Shrinkage estimation of the realized relationship matrix

Gene disruption by structural mutations drives selection in US rice breeding over the last century

The genetic basis of general plant vigor is of major interest to food producers, yet the trait is recalcitrant to genetic mapping because of the number of loci involved, their small effects, and linkage. Observations of heterosis in many crops suggests that recessive, malfunctioning versions of genes are a major cause of poor performance, yet we have little information on the mutational spectrum underlying these disruptions. To address this question, we generated a long-read assembly of a tropical japonica rice (Oryza sativa) variety, Carolina Gold, which allowed us to identify structural mutations (>50 bp) and orient them with respect to their ancestral state using the outgroup, Oryza glaberrima. Supporting prior work, we find substantial genome expansion in the sativa branch. While transposable elements (TEs) account for the largest share of size variation, the majority of events are not directly TE-mediated. Tandem duplications are the most common source of insertions and are highly enriched among 50-200bp mutations. To explore the relative impact of various mutational classes on crop fitness, we then track these structural events over the last century of US rice improvement using 101 resequenced varieties. Within this material, a pattern of temporary hybridization between medium and long-grain varieties was followed by recent divergence. During this long-term selection, structural mutations that impact gene exons have been removed at a greater rate than intronic indels and single-nucleotide mutations. These results support the use of ab initio estimates of mutational burden, based on structural data, as an orthogonal predictor in genomic selection.

Some crop varieties have superior performance across years and environments. In hybrids, harmful mutations in one parent are masked by the ancestral alleles in the other parent, resulting in increased vigor. Unfortunately, these mutations are very difficult to identify precisely because, individually, they only have a small effect. In this study, we use long-read sequencing to characterize the entire mutational spectrum between two rice varieties. We then track these mutations through the last century of rice breeding. We show that large structural mutations in exons are selected against at a greater rate than any other mutational class. These findings illuminate the nature of deleterious alleles and will guide attempts to predict variety vigor based solely on genomic information.

Connecting tree-ring phenotypes, genetic associations and transcriptomics to decipher the genomic architecture of drought adaptation in a widespread conifer

As boreal forests face significant threats from climate change, understanding evolutionary trajectories of coniferous species has become fundamental to adapting management and conservation to a drying climate. We examined the genomic architecture underlying adaptive variation related to drought tolerance in 43 populations of a widespread boreal conifer, white spruce (Piceaglauca [Moench] Voss), by combining genotype–environment associations, genotype–phenotype associations, and transcriptomics. Adaptive genetic variation was identified by correlating allele frequencies for 6,153 single nucleotide polymorphisms from 2,606 candidate genes with temperature, precipitation and aridity gradients, and testing for significant associations between genotypes and 11 dendrometric and drought‐related traits (i.e., anatomical, growth response and climate‐sensitivity traits) using a polygenic model. We identified a set of 285 genes significantly associated with a climatic factor or a phenotypic trait, including 110 that were differentially expressed in response to drought under greenhouse‐controlled conditions. The interlinked phenotype–genotype–environment network revealed eight high‐confidence genes involved in white spruce adaptation to drought, of which four were drought‐responsive in the expression analysis. Our findings represent a significant step toward the characterization of the genomic basis of drought tolerance and adaptation to climate in conifers, which is essential to enable the establishment of resilient forests in view of new climate conditions.

see also the Perspective by Lars Opgenoorth and Christian Rellstab

Genomic Selection for F1 Hybrid Breeding in Strawberry (Fragaria × ananassa)

Cultivated strawberry is the most widely consumed fruit crop in the world, and therefore, many breeding programs are underway to improve its agronomic traits such as fruit quality. Strawberry cultivars were vegetatively propagated through runners and carried a high risk of infection with viruses and insects. To solve this problem, the development of F₁ hybrid seeds has been proposed as an alternative breeding strategy in strawberry. In this study, we conducted a potential assessment of genomic selection (GS) in strawberry F₁ hybrid breeding. A total of 105 inbred lines were developed as candidate parents of strawberry F₁ hybrids. In addition, 275 parental combinations were randomly selected from the 105 inbred lines and crossed to develop test F₁ hybrids for GS model training. These populations were phenotyped for petiole length, leaf area, Brix, fruit hardness, and pericarp color. Whole-genome shotgun sequencing of the 105 inbred lines detected 20,811 single nucleotide polymorphism sites that were provided for subsequent GS analyses. In a GS model construction, inclusion of dominant effects showed a slight advantage in GS accuracy. In the across population prediction analysis, GS models using the inbred lines showed predictability for the test F₁ hybrids and vice versa, except for Brix. Finally, the GS models were used for phenotype prediction of 5,460 possible F₁ hybrids from 105 inbred lines to select F₁ hybrids with high fruit hardness or high pericarp color. These F₁ hybrids were developed and phenotyped to evaluate the efficacy of the GS. As expected, F₁ hybrids that were predicted to have high fruit hardness or high pericarp color expressed higher observed phenotypic values than the F₁ hybrids that were selected for other objectives. Through the analyses in this study, we demonstrated that GS can be applied for strawberry F₁ hybrid breeding.

