Efficiency of genomic selection with models including dominance effect in the context of Eucalyptus breeding

We developed a simulation study to test the efficiency of genomic selection (GS) in the case of Eucalyptus breeding. We simulated a recurrent selection scheme for clone production over four breeding cycles. Scenarios crossing broad sense heritabilities (H ²?=?0.6 and 0.1) and dominance to additive variance ratios (R?=?0.1; 0.5; and 1) were compared. GS was performed with 1,000 SNPs and 22 QTLs per Morgan and tested against phenotypic selection (PS) based on best linear unbiased prediction of parents and clones. When the training population was made up of the first cycle progeny tests and the candidate populations were the progeny tests of three successive cycles, GS accuracy decreased with breeding cycles (e.g., from 0.9 to 0.4 with H ²?=?0.6 and R?=?0.1), whereas PS presented constant performances (accuracy of 0.8 with H ²?=?0.6 and R?=?0.1). When the training population set was updated by associating data of previous cycles, GS accuracy was improved from 25 % to 418 %, especially with H ²?=?0.1. The GS model including dominance effects performed better in clone selection (genotypic value) when dominance effects were preponderant (R?=?1), heritability was high (H ²?=?0.6 and with an updated training set), but no improvement was detected for parent selection (breeding value). The genetic gains over cycles were lower with the GS method without updating the data set but, with an updated training set, were similar to PS. However, the genetic gain per unit time with GS was 1.5 to 3 times higher than with PS for breeding and clone populations. These results highlight the value of GS in Eucalyptus breeding.     

Integrated genomic selection for rapid improvement of crops

An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.     

Accuracy of genomic selection models in a large population of open-pollinated families in white spruce

Genomic selection (GS) is of interest in breeding because of its potential for predicting the genetic value of individuals and increasing genetic gains per unit of time. To date, very few studies have reported empirical results of GS potential in the context of large population sizes and long breeding cycles such as for boreal trees. In this study, we assessed the effectiveness of marker-aided selection in an undomesticated white spruce (Picea glauca (Moench) Voss) population of large effective size using a GS approach. A discovery population of 1694 trees representative of 214 open-pollinated families from 43 natural populations was phenotyped for 12 wood and growth traits and genotyped for 6385 single-nucleotide polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to predict estimated breeding values using all the available SNPs or SNP subsets of the largest absolute effects, and they were validated using various cross-validation schemes. The accuracy of genomic estimated breeding values (GEBVs) varied from 0.327 to 0.435 when the training and the validation data sets shared half-sibs that were on average 90% of the accuracies achieved through traditionally estimated breeding values. The trend was also the same for validation across sites. As expected, the accuracy of GEBVs obtained after cross-validation with individuals of unknown relatedness was lower with about half of the accuracy achieved when half-sibs were present. We showed that with the marker densities used in the current study, predictions with low to moderate accuracy could be obtained within a large undomesticated population of related individuals, potentially resulting in larger gains per unit of time with GS than with the traditional approach.     

Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding

Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S_2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r² = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (F_ST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.     

Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center''s (CIMMYT''s) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT''s maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.     

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement.     

Dynamics of long-term genomic selection

Background

Simulation and empirical studies of genomic selection (GS) show accuracies sufficient to generate rapid gains in early selection cycles. Beyond those cycles, allele frequency changes, recombination, and inbreeding make analytical prediction of gain impossible. The impacts of GS on long-term gain should be studied prior to its implementation.

Methods

A simulation case-study of this issue was done for barley, an inbred crop. On the basis of marker data on 192 breeding lines from an elite six-row spring barley program, stochastic simulation was used to explore the effects of large or small initial training populations with heritabilities of 0.2 or 0.5, applying GS before or after phenotyping, and applying additional weight on low-frequency favorable marker alleles. Genomic predictions were from ridge regression or a Bayesian analysis.

Results

Assuming that applying GS prior to phenotyping shortened breeding cycle time by 50%, this practice strongly increased early selection gains but also caused the loss of many favorable QTL alleles, leading to loss of genetic variance, loss of GS accuracy, and a low selection plateau. Placing additional weight on low-frequency favorable marker alleles, however, allowed GS to increase their frequency earlier on, causing an initial increase in genetic variance. This dynamic led to higher long-term gain while mitigating losses in short-term gain. Weighted GS also increased the maintenance of marker polymorphism, ensuring that QTL-marker linkage disequilibrium was higher than in unweighted GS.

