 to as little as .11. Additive genetic gain was either little compromised or increased. The results suggest that information linking expected levels of genetic merit and inbreeding can be used to find the preferred selection strategy. Zusammenfassung Gemeinsame Kontrolle von Zuchtfortschritt und Inzucht bei Partnerselektion Es wurde stochastische Simulation zur Auswertung einer Reihe von Selektionsstrategien hinsichtlich Zuchtwertzuwachs und Inzucht verwendet. Strategien mit Selektion auf der Basis von BLUP ebvs oder individuellem Phanotyp mit nachfolgender Zufallspaarung wurden mit Partnerselektionsstrategien verglichen, die Portfolioanalyse zur gemeinsamen Beachtung von Zuchtwert und Inzucht verwendeten. Eine Methode adaptierter MITTELWERTE TOTALER ABSOLUTER ABWEICHUNGEN (MOTAD) Methode wurde beim Partnerselektionsmodell zur Definition optimaler Paarungen in Hinblick auf Gesamtzuchtwert und Inzucht bei einer Populationsheritabilitat von 0,2 verwendet. Verglichen mit Zufallspaarung nach Selektion auf BLUP ebvs waren die Inzuchtgrade nach 10 Selektionsjahren von 0,23 auf 0,11 reduziert und additiver Zuchtfortschritt war dabei wenig beeintrachtigt oder nahm sogar zu. Die Ergebnisse weisen darauf hin, das Information, die Zuchtwert und Inzucht verbindet, zur Identifikation erwunschter Selektionsstrategien fuhren kann.

Summary The stability of phenotypic, additive genetic and environmental variances of thorax length of Drosophila melanogaster in pure and synthetic strains was examined in two different environments. Two pure strains from different geographic locations (Melbourne and Townsville) were used, together with three synthetic populations formed from them. The existence of differences in thorax length between the Melbourne and Townsville populations, genotype by environment interaction, and heterosis in crosses between these populations indicate that they are genetically different. Thus geographic separation can cause differences in mean thorax length of flies from different populations. Both the difference in selection histories between the two localities and drift could lead to these differences. Up to the thirty fifth generation there was no evidence of any reduction in the difference between the Melbourne and Townsville populations, in either laboratory environment. The genetic differentiation of strains therefore may be maintained over many generations under new environmental conditions. The fluctuation over generations of heterosis of thorax length is possibly caused by the fluctuation of the rate of loss of favourable epistatic interaction in crossbred genotypes in combination with natural selection effects. Vp was significantly higher in poor than in the good environment. This higher Vp in the poor environment is most likly due to higher non additive genetic variance. Vp was also significantly influenced by strain. In general, Vp values of synthetic strains were higher than those of pure strains in both environments. Finally, the additive and environmental variances of thorax length were relatively stable across strains, generations and environments. Zusammenfassung Wirkung von Herkunften, Kreuzungen und Umwelten auf additiv-genetische und phanotypische Varianzen in Drosophila melanogaster Die Stabilitat phanotypischer, additiv-genetischer und umweltbedingter Varianzen der Thoraxlange von Drosophila melanogaster in reinen und synthetischen Herkunften wurde in zwei verschiedenen Umwelten uberpruft. Zwei reine Herkunfte von verschiedenen Gegenden (Melboune und Townsville) wurden zusammen mit drei zwischen ihnen gebildeten synthetischen Populationen untersucht. Unterschiede in Thoraxlange zwischen Melbourne- und Townsvilleherkunften, Genotypumweltinteraktionen und Heterosis in Kreuzungen zwischen diesen Populationen zeigen, das sie sich genetisch unterscheiden. Die geographische Trennung kann also Unterschiede in der mittleren Thoraxlange zur Folge haben, wobei unterschiedliche Selektionsgeschichte in beiden Gegenden und Drift dies verursachen konnen. Bis zur 35. Generation gab es in keinem Labormilieu einen Hinweis auf eine Reduktion der Unterschiede zwischen den beiden Populationen. Die genetische Differenz der Herkunfte erhalt sich daher auch unter neuen Umweltverhaltnissen uber viele Generationen. Die Schwankung in Heterosis fur Thoraxlange ist moglicherweise durch Schwankungen in der Verlustrate gunstiger epistatischer Interaktionswirkungen in Kreuzungsgenotypen zusammen mit naturlichen Selektionswirkungen verursacht. Vp war durch Umweltbedingungen signifikant beeinflust und hoher in schlechtem als in gutem Milieu. Der hohe Wert in schlechtem Milieu ist wahrscheinlich auf nicht-additiv-genetische Varianz zuruckzufuhren. Vp wurde auch signifikant durch Herkunft beeinflust und Werte in synthetischen Linien waren hoher Linien in beiden Milieus. Additive und umweltbedingte Varianzen waren uber Linie, Generationen und Umwelt relativ stabil.