SNP-based analysis reveals unexpected features of genetic diversity, parental contributions and pollen contamination in a white spruce breeding program

Accurate monitoring of genetic diversity levels of seedlots and mating patterns of parents from seed orchards are crucial to ensure that tree breeding programs are long-lasting and will deliver anticipated genetic gains. We used SNP genotyping to characterize founder trees, five bulk seed orchard seedlots, and trees from progeny trials to assess pollen contamination and the impact of severe roguing on genetic diversity and parental contributions in a first-generation open-pollinated white spruce clonal seed orchard. After severe roguing (eliminating 65% of the seed orchard trees), we found a slight reduction in the Shannon Index and a slightly negative inbreeding coefficient, but a sharp decrease in effective population size (eightfold) concomitant with sharp increase in coancestry (eightfold). Pedigree reconstruction showed unequal parental contributions across years with pollen contamination levels between 12 and 51% (average 27%) among seedlots, and 7-68% (average 30%) among individual genotypes within a seedlot. These contamination levels were not correlated with estimates obtained using pollen flight traps. Levels of pollen contamination also showed a Pearson's correlation of 0.92 with wind direction, likely from a pollen source 1 km away from the orchard under study. The achievement of 5% genetic gain in height at rotation through eliminating two-thirds of the orchard thus generated a loss in genetic diversity as determined by the reduction in effective population size. The use of genomic profiles revealed the considerable impact of roguing on genetic diversity, and pedigree reconstruction of full-sib families showed the unanticipated impact of pollen contamination from a previously unconsidered source.

Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools

KEY MESSAGE

High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools. Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, "Kemater Landmais Gelb" (KE) and "Petkuser Ferdinand Rot" (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic ([Formula: see text]) correlations between GER severity and three agronomic traits were low (r =  - 0.27 to 0.20; [Formula: see text]=  - 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies ([Formula: see text]) of weighted GS (wRR-BLUP) were higher than [Formula: see text] of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low [Formula: see text] that could be improved by using fixed marker effects in the GS model.

Genome-wide association of rice response to blast fungus identifies loci for robust resistance under high nitrogen

BACKGROUND

Nitrogen fertilization is known to increase disease susceptibility, a phenomenon called Nitrogen-Induced Susceptibility (NIS). In rice, this phenomenon has been observed in infections with the blast fungus Magnaporthe oryzae. A previous classical genetic study revealed a locus (NIS1) that enhances susceptibility to rice blast under high nitrogen fertilization. In order to further address the underlying genetics of plasticity in susceptibility to rice blast after fertilization, we analyzed NIS under greenhouse-controlled conditions in a panel of 139 temperate japonica rice strains. A genome-wide association analysis was conducted to identify loci potentially involved in NIS by comparing susceptibility loci identified under high and low nitrogen conditions, an approach allowing for the identification of loci validated across different nitrogen environments. We also used a novel NIS Index to identify loci potentially contributing to plasticity in susceptibility under different nitrogen fertilization regimes.

RESULTS

A global NIS effect was observed in the population, with the density of lesions increasing by 8%, on average, under high nitrogen fertilization. Three new QTL, other than NIS1, were identified. A rare allele of the RRobN1 locus on chromosome 6 provides robust resistance in high and low nitrogen environments. A frequent allele of the NIS2 locus, on chromosome 5, exacerbates blast susceptibility under the high nitrogen condition. Finally, an allele of NIS3, on chromosome 10, buffers the increase of susceptibility arising from nitrogen fertilization but increases global levels of susceptibility. This allele is almost fixed in temperate japonicas, as a probable consequence of genetic hitchhiking with a locus involved in cold stress adaptation.

CONCLUSIONS

Our results extend to an entire rice subspecies the initial finding that nitrogen increases rice blast susceptibility. We demonstrate the usefulness of estimating plasticity for the identification of novel loci involved in the response of rice to the blast fungus under different nitrogen regimes.

Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

Genomic selection with genome-wide distributed molecular markers has evolved into a well-implemented tool in many breeding programs during the last decade. The resistance against Fusarium head blight (FHB) in wheat is probably one of the most thoroughly studied systems within this framework. Aside from the genome, other biological strata like the transcriptome have likewise shown some potential in predictive breeding strategies but have not yet been investigated for the FHB-wheat pathosystem. The aims of this study were thus to compare the potential of genomic with transcriptomic prediction, and to assess the merit of blending incomplete transcriptomic with complete genomic data by the single-step method. A substantial advantage of gene expression data over molecular markers has been observed for the prediction of FHB resistance in the studied diversity panel of breeding lines and released cultivars. An increase in prediction ability was likewise found for the single-step predictions, although this can mostly be attributed to an increased accuracy among the RNA-sequenced genotypes. The usage of transcriptomics can thus be seen as a complement to already established predictive breeding pipelines with pedigree and genomic data, particularly when more cost-efficient multiplexing techniques for RNA-sequencing will become more accessible in the future.

Haplotype- and SNP-Based GWAS for Growth and Wood Quality Traits in Eucalyptus cladocalyx Trees under Arid Conditions

The agricultural and forestry productivity of Mediterranean ecosystems is strongly threatened by the adverse effects of climate change, including an increase in severe droughts and changes in rainfall distribution. In the present study, we performed a genome-wide association study (GWAS) to identify single-nucleotide polymorphisms (SNPs) and haplotype blocks associated with the growth and wood quality of Eucalyptus cladocalyx, a tree species suitable for low-rainfall sites. The study was conducted in a progeny-provenance trial established in an arid site with Mediterranean patterns located in the southern Atacama Desert, Chile. A total of 87 SNPs and 3 haplotype blocks were significantly associated with the 6 traits under study (tree height, diameter at breast height, slenderness coefficient, first bifurcation height, stem straightness, and pilodyn penetration). In addition, 11 loci were identified as pleiotropic through Bayesian multivariate regression and were mainly associated with wood hardness, height, and diameter. In general, the GWAS revealed associations with genes related to primary metabolism and biosynthesis of cell wall components. Additionally, associations coinciding with stress response genes, such as GEM-related 5 and prohibitin-3, were detected. The findings of this study provide valuable information regarding genetic control of morphological traits related to adaptation to arid environments.

