Development of microsatellite markers in autopolyploid sugarcane and comparative analysis of conserved microsatellites in sorghum and sugarcane

Sugarcane has become an increasingly important first-generation biofuel crop in tropical and subtropical regions. It has a large, complex, polyploid genome that has hindered the progress of genomic research and marker-assisted selection. Genetic mapping and ultimately genome sequence assembly require a large number of DNA markers. Simple sequence repeats (SSRs) are widely used in genetic mapping because of their abundance, high rates of polymorphism, and ease of use. The objectives of this study were to develop SSR markers for construction of a saturated genetic map and to characterize the frequency and distribution of SSRs in a polyploid genome. SSR markers were mined from expressed sequence tag (EST), reduced representation library genomic sequences, and bacterial artificial chromosome (BAC) sequences. A total of 5,675 SSR markers were surveyed in a segregating population. The overall successful amplification and polymorphic rates were 87.9 and 16.4%, respectively. The trinucleotide repeat motifs were most abundant, with tri- and hexanucleotide motifs being the most abundant for the ESTs. BAC and genomic SSRs were mostly AT-rich while the ESTs were relatively GC-rich due to codon bias. These markers were also aligned to the sorghum genome, resulting in 1,203 markers mapped in the sorghum genome. This set of SSRs conserved in sugarcane and sorghum would be the most informative for mapping quantitative trait loci in sugarcane and for comparative genomic analyses. This large collection of SSR markers is a valuable resource for sugarcane genomic research and crop improvement.     

Molecular profiling demonstrates limited diversity amongst geographically separate strains of Ustilago scitaminea

Intraspecies diversity within Ustilago scitaminea isolates from South Africa, Reunion Island, Hawaii and Guadeloupe was assessed by RAPDs, bE mating-type gene detection, rDNA sequence analysis, microscopy and germination and morphological studies. Except for sequence data, the other analyses yielded no differences in the isolates that could be used in a phylogenetic separation. Mycelial DNA of the SA isolate shared 100% sequence identity with that of mycelial DNA cultured from in vitro produced teliospores of the parent cultivar. Overall the ITS1 and ITS2 regions were found to have 96.1% and 96.9% sequence identity with a total of 17 and 21 base changes, respectively, amongst the isolates. The Reunion Island isolate was shown to be most distantly related by 3.6% to the other isolates, indicating a single clonal lineage. The lack of germination in teliospores from Guadeloupe may be attributed to changes in temperature and humidity during transportation.     

Consistency statistics and genetic parameters for taste panel assessed meat quality traits and their relationship with carcass quality traits in a commercial population of Angus-sired beef cattle

Sensory traits, such as juiciness and tenderness, are known to be important to the consumer and thus will influence their consumption of meat, specifically beef. These traits are difficult to measure and often require the use of taste panels to assess the complex parameters involved in the eating experience. Such panels are potentially a large source of measurement error, which may reduce the effectiveness of breeding programmes based on the data they generate. The aim of this study was to assess the quality of such taste panel-derived sensory traits as well as calculating genetic parameters and residual correlations for these traits along with a further set of traditional carcass quality traits. The study examined a sample of 443 Aberdeen Angus-cross animals collected from 14 breeder-finisher farms throughout Scotland. To assess the quality of the taste panel measurements, three consistency statistics were calculated: (i) panel-member consistency, i.e. the extent to which an individual panel member varied in their scoring for a given trait over the period of the experiment; (ii) repeatability, i.e. the consistency with which an individual panel member was able to score a trait on repeated samples from the same animal; and (iii) reproducibility, i.e. the extent to which taste panel members agreed with each other when scoring a trait. These consistency statistics were moderately high, particularly for panel-member consistency and reproducibility, with values ranging from 0.48 to 0.81 and 0.43 to 0.73 respectively. Estimated heritabilities were low for most of the sensory taste-panel-evaluated traits where the maximum value was 0.16 for overall liking. Residual correlations were high between many of the closely related sensory traits, although few significant correlations were found between the carcass quality data and meat quality traits.     