Conclusions

Losing favorable alleles that are in weak linkage disequilibrium with markers is perhaps inevitable when using GS. Placing additional weight on low-frequency favorable alleles, however, may reduce the rate of loss of such alleles to below that of phenotypic selection. Applying such weights at the beginning of GS implementation is important.     

Training genomic selection models across several breeding cycles increases genetic gain in oil palm in silico study

Genomic selection (GS) is expected to increase the rate of genetic gain in oil palm. In a GS scheme, breeding cycles with progeny tests (phenotypic selection, PS) used to calibrate the GS predictive model and for selection alternate with GS cycles, making it possible to train the GS model with aggregated data from several cycles. To evaluate this possibility, we simulated four cycles of hybrid breeding for bunch production and compared two methods of calibrating the GS model, one using aggregated data from the two most recent cycles (Tr2Gen), the other using data from the last cycle (Tr1Gen). We also compared a GS scheme with two PS cycles and two GS cycles (2PT-2noPT), and a scheme with PS every other cycle and GS otherwise (PT-noPT). We showed that Tr2Gen had a 10.7% higher genetic gain per cycle than Tr1Gen, mostly due to increased selection accuracy, particularly in across-cycle selection, despite the decreased relationship between training individuals and selection candidates. After four cycles, Tr2Gen had a 5% higher cumulative genetic gain than Tr1Gen, with a lower coefficient of variation. PT-noPT benefited more from the advantages offered by Tr2Gen than 2PT-2noPT. Over four breeding cycles, combining PT-noPT and Tr2Gen largely outperformed conventional reciprocal recurrent selection (RRS), with an increase in annual genetic gain ranging from 37.6 to 57.5%, depending on the number of GS candidates. This study confirms the advantages of GS over RRS and indicated that oil palm breeders should train GS models using all data from past breeding cycles.     

Optimizing the allocation of resources for genomic selection in one breeding cycle

Key message

We developed a universally applicable planning tool for optimizing the allocation of resources for one cycle of genomic selection in a biparental population. The framework combines selection theory with constraint numerical optimization and considers genotype? ×? environment interactions.

Abstract

Genomic selection (GS) is increasingly implemented in plant breeding programs to increase selection gain but little is known how to optimally allocate the resources under a given budget. We investigated this problem with model calculations by combining quantitative genetic selection theory with constraint numerical optimization. We assumed one selection cycle where both the training and prediction sets comprised double haploid (DH) lines from the same biparental population. Grain yield for testcrosses of maize DH lines was used as a model trait but all parameters can be adjusted in a freely available software implementation. An extension of the expected selection accuracy given by Daetwyler et al. (2008) was developed to correctly balance between the number of environments for phenotyping the training set and its population size in the presence of genotype?×?environment interactions. Under small budget, genotyping costs mainly determine whether GS is superior over phenotypic selection. With increasing budget, flexibility in resource allocation increases greatly but selection gain leveled off quickly requiring balancing the number of populations with the budget spent for each population. The use of an index combining phenotypic and GS predicted values in the training set was especially beneficial under limited resources and large genotype × environment interactions. Once a sufficiently high selection accuracy is achieved in the prediction set, further selection gain can be achieved most efficiently by massively expanding its size. Thus, with increasing budget, reducing the costs for producing a DH line becomes increasingly crucial for successfully exploiting the benefits of GS.     

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture,Training Population Composition,Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite,Tropical Rice Breeding Lines

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute''s (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.     

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical P<0.05) were identified across the three lines. With GS, 70 selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167–0.198) and BLUP (0.105–0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.     

Performance of multi-trait genomic selection for <Emphasis Type="Italic">Eucalyptus robusta</Emphasis> breeding program