Candidate genes and SNPs associated with stomatal conductance under drought stress in Vitis

BACKGROUND

Understanding the complexity of the vine plant's response to water deficit represents a major challenge for sustainable winegrowing. Regulation of water use requires a coordinated action between scions and rootstocks on which cultivars are generally grafted to cope with phylloxera infestations. In this regard, a genome-wide association study (GWAS) approach was applied on an 'ad hoc' association mapping panel including different Vitis species, in order to dissect the genetic basis of transpiration-related traits and to identify genomic regions of grape rootstocks associated with drought tolerance mechanisms. The panel was genotyped with the GrapeReSeq Illumina 20 K SNP array and SSR markers, and infrared thermography was applied to estimate stomatal conductance values during progressive water deficit.

RESULTS

In the association panel the level of genetic diversity was substantially lower for SNPs loci (0.32) than for SSR (0.87). GWAS detected 24 significant marker-trait associations along the various stages of drought-stress experiment and 13 candidate genes with a feasible role in drought response were identified. Gene expression analysis proved that three of these genes (VIT_13s0019g03040, VIT_17s0000g08960, VIT_18s0001g15390) were actually induced by drought stress. Genetic variation of VIT_17s0000g08960 coding for a raffinose synthase was further investigated by resequencing the gene of 85 individuals since a SNP located in the region (chr17_10,497,222_C_T) was significantly associated with stomatal conductance.

CONCLUSIONS

Our results represent a step forward towards the dissection of genetic basis that modulate the response to water deprivation in grape rootstocks. The knowledge derived from this study may be useful to exploit genotypic and phenotypic diversity in practical applications and to assist further investigations.

Genome-Wide Association Studies of Mineral Content in Common Bean

Micronutrient malnutrition is one of the main public health problems in many parts of the world. This problem raises the attention of all valuable sources of micronutrients for the human diet, such as common bean (Phaseolus vulgaris L.). In this research, a panel of 174 accessions representing Croatian common bean landraces was phenotyped for seed content of eight nutrients (N, P, K, Ca, Mg, Fe, Zn, and Mn), and genotyped using 6,311 high-quality DArTseq-derived SNP markers. A genome-wide association study (GWAS) was then performed to identify new genetic sources for improving seed mineral content. Twenty-two quantitative trait nucleotides (QTN) associated with seed nitrogen content were discovered on chromosomes Pv01, Pv02, Pv03, Pv05, Pv07, Pv08, and Pv10. Five QTNs were associated with seed phosphorus content, four on chromosome Pv07, and one on Pv08. A single significant QTN was found for seed calcium content on chromosome Pv09 and for seed magnesium content on Pv08. Finally, two QTNs associated with seed zinc content were identified on Pv06 while no QTNs were found to be associated with seed potassium, iron, or manganese content. Our results demonstrate the utility of GWAS for understanding the genetic architecture of seed nutritional traits in common bean and have utility for future enrichment of seed with macro- and micronutrients through genomics-assisted breeding.

Intercontinental trials reveal stable QTL for Northern corn leaf blight resistance in Europe and in Brazil

KEY MESSAGE

NCLB is the most devastating leaf disease in European maize, and the introduction of Brazilian resistance donors can efficiently increase the resistance levels of European maize germplasm. Northern corn leaf blight (NCLB) is one of the most devastating leaf pathogens in maize (Zea mays L.). Maize cultivars need to be equipped with broad and stable NCLB resistance to cope with production intensification and climate change. Brazilian germplasm is a great source to increase low NCLB resistance levels in European materials, but little is known about their effect in European environments. To investigate the usefulness of Brazilian germplasm as NCLB resistance donors, we conducted multi-parent QTL mapping, evaluated the potential of marker-assisted selection as well as genome-wide selection of 742 F1-derived DH lines. The line per se performance was evaluated in one location in Brazil and six location-by-year combinations (= environments) in Europe, while testcrosses were assessed in two locations in Brazil and further 10 environments in Europe. Jointly, we identified 17 QTL for NCLB resistance explaining 3.57-30.98% of the genotypic variance each. Two of these QTL were detected in both Brazilian and European environments indicating the stability of these QTL in contrasting ecosystems. We observed moderate to high genomic prediction accuracies between 0.58 and 0.83 depending on population and continent. Collectively, our study illustrates the potential use of tropical resistance sources to increase NCLB resistance level in applied European maize breeding programs.

Estimation of dynamic SNP-heritability with Bayesian Gaussian process models

Motivation

Improved DNA technology has made it practical to estimate single-nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth- and development-related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty.

Results

We introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo method which allows full uncertainty quantification. Several datasets are analysed and our results clearly illustrate that the 95% credible intervals of the proposed joint estimation method (which ‘borrows strength’ from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 software and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals.