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.     

Genetic control of yield related stalk traits in sugarcane

A major focus of sugarcane variety improvement programs is to increase sugar yield, which can be accomplished by either increasing the sugar content of the cane or by increasing cane yield, as the correlation between these traits is low. We used a cross between an Australian sugarcane variety Q165, and a Saccharum officinarum accession, IJ76-514, to dissect the inheritance of yield-related traits in the complex polyploid sugarcane. A population of 227 individuals was grown in a replicated field trial and evaluated over 3 years for stalk weight, stalk diameter, stalk number, stalk length and total biomass. Over 1,000 AFLP and SSR markers were scored across the population and used to identify quantitative trait loci (QTL). In total, 27 regions were found that were significant at the 5% threshold using permutation tests with at least one trait; individually, they explained from 4 to 10% of the phenotypic variation and up to 46% were consistent across years. With the inclusion of digeneic interactions, from 28 to 60% of the variation was explained for these traits. The 27 genomic regions were located on 22 linkage groups (LGs) in six of the eight homology groups (HGs) indicating that a number of alleles or quantitative trait alleles (QTA) at each QTL contribute to the trait; from one to three alleles had an effect on the traits for each QTL identified. Alleles of a candidate gene, TEOSINTE BRANCHED 1 (TB1), the major gene controlling branching in maize, were mapped in this population using either an SSR or SNP markers. Two alleles showed some association with stalk number, but unlike maize, TB1 is not a major gene controlling branching in sugarcane but only has a minor and variable effect.     

Genomic Selection Using Low-Density Marker Panels

Genomic selection (GS) using high-density single-nucleotide polymorphisms (SNPs) is promising to improve response to selection in populations that are under artificial selection. High-density SNP genotyping of all selection candidates each generation, however, may not be cost effective. Smaller panels with SNPs that show strong associations with phenotype can be used, but this may require separate SNPs for each trait and each population. As an alternative, we propose to use a panel of evenly spaced low-density SNPs across the genome to estimate genome-assisted breeding values of selection candidates in pedigreed populations. The principle of this approach is to utilize cosegregation information from low-density SNPs to track effects of high-density SNP alleles within families. Simulations were used to analyze the loss of accuracy of estimated breeding values from using evenly spaced and selected SNP panels compared to using all high-density SNPs in a Bayesian analysis. Forward stepwise selection and a Bayesian approach were used to select SNPs. Loss of accuracy was nearly independent of the number of simulated quantitative trait loci (QTL) with evenly spaced SNPs, but increased with number of QTL for the selected SNP panels. Loss of accuracy with evenly spaced SNPs increased steadily over generations but was constant when the smaller number individuals that are selected for breeding each generation were also genotyped using the high-density SNP panel. With equal numbers of low-density SNPs, panels with SNPs selected on the basis of the Bayesian approach had the smallest loss in accuracy for a single trait, but a panel with evenly spaced SNPs at 10 cM was only slightly worse, whereas a panel with SNPs selected by forward stepwise selection was inferior. Panels with evenly spaced SNPs can, however, be used across traits and populations and their performance is independent of the number of QTL affecting the trait and of the methods used to estimate effects in the training data and are, therefore, preferred for broad applications in pedigreed populations under artificial selection.     

Efficiency of multi-trait,indirect, and trait-assisted genomic selection for improvement of biomass sorghum

Key message

We compare genomic selection methods that use correlated traits to help predict biomass yield in sorghum, and find that trait-assisted genomic selection performs best.

