Extended linkage disequilibrium in noncoding regions in a conifer, Cryptomeria japonica

We measured linkage disequilibrium in mostly noncoding regions of Cryptomeria japonica, a conifer belonging to Cupressaceae. Linkage disequilibrium was extensive and did not decay even at a distance of 100 kb. The average estimate of the population recombination rate per base pair was 1.55 × 10(-5) and was <1/70 of that in the coding regions. We discuss the impact of low recombination rates in a large part of the genome on association studies.     

DNA variation in a conifer,Cryptomeria japonica (Cupressaceae sensu lato)

We investigated the nucleotide variation of a conifer, Cryptomeria japonica, and the divergence between this species and its closest relative, Taxodium distichum, at seven nuclear loci (Acl5, Chi1, Ferr, GapC, HemA, Lcyb, and Pat). Samples of C. japonica were collected from three areas, Kantou-Toukai, Hokuriku, and Iwate. No apparent geographic differentiation was found among these samples. However, the frequency spectrum of the nucleotide polymorphism revealed excesses of intermediate-frequency variants, which suggests that the population was not panmictic and a constant size in the past. The average nucleotide diversity, pi, for silent sites was 0.00383. However, values of pi for silent sites vary among loci. Comparisons of polymorphism to divergence among loci (the HKA test) showed that the polymorphism at the Acl5 locus was significantly lower. We also observed a nearly significant excess of replacement polymorphisms at the Lcyb locus. These results suggested possibilities of natural selection acting at some of the loci. Intragenic recombination was detected only once at the Chi1 locus and was not detected at the other loci. The low level of population recombination rate, 4Nr, seemed to be due to both low level of recombination, r, and small population size, N.     

A simple allele-specific PCR marker for identifying male-sterile trees: Towards DNA marker-assisted selection in the Cryptomeria japonica breeding program

The number of people in Japan suffering from Cryptomeria japonica pollinosis has risen considerably since the 1970s as the area planted with this species has increased. In order to reduce the amount of pollen dispersed, breeding programs using trees with male-sterile genes have been implemented. We have constructed partial linkage maps surrounding a male sterility gene (ms-1) in four families of C. japonica to facilitate this process. The marker most closely linked to ms-1 was different in the four mapping families: gSNP00438, gSNP01452, estSNP00083, and estSNP01228 in the TO13S family (3.1 cM from ms-1); gSNP05835 and gSNP06239 in the S3T67 family (2.0 cM from ms-1); gSNP05835 in the F1N4 family (1.5 cM from ms-1); and gSNP06239 in the T5 family (4.2 cM from ms-1). This is probably mainly due to genetic differences between the parents used to produce the mapping families. However, in all four families, the accuracy with which male-sterile trees could be identified using the closest markers was more than 96.0 %. These results suggested that marker-assisted selection of male-sterile trees within a given family is feasible using the closest flanking markers to the ms-1 locus. We also developed an allele-specific PCR marker for identifying male-sterile trees in the TO13S family from which male-sterile seedlings are produced. Allele-specific PCR using three primer combinations produced two clear fragments, which could be easily separated by agarose gel electrophoresis: one fragment with a molecular weight of 410 bp, which was present in all samples and could thus be used as a positive control, and another of lower molecular weight (196 bp), which was specific for male-sterile trees. This marker makes it possible to carry out a simple and economical PCR assay for the detection of the SNP linked to the target gene without the need to use fluorescent labels. This study shows how a simple allele-specific PCR marker for an important major gene in a forest tree species can be developed using information from a high-density linkage map. In addition, our results will facilitate the first application of MAS (marker assisted selection) in conifers because the male sterility in C. japonica has several advantages and may be one of the best examples for MAS in conifers.     

Use of field data in pig genomic selection schemes: a simulation study

The aim of this study was to test how genetic gain for a trait not measured on the nucleus animals could be obtained within a genomic selection pig breeding scheme. Stochastic simulation of a pig breeding program including a breeding nucleus, a multiplier to produce and disseminate semen and a production tier where phenotypes were recorded was performed to test (1) the effect of obtaining phenotypic records from offspring of nucleus animals, (2) the effect of genotyping production animals with records for the purpose of including them in a genomic selection reference population or (3) to combine the two approaches. None of the tested strategies affected genetic gain if the trait under investigation had a low economic value of only 10% of the total breeding goal. When the relative economic weight was increased to 30%, a combination of the methods was most effective. Obtaining records from offspring of already genotyped nucleus animals had more impact on genetic gain than to genotype more distant relatives with phenotypes to update the reference population. When records cannot be obtained from offspring of nucleus animals, genotyping of production animals could be considered for traits with high economic importance.     