Availability and implementation

The C++ implementation dynBGP and simulated data are available in GitHub: https://github.com/aarjas/dynBGP. The programmes can be run in R. Real datasets are available in QTL archive: https://phenome.jax.org/centers/QTLA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-wide dissection of hybridization for fiber quality- and yield-related traits in upland cotton

An evaluation of combining ability can facilitate the selection of suitable parents and superior F₁ hybrids for hybrid cotton breeding, although the molecular genetic basis of combining ability has not been fully characterized. In the present study, 282 female parents were crossed with four male parents in accordance with the North Carolina II mating scheme to generate 1128 hybrids. The parental lines were genotyped based on restriction site-associated DNA sequencing and 306 814 filtered single nucleotide polymorphisms were used for genome-wide association analysis involving the phenotypes, general combining ability (GCA) values, and specific combining ability values of eight fiber quality- and yield-related traits. The main results were: (i) all parents could be clustered into five subgroups based on population structure analyses and the GCA performance of the female parents had significant differences between subgroups; (ii) 20 accessions with a top 5% GCA value for more than one trait were identified as elite parents for hybrid cotton breeding; (iii) 120 significant single nucleotide polymorphisms, clustered into 66 quantitative trait loci, such as the previously reported Gh_A07G1769 and GhHOX3 genes, were found to be significantly associated with GCA; and (iv) identified quantitative trait loci for GCA had a cumulative effect on GCA of the accessions. Overall, our results suggest that pyramiding the favorable loci for GCA may improve the efficiency of hybrid cotton breeding.

Impacts of dominance effects on genomic prediction of sorghum hybrid performance

Non-additive (dominance and epistasis) effects have remarkable influences on hybrid performance, e.g., via heterosis. Nevertheless, only additive effects are often considered in genomic predictions (GP). In this study, we demonstrated the importance of dominance effects in the prediction of hybrid performance in bioenergy sorghum [Sorghum bicolor (L.) Moench]. The dataset contained more than 400 hybrids between 200 inbred lines and two testers. The hybrids exhibited considerable heterosis in culm length and fresh weight, and the degree of heterosis was consistent with the genetic distance from the corresponding tester. The degree of heterosis was further different among subpopulations. Conversely, Brix exhibited limited heterosis. Regarding GP, we examined three statistical models and four training dataset types. In most of the dataset types, genomic best linear unbiased prediction (GBLUP) with additive effects had lower prediction accuracy than GBLUP with additive and dominance effects (GBLUP-AD) and Gaussian kernel regression (GK). The superiority of GBLUP-AD and GK depended on the level of dominance variance, which was high for culm length and fresh weight, and low for Brix. Considering subpopulations, the influence of dominance was more complex. Our findings highlight the importance of considering dominance effects in GP models for sorghum hybrid breeding.

Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits

With climate change, the pressure on tree breeding to provide varieties with improved resilience to biotic and abiotic stress is increasing. As such, pest resistance is of high priority but has been neglected in most tree breeding programs, given the complexity of phenotyping for these traits and delays to assess mature trees. In addition, the existing genetic variation of resistance and its relationship with productivity should be better understood for their consideration in multitrait breeding. In this study, we evaluated the prospects for genetic improvement of the levels of acetophenone aglycones (AAs) in white spruce needles, which have been shown to be tightly linked to resistance to spruce budworm. Furthermore, we estimated the accuracy of genomic selection (GS) for these traits, allowing selection at a very early stage to accelerate breeding. A total of 1,516 progeny trees established on five sites and belonging to 136 full-sib families from a mature breeding population in New Brunswick were measured for height growth and genotyped for 4,148 high-quality SNPs belonging to as many genes along the white spruce genome. In addition, 598 trees were assessed for levels of AAs piceol and pungenol in needles, and 578 for wood stiffness. GS models were developed with the phenotyped trees and then applied to predict the trait values of unphenotyped trees. AAs were under moderate-to-high genetic control (h 2: 0.43-0.57) with null or marginally negative genetic correlations with other traits. The prediction accuracy of GS models (GBLUP) for AAs was high (PAAC: 0.63-0.67) and comparable or slightly higher than pedigree-based (ABLUP) or BayesCπ models. We show that AA traits can be improved and that GS speeds up the selection of improved trees for insect resistance and for growth and wood quality traits. Various selection strategies were tested to optimize multitrait gains.

Genome-Wide Association Analysis Identified BMPR1A as a Novel Candidate Gene Affecting the Number of Thoracic Vertebrae in a Large White × Minzhu Intercross Pig Population

Simple Summary

The number of thoracic vertebrae (NTV) and number of vertebrae (NV) varies among pig breeds with a high correlation of about 0.8. It is important to discover variants associated with the NTV by considering the effect of the NV in pig. The results suggest that regulation variants on SSC7 might play crucial roles in the NTV and the FOS on SSC7 should be further studied as a critical candidate gene. In addition, BMPR1A was identified as a novel candidate gene affecting the NTV in pigs.