Abstract

Genomic selection (GS) is usually performed on a single trait, but correlated traits can also help predict a focal trait through indirect or multi-trait GS. In this study, we use a pre-breeding population of biomass sorghum to compare strategies that use correlated traits to improve prediction of biomass yield, the focal trait. Correlated traits include moisture, plant height measured at monthly intervals between planting and harvest, and the area under the growth progress curve. In addition to single- and multi-trait direct and indirect GS, we test a new strategy called trait-assisted GS, in which correlated traits are used along with marker data in the validation population to predict a focal trait. Single-trait GS for biomass yield had a prediction accuracy of 0.40. Indirect GS performed best using area under the growth progress curve to predict biomass yield, with a prediction accuracy of 0.37, and did not differ from indirect multi-trait GS that also used moisture information. Multi-trait GS and single-trait GS yielded similar results, indicating that correlated traits did not improve prediction of biomass yield in a standard GS scenario. However, trait-assisted GS increased prediction accuracy by up to \(50\%\) when using plant height in both the training and validation populations to help predict yield in the validation population. Coincidence between selected genotypes in phenotypic and genomic selection was also highest in trait-assisted GS. Overall, these results suggest that trait-assisted GS can be an efficient strategy when correlated traits are obtained earlier or more inexpensively than a focal trait.

     

Genome‐enabled prediction of reproductive traits in Nellore cattle using parametric models and machine learning methods

This study aimed to assess the predictive ability of different machine learning (ML) methods for genomic prediction of reproductive traits in Nellore cattle. The studied traits were age at first calving (AFC), scrotal circumference (SC), early pregnancy (EP) and stayability (STAY). The numbers of genotyped animals and SNP markers available were 2342 and 321 419 (AFC), 4671 and 309 486 (SC), 2681 and 319 619 (STAY) and 3356 and 319 108 (EP). Predictive ability of support vector regression (SVR), Bayesian regularized artificial neural network (BRANN) and random forest (RF) were compared with results obtained using parametric models (genomic best linear unbiased predictor, GBLUP, and Bayesian least absolute shrinkage and selection operator, BLASSO). A 5‐fold cross‐validation strategy was performed and the average prediction accuracy (ACC) and mean squared errors (MSE) were computed. The ACC was defined as the linear correlation between predicted and observed breeding values for categorical traits (EP and STAY) and as the correlation between predicted and observed adjusted phenotypes divided by the square root of the estimated heritability for continuous traits (AFC and SC). The average ACC varied from low to moderate depending on the trait and model under consideration, ranging between 0.56 and 0.63 (AFC), 0.27 and 0.36 (SC), 0.57 and 0.67 (EP), and 0.52 and 0.62 (STAY). SVR provided slightly better accuracies than the parametric models for all traits, increasing the prediction accuracy for AFC to around 6.3 and 4.8% compared with GBLUP and BLASSO respectively. Likewise, there was an increase of 8.3% for SC, 4.5% for EP and 4.8% for STAY, comparing SVR with both GBLUP and BLASSO. In contrast, the RF and BRANN did not present competitive predictive ability compared with the parametric models. The results indicate that SVR is a suitable method for genome‐enabled prediction of reproductive traits in Nellore cattle. Further, the optimal kernel bandwidth parameter in the SVR model was trait‐dependent, thus, a fine‐tuning for this hyper‐parameter in the training phase is crucial.     

Genome-wide association analysis for feed efficiency in Angus cattle

Estimated breeding values for average daily feed intake (AFI; kg/day), residual feed intake (RFI; kg/day) and average daily gain (ADG; kg/day) were generated using a mixed linear model incorporating genomic relationships for 698 Angus steers genotyped with the Illumina BovineSNP50 assay. Association analyses of estimated breeding values (EBVs) were performed for 41,028 single nucleotide polymorphisms (SNPs), and permutation analysis was used to empirically establish the genome-wide significance threshold (P < 0.05) for each trait. SNPs significantly associated with each trait were used in a forward selection algorithm to identify genomic regions putatively harbouring genes with effects on each trait. A total of 53, 66 and 68 SNPs explained 54.12% (24.10%), 62.69% (29.85%) and 55.13% (26.54%) of the additive genetic variation (when accounting for the genomic relationships) in steer breeding values for AFI, RFI and ADG, respectively, within this population. Evaluation by pathway analysis revealed that many of these SNPs are in genomic regions that harbour genes with metabolic functions. The presence of genetic correlations between traits resulted in 13.2% of SNPs selected for AFI and 4.5% of SNPs selected for RFI also being selected for ADG in the analysis of breeding values. While our study identifies panels of SNPs significant for efficiency traits in our population, validation of all SNPs in independent populations will be necessary before commercialization.     

Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.     

Detection and validation of EST-SSR markers associated with sugar-related traits in sugarcane using linkage and association mapping

Sugar-related traits are of great importance in sugarcane breeding. In the present study, quantitative trait loci (QTL) mapping validated with association mapping was used to identify expressed sequence tag-simple sequence repeats (EST-SSRs) associated with sugar-related traits. For linkage mapping, 524 EST-SSRs, 241 Amplified Fragment Length Polymorphisms, and 10 genomic SSR markers were mapped using 283 F₁ progenies derived from an interspecific cross. Six regions were identified using Multiple QTL Mapping, and 14 unlinked markers using single marker analysis. Association analysis was performed on a set of 200 accessions, based on the mixed linear model. Validation of the EST-SSR markers using association mapping within the target QTL genomic regions identified two EST-SSR markers showing a putative relationship with uridine diphosphate (UDP) glycosyltransferase, and beta-amylase, which are associated with pol and sugar yield. These functional markers can be used for marker-assisted selection of sugarcane.     

Accelerating the Switchgrass (Panicum virgatum L.) Breeding Cycle Using Genomic Selection Approaches

Switchgrass (Panicum virgatum L.) is a perennial grass undergoing development as a biofuel feedstock. One of the most important factors hindering breeding efforts in this species is the need for accurate measurement of biomass yield on a per-hectare basis. Genomic selection on simple-to-measure traits that approximate biomass yield has the potential to significantly speed up the breeding cycle. Recent advances in switchgrass genomic and phenotypic resources are now making it possible to evaluate the potential of genomic selection of such traits. We leveraged these resources to study the ability of three widely-used genomic selection models to predict phenotypic values of morphological and biomass quality traits in an association panel consisting of predominantly northern adapted upland germplasm. High prediction accuracies were obtained for most of the traits, with standability having the highest ten-fold cross validation prediction accuracy (0.52). Moreover, the morphological traits generally had higher prediction accuracies than the biomass quality traits. Nevertheless, our results suggest that the quality of current genomic and phenotypic resources available for switchgrass is sufficiently high for genomic selection to significantly impact breeding efforts for biomass yield.     

The Sugarcane Genome Challenge: Strategies for Sequencing a Highly Complex Genome

Sugarcane cultivars derive from interspecific hybrids obtained by crossing Saccharum officinarum and Saccharum spontaneum and provide feedstock used worldwide for sugar and biofuel production. The importance of sugarcane as a bioenergy feedstock has increased interest in the generation of new cultivars optimised for energy production. Cultivar improvement has relied largely on traditional breeding methods, which may be limited by the complexity of inheritance in interspecific polyploid hybrids, and the time-consuming process of selection of plants with desired agronomic traits. In this sense, molecular genetics can assist in the process of developing improved cultivars by generating molecular markers that can be used in the breeding process or by introducing new genes into the sugarcane genome. For meeting each of these, and additional goals, biotechnologists would benefit from a reference genome sequence of a sugarcane cultivar. The sugarcane genome poses challenges that have not been addressed in any prior sequencing project, due to its highly polyploid and aneuploid genome structure with a complete set of homeologous genes predicted to range from 10 to 12 copies (alleles) and to include representatives from each of two different species. Although sugarcane’s monoploid genome is about 1 Gb, its highly polymorphic nature represents another significant challenge for obtaining a genuine assembled monoploid genome. With a rich resource of expressed-sequence tag (EST) data in the public domain, the present article describes tools and strategies that may aid in the generation of a reference genome sequence.     

MHC polymorphism and disease resistance to vibrio anguillarum in 8 families of half-smooth tongue sole (Cynoglossus semilaevis)

Background

Most quantitative traits are controlled by multiple quantitative trait loci (QTL). The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects) and marker pairs (epistatic effects) to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement.

