Genomic preselection with genotyping-by-sequencing increases performance of commercial oil palm hybrid crosses

Genomic selection for morphological and yield-related traits using genome-wide SNPs in oil palm

Oil palm is the most important oil crop worldwide. Colombia is the fourth largest producer, primarily relying on production from interspecific hybrids, derived from crosses between Elaeis oleifera and Elaeis guineensis (OxG). However, conventional breeding can take up to 20 years to generate a new variety. Therefore, reducing the breeding cycle while improving the genetic gain for complex traits is desirable. Genomic selection (GS) is an approach with the potential to achieve this goal. In this study, we evaluated 431 F1 interspecific hybrids (OxG) and 444 backcrosses (BC1) for morphological and yield-related traits. Genomic predictions were performed with the G-BLUP model using three different population datasets for training the model: the same population (TRN1), the other population (TRN2), and both populations (TRN1+2). Higher multi-family prediction accuracies were obtained for foliar area (0.3 in OxG) and trunk height (0.47 in BC1) when the model was trained with TRN1. Single-family prediction accuracies were lower in the OxG compared to BC1 families for traits such as trunk diameter, trunk height, bunch number, and yield using TRN1. Conversely, lower prediction accuracies were obtained for most traits when the model was trained using TRN2 (< 0.1). Multi-trait models showed a substantial increase of the predictions for traits such as yield (0.22 for OxG and 0.44 for BC1), because of the genetic correlations between traits. The results herein highlighted the potential of GS for parental selection in OxG and BC1 populations, but further studies are required to improve the models to select individuals by their genetic value.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01341-5.

Genomic selection in tropical perennial crops and plantation trees: a review

To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01326-4.

Genome properties of key oil palm (Elaeis guineensis Jacq.) breeding populations

A good knowledge of the genome properties of the populations makes it possible to optimize breeding methods, in particular genomic selection (GS). In oil palm (Elaeis guineensis Jacq), the world's main source of vegetable oil, this would provide insight into the promising GS results obtained so far. The present study considered two complex breeding populations, Deli and La Mé, with 943 individuals and 7324 single-nucleotide polymorphisms (SNPs) from genotyping-by-sequencing. Linkage disequilibrium (LD), haplotype sharing, effective size (N_e), and fixation index (F_st) were investigated. A genetic linkage map spanning 1778.52 cM and with a recombination rate of 2.85 cM/Mbp was constructed. The LD at r²=0.3, considered the minimum to get reliable GS results, spanned over 1.05 cM/0.22 Mbp in Deli and 0.9 cM/0.21 Mbp in La Mé. The significant degree of differentiation existing between Deli and La Mé was confirmed by the high F_st value (0.53), the pattern of correlation of SNP heterozygosity and allele frequency among populations, and the decrease of persistence of LD and of haplotype sharing among populations with increasing SNP distance. However, the level of resemblance between the two populations over short genomic distances (correlation of r values between populations >0.6 for SNPs separated by <0.5 cM/1 kbp and percentage of common haplotypes >40% for haplotypes <3600 bp/0.20 cM) likely explains the superiority of GS models ignoring the parental origin of marker alleles over models taking this information into account. The two populations had low N_e (<5). Population-specific genetic maps and reference genomes are recommended for future studies.

Oil Palm Breeding in the Modern Era: Challenges and Opportunities

Oil palm, a cross-pollinated crop with long generation time, poses a lot of challenges in achieving sustainable oil palm with high yield and quality. The African oil palm (Elaeis guineensis Jacq.) is the most productive and versatile oil-yielding crop in the world, producing more than any other oil-yielding crop. Despite recent challenges, such as stress tolerance, superior oil quality, disease tolerance, and the need for new market niches, there is a growing need to explore and develop new varieties with high yield potential and the genetic diversity required to maintain oil palm yield stability. Breeding is an indispensable part of producing high-quality planting materials to increase oil palm yield. Biotechnological technologies have transformed conventional plant breeding approaches by introducing novel genotypes for breeding. Innovative pre-breeding and breeding approaches, such as identifying candidate genes in wild or land races using genomics tools, can pave the way for genetic improvement in oil palm. In this review, we highlighted the modern breeding tools, including genomics, marker-assisted breeding, genetic engineering, and genome editing techniques in oil palm crops, and we explored certain concerns connected to the techniques and their applications in practical breeding.