Abstract

The number of vertebrae (NV), especially the number of thoracic vertebrae (NTV), varies among pig breeds. The NTV is controlled by vertebral segmentation and the number of somites during embryonic development. Although there is a high correlation between the NTV and NV, studies on a fixed NV have mainly considered the absolute numbers of thoracic vertebrae instead of vertebral segmentation. Therefore, this study aimed to discover variants associated with the NTV by considering the effect of the NV in pigs. The NTV and NV of 542 F2 individuals from a Large White × Minzhu pig crossbreed were recorded. All animals were genotyped for VRTN g.19034 A > C, LTBP2 c.4481A > C, and 37 missense or splice variants previously reported in a 951-kb interval on SSC7 and 147 single nucleotide polymorphisms (SNPs) on SSC14. To identify NTV-associated SNPs, we firstly performed a genome-wide association study (GWAS) using the Q + K (population structure + kinship matrix) model in TASSEL. With the NV as a covariate, the obtained data were used to identify the SNPs with the most significant genome-wide association with the NTV by performing a GWAS on a PorcineSNP60K Genotyping BeadChip. Finally, a conditional GWAS was performed by fixing this SNP. The GWAS showed that 31 SNPs on SSC7 have significant genome-wide associations with the NTV. No missense or splice variants were found to be associated with the NTV significantly. A linkage disequilibrium analysis suggested the existence of quantitative trait loci (QTL) in a 479-Kb region on SSC7, which contained a critical candidate gene FOS for the NTV in pigs. Subsequently, a conditional GWAS was performed by fixing M1GA0010658, the most significant of these SNPs. Two SNPs in BMPR1A were found to have significant genome-wide associations and a significant dominant effect. The leading SNP, S14_87859370, accounted for 3.86% of the phenotypic variance. Our study uncovered that regulation variants in FOS on SSC7 and in BMPR1A on SSC14 might play important roles in controlling the NTV, and thus these genetic factors may be harnessed for increasing the NTV in pigs.

Genomic selection for heterobothriosis resistance concurrent with body size in the tiger pufferfish, Takifugu rubripes

Parasite resistance traits in aquaculture species often have moderate heritability, indicating the potential for genetic improvements by selective breeding. However, parasite resistance is often synonymous with an undesirable negative correlation with body size. In this study, we first tested the feasibility of genomic selection (GS) on resistance to heterobothriosis, caused by the monogenean parasite Heterobothrium okamotoi, which leads to huge economic losses in aquaculture of the tiger pufferfish Takifugu rubripes. Then, using a simulation study, we tested the possibility of simultaneous improvement of parasite resistance, assessed by parasite counts on host fish (HC), and standard length (SL). Each trait showed moderate heritability (square-root transformed HC: h2 = 0.308 ± 0.123, S.E.; SL: h2 = 0.405 ± 0.131). The predictive abilities of genomic prediction among 12 models, including genomic Best Linear Unbiased Predictor (GBLUP), Bayesian regressions, and machine learning procedures, were also moderate for both transformed HC (0.248‒0.344) and SL (0.340‒0.481). These results confirmed the feasibility of GS for this trait. Although an undesirable genetic correlation was suggested between transformed HC and SL (rg = 0.228), the simulation study suggested the desired gains index can help achieve simultaneous genetic improvements in both traits.

Accuracy of genomic selection and long-term genetic gain for resistance to Verticillium wilt in strawberry

Verticillium wilt, a soil-borne disease caused by the fungal pathogen Verticillium dahliae, threatens strawberry (Fragaria × ananassa) production worldwide. The development of resistant cultivars has been a persistent challenge, in part because the genetics of resistance is complex. The heritability of resistance and genetic gains in breeding for resistance to this pathogen have not been well documented. To elucidate the genetics, assess long-term genetic gains, and estimate the accuracy of genomic selection for resistance to Verticillium wilt, we analyzed a genetically diverse population of elite and exotic germplasm accessions (n = 984), including 245 cultivars developed since 1854. We observed a full range of phenotypes, from highly susceptible to highly resistant: < 3% were classified as highly resistant, whereas > 50% were classified as moderately to highly susceptible. Broad-sense heritability estimates ranged from 0.70-0.76, whereas narrow-sense genomic heritability estimates ranged from 0.33-0.45. We found that genetic gains in breeding for resistance to Verticillium wilt have been negative over the last 165 years (mean resistance has decreased over time). We identified several highly resistant accessions that might harbor favorable alleles that are either rare or non-existent in modern populations. We did not observe the segregation of large-effect loci. The accuracy of genomic predictions ranged from 0.38-0.53 among years and whole-genome regression methods. We show that genomic selection has promise for increasing genetic gains and accelerating the development of resistant cultivars in strawberry by shortening selection cycles and enabling selection in early developmental stages without phenotyping.

Genome-wide association mapping for wheat blast resistance in CIMMYT's international screening nurseries evaluated in Bolivia and Bangladesh

Wheat blast caused by the fungus Magnaporthe oryzae pathotype Triticum (MoT) is an emerging threat to wheat production. To identify genomic regions associated with blast resistance against MoT isolates in Bolivia and Bangladesh, we performed a large genome-wide association mapping study using 8607 observations on 1106 lines from the International Maize and Wheat Improvement Centre’s International Bread Wheat Screening Nurseries (IBWSNs) and Semi-Arid Wheat Screening Nurseries (SAWSNs). We identified 36 significant markers on chromosomes 2AS, 3BL, 4AL and 7BL with consistent effects across panels or site-years, including 20 markers that were significant in all the 49 datasets and tagged the 2NS translocation from Aegilops ventricosa. The mean blast index of lines with and without the 2NS translocation was 2.7 ± 4.5 and 53.3 ± 15.9, respectively, that substantiates its strong effect on blast resistance. Furthermore, we fingerprinted a large panel of 4143 lines for the 2NS translocation that provided excellent insights into its frequency over years and indicated its presence in 94.1 and 93.7% of lines in the 2019 IBWSN and SAWSN, respectively. Overall, this study reinforces the effectiveness of the 2NS translocation for blast resistance and emphasizes the urgent need to identify novel non-2NS sources of blast resistance.

