The effect of genomic information on optimal contribution selection in livestock breeding programs

BACKGROUND: Long-term benefits in animal breeding programs require that increases in genetic merit be balanced with the need to maintain diversity (lost due to inbreeding). This can be achieved by using optimal contribution selection. The availability of high-density DNA marker information enables the incorporation of genomic data into optimal contribution selection but this raises the question about how this information affects the balance between genetic merit and diversity. METHODS: The effect of using genomic information in optimal contribution selection was examined based on simulated and real data on dairy bulls. We compared the genetic merit of selected animals at various levels of co-ancestry restrictions when using estimated breeding values based on parent average, genomic or progeny test information. Furthermore, we estimated the proportion of variation in estimated breeding values that is due to within-family differences. RESULTS: Optimal selection on genomic estimated breeding values increased genetic gain. Genetic merit was further increased using genomic rather than pedigree-based measures of co-ancestry under an inbreeding restriction policy. Using genomic instead of pedigree relationships to restrict inbreeding had a significant effect only when the population consisted of many large full-sib families; with a half-sib family structure, no difference was observed. In real data from dairy bulls, optimal contribution selection based on genomic estimated breeding values allowed for additional improvements in genetic merit at low to moderate inbreeding levels. Genomic estimated breeding values were more accurate and showed more within-family variation than parent average breeding values; for genomic estimated breeding values, 30 to 40% of the variation was due to within-family differences. Finally, there was no difference between constraining inbreeding via pedigree or genomic relationships in the real data. CONCLUSIONS: The use of genomic estimated breeding values increased genetic gain in optimal contribution selection. Genomic estimated breeding values were more accurate and showed more within-family variation, which led to higher genetic gains for the same restriction on inbreeding. Using genomic relationships to restrict inbreeding provided no additional gain, except in the case of very large full-sib families

Genomic selection in rubber tree breeding: A comparison of models and methods for managing G×E interactions

Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs

Benchmarking database systems for Genomic Selection implementation

Motivation: With high-throughput genotyping systems now available, it has become feasible to fully integrate genotyping information into breeding programs. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able to collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize it in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs, one would need an efficient genotyping data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems. Results: We found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix

Future trends in Animal Breeding due to new genetic tecnologies

The Darwin theory of evolution by natural selection is based on three principles: (a) variation; (b) inheritance; and (c) natural selection. Here, I take these principles as an excuse to review some topics related to the future research prospects in Animal Breeding. With respect to the first principle I describe two forms of variation different from mutation that are becoming increasingly important: variation in copy number and microRNAs. With respect to the second principle I comment on the possible relevance of non-mendelian inheritance, the so-called epigenetic effects, of which the genomic imprinting is the best characterized in domestic species. Regarding selection principle I emphasize the importance of selection for social traits and how this could contribute to both productivity and animal welfare. Finally, I analyse the impact of molecular biology in Animal Breeding, the achievements and limitations of quantitative trait locus and classical marker-assisted selection and the future of genomic selectio

Implications of avoiding overlap between training and testing data sets when evaluating genomic predictions of genetic merit

The aim of this study was to evaluate and quantify the importance of avoiding overlap between training and testing subsets of data when evaluating the effectiveness of predictions of genetic merit based on genetic markers. Genomic selection holds great potential for increasing the accuracy of selection in young bulls and is likely to lead quickly to more widespread use of these young bulls with a shorter generation interval and faster genetic improvement. Practical implementations of genomic selection in dairy cattle commonly involve results of national genetic evaluations being used as the dependent variable to evaluate the predictive ability of genetic markers. Selection index theory was used to demonstrate how ignoring correlations among errors of prediction between animals in training and testing sets could result in overestimates of accuracy of genomic predictions. Correlations among errors of prediction occur when estimates of genetic merit of training animals used in prediction are taken from the same genetic evaluation as estimates for validation of animals. Selection index theory was used to show a substantial degree of error correlation when animals used for testing genomic predictions are progeny of training animals, when heritability is low, and when the number of recorded progeny for both training and testing animals is low. Even when training involves a dependent variable that is not influenced by the progeny records of testing animals (i.e., historic proofs), error correlations can still result from records of relatives of training animals contributing to both the historic proofs and the predictions of genetic merit of testing animals. A simple simulation was used to show how an error correlation could result in spurious confirmation of predictive ability that was overestimated in the training population because of ascertainment bias. Development of a method of testing genomic selection predictions that allows unbiased testing when training and testing variables are estimated breeding values from the same genetic evaluation would simplify training and testing of genomic predictions. In the meantime, a 4-step approach for separating records used for training from those used for testing after correction of fixed effects is suggested when use of progeny averages of adjusted records (e.g., daughter yield deviations) would result in inefficient use of the information available in the data.</p

The Adaptive Significance of Natural Genetic Variation in the DNA Damage Response of Drosophila melanogaster.

