Choice of shrinkage parameter and prediction of genomic breeding values in elite maize breeding populations

Genomic selection (GS) is a promising alternative to marker‐assisted selection particularly for quantitative traits. In this study, we examined the prediction accuracy of genomic breeding values by using ridge regression best linear unbiased prediction in combination with fivefold cross‐validation based on empirical data of a commercial maize breeding programme. The empirical data is composed of 930 testcross progenies derived from 11 segregating families evaluated at six environments for grain yield and grain moisture. Accuracy to predict genomic breeding values was affected by the choice of the shrinkage parameter λ², by unbalanced family size, by size of the training population and to a lower extent by the number of markers. Accuracy of genomic breeding values was high suggesting that the selection gain can be improved implementing GS in elite maize breeding programmes.     

Potential of genomic selection in rapeseed (Brassica napus L.) breeding

Genomic selection employs genome‐wide marker data to predict genomic breeding values. In this study, a population consisting of 391 lines of elite winter oilseed rape derived from nine families was used to evaluate the prospects of genomic selection in rapeseed breeding. All lines have been phenotyped for six morphological, quality‐ and yield‐related traits and genotyped with genome‐wide SNP markers. We used ridge regression best linear unbiased prediction in combination with cross‐validation and obtained medium to high prediction accuracies for the studied traits. Our results illustrate that among‐family variance contributes to the prediction accuracy and can lead to an overestimation of the prospects of genomic selection within single segregating families. We also tested a scenario where estimation of effects was carried out without individuals from the family in which breeding values were predicted, which yielded lower but nevertheless attractive prediction accuracies. Taken together, our results suggest that genomic selection can be a valuable genomic approach for complex agronomic traits towards a knowledge‐based breeding in rapeseed.     

Hybrid breeding for biomass yield in winter triticale: II. Combining ability and hybrid prediction

Accurate hybrid prediction and knowledge about the relative contribution of general (GCA) and specific combining ability (SCA) are of utmost importance for efficient hybrid breeding. We therefore evaluated 91 triticale single-cross hybrids in field trials at seven environments for plant height, heading time, fresh biomass, dry matter content and dry biomass. Fresh and dry biomass showed the highest proportion (23%) of variance due to SCA. Prediction accuracies based on GCA were slightly higher than based on mid-parent values. Utilizing parental kinship information yielded the highest prediction accuracies when both parental lines have been tested in other hybrid combinations, but still moderate-to-low prediction accuracies for two untested parents. Thus, hybrid prediction for biomass traits in triticale is currently promising based on mid-parent values as emphasized by our simulation study, but can be expected to shift to GCA-based prediction with an increasing importance of GCA due to selection in hybrid breeding. Moreover, the performance of potential hybrids between newly developed lines can be predicted with moderate accuracy using genomic relationship information.     

Prediction of heterosis for grain yield in rice using 'key' informative EST-SSR markers

This study was undertaken to assess the comparative potential of 25 Expressed Sequence Tag derived simple sequence repeats (EST-SSRs) and 25 genomic SSRs in the prediction of grain yield heterosis using a set of nine cytoplasmic male sterile (CMS) lines and 32 restorer lines of rice. EST-SSRs and genomic SSRs exhibited an average Polymorphism Information Content value of 0.37 and 0.45, respectively. The coefficient of marker polymorphism among parental lines with respect to a set of hypervariable EST and genomic SSRs was correlated with standard heterosis for grain yield of six public bred rice hybrids. EST-SSRs gave a better correlation (r = 0.75) as compared with genomic SSRs (r = 0.09). When 10 'key' informative EST-SSR markers which showed a higher positive correlation with grain yield heterosis were validated in a new set of 14 experimental hybrids, the markers exhibited a higher correlation (r = 0.79), indicating the predictive value of these EST-SSRs. We recommend these 10 'key' informative EST-SSR markers for analysis of genetic diversity of parental lines and prediction of heterosis in hybrid rice breeding programmes.     