Genomic predictions for enteric methane production are improved by metabolome and microbiome data in sheep (Ovis aries)

Methane production from rumen methanogenesis contributes approximately 71% of greenhouse gas emissions from the agricultural sector. This study has performed genomic predictions for methane production from 99 sheep across 3 yr using a residual methane phenotype that is log methane yield corrected for live weight, rumen volume, and feed intake. Using genomic relationships, the prediction accuracies (as determined by the correlation between predicted and observed residual methane production) ranged from 0.058 to 0.220 depending on the time point being predicted. The best linear unbiased prediction algorithm was then applied to relationships between animals that were built on the rumen metabolome and microbiome. Prediction accuracies for the metabolome-based relationships for the two available time points were 0.254 and 0.132; the prediction accuracy for the first microbiome time point was 0.142. The second microbiome time point could not successfully predict residual methane production. When the metabolomic relationships were added to the genomic relationships, the accuracy of predictions increased to 0.274 (from 0.201 when only the genomic relationship was used) and 0.158 (from 0.081 when only the genomic relationship was used) for the two time points, respectively. When the microbiome relationships from the first time point were added to the genomic relationships, the maximum prediction accuracy increased to 0.247 (from 0.216 when only the genomic relationship was used), which was achieved by giving the genomic relationships 10 times more weighting than the microbiome relationships. These accuracies were higher than the genomic, metabolomic, and microbiome relationship matrixes achieved alone when identical sets of animals were used.

High-Throughput Genome-Wide Genotyping To Optimize the Use of Natural Genetic Resources in the Grassland Species Perennial Ryegrass (Lolium perenne L.)

The natural genetic diversity of agricultural species is an essential genetic resource for breeding programs aiming to improve their ecosystem and production services. A large natural ecotype diversity is usually available for most grassland species. This could be used to recombine natural climatic adaptations and agronomic value to create improved populations of grassland species adapted to future regional climates. However describing natural genetic resources can be long and costly. Molecular markers may provide useful information to help this task. This opportunity was investigated for Lolium perenne L., using a set of 385 accessions from the natural diversity of this species collected right across Europe and provided by genebanks of several countries. For each of these populations, genotyping provided the allele frequencies of 189,781 SNP markers. GWAS were implemented for over 30 agronomic and/or putatively adaptive traits recorded in three climatically contrasted locations (France, Belgium, Germany). Significant associations were detected for hundreds of markers despite a strong confounding effect of the genetic background; most of them pertained to phenology traits. It is likely that genetic variability in these traits has had an important contribution to environmental adaptation and ecotype differentiation. Genomic prediction models calibrated using natural diversity were found to be highly effective to describe natural populations for almost all traits as well as commercial synthetic populations for some important traits such as disease resistance, spring growth or phenological traits. These results will certainly be valuable information to help the use of natural genetic resources of other species.

Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple

Texture is a complex trait and a major component of fruit quality in apple. While the major effect of MdPG1, a gene controlling firmness, has already been exploited in elite cultivars, the genetic basis of crispness remains poorly understood. To further improve fruit texture, harnessing loci with minor effects via genomic selection is therefore necessary. In this study, we measured acoustic and mechanical features in 537 genotypes to dissect the firmness and crispness components of fruit texture. Predictions of across-year phenotypic values for these components were calculated using a model calibrated with 8,294 SNP markers. The best prediction accuracies following cross-validations within the training set of 259 genotypes were obtained for the acoustic linear distance (0.64). Predictions for biparental families using the entire training set varied from low to high accuracy, depending on the family considered. While adding siblings or half-siblings into the training set did not clearly improve predictions, we performed an optimization of the training set size and composition for each validation set. This allowed us to increase prediction accuracies by 0.17 on average, with a maximal accuracy of 0.81 when predicting firmness in the 'Gala' × 'Pink Lady' family. Our results therefore identified key genetic parameters to consider when deploying genomic selection for texture in apple. In particular, we advise to rely on a large training population, with high phenotypic variability from which a 'tailored training population' can be extracted using a priori information on genetic relatedness, in order to predict a specific target population.

Stable SNP Allele Associations With High Grain Zinc Content in Polished Rice (Oryza sativa L.) Identified Based on ddRAD Sequencing

Polished rice is widely consumed staple food across the globe, however, it contains limited nutrients especially iron (Fe) and zinc (Zn). To identify promising genotypes for grain Zn, a total of 40 genotypes consisting 20 rice landraces, and 20 released high yielding rice varieties were evaluated in three environments (wet seasons 2014, 2015 and 2016) for nine traits including days to 50% flowering (DFF), plant height (PH), panicle length (PL), total number of tillers (TNT), single plant yield (SPY), Fe and Zn in brown (IBR, ZBR) and polished rice (IPR, ZPR). Additive Main Effect and Multiplicative Interaction (AMMI), Genotype and Genotype × Environment Interaction (GGE) analyses identified genotypes G22 (Edavankudi Pokkali), G17 (Taraori Basmati), G27 (Chittimuthyalu) and G26 (Kalanamak) stable for ZPR and G8 (Savitri) stable for SPY across three environments. Significant negative correlation between yield and grain Zn was reaffirmed. Regression analysis indicated the contribution of traits toward ZPR and SPY and also desirable level of grain Zn in brown rice. A total of 39,137 polymorphic single nucleotide polymorphisms (SNPs) were obtained through double digest restriction site associated DNA (dd-RAD) sequencing of 40 genotypes. Association analyses with nine phenotypic traits revealed 188 stable SNPs with six traits across three environments. ZPR was associated with SNPs located in three putative candidate genes (LOC_Os03g47980, LOC_Os07g47950 and LOC_Os07g48050) on chromosomes 3 and 7. The genomic region of chromosome 7 co localized with reported genomic regions (rMQTL₇.₁) and OsNAS3 candidate gene. SPY was found to be associated with 12 stable SNPs located in 11 putative candidate genes on chromosome 1, 6, and 12. Characterization of rice landraces and varieties in terms of stability for their grain Zn and yield identified promising donors and recipients along with genomic regions in the present study to be deployed rice Zn biofortification breeding program.