Despite decades of work, our understanding of the distribution of fitness effects of segregating genetic variants in natural populations remains largely incomplete. One form of selection that can maintain genetic variation is spatially varying selection, such as that leading to latitudinal clines. While the introduction of population genomic approaches to understanding spatially varying selection has generated much excitement, little successful effort has been devoted to moving beyond genome scans for selection to experimental analysis of the relevant biology and the development of experimentally motivated hypotheses regarding the agents of selection; it remains an interesting question as to whether the vast majority of population genomic work will lead to satisfying biological insights. Here, motivated by population genomic results, we investigate how spatially varying selection in the genetic model system, Drosophila melanogaster, has led to genetic differences between populations in several components of the DNA damage response. UVB incidence, which is negatively correlated with latitude, is an important agent of DNA damage. We show that sensitivity of early embryos to UVB exposure is strongly correlated with latitude such that low latitude populations show much lower sensitivity to UVB. We then show that lines with lower embryo UVB sensitivity also exhibit increased capacity for repair of damaged sperm DNA by the oocyte. A comparison of the early embryo transcriptome in high and low latitude embryos provides evidence that one mechanism of adaptive DNA repair differences between populations is the greater abundance of DNA repair transcripts in the eggs of low latitude females. Finally, we use population genomic comparisons of high and low latitude samples to reveal evidence that multiple components of the DNA damage response and both coding and non-coding variation likely contribute to adaptive differences in DNA repair between populations

Genomic selection on cacao for disease resistance : S04T08

Under favorable conditions, diseases can cause losses up to 100% in cacao. Breeding has been the most effective strategy in reducing these losses. However, breeding is limited by the: a) long generation time; b) large tree size (9 m2/tree); c) multiple measurements per year; d) low heritability; e) requirement of the presence of the pathogen; among others. Genomic selection (GS) overcomes some of these problems. In 2011, a project on genomic selection involving Ceplac and Cirad was started, aiming to implement GS on breeding for diseases resistance and yield. In 2013, a second project is being started involving Ceplac, Cirad and Catie, aiming preventive breeding for moniliasis (a disease still absent in Brazil) resistance through GS. In the first project, a training population of 380 progenies, out of 4000 trees of the third cycle of Cepec´s breeding program, and 90 founder and pedigree clones were chosen. In the second project, a training population of 470 trees was chosen among 2500 trees established in progeny trials and evaluated for moniliasis at CATIE. Founder parents (resistant to moniliasis) of this population was also introduced in Brazil and involved in crosses for preventive breeding. Prediction equations developed in Costa Rica will be used in Brazil for selection in the absence of the pathogen. Aiming to have accurate "phenotype" measurements for the prediction equations, BLUP values from historical data have been used (around 800 thousand data points, of 15 thousand trees). The overall strategy and partial results of these projects will be presented. Work supported by Agropolis Foundation, Fapesb, Capes, Ceplac. (Texte intégral

Local adaptation drives the diversification of effectors in the fungal wheat pathogen Parastagonospora nodorum in the United States

Filamentous fungi rapidly evolve in response to environmental selection pressures in part due to their genomic plasticity. Parastagonospora nodorum, a fungal pathogen of wheat and causal agent of septoria nodorum blotch, responds to selection pressure exerted by its host, influencing the gain, loss, or functional diversification of virulence determinants, known as effector genes. Whole genome resequencing of 197 P. nodorum isolates collected from spring, durum, and winter wheat production regions of the United States enabled the examination of effector diversity and genomic regions under selection specific to geographically discrete populations. 1,026,859 SNPs/InDels were used to identify novel loci, as well as SnToxA and SnTox3 as factors in disease. Genes displaying presence/absence variation, predicted effector genes, and genes localized on an accessory chromosome had significantly higher pN/pS ratios, indicating a higher rate of sequence evolution. Population structure analyses indicated two P. nodorum populations corresponding to the Upper Midwest (Population 1) and Southern/Eastern United States (Population 2). Prevalence of SnToxA varied greatly between the two populations which correlated with presence of the host sensitivity gene Tsn1 in the most prevalent cultivars in the corresponding regions. Additionally, 12 and 5 candidate effector genes were observed to be under diversifying selection among isolates from Population 1 and 2, respectively, but under purifying selection or neutrally evolving in the opposite population. Selective sweep analysis revealed 10 and 19 regions that had recently undergone positive selection in Population 1 and 2, respectively, involving 92 genes in total. When comparing genes with and without presence/absence variation, those genes exhibiting this variation were significantly closer to transposable elements. Taken together, these results indicate that P. nodorum is rapidly adapting to distinct selection pressures unique to spring and winter wheat production regions by rapid adaptive evolution and various routes of genomic diversification, potentially facilitated through transposable element activity