Frequency-dependent selection on information-transfer strategies at breeding colonies: a simulation study

Through computer simulations, we model three different foodfinding strategies: searcher, no information transfer, watcher,limited information transfer; follower, full information transfer.The aim of this article was to study how frequency-dependentselection affects the proportion of these strategies at a simulatedcolony under different patterns of food distribution. Furthermore,we determined how information transfer in a population witha mixed evolutionarily stable strategy (ESS) modified the averageforaging efficiency of an individual compared to that of anindividual in a population with mutual information exchange.We found that the proportion of information gaining strategiesincreases as the food resources become more clumped. The improvementin foraging efficiency through the operation of an informationcenter need not require mutuality in information exchange. Onthe basis of the presented study, at the ESS only a small percentageof colony members need discover food patches, yet the foragingefficiency may be high because of the operation of an informationcenter.     

Two-hundred-years old shoots of the epiphytic moss Brotherella henonii preserved in a bark pocket of the conifer Cryptomeria japonica

Abstract

Schistidium flaccidum (De Not.) Ochyra is recorded as new to Britain from Snowdon, Wales. It may be distinguished from the other British species of the genus by its poculiform capsule, absence of peristome and small, dense tufts. Notes are given on its separation from Grimmia anodon, which it resembles more closely.     

Integrating genomic selection into dairy cattle breeding programmes: a review

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.     

Genome scanning for detecting adaptive genes along environmental gradients in the Japanese conifer,Cryptomeria japonica

Local adaptation is important in evolutionary processes and speciation. We used multiple tests to identify several candidate genes that may be involved in local adaptation from 1026 loci in 14 natural populations of Cryptomeria japonica, the most economically important forestry tree in Japan. We also studied the relationships between genotypes and environmental variables to obtain information on the selective pressures acting on individual populations. Outlier loci were mapped onto a linkage map, and the positions of loci associated with specific environmental variables are considered. The outlier loci were not randomly distributed on the linkage map; linkage group 11 was identified as a genomic island of divergence. Three loci in this region were also associated with environmental variables such as mean annual temperature, daily maximum temperature, maximum snow depth, and so on. Outlier loci identified with high significance levels will be essential for conservation purposes and for future work on molecular breeding.     

基因组选择技术在农业动物育种中的应用

基因组选择(genomic selection, GS)是畜禽经济性状遗传改良的重要方法。随着高密度SNP芯片和二代测序价格的下降,GS技术越来越多被应用于奶牛、猪、鸡等农业动物育种中。然而,降低全基因组SNP分型成本、提高基因组育种值(genomic estimated breeding value,GEBV)估计准确性仍然是GS研究的主要难题。本文从全基因组SNP分型策略和GEBV估计模型两个方面进行了综述,并对目前GS技术在主要畜禽品种中的应用现状进行了介绍,以期为GS在农业动物育种中的深入开展提供借鉴和参考。     

Hybrid breeding of rice via genomic selection

Hybrid breeding is the main strategy for improving productivity in many crops, especially in rice and maize. Genomic hybrid breeding is a technology that uses whole‐genome markers to predict future hybrids. Predicted superior hybrids are then field evaluated and released as new hybrid cultivars after their superior performances are confirmed. This will increase the opportunity of selecting true superior hybrids with minimum costs. Here, we used genomic best linear unbiased prediction to perform hybrid performance prediction using an existing rice population of 1495 hybrids. Replicated 10‐fold cross‐validations showed that the prediction abilities on ten agronomic traits ranged from 0.35 to 0.92. Using the 1495 rice hybrids as a training sample, we predicted six agronomic traits of 100 hybrids derived from half diallel crosses involving 21 parents that are different from the parents of the hybrids in the training sample. The prediction abilities were relatively high, varying from 0.54 (yield) to 0.92 (grain length). We concluded that the current population of 1495 hybrids can be used to predict hybrids from seemingly unrelated parents. Eventually, we used this training population to predict all potential hybrids of cytoplasm male sterile lines from 3000 rice varieties from the 3K Rice Genome Project. Using a breeding index combining 10 traits, we identified the top and bottom 200 predicted hybrids. SNP genotypes of the training population and parameters estimated from this training population are available for general uses and further validation in genomic hybrid prediction of all potential hybrids generated from all varieties of rice.     