Training Population Design With the Use of Regional Fusarium Head Blight Nurseries to Predict Independent Breeding Lines for FHB Traits

Fusarium head blight (FHB) is a devastating disease in cereals around the world. Because it is quantitatively inherited and technically difficult to reproduce, breeding to increase resistance in wheat germplasm is difficult and slow. Genomic selection (GS) is a form of marker-assisted selection (MAS) that simultaneously estimates all locus, haplotype, or marker effects across the entire genome to calculate genomic estimated breeding values (GEBVs). Since its inception, there have been many studies that demonstrate the utility of GS approaches to breeding for disease resistance in crops. In this study, the Uniform Northern (NUS) and Uniform Southern (SUS) soft red winter wheat scab nurseries (a total 452 lines) were evaluated as possible training populations (TP) to predict FHB traits in breeding lines of the UK (University of Kentucky) wheat breeding program. DON was best predicted by the SUS; Fusarium damaged kernels (FDK), FHB rating, and two indices, DSK index and DK index were best predicted by NUS. The highest prediction accuracies were obtained when the NUS and SUS were combined, reaching up to 0.5 for almost all traits except FHB rating. Highest prediction accuracies were obtained with bigger TP sizes (300-400) and there were not significant effects of TP optimization method for all traits, although at small TP size, the PEVmean algorithm worked better than other methods. To select for lines with tolerance to DON accumulation, a primary breeding target for many breeders, we compared selection based on DON BLUES with selection based on DON GEBVs, DSK GEBVs, and DK GEBVs. At selection intensities (SI) of 30-40%, DSK index showed the best performance with a 4-6% increase over direct selection for DON. Our results confirm the usefulness of regional nurseries as a source of lines to predict GEBVs for local breeding programs, and shows that an index that includes DON, together with FDK and FHB rating could be an excellent choice to identify lines with low DON content and an overall improved FHB resistance.

Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat

The use of haplotypes may improve the accuracy of genomic prediction over single SNPs because haplotypes can better capture linkage disequilibrium and genomic similarity in different lines and may capture local high-order allelic interactions. Additionally, prediction accuracy could be improved by portraying population structure in the calibration set. A set of 383 advanced lines and cultivars that represent the diversity of the University of Minnesota wheat breeding program was phenotyped for yield, test weight, and protein content and genotyped using the Illumina 90K SNP Assay. Population structure was confirmed using single SNPs. Haplotype blocks of 5, 10, 15, and 20 adjacent markers were constructed for all chromosomes. A multi-allelic haplotype prediction algorithm was implemented and compared with single SNPs using both k-fold cross validation and stratified sampling optimization. After confirming population structure, the stratified sampling improved the predictive ability compared with k-fold cross validation for yield and protein content, but reduced the predictive ability for test weight. In all cases, haplotype predictions outperformed single SNPs. Haplotypes of 15 adjacent markers showed the best improvement in accuracy for all traits; however, this was more pronounced in yield and protein content. The combined use of haplotypes of 15 adjacent markers and training population optimization significantly improved the predictive ability for yield and protein content by 14.3 (four percentage points) and 16.8% (seven percentage points), respectively, compared with using single SNPs and k-fold cross validation. These results emphasize the effectiveness of using haplotypes in genomic selection to increase genetic gain in self-fertilized crops.

Genome-wide mapping and allelic fingerprinting provide insights into the genetics of resistance to wheat stripe rust in India, Kenya and Mexico

Stripe or yellow rust (YR) caused by Puccinia striiformis Westend. f. sp. tritici Erikss. is a persistent biotic-stress threatening global wheat production. To broaden our understanding of the shared genetic basis of YR resistance across multi-site and multi-year evaluations, we performed a large genome-wide association study using 43,706 YR observations on 23,346 wheat lines from the International Maize and Wheat Improvement Center evaluated between 2013 and 2019 at sites in India, Kenya and Mexico, against predominant races prevalent in the countries. We identified 114 repeatable markers tagging 20 quantitative trait loci (QTL) associated with YR on ten chromosomes including 1D, 2A, 2B, 2D, 3A, 4A, 4D, 5A, 5B and 6B, among which four QTL, QYr.cim-2DL.2, QYr.cim-2AS.1, QYr.cim-2BS.2 and QYr.cim-2BS.3 were significant in more than ten datasets. Furthermore, we report YR-associated allelic fingerprints for the largest panel of wheat breeding lines (52,067 lines) till date, creating substantial opportunities for YR favorable allele enrichment using molecular markers. Overall, the markers and fingerprints reported in this study provide excellent insights into the genetic architecture of YR resistance in different geographical regions, time-periods and wheat germplasm and are a huge resource to the global wheat breeding community for accelerating YR resistance breeding efforts.