In forest tree genetic improvement, multi-trait genomic selection (GS) may have advantages in improving the accuracy of the genotype estimation and shortening selection cycles. For the breeding of Eucalyptus robusta, one of the most exotic planted species in Madagascar, volume at 49 months (V49), total lignin (TL), and holo-cellulose (Holo) were considered. For GS, 2919 single nucleotide polymorphisms (SNP) were used with the genomic best linear unbiased predictor (GBLUP) method, which was as efficient as the reproducing kernel Hilbert space (RKHS) and elastic net methods (EN), but more adapted to multi-trait modeling. The efficiency of individual I model, including the genomic data, was much higher than the provenance effect P model. For example, with V49, mean goodness-of-fit was: r_{I_Full} =?0.79, r_{P_Full} =?0.37 for I and P, respectively. The prediction accuracies using the cross-validation procedure were lower for V49: r_I =?0.29 r_P =?0.28. The genetic gains resulting from the indexes associating (V49, TL) and (V49, Holo) were higher using I than for the P model; for V49, the relative genetic gain was 37 and 20%, respectively, with 5% of selection intensity. The single-trait approach was as efficient as the multi-trait approach given the weak correlations between V49 and TL or Holo. The I model also brings greater diversity: for V49 the number of provenances represented in a selected population was two and three with the P model, and 6 and 16 with the I model.     

Practical application of genomic selection in a doubled-haploid winter wheat breeding program

Crop improvement is a long-term, expensive institutional endeavor. Genomic selection (GS), which uses single nucleotide polymorphism (SNP) information to estimate genomic breeding values, has proven efficient to increasing genetic gain by accelerating the breeding process in animal breeding programs. As for crop improvement, with few exceptions, GS applicability remains in the evaluation of algorithm performance. In this study, we examined factors related to GS applicability in line development stage for grain yield using a hard red winter wheat (Triticum aestivum L.) doubled-haploid population. The performance of GS was evaluated in two consecutive years to predict grain yield. In general, the semi-parametric reproducing kernel Hilbert space prediction algorithm outperformed parametric genomic best linear unbiased prediction. For both parametric and semi-parametric algorithms, an upward bias in predictability was apparent in within-year cross-validation, suggesting the prerequisite of cross-year validation for a more reliable prediction. Adjusting the training population’s phenotype for genotype by environment effect had a positive impact on GS model’s predictive ability. Possibly due to marker redundancy, a selected subset of SNPs at an absolute pairwise correlation coefficient threshold value of 0.4 produced comparable results and reduced the computational burden of considering the full SNP set. Finally, in the context of an ongoing breeding and selection effort, the present study has provided a measure of confidence based on the deviation of line selection from GS results, supporting the implementation of GS in wheat variety development.     

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Key message

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials.

Abstract

The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

     

Genomic selection in soybean: accuracy and time gain in relation to phenotypic selection

Genomic selection (GS) can potentially accelerate genetic improvement of soybean [Glycine max L. (Merrill)] by reducing the time to complete breeding cycles. The objectives of this study were to (1) explore the accuracy of GS in soybean, (2) evaluate the contribution of intrapopulational structure to the accuracy of GS, and (3) compare the efficiencies of phenotypic selection and GS in soybean. For this, phenotypic and genotypic data were collected from 324 soybean genotypes (243 recombinant inbred lines and 81 cultivars) and GS was performed for five yield related traits. BayesB methodology with a 10-fold cross-validation was used to compute accuracies. The GS accuracies were evaluated for grain yield, plant height, insertion of first pod, days to maturity, and 1000-grain weight at eight locations. We found that GS can reduce the time required to complete a selection cycle in soybean, which can lead to increased production of this commercially important crop. Furthermore, genotypic accuracy was similar regardless of population structure correction.     

Genome-based prediction of maize hybrid performance across genetic groups,testers, locations,and years

Key message

The calibration data for genomic prediction should represent the full genetic spectrum of a breeding program. Data heterogeneity is minimized by connecting data sources through highly related test units.

Abstract

One of the major challenges of genome-enabled prediction in plant breeding lies in the optimum design of the population employed in model training. With highly interconnected breeding cycles staggered in time the choice of data for model training is not straightforward. We used cross-validation and independent validation to assess the performance of genome-based prediction within and across genetic groups, testers, locations, and years. The study comprised data for 1,073 and 857 doubled haploid lines evaluated as testcrosses in 2 years. Testcrosses were phenotyped for grain dry matter yield and content and genotyped with 56,110 single nucleotide polymorphism markers. Predictive abilities strongly depended on the relatedness of the doubled haploid lines from the estimation set with those on which prediction accuracy was assessed. For scenarios with strong population heterogeneity it was advantageous to perform predictions within a priori defined genetic groups until higher connectivity through related test units was achieved. Differences between group means had a strong effect on predictive abilities obtained with both cross-validation and independent validation. Predictive abilities across subsequent cycles of selection and years were only slightly reduced compared to predictive abilities obtained with cross-validation within the same year. We conclude that the optimum data set for model training in genome-enabled prediction should represent the full genetic and environmental spectrum of the respective breeding program. Data heterogeneity can be reduced by experimental designs that maximize the connectivity between data sources by common or highly related test units.     

Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture

Key Message

Genomic prediction using the Brassica 60 k genotyping array is efficient in oilseed rape hybrids. Prediction accuracy is more dependent on trait complexity than on the prediction model.

Abstract

In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F₁-hybrid performance, and selection of well-paired parents requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrids, in general, dramatically exceeds breeding capacity and budget. Integration of genomic selection (GS) could substantially increase the number of potential combinations that can be evaluated. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously predicted in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. In this study, we evaluated the usefulness of the Illumina Brassica 60 k SNP array for genomic prediction and compared three alternative approaches based on a homoscedastic ridge regression BLUP and three Bayesian prediction models that considered general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal doubled haploid lines and five male-sterile testers. Predictive ability was evaluated for seven agronomic traits. We demonstrate that the Brassica 60 k genotyping array is an adequate and highly valuable platform to implement genomic prediction of hybrid performance in oilseed rape. Furthermore, we present first insights into the application of established statistical models for prediction of important agronomical traits with contrasting patterns of polygenic control.

     

Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement

To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic selection (GS) is a new technique that helps accelerate the rate of genetic gain in breeding by using whole-genome data to predict the breeding value of offspring. Here, we describe a new GS model that combines RR-BLUP with markers fit as fixed effects selected from the results of a genome-wide-association study (GWAS) on the RR-BLUP training data. We term this model GS + de novo GWAS. In a breeding population of tropical rice, GS + de novo GWAS outperformed six other models for a variety of traits and in multiple environments. On the basis of these results, we propose an extended, two-part breeding design that can be used to efficiently integrate novel variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains.     

Dissection of complex traits in forest trees — opportunities for marker-assisted selection

Due to their long reproductive cycles and the time to expression of mature traits, marker-assisted selection is particularly attractive for tree breeding. In this review, we discuss different approaches used for developing markers and propose a method for application of markers in low linkage disequilibrium (LD) populations. Identification of useful markers for application in tree breeding is mainly based on two approaches, quantitative trait locus (QTL) mapping and association genetic studies. While several studies have identified significant markers, effect of the individual markers is low making it difficult to utilize them in breeding programs. Recently, genomic selection (GS) was proposed for overcoming some of these difficulties. In GS, high density markers are used for predicting phenotypes from genotypes. Currently small effective populations with high LD are being tested for GS in tree breeding. For wider application, GS needs to be applied in low LD populations which are found in many tree breeding programs. Here we propose an approach in which the significant markers from association studies may be used for developing prediction models in low LD populations using the same methods as in GS. Preliminary analyses indicate that a modest numbers of markers may be sufficient for developing prediction models in low LD populations. GS based on large numbers of random markers or small numbers of associated markers is poised to make marker-assisted selection a reality in forest tree breeding.     

Predicting Growth Traits with Genomic Selection Methods in Zhikong Scallop (<Emphasis Type="Italic">Chlamys farreri</Emphasis>)

Selective breeding is a common and effective approach for genetic improvement of aquaculture stocks with parental selection as the key factor. Genomic selection (GS) has been proposed as a promising tool to facilitate selective breeding. Here, we evaluated the predictability of four GS methods in Zhikong scallop (Chlamys farreri) through real dataset analyses of four economical traits (e.g., shell length, shell height, shell width, and whole weight). Our analysis revealed that different GS models exhibited variable performance in prediction accuracy depending on genetic and statistical factors, but non-parametric method, including reproducing kernel Hilbert spaces regression (RKHS) and sparse neural networks (SNN), generally outperformed parametric linear method, such as genomic best linear unbiased prediction (GBLUP) and BayesB. Furthermore, we demonstrated that the predictability relied mainly on the heritability regardless of GS methods. The size of training population and marker density also had considerable effects on the predictive performance. In practice, increasing the training population size could better improve the genomic prediction than raising the marker density. This study is the first to apply non-linear model and neural networks for GS in scallop and should be valuable to help develop strategies for aquaculture breeding programs.