Accuracy of within‐ and among‐family genomic prediction in triticale

Genomic prediction has emerged as a powerful genomic tool to assist breeding of complex traits. In this study, we employed a population of 647 triticale doubled haploid lines derived from four families to assess the potential of this approach for triticale breeding. All lines were phenotyped for grain yield, thousand‐kernel weight, biomass yield, plant height, frost tolerance and Fusarium head blight resistance. The obtained prediction accuracies were moderate to high and consisted to varying degrees of within‐ and among‐family variance, in line with the different degrees of phenotypic differences between family means. The prediction accuracy within individual families also varied with the genetic complexity of the traits and was generally highest based on effect estimation with lines from the respective family, whereas the prediction accuracy decreased with decreasing relatedness among the families. Taken together, our results illustrate the potential of genomic prediction to increase selection gain in triticale breeding, but the composition of the training set is of utmost importance, and consequently, the implementation of this approach in applied breeding programmes is not straightforward.     

Genomic prediction of sunflower hybrid performance

Predicting single‐cross performance is of high importance to improve the efficiency of sunflower (Helianthus annuus L.) hybrid breeding programmes. We used experimental data from inter‐ and intragroup sunflower hybrids and their parental lines adapted to Central Europe to (i) study the genetic diversity and combining ability and (ii) examine the accuracy to predict hybrid performance based on phenotypic and genomic data. We evaluated 133 intragroup and 104 intergroup hybrids with their parental lines in replicated trials at four environments for grain yield, oil yield and oil content. Furthermore, the parental lines were fingerprinted with 572 AFLP markers. Variance due to specific combining ability was comparable for intergroup and intragroup crosses. This suggested a lack of clearly defined heterotic groups for the sample of studied sunflower lines. Prediction accuracy of hybrid performance based on general combining ability effects was high and could not be increased using genomic selection approaches. For situations where no information on GCA effects of parental lines was available, hybrid prediction based on genomic selection methods was accurate for groups of related lines. For groups of unrelated lines, however, we observed a strong decrease in the prediction accuracy. This suggests that prediction of hybrid performance for crosses based on genetically distant parents remains challenging.     

Genomic prediction for yields,processing and nutritional quality traits in cultivated potato (Solanum tuberosum L.)

Current potato breeding approaches are hampered by several factors including costly seed tubers, tetrasomic inheritance and inbreeding depression. Genomic selection (GS) demonstrated interesting results regardless of the ploidy level, and can be harnessed to circumvent these problems. In this work, three GS models were evaluated using 50,107 informative SilicoDArT markers and 11 traits in two values for cultivation and use (VCU) potato trials. Two key breeding problems modelled included predicting the performance of (i) new and unphenotyped clones (cross‐validation) and (ii) a VCU using another as training set (TS). GS models performed comparably. Cross‐validation accuracy was high for D35, D45, DMW and BVAL, in ascending order. Prediction accuracies of the VCUs were highly correlated, but the best prediction was obtained for the smaller VCU using the bigger as TS. Cross‐validation and VCU prediction accuracies were higher when bigger TSs were used. The findings herein indicate that GS can be attractively integrated in potato breeding, particularly in early clonal generations to predict and select for traits with low heritability which would otherwise require more testing years, environments and resources.     

Best linear unbiased prediction of triticale hybrid performance

Predicting single-cross performance is of special interest in hybrid breeding of triticale. We used molecular and phenotypic data of factorial triticale crosses and compared several approaches to predict their single-cross performance. Twenty-three inbred lines and their 76 incomplete factorial crosses were field evaluated for grain yield, plant height, and heading time at five locations in Central Europe. In addition, the parental lines were genotyped with 52 SSR markers. Plant height and heading time were predicted with high accuracy based on mid-parent performance. In contrast, prediction of hybrid performance based on mid-parent value was not accurate for grain yield. Using general combining ability effects led to an enhanced prediction accuracy of hybrid grain yield performance. This accuracy could be slightly improved using best linear unbiased prediction approaches. The prediction accuracy was considerably high even if the number of tested hybrids was small. Consequently, best linear unbiased prediction of hybrid performance is a promising tool for hybrid triticale breeding programs.     