Summary Ambiguous paternity can be incorporated into the mixed model equations (MME) by including the average numerator relatinship matrix (average A), which averages the true sire-offspring relationship over the putative sires. A previous study has shown that some overestimation of genetic trend results from this substitution. A population of 40 breeding females and 2 breeding males was simulated 1,000 times with either random mating or sequential selection continuing for 8 breeding cycles. In the selection case candidates were ranked on estimated breeding values (EBVs) calculated from the MME with an animal model and the average A. Variances of the EBVs and prediction errors were computed. The results showed the average A incorrectly perceives both the variance of family sizes among males and the variance loss due to selection to be smaller. This will lead to an overestimation of genetic trend. Zusammenfassung Folgerungen aus der Anwendung einer durchschnittlichen Verwandtschaftsmatrix bei der Zuchtwertschatzung fur eine Population mit mehreren Vatern in einer Paarungsgruppe In den Mischmodellgleichungen kann eine unklare vaterliche Abstammung durch die Verwendung einer durchschnittlichen Verwandtschaftsmatrix, die die Abstammung zu gleichen Teilen uber die moglichen Vater aufteilt, berucksichtigt werden. Eine fruhere Arbeit hat gezeigt, das diese Masnahme zu einer gewissen Uberschatzung des genetischen Fortschritts fuhrt. Eine Population mit 40 weiblichen und 2 mannlichen Tieren wurde 1000mal uber 8 Paarungsperioden simuliert und zwar mit und ohne gerichteter Selektion. Im Falle der Selektion wurden die Tiere aufgrund der mit einem Tiermodell und der durchschnittlichen Verwandtschaftsmatrix geschatzten Zuchtwerte geordnet. Die Varianzen der Zuchtwerte und der Schatzfehler wurden berechnet. Die Ergebnisse zeigen, das durch eine durch-schnittliche Verwandtschaftsmatrix die Varianz der Grose der Nachkommensgruppen der Vater und der Verlust an genetischer Varianz aufgrund der Selektion unterschatzt wird. Dies fuhrt zu einer Uberschatzung des genetischen Fortschritts.

Background Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data. Methods A long-range phasing and haplotype library imputation algorithm was developed. It combines information from surrogate parents and long haplotypes to resolve phase in a manner that is not dependent on the family structure of a dataset or on the presence of pedigree information. Results The algorithm performed well in both simulated and real livestock and human datasets in terms of both phasing accuracy and computation efficiency. The percentage of alleles that could be phased in both simulated and real datasets of varying size generally exceeded 98% while the percentage of alleles incorrectly phased in simulated data was generally less than 0.5%. The accuracy of phasing was affected by dataset size, with lower accuracy for dataset sizes less than 1000, but was not affected by effective population size, family data structure, presence or absence of pedigree information, and SNP density. The method was computationally fast. In comparison to a commonly used statistical method (fastPHASE), the current method made about 8% less phasing mistakes and ran about 26 times faster for a small dataset. For larger datasets, the differences in computational time are expected to be even greater. A computer program implementing these methods has been made available. Conclusions The algorithm and software developed in this study make feasible the routine phasing of high-density SNP chips in large datasets.

Background Mate selection can be used as a framework to balance key technical, cost and logistical issues while implementing a breeding program at a tactical level. The resulting mating lists accommodate optimal contributions of parents to future generations, in conjunction with other factors such as progeny inbreeding, connection between herds, use of reproductive technologies, management of the genetic distribution of nominated traits, and management of allele/genotype frequencies for nominated QTL/markers. Methods This paper describes a mate selection algorithm that is widely used and presents an extension that makes it possible to apply constraints on certain matings, as dictated through a group mating permission matrix. Results This full algorithm leads to simpler applications, and to computing speed for the scenario tested, which is several hundred times faster than the previous strategy of penalising solutions that break constraints. Conclusions The much higher speed of the method presented here extends the use of mate selection and enables implementation in relatively large programs across breeding units.