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Genomic selection refers to the use of dense markers covering the whole genome to estimate the breeding value of selection candidates for a quantitative trait. This paper considers prediction of breeding value based on a linear combination of the markers. In this case the best estimate of each marker's effect is the expectation of the effect conditional on the data. To calculate this requires a prior distribution of marker effects. If the marker effects are normally distributed with constant variance, BLUP can be used to calculate the estimated effects of the markers and hence the estimated breeding value (EBV). In this case the model is equivalent to a conventional animal model in which the relationship matrix among the animals is estimated from the markers instead of the pedigree. The accuracy of the EBV can approach 1.0 but a very large amount of data is required. An alternative model was investigated in which only some markers have non-zero effects and these effects follow a reflected exponential distribution. In this case the expected effect of a marker is a non-linear function of the data such that apparently small effects are regressed back almost to zero and consequently these markers can be deleted from the model. The accuracy in this case is considerably higher than when marker effects are normally distributed. If genomic selection is practiced for several generations the response declines in a manner that can be predicted from the marker allele frequencies. Genomic selection is likely to lead to a more rapid decline in the selection response than phenotypic selection unless new markers are continually added to the prediction of breeding value. A method to find the optimum index to maximise long term selection response is derived. This index varies the weight given to a marker according to its frequency such that markers where the favourable allele has low frequency receive more weight in the index.

Datasets comprised of large sets of both predictor and outcome variables are becoming more widely used in research. In addition to the well-known problems of model complexity and predictor variable selection, predictive modelling with such large data also presents a relatively novel and under-studied challenge of outcome variable selection. Certain outcome variables in the data may not be adequately predicted by the given sets of predictors. In this paper, we propose the method of Sparse Multivariate Principal Covariates Regression that addresses these issues altogether by expanding the Principal Covariates Regression model to incorporate sparsity penalties on both of predictor and outcome variables. Our method is one of the first methods that perform variable selection for both predictors and outcomes simultaneously. Moreover, by relying on summary variables that explain the variance in both predictor and outcome variables, the method offers a sparse and succinct model representation of the data. In a simulation study, the method performed better than methods with similar aims such as sparse Partial Least Squares at prediction of the outcome variables and recovery of the population parameters. Lastly, we administered the method on an empirical dataset to illustrate its application in practice.

The Pacific oyster Crassostrea gigas is one of the most commercially important aquaculture species worldwide. The occurrence of inbreeding is common in the breeding practice of oysters due to limited population size, successive high selection pressure, and genetic drift. To determine the potential effects of inbreeding on the potential of genetic improvement for fast growth of oysters, genetic parameters of growth traits in the 7 th to 9 th generation of the orange-shell strain (a typical inbred population) were estimated. At the grow-out stage, the average genetic gain (GG), selection response (SR), and realized heritability ( h R 2 ) for shell height were 6.06–7.06%, 0.43–0.50, and 0.24–0.27, respectively. Meanwhile, the average GG, SR, and h R 2 for body weight were 6.03–7.36%, 0.38–0.41, and 0.21–0.22. There were no significant differences in the corresponding genetic parameters among the three generations ( P  > 0.05). Genetic parameters were estimated for an unselected population of C. gigas that served as a control group and were significantly higher than those of the orange-shell strain ( P  < 0.05). The results of this study suggest that inbreeding reduced the potential for genetic improvement of the rapid growth of oysters. However, some easy-to-operate and low-cost measures including the large size of broodstock, balanced sex ratio, and artificial spawning have been applied to the mass selection of the orange-shell strain, which prevents the situation from getting worse. These results will contribute to a better understanding of the negative effects of inbreeding on C. gigas and the management of inbreeding in shellfish breeding programs.