Modified full-sib selection and best linear unbiased prediction of progeny performance in a European F2 maize population

Four cycles of modified recurrent full‐sib (FS) selection were conducted in an intermated F₂ population of European flint maize. The objectives of our study were to monitor trends across selection cycles in the estimates of population mean, inbreeding coefficients and variance components, and to investigate the usefulness of best linear unbiased prediction (BLUP) of progeny performance under the recurrent FS selection scheme applied. We used a selection rate of 25% for a selection index, based on grain yield and dry matter content. A pseudo‐factorial mating scheme was used for recombination. In this scheme, the selected FS families were divided into an upper‐ranking group of parents mated to the lower‐ranking group. Variance components were estimated with restricted maximum likelihood (REML). Average grain yield increased 1.2 t/ha per cycle, average grain moisture decreased 20.1 g/kg per cycle, and the selection index relative to the F₂ check entries decreased 0.3% per cycle. For a more precise calculation of selection response, the four cycles should be tested together in multi‐environmental trials. We observed a significant decrease in additive variance in the selection index, suggesting smaller future selection response. Predictions of FS family performance in Cn + 1 based on mean performance of parental FS families in Cn were of equal precision as those based on the mean additive genetic BLUP of their parents, and corresponding correlations were of moderate size for grain moisture and selection index.     

Genomic prediction of grain yield in commercial Finnish oat (Avena sativa) and barley (Hordeum vulgare) breeding programmes

Genomic selection has been adopted in many plant breeding programmes. In this paper, we cover some aspects of information necessary before starting genomic selection. Spring oat and barley breeding data sets from commercial breeding programmes were studied using single, multitrait and trait-assisted models for predicting grain yield. Heritabilities were higher when estimated using multitrait models compared to single-trait models. However, no corresponding increase in prediction accuracy was observed in a cross-validation scenario. On the other hand, forward prediction showed a slight, but not significant, increase in accuracy of genomic estimated breeding values for breeding cohorts when a multitrait model was applied. When a correlated trait was used in a trait-assisted model, on average the accuracies increased by 9%–14% for oat and by 11%–28% for barley compared with a single-trait model. Overall, accuracies in forward validation varied between breeding cohorts and years for grain yield. Forward prediction accuracies for multiple cohorts and multiple years’ data are reported for oat for the first time.     

Evaluation of genomic approaches for marker‐based improvement of lodging tolerance in triticale

Lodging tolerance is an important agronomic trait as it can have a severe negative impact on grain yield and quality. Here, we used a large mapping population of 647 doubled haploid triticale lines derived from four families to dissect the genetic architecture underlying lodging tolerance and to assess different approaches for a genomics‐based improvement of the trait. The plants were evaluated for lodging in two environments and genotyped with 1710 genomewide DArT markers. We observed a large genotypic variation for lodging and transgressive segregation in all families. Employing two complementary QTL mapping approaches, we identified both main effect and epistatic QTL. Using cross‐validation, we showed that the proportion of genotypic variance explained by the detected QTL is low, thus limiting the efficiency of marker‐assisted selection to improve this trait. By contrast, the cross‐validated predictive ability of genomic prediction was approximately twice as high as that of the QTL‐based selection approaches. In conclusion, our results show that lodging tolerance is a complex trait that can be improved by classical breeding but also assisted by marker‐based approaches.     