Genome-wide association and genomic selection in animal breeding

Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.     

Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program

Recent genomic evaluation studies using real data and predicting genetic gain by modeling breeding programs have reported moderate expected benefits from the replacement of classic selection schemes by genomic selection (GS) in small ruminants. The objectives of this study were to compare the cost, monetary genetic gain and economic efficiency of classic selection and GS schemes in the meat sheep industry. Deterministic methods were used to model selection based on multi-trait indices from a sheep meat breeding program. Decisional variables related to male selection candidates and progeny testing were optimized to maximize the annual monetary genetic gain (AMGG), that is, a weighted sum of meat and maternal traits annual genetic gains. For GS, a reference population of 2000 individuals was assumed and genomic information was available for evaluation of male candidates only. In the classic selection scheme, males breeding values were estimated from own and offspring phenotypes. In GS, different scenarios were considered, differing by the information used to select males (genomic only, genomic+own performance, genomic+offspring phenotypes). The results showed that all GS scenarios were associated with higher total variable costs than classic selection (if the cost of genotyping was 123 euros/animal). In terms of AMGG and economic returns, GS scenarios were found to be superior to classic selection only if genomic information was combined with their own meat phenotypes (GS-Pheno) or with their progeny test information. The predicted economic efficiency, defined as returns (proportional to number of expressions of AMGG in the nucleus and commercial flocks) minus total variable costs, showed that the best GS scenario (GS-Pheno) was up to 15% more efficient than classic selection. For all selection scenarios, optimization increased the overall AMGG, returns and economic efficiency. As a conclusion, our study shows that some forms of GS strategies are more advantageous than classic selection, provided that GS is already initiated (i.e. the initial reference population is available). Optimizing decisional variables of the classic selection scheme could be of greater benefit than including genomic information in optimized designs.     

基因组选择在猪杂交育种中的应用

基因组选择是指在全基因组范围内通过基因组中大量的标记信息估计出个体全基因组范围的育种值,可进一步提升育种效率和准确性,目前在猪纯繁育种中得到广泛应用。但有研究表明,现有的基因组选择方法在猪杂交育种上的应用效果并不理想,在跨群体条件下预测准确性极低。杂交作为养猪业中最为广泛的育种手段之一,通过结合基因组选择理论进一步提升猪的生产性能,具有重要的经济和研究价值。本文综述了基因组选择的发展及其在猪育种中的应用现状,并结合国内外猪杂交育种的方式,分析了目前基因组选择方法在猪杂交育种应用方面的不足,旨在为未来基因组选择在猪杂交育种中的合理应用提供参考。     

Optimised parent selection and minimum inbreeding mating in small aquaculture breeding schemes: a simulation study

The effectiveness of low cost breeding scheme designs for small aquaculture breeding programmes were assessed for their ability to achieve genetic gain while managing inbreeding using stochastic simulation. Individuals with trait data were simulated over 15 generations with selection on a single trait. Combinations of selection methods, mating strategies and genetic evaluation options were evaluated with and without the presence of common environmental effects. An Optimal Parent Selection (OPS) method using semi-definite programming was compared with a truncation selection (TS) method. OPS constrains the rate of inbreeding while maximising genetic gain. For either selection method, mating pairs were assigned from the selected parents by either random mating (RM) or Minimum Inbreeding Mating (MIM), which used integer programming to determine mating pairs. Offspring were simulated for each mating pair with equal numbers of offspring per pair and these offspring were the candidates for selection of parents of the next generation. Inbreeding and genetic gain for each generation were averaged over 25 replicates. Combined OPS and MIM led to a similar level of genetic gain to TS and RM, but inbreeding levels were around 75% lower than TS and RM after 15 generations. Results demonstrate that it would be possible to manage inbreeding over 15 generations within small breeding programmes comprised of 30 to 40 males and 30 to 40 females with the use of OPS and MIM. Selection on breeding values computed using Best Linear Unbiased Prediction (BLUP) with all individuals genotyped to obtain pedigree information resulted in an 11% increase in genetic merit and a 90% increase in the average inbreeding coefficient of progeny after 15 generations compared with selection on raw phenotype. Genetic evaluation strategies using BLUP wherein elite individuals by raw phenotype are genotyped to obtain parentage along with a range of different samples of remaining individuals did not increase genetic progress in comparison to selection on raw phenotype. When common environmental effects on full-sib families were simulated, performance of small breeding scheme designs was little affected. This was because the majority of selection must anyway be applied within family due to inbreeding constraints.     

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.

     

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.