Improving the accuracy of genomic predictions in an outcrossing species with hybrid cultivars between heterozygote parents: a case study of oil palm (Elaeis guineensis Jacq.)

Genomic selection (GS) is a method of marker-assisted selection revolutionizing crop improvement, but it can still be optimized. For hybrid breeding between heterozygote parents of different populations or species, specific aspects can be considered to increase GS accuracy: (1) training population genotyping, i.e., only genotyping the hybrid parents or also a sample of hybrid individuals, and (2) marker effects modeling, i.e., using population-specific effects of single nucleotide polymorphism alleles model (PSAM) or across-population SNP genotype model (ASGM). Here, this was investigated empirically for the prediction of the performances of oil palm hybrids for yield traits. The GS model was trained on 352 hybrid crosses and validated on 213 independent hybrid crosses. The training and validation hybrid parents and 399 training hybrid individuals were genotyping by sequencing. Despite the small proportion of hybrid individuals genotyped and low parental heterozygosity, GS prediction accuracy increased on average by 5% (range 1.4-31.3%, depending on trait and model) when training was done using genomic data on hybrids and parents compared with only parental genomic data. With ASGM, GS prediction accuracy increased on average by 3% (- 10.2 to 40%, depending on trait and genotyping strategy) compared with PSAM. We conclude that the best GS strategy for oil palm is to aggregate genomic data of parents and hybrid individuals and to ignore the parental origin of marker alleles (ASGM). To gain a better insight into these results, future studies should examine the respective effect of capturing genetic variability within crosses and taking segregation distortion into account when genotyping hybrid individuals, and investigate the factors controlling the relative performances of ASGM and PSAM in hybrid crops.

Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning

Oil palm is one of the main crops grown to help achieve sustainability in Malaysia. The selection of the best breeds will produce quality crops and increase crop yields. This study aimed to examine machine learning (ML) in oil palm breeding (OPB) using factors other than genetic data. A new conceptual framework to adopt the ML in OPB will be presented at the end of this paper. At first, data types, phenotype traits, current ML models, and evaluation technique will be identified through a literature survey. This study found that the phenotype and genotype data are widely used in oil palm breeding programs. The average bunch weight, bunch number, and fresh fruit bunch are the most important characteristics that can influence the genetic improvement of progenies. Although machine learning approaches have been applied to increase the productivity of the crop, most studies focus on molecular markers or genotypes for plant breeding, rather than on phenotype. Theoretically, the use of phenotypic data related to offspring should predict high breeding values by using ML. Therefore, a new ML conceptual framework to study the phenotype and progeny data of oil palm breeds will be discussed in relation to achieving the Sustainable Development Goals (SDGs).

Insights on Genetic Diversity, Population Structure, and Linkage Disequilibrium in Globally Diverse Coconut Accessions Using Genotyping-by-Sequencing

Genotyping-by-sequencing (GBS) has emerged as a cost-effective approach for genome-wide discovery of single-nucleotide polymorphism (SNP) markers and high-throughput genotyping. In this study, 96 coconut palms, representing 16 accessions from globally diverse origins, were genotyped using the GBS strategy. A total of 10,835 high-quality SNPs, which were identified after stringent filtering, were utilized to assess genetic diversity, population structure, and linkage disequilibrium (LD) analyses. The polymorphism information content (PIC) values of SNPs ranged from 0.1 to 0.4, with a large proportion of SNPs (8633 nos.; 79.7%) having a higher PIC in the range of 0.3-0.4. The genetic diversity analysis revealed the existence of a high level of variation in coconut accessions, with an average expected heterozygosity (H_e) value of 0.43. Unweighted neighbor-joining phylogenetic tree and Bayesian-based model population structure grouped coconut genotypes into four main clusters. The accessions are generally clustered based on their height (tall or dwarf), with a few accession clusterings based on geographical origins. Investigation of LD pattern in coconut indicated a relatively rapid LD decay with a short range (9 kb). The results obtained in this study will contribute to enhancing the capacity of coconut researchers to utilize genetic diversity for further genetic improvement. In addition, it would open up possibilities for performing genomic studies such as genome-wide association studies and genomic selection to accelerate the efficiency and speed of coconut genetic improvement.