Background Artificial selection provides a powerful approach to study the genetics of adaptation. Using selective-sweep mapping, it is possible to identify genomic regions where allele-frequencies have diverged during selection. To avoid false positive signatures of selection, it is necessary to show that a sweep affects a selected trait before it can be considered adaptive. Here, we confirm candidate, genome-wide distributed selective sweeps originating from the standing genetic variation in a long-term selection experiment on high and low body weight of chickens. Results Using an intercross between the two divergent chicken lines, 16 adaptive selective sweeps were confirmed based on their association with the body weight at 56 days of age. Although individual additive effects were small, the fixation for alternative alleles across the loci contributed at least 40 % of the phenotypic difference for the selected trait between these lines. The sweeps contributed about half of the additive genetic variance present within and between the lines after 40 generations of selection, corresponding to a considerable portion of the additive genetic variance of the base population. Conclusions Long-term, single-trait, bi-directional selection in the Virginia chicken lines has resulted in a gradual response to selection for extreme phenotypes without a drastic reduction in the genetic variation. We find that fixation of several standing genetic variants across a highly polygenic genetic architecture made a considerable contribution to long-term selection response. This provides new fundamental insights into the dynamics of standing genetic variation during long-term selection and adaptation.

Birth weight (BW) in animal production is an economically important trait in prolific species. The laboratory mouse (Mus musculus) is used as an experimental animal because it is considered a suitable model for prolific species such as rabbits and pigs. Two mouse lines were divergently selected for birth weight variability with a third line of non-selected control population of the same origin as the animals starting the experiment. The objective of this study was, therefore, to compare and evaluate the differential response of each line. The animals were from the 17th generation of both low and high BW variability lines of the divergent selection experiment, including in addition animals from the control line. The dataset contained 389 records from 48 litters of the high line, 734 records from 73 litters of the low line, and 574 records from 71 litters of the control line. The studied traits were as follows: the BW, the BW variance, the BW standard deviation, the BW coefficient of variation within-litter, the weaning weight (WW), the litter size at birth and at weaning, the weight gain, and the preweaning survival. The model included the line effect jointly with the parturition number and its interaction, the linear and quadratic LS as covariates except for the LS trait itself when analyzing litter traits, as well as the pup sex when analyzing individual traits. The low line had a lower BW and WW, but a higher litter size, and greater robustness owing to a higher survival at weaning. As a model of livestock animals, the findings from this experiment led to a proposal of selection for pig production that would combine an increase in litter size with higher survival and welfare. Compared with the control line, a much higher response was observed in the low variability line than in the high line, making it extremely satisfactory given that homogeneity provides advantages in terms of animal welfare and robustness.

Fiber properties measured by high volume instrument (HVI) and advanced fiber information system (AFIS) are currently being used in Upland cotton ( Gossypium hirsutum L.) breeding. It would be interesting to know if it is equally efficient in selection for parameters measured by the two instrumental systems and how are the correlated selection responses of the parameters between the two systems. A study was designed to determine the correlated selection responses between fiber properties measured by the two systems in F 3 , F 4 , and F 5 generations of two crosses, FM 832 × SP 205 and MD 52ne × JJ 1145ne. Selections for HVI-fiber length, HVI-short fiber content, and HVI-maturity were made in the F 3 generation and the correlated responses of AFIS-fiber properties to the selections were estimated in the subsequent F 4 and F 5 generations. Selections for AFIS-fiber length, AFIS-short fiber content, and AFIS-maturity were made in the F 4 generation and the correlated responses of HVI-fiber properties to the selections were estimated in the F 5 generation. Moderate to high realized heritability was identified for both HVI- and AFIS-fiber properties. Significant correlated selection responses of fiber length and maturity between the HVI and AFIS methods were consistently observed in both crosses, suggesting similar genetic basis for fiber length and maturity measured by HVI and AFIS. Therefore, equal effectiveness is expected of the selection for both traits by the two instrumental systems. Correlated selection responses of short fiber content parameters between the HVI and AFIS methods were not consistent, suggesting a possible different genetic basis or differential variations of measurement quality between the two systems.

In many dual-task situations, responses to the second of two tasks are slowed when the time between tasks is short. The response-selection bottleneck model of dual-task performance accounts for this phenomenon by assuming that central processing of the second task is blocked by a bottleneck until central processing of Task 1 is complete. This assumption could be called into question if it could be demonstrated that the response to Task 2 affected the central processing of Task 1, a backward response compatibility effect. Such effects are well-established in younger adults. Backward compatibility effects in older (as well as younger) adults were explored in two experiments. The first experiment found clear backward response compatibility effects for younger adults but no evidence of them for older adults. The second experiment explored backward stimulus compatibility and found similar effects in both younger and older adults. Evidence possibly consistent with some pre-bottleneck processing of Task 2 central stages also was found in the second experiment in both age groups. For younger adults, the results provide further evidence falsifying the claim of an immutable response selection bottleneck. For older adults, the evidence suggested that Task 2 affects Task 1 when there is stimulus compatibility but not when there is response compatibility.