基于单拷贝SNP标记的棉花杂交种纯度高通量检测技术

利用有代表性的材料进行SNP位点的全基因组扫描分析与综合评估,基于KASP技术开发1套适用于我国棉花杂交种纯度高通量检测的核心SNP组合。从63K的棉花全基因组芯片中筛选获得具有单拷贝特性的SNP标记分别为5474个(中棉所TM-1基因组版本)和1850个(南京农大TM-1基因组版本)。根据芯片扫描分析结果,权衡考虑位点多态性、分型效果、纯合率与杂合率等因素,最终从每条染色体上优选1个杂交种杂合率高且分型效果理想的核心SNP位点,合计26个。采用KrakenTM软件将SNP位点转化成KASP标记,利用SNPline平台进行SNP分型检测,实现了对大量样品的高通量基因分型,尤其适用于品种纯度快速检测,为SNP标记技术在棉花品种鉴定及指纹数据库构建等方面的应用奠定基础。     

基于智能算法优化SVM的横向通风过程中温度场预测方法探究

将改进的智能预测技术应用于储粮横向通风过程中的粮堆温度预测,为粮食通风智能预测与决策提供了一种新思路。选取河北清苑国家粮食储备库冬季横向通风的实时监测数据,在分析主要影响因素的基础上,应用三种智能优化算法——网格寻优算法、GA遗传算法寻优、PSO粒子群算法,结合回归支持向量机理论,对粮堆的通风过程进行建模。结果表明,优化过的回归预测模型能较好地拟合粮食温度与其他变量之间的非线性关系,尤其是当样本数量较为有限时,该方法具有更高的拟合精度,更适合对储粮通风这一强非线性过程的预测研究,对于人工干预操作具有一定的现实指导意义。     

Rapid and convenient gel‐free screening of SCAR markers in wheat using SYBR green‐based melt‐profiling

Sequence characterized amplified region (SCAR) markers that are highly desirable in crop breeding for marker‐assisted selection (MAS) are routinely analysed by gel‐based methods that are low‐throughput, time‐consuming and laborious. In this study, we showed a rapid and convenient method for analysis of SCAR markers in a gel‐free manner. Seven SCAR markers, linked to rust resistance genes (Sr24, Sr26 and Sr31) and seed quality traits (Pina, Pinb and Glu‐D1) in wheat (Triticum aestivum), were amplified on a real‐time PCR machine using custom reaction mixture. Subsequently, melting curve analysis was performed, to assess the specificity of amplicons. Using the amplicon‐specific melt‐profiles, the presence/absence of SCAR markers was analysed in fifteen genotypes and five F₂ populations. Unlike the fluorescence‐based in‐tube detection methods, the present method used the amplicon‐specific melt‐profiles to evaluate the status of the SCAR markers, thus eliminating the need for gel‐based analysis. Results also showed feasibility of multiplex analysis of two markers with well‐separated melting profiles. Overall, the approach is a rapid, convenient and cost‐effective method for high‐throughput screening of SCAR markers.     

Construction of chromosomal segment substitution lines and genetic dissection of introgressed segments associated with yield determination in the parents of a super‐hybrid rice

Heterosis is a phenomenon whereby hybrids of inbred lines produce favourable phenotypes that exceed those of their parents. Traits of interest are higher yield and stronger stress tolerance. The two‐line super‐hybrid rice ‘Liangyoupei9’ (LYP9) shows superiority to both its elite inbred line ‘93‐11’ and ‘Pei'ai64s’ (‘PA64s’) parents and conventional hybrids. However, the genetic basis of its hybrid vigour, especially yield determination, remains elusive. In the present study, a set of 156 chromosome segment substitution lines (CSSLs) carrying overlapping segments from ‘PA64s’ in a genetic background of ‘93‐11’ were constructed and planted in six environments. Three major agronomic traits, viz. panicle length (PL), heading date (HD) and plant height (PH), and five yield‐related traits, viz. grain weight per panicle (GWP), number of grains per panicle (GPP), 1000‐grain weight (TGW), seed set (SS) and number of panicles of per plant (PPP), were evaluated over 3 years. Quantitative trait loci (QTL) analysis was conducted using a likelihood ratio test based on stepwise regression. Forty‐six putative QTL distributed on 11 chromosomes were detected in more than one year. Remarkably, GWP of four CSSLs carrying positive yield QTL outperformed the recurrent parent ‘93‐11’ by more than 15%, in at least two environments. These results indicate that CSSLs are effective in identifying yield‐associated traits, and lines harbouring such QTL will be rich in resources for future molecular breeding programmes.     