Results

In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait) for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive) effects were used for prediction. When the interaction (epistatic) effects were also included in the model, the squared correlation coefficient reached 0.78.

Conclusions

This study provided an excellent example for the application of genome selection to plant breeding.     

Comparative analysis of QTLs affecting plant height and flowering among closely-related diploid and polyploid genomes.

Quantitative trait loci (QTLs) affecting plant height and flowering were studied in the two Saccharum species from which modern sugarcane cultivars are derived. Two segregating populations derived from interspecific crosses between Saccharum officinarum and Saccharum spontaneum were genotyped with 735 DNA markers. Among the 65 significant associations found between these two traits and DNA markers, 35 of the loci were linked to sugarcane genetic maps and 30 were unlinked DNA markers. Twenty-one of the 35 mapped QTLs were clustered in eight genomic regions of six sugarcane homologous groups. Some of these could be divergent alleles at homologous loci, making the actual number of genes implicated in these traits much less than 35. Four QTL clusters controlling plant height in sugarcane corresponded closely to four of the six plant-height QTLs previously mapped in sorghum. One QTL controlling flowering in sugarcane corresponded to one of three flowering QTLs mapped in sorghum. The correspondence in locations of QTLs affecting plant height and flowering in sugarcane and sorghum reinforce the notion that the simple sorghum genome is a valuable "template" for molecular dissection of the much more complex sugarcane genome.     

Accounting for Genetic Architecture Improves Sequence Based Genomic Prediction for a Drosophila Fitness Trait

The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology. Here, we used the Drosophila Genetic Reference Panel to perform both a genome wide association analysis and genomic prediction for the fitness-related trait chill coma recovery time. We found substantial total genetic variation for chill coma recovery time, with a genetic architecture that differs between males and females, a small number of molecular variants with large main effects, and evidence for epistasis. Although the top additive variants explained 36% (17%) of the genetic variance among lines in females (males), the predictive ability using genomic best linear unbiased prediction and a relationship matrix using all common segregating variants was very low for females and zero for males. We hypothesized that the low predictive ability was due to the mismatch between the infinitesimal genetic architecture assumed by the genomic best linear unbiased prediction model and the true genetic architecture of chill coma recovery time. Indeed, we found that the predictive ability of the genomic best linear unbiased prediction model is markedly improved when we combine quantitative trait locus mapping with genomic prediction by only including the top variants associated with main and epistatic effects in the relationship matrix. This trait-associated prediction approach has the advantage that it yields biologically interpretable prediction models.     

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。     

Target enrichment sequencing of 307 germplasm accessions identified ancestry of ancient and modern hybrids and signatures of adaptation and selection in sugarcane (Saccharum spp.), a ‘sweet’ crop with ‘bitter’ genomes

Sugarcane (Saccharum spp.) is a highly energy‐efficient crop primarily for sugar and bio‐ethanol production. Sugarcane genetics and cultivar improvement have been extremely challenging largely due to its complex genomes with high polyploidy levels. In this study, we deeply sequenced the coding regions of 307 sugarcane germplasm accessions. Nearly five million sequence variations were catalogued. The average of 98× sequence depth enabled different allele dosages of sequence variation to be differentiated in this polyploid collection. With selected high‐quality genome‐wide SNPs, we performed population genomic studies and environmental association analysis. Results illustrated that the ancient sugarcane hybrids, S. barberi and S. sinense, and modern sugarcane hybrids are significantly different in terms of genomic compositions, hybridization processes and their potential ancestry contributors. Linkage disequilibrium (LD) analysis showed a large extent of LD in sugarcane, with 962.4 Kbp, 2739.2 Kbp and 3573.6 Kbp for S. spontaneum, S. officinarum and modern S. hybrids respectively. Candidate selective sweep regions and genes were identified during domestication and historical selection processes of sugarcane in addition to genes associated with environmental variables at the original locations of the collection. This research provided an extensive amount of genomic resources for sugarcane community and the in‐depth population genomic analyses shed light on the breeding and evolution history of sugarcane, a highly polyploid species.