Genetic trends for clinical mastitis (CM), ketosis (KET), retained placenta (RP), and 305-d protein yield (PY305) were calculated for 2 Norwegian dairy cattle selection experiments. The first experiment, accomplished from 1978 to 1989, included groups selected for high (HMP) and low milk production (LMP). The second experiment started in 1989 and included selection for high protein yield (HPY) and low mastitis frequency (LCM). In both experiments proven sires from the active breeding program of Norwegian Red were used as sires. To take into account that selection of sires was external to the experiment, all available data from the Norwegian Red population, including disease records for 2.7million first-lactation cows, were analyzed with a multivariate animal model. Estimated breeding values for cows in the experiments were extracted from this analysis to calculate genetic trends in the selection groups. Genetic trends for PY305 were, as expected, positive for the HMP and HPY groups, and negative for LMP and LCM. The HMP group showed increasing genetic trends for all 3 diseases, arguably a correlated response after selection for increased milk production, whereas the LCM group showed decreasing genetic trends for CM, KET, and RP. The genetic trends for KET and RP in the LCM group are most likely correlated responses after selection against CM. After 5 cow-generations the genetic difference between HPY and LCM was 10 percentage units CM, 1.5 percentage units KET, and 0.5 percentage units RP.

Key messageLarge genetic improvement can be achieved by simultaneous genomic selection for grain yield and protein content when combining different breeding strategies in the form of selection indices.Genomic selection has been implemented in many national and international breeding programmes in recent years. Numerous studies have shown the potential of this new breeding tool; few have, however, taken the simultaneous selection for multiple traits into account that is though common practice in breeding programmes. The simultaneous improvement in grain yield and protein content is thereby a major challenge in wheat breeding due to a severe negative trade-off. Accordingly, the potential and limits of multi-trait selection for this particular trait complex utilizing the vast phenotypic and genomic data collected in an applied wheat breeding programme were investigated in this study. Two breeding strategies based on various genomic-selection indices were compared, which (1) aimed to select high-protein genotypes with acceptable yield potential and (2) develop high-yielding varieties, while maintaining protein content. The prediction accuracy of preliminary yield trials could be strongly improved when combining phenotypic and genomic information in a genomics-assisted selection approach, which surpassed both genomics-based and classical phenotypic selection methods both for single trait predictions and in genomic index selection across years. The employed genomic selection indices mitigated furthermore the negative trade-off between grain yield and protein content leading to a substantial selection response for protein yield, i.e. total seed nitrogen content, which suggested that it is feasible to develop varieties that combine a superior yield potential with comparably high protein content, thus utilizing available nitrogen resources more efficiently.

Top ranking varieties are tested in multiple environments before and after registration in order to assess their value for cultivation and use (VCU testing). Recently, interest has increased in obtaining varieties specifically adapted to organic farming conditions. This raised the question if an independent system of trials may be required for this purpose. To help answering this question, through the exchange network of European cereal researchers SUSVAR (www.cost860.dk), a number of data sets of agronomic traits from barley, wheat and winter triticale, from trials performed in Denmark, Sweden, The Netherlands, France, Switzerland, UK and Germany, were made available and analysed using an approach based on mixed models involving parameters describing genetic correlation between the two types of experiments, i.e., organic and non-organic (high or low input). Estimated variance components and correlations were used to evaluate response to selection and index selection. The response to index selection was analysed as a function of the fraction of available trials assigned to the organic system. The genetic correlations were interpreted in terms of ranking agreement. We found high genetic correlations between both systems for most traits in all countries. Despite high genetic correlations, the chances of very good agreement in observed rankings were moderate. Combining information from both organic and non-organic systems is shown to be beneficial. Further, ignoring information from non-organic trials when making decisions regarding performance under organic conditions is a sub-optimal strategy.