In silico QTL mapping in an oil palm breeding program reveals a quantitative and complex genetic resistance to Ganoderma boninense

Basal stem rot caused by Ganoderma boninense is the major threat to oil palm cultivation in Southeast Asia, which accounts for 80% of palm oil production worldwide, and this disease is increasing in Africa. The use of resistant planting material as part of an integrated pest management of this disease is one sustainable solution. However, breeding for Ganoderma resistance requires long-term and costly research, which could greatly benefit from marker-assisted selection (MAS). In this study, we evaluated the effectiveness of an in silico genetic mapping approach that took advantage of extensive data recorded in an ongoing breeding program. A pedigree-based QTL mapping approach applied to more than 10 years' worth of data collected during pre-nursery tests revealed the quantitative nature of Ganoderma resistance and identified underlying loci segregating in genetic diversity that is directly relevant for the breeding program supporting the study. To assess the consistency of QTL effects between pre-nursery and field environments, information was collected on the disease status of the genitors planted in genealogical gardens and modeled with pre-nursery-based QTL genotypes. In the field, individuals were less likely to be infected with Ganoderma when they carried more favorable alleles at the pre-nursery QTL. Our results pave the way for a MAS of Ganoderma resistant and high yielding planting material, and the provided proof-of-concept of this efficient and cost-effective approach could motivate similar studies based on diverse breeding programs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01246-9.

Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population

Background

Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations.

Results

Narrow-sense heritability of yield and yield stability was low (h²=0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r=0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474g/year, more than double that of traditional breeding methods (226g/year), due to the halving of generation length from 8 to 4years.

Conclusions

The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07694-z.

Analysis of genetic diversity and discrimination of Oil Palm DxP populations based on the origins of pisifera elite parents

A total of 251 Dura cross Pisifera (DxP) hybrid palms from six populations descending from six parental African Pisifera origins and involving 12 progenies were analyzed with 19 selected Simple Sequence Repeats (SSR) markers. A total of 110 alleles were produced, ranging from three to eight per SSR, with a mean of 5.8 alleles per SSR locus. Of these, 68.5% were considered shared alleles by more than one population and the remaining 31.5% were population specific alleles. They generated between six and 21 haplotypes in all populations, and depending on the SSR marker, between one and 10 haplotypes within populations. Various parameters for analyzing genetic variability, differentiation and genetic structure were computed using GenAlEx, Structure and Darwin software. The obtained results confirmed microsatellites as a robust, feasible and trustful method for obtaining DNA fingerprints, tracing the source of oil palm samples. With respect to the authenticity of materials or for solving legitimacy issues, accession belonging to each population by SSR markers could be distinguished, but additional SSR should be screened for improving this process.

Molecular approaches for improving oil palm for oil

The oil palm, originating from Africa, is the most productive oil crop species. Palm oil is an important source of edible oil. Its current global plantation area is over 23 million ha. The theoretical oil yield potential of the oil palm is 18.2 tons/ha/year. However, current average oil yield is only 3.8 tons/ha/year. In the past 100 years, conventional breeding and improvement of field management played important roles in increasing oil yield. However, conventional breeding for trait improvement was limited by its very long (10-20 years) phenotypic selection cycle, although it improved oil yield by ~10-20% per generation. Molecular breeding using novel molecular technologies will accelerate genetic improvement and may reduce the need to deforest and to use arable land for expanding oil palm plantations, which in turn makes palm oil more sustainable. Here, we comprehensively synthesize information from relevant literature of the technologies, achievements, and challenges of molecular approaches, including tissue culture, haploid breeding, mutation breeding, marker-assisted selection (MAS), genomic selection (GS), and genome editing (GE). We propose the characteristics of ideal palms and suggest a road map to breed ideal palms for sustainable palm oil.

Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review

Although hybrid crop varieties are among the most popular agricultural innovations, the rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower and more resource-intensive than inbred breeding, but it allows systematic improvement of a population by recurrent selection and exploitation of heterosis simultaneously. Inbred parental lines can identically reproduce both themselves and their F1 progeny indefinitely, whereas outbred lines cannot, so uniform outbred lines must be bred indirectly through their inbred parents to harness heterosis. Heterosis is an expected consequence of whole-genome non-additive effects at the population level over evolutionary time. Understanding heterosis from the perspective of molecular genetic mechanisms alone may be elusive, because heterosis is likely an emergent property of populations. Hybrid breeding is a process of recurrent population improvement to maximize hybrid performance. Hybrid breeding is not maximization of heterosis per se, nor testing random combinations of individuals to find an exceptional hybrid, nor using heterosis in place of population improvement. Though there are methods to harness heterosis other than hybrid breeding, such as use of open-pollinated varieties or clonal propagation, they are not currently suitable for all crops or production environments. The use of genomic selection can decrease cycle time and costs in hybrid breeding, particularly by rapidly establishing heterotic pools, reducing testcrossing, and limiting the loss of genetic variance. Open questions in optimal use of genomic selection in hybrid crop breeding programs remain, such as how to choose founders of heterotic pools, the importance of dominance effects in genomic prediction, the necessary frequency of updating the training set with phenotypic information, and how to maintain genetic variance and prevent fixation of deleterious alleles.

Optimizing imputation of marker data from genotyping-by-sequencing (GBS) for genomic selection in non-model species: Rubber tree (Hevea brasiliensis) as a case study

Genotyping-by-sequencing (GBS) provides the marker density required for genomic predictions (GP). However, GBS gives a high proportion of missing SNP data which, for species without a chromosome-level genome assembly, must be imputed without knowing the SNP physical positions. Here, we compared GP accuracy with seven map-independent and two map-dependent imputation approaches, and when using all SNPs against the subset of genetically mapped SNPs. We used two rubber tree (Hevea brasiliensis) datasets with three traits. The results showed that the best imputation approaches were LinkImputeR, Beagle and FImpute. Using the genetically mapped SNPs increased GP accuracy by 4.3%. Using LinkImputeR on all the markers allowed avoiding genetic mapping, with a slight decrease in GP accuracy. LinkImputeR gave the highest level of correctly imputed genotypes and its performances were further improved by its ability to define a subset of SNPs imputed optimally. These results will contribute to the efficient implementation of genomic selection with GBS. For Hevea, GBS is promising for rubber yield improvement, with GP accuracies reaching 0.52.

Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of "selective sweeps." Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees' phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging "big data" approaches (machine learning-ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.

Genomic predictions improve clonal selection in oil palm (Elaeis guineensis Jacq.) hybrids

The prediction of clonal genetic value for yield is challenging in oil palm (Elaeis guineensis Jacq.). Currently, clonal selection involves two stages of phenotypic selection (PS): ortet preselection on traits with sufficient heritability among a small number of individuals in the best crosses in progeny tests, and final selection on performance in clonal trials. The present study evaluated the efficiency of genomic selection (GS) for clonal selection. The training set comprised almost 300 Deli × La Mé crosses phenotyped for eight palm oil yield components and the validation set 42 Deli × La Mé ortets. Genotyping-by-sequencing (GBS) revealed 15,054 single nucleotide polymorphisms (SNP). The effects of the SNP dataset (density and percentage of missing data) and two GS modeling approaches, ignoring (ASGM) and considering (PSAM) the parental origin of alleles, were assessed. The results showed prediction accuracies ranging from 0.08 to 0.70 for ortet candidates without data records, depending on trait, SNP dataset and modeling. ASGM was better (on average slightly more accurate, less sensitive to SNP dataset and simpler), although PSAM appeared interesting for a few traits. With ASGM, the number of SNPs had to reach 7,000, while the percentage of missing data per SNP was of secondary importance, and GS prediction accuracies were higher than those of PS for most of the traits. Finally, this makes possible two practical applications of GS, that will increase genetic progress by improving ortet preselection before clonal trials: (1) preselection at the mature stage on all yield components jointly using ortet genotypes and phenotypes, and (2) genomic preselection on more yield components than PS, among a large population of the best possible crosses at nursery stage.