Identification of RAPD markers for resistance to coffee berry disease, Colletotrichum kahawae, in arabica coffee

Resistance to Coffee Berry Disease (CBD) in Arabica coffee is controlled by at least three genes which are present in the varieties Hibrido de Timor (T gene), Catimor (T gene), Rume Sudan (R and k genes) and K7 (k gene). Hibrido de Timor, Catimor and Rume Sudan are genetically distant from most of the commercial cultivars, and the utilisation of molecular markers would greatly improve the efficiency of breeding programmes concerned with CBD resistance. The objectives of the present work were therefore: (1) to identify random amplified polymorphic DNA (RAPD) markers associated with CBD resistance and (2) to identify markers which could be used to select against the genetic background of the resistance donors. Identification of RAPD markers was carried out in three steps. The first step involved the comparison of the RAPD profiles between the susceptible cultivars and the resistant donors. This was followed by comparison of the RAPD profiles between resistant and susceptible types of each donor variety. The final step involved assay of the resistance markers in the first and the second backcrosses between these donors and the recurrent parent. High genetic variability was demonstrated in Catimor, and to some extent in Rume Sudan. Three RAPD markers were shown to be closely associated to the T gene. Attempts to identify markers associated with the R and k genes were less rewarding. The implications of the current observations in relation to breeding for CBD resistance in Arabica coffee are discussed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Marker-based screening of maize inbred lines using support vector machine regression

The phenomenon of heterosis is widely used in hybrid breeding programmes, despite the fact that no satisfactory molecular explanation is available. Estimators of quantitative genetic components like GCA and SCA values are tools used by the plant breeder to identify superior parental individuals and to search for high heterosis combinations. Obtaining these estimators usually requires the creation of new parental combinations and testing their offspring in multi-environment field trials. In this study we explore the use of ɛ-insensitive Support Vector Machine Regression (ɛ-SVR) for the prediction of GCA and SCA values from the molecular marker scores of parental inbred lines as an alternative to these field trials. Prediction accuracies are obtained by means of cross-validation on a grain maize data set from the private breeding company RAGT R2n. Results indicate that the proposed method allows the routine screening of new inbred lines despite the fact that predicting the SCA value of an untested hybrid remains problematic with the available molecular marker information and standard kernel functions. The genotypical performance of a testcross hybrid, originating from a cross between an untested inbred line and a well-known complementary tester, can be predicted with moderate to high accuracy while this cannot be said for a cross between two untested inbred lines.     

Identification of a diverse mini‐core panel of Indian rice germplasm based on genotyping using microsatellite markers

Identification of a small core germplasm set representing the available genetic diversity is essential for its proper evaluation and subsequent utilization in rice improvement programmes. For constituting a small diverse mini‐core panel of Indian rice germplasm, a representative set of 6912 accessions drawn based on their geographic origin from the whole rice germplasm collection available in the National Gene Bank was genotyped using 36 microsatellite markers. Automated fragment analysis of amplicons yielded a total of 435 alleles, with an average 12.4 and range of 3–29 alleles per locus. Polymorphism information content (PIC) ranged from 0.08 (RGNMS190) to 0.86 (RM552) with an average of 0.528. Based on genotyping data, a mini‐core consisting of 98 genotypes was identified. Ninety‐four per cent of the alleles present in the core set were present in the mini‐core. The identified small but diverse panel will be useful for further intensive trait‐specific evaluation and utilization in allele mining.