Construction of a high density linkage map in Oil Palm using SPET markers

A high-density genetic linkage map from a controlled cross of two oil palm (Elaeis guineensis) genotypes was constructed based on Single Primer Enrichment Technology (SPET) markers. A 5K panel of hybridization probes were used for this purpose which was derived from previously developed SNP primers in oil palm. Initially, 13,384 SNPs were detected which were reduced to 13,073 SNPs after filtering for only bi-allelic SNP. Around 75% of the markers were found to be monomorphic in the progeny, reducing the markers left for linkage mapping to 3,501. Using Lep-MAP3 software, a linkage map was constructed which contained initially 2,388 markers and had a total length of 1,370 cM. In many cases several adjacent SNP were located on the same locus, due to missing recombination events between them, leading to a total of 1,054 loci on the 16 LG. Nevertheless, the marker density of 1.74 markers per cM (0.57 cM/marker) should allow the detection of QTLs in the future. This study shows that cost efficient SPET markers are suitable for linkage map construction in oil palm and probably, also in other species.

High-Density Linkage Map and QTLs for Growth in Snapper (Chrysophrys auratus)

Characterizing the genetic variation underlying phenotypic traits is a central objective in biological research. This research has been hampered in the past by the limited genomic resources available for most non-model species. However, recent advances in sequencing technologies and related genotyping methods are rapidly changing this. Here we report the use of genome-wide SNP data from the ecologically and commercially important marine fish species Chrysophrys auratus (snapper) to 1) construct the first linkage map for this species, 2) scan for growth QTL, and 3) search for putative candidate genes in the surrounding QTL regions. The newly constructed linkage map contained ∼11K SNP markers and is one of the densest maps to date in the fish family Sparidae. Comparisons with genome scaffolds of the recently assembled snapper genome indicated that marker placement was mostly consistent between the scaffolds and linkage map (R = 0.7), but that at fine scales (< 5 cM) some precision limitations occurred. Of the 24 linkage groups, which likely reflect the 24 chromosomes of this species, three were found to contain QTL with genome-wide significance for growth-related traits. A scan of 13 candidate growth genes located the growth hormone, myogenin, and parvalbumin genes within 5.3, 9.6, and 25.0 cM of these QTL, respectively. The linkage map and QTL found in this study will advance the investigation of genome structure and aquaculture breeding efforts in this and related species.

Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations

BACKGROUND

Switchgrass breeders need to improve the rates of genetic gain in many bioenergy-related traits in order to create improved cultivars that are higher yielding and have optimal biomass composition. One way to achieve this is through genomic selection. However, the heritability of traits needs to be determined as well as the accuracy of prediction in order to determine if efficient selection is possible.

RESULTS

Using five distinct switchgrass populations comprised of three lowland, one upland and one hybrid accession, the accuracy of genomic predictions under different cross-validation strategies and prediction methods was investigated. Individual genotypes were collected using GBS while kin-BLUP, partial least squares, sparse partial least squares, and BayesB methods were employed to predict yield, morphological, and NIRS-based compositional data collected in 2012-2013 from a replicated Nebraska field trial. Population structure was assessed by F statistics which ranged from 0.3952 between lowland and upland accessions to 0.0131 among the lowland accessions. Prediction accuracy ranged from 0.57-0.52 for cell wall soluble glucose and fructose respectively, to insignificant for traits with low repeatability. Ratios of heritability across to within-population ranged from 15 to 0.6.

CONCLUSIONS

Accuracy was significantly affected by both cross-validation strategy and trait. Accounting for population structure with a cross-validation strategy constrained by accession resulted in accuracies that were 69% lower than apparent accuracies using unconstrained cross-validation. Less accurate genomic selection is anticipated when most of the phenotypic variation exists between populations such as with spring regreening and yield phenotypes.