Adaptive genetic variation to drought in a widely distributed conifer suggests a potential for increasing forest resilience in a drying climate

Drought intensity and frequency are increasing under global warming, with soil water availability now being a major factor limiting tree growth in circumboreal forests. Still, the adaptive capacity of trees in the face of future climatic regimes remains poorly documented. Using 1481 annually resolved tree-ring series from 29-yr-old trees, we evaluated the drought sensitivity of 43 white spruce (Picea glauca (Moench) Voss) populations established in a common garden experiment. We show that genetic variation among populations in response to drought plays a significant role in growth resilience. Local genetic adaptation allowed populations from drier geographical origins to grow better, as indicated by higher resilience to extreme drought events, compared with populations from more humid geographical origins. The substantial genetic variation found for growth resilience highlights the possibility of selecting for drought resilience in boreal conifers. As a major research outcome, we showed that adaptive genetic variation in response to changing local conditions can shape drought vulnerability at the intraspecific level. Our findings have wide implications for forest ecosystem management and conservation.

Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality

Trait-assisted genomic prediction approach is a way to improve genetic gain by cost unit, by reducing budget allocated to phenotyping or by increasing the program's size for the same budget. This study compares different strategies of genomic prediction to optimize resource allocation in breeding schemes by using information from cheaper correlated traits to predict a more expensive trait of interest. We used bread wheat baking score (BMS) calculated for French registration as a case study. To conduct this project, 398 lines from a public breeding program were genotyped and phenotyped for BMS and correlated traits in 11 locations in France between 2000 and 2016. Single-trait (ST), multi-trait (MT) and trait-assisted (TA) strategies were compared in terms of predictive ability and cost. In MT and TA strategies, information from dough strength (W), a cheaper trait correlated with BMS (r = 0.45), was evaluated in the training population or in both the training and the validation sets, respectively. TA models allowed to reduce the budget allocated to phenotyping by up to 65% while maintaining the predictive ability of BMS. TA models also improved the predictive ability of BMS compared to ST models for a fixed budget (maximum gain: + 0.14 in cross-validation and + 0.21 in forward prediction). We also demonstrated that the budget can be further reduced by approximately one fourth while maintaining the same predictive ability by reducing the number of phenotypic records to estimate BMS adjusted means. In addition, we showed that the choice of the lines to be phenotyped can be optimized to minimize cost or maximize predictive ability. To do so, we extended the mean of the generalized coefficient of determination (CD_mean) criterion to the multi-trait context (CD_multi).

The pulse of the tree is under genetic control: eucalyptus as a case study

The pulse of the tree (diurnal cycle of stem radius fluctuations) has been widely studied as a way of analyzing tree responses to the environment, including the phenotypic plasticity of tree-water relationships in particular. However, the genetic basis of this daily phenotype and its interplay with the environment remain largely unexplored. We characterized the genetic and environmental determinants of this response, by monitoring daily stem radius fluctuation (dSRF) on 210 trees from a Eucalyptus urophylla × E. grandis full-sib family over 2 years. The dSRF signal was broken down into hydraulic capacitance, assessed as the daily amplitude of shrinkage (DA), and net growth, estimated as the change in maximum radius between two consecutive days (ΔR). The environmental determinants of these two traits were clearly different: DA was positively correlated with atmospheric variables relating to water demand, while ΔR was associated with soil water content. The heritability for these two traits ranged from low to moderate over time, revealing a time-dependent or environment-dependent complex genetic determinism. We identified 686 and 384 daily quantitative trait loci (QTL) representing 32 and 31 QTL regions for DA and ΔR, respectively. The identification of gene networks underlying the 27 major genomics regions for both traits generated additional hypotheses concerning the biological mechanisms involved in response to water demand and supply. This study highlights that environmentally induced changes in daily stem radius fluctuation are genetically controlled in trees and suggests that these daily responses integrated over time shape the genetic architecture of mature traits.

Genome-Wide Association Mapping of Floral Traits in Cultivated Sunflower (Helianthus annuus)

Floral morphology and pigmentation are both charismatic and economically relevant traits associated with cultivated sunflower (Helianthus annuus L.). Recent work has linked floral morphology and pigmentation to pollinator efficiency and seed yield. Understanding the genetic architecture of such traits is essential for crop improvement, and gives insight into the role of genetic constraints in shaping floral diversity. A diversity panel of 288 sunflower genotypes was phenotyped for a variety of morphological, phenological, and color traits in both a greenhouse and a field setting. Association mapping was performed using 5788 SNP markers using a mixed linear model approach. Several dozen markers across 10 linkage groups were significantly associated with variation in morphological and color trait variation. Substantial trait plasticity was observed between greenhouse and field phenotyping, and associations differed between environments. Color traits mapped more strongly than morphology in both settings, with markers together explaining 16% of petal carotenoid content in the greenhouse, and 17% and 24% of variation in disc anthocyanin presence in the field and greenhouse, respectively. Morphological traits like disc size mapped more strongly in the field, with markers together explaining up to 19% of disc size variation. Loci identified here through association mapping within cultivated germplasm differ from those identified through biparental crosses between modern cultivated sunflower and either its wild progenitor or domesticated landraces. Several loci lie within genomic regions involved in domestication. Differences between phenotype expression under greenhouse and field conditions highlight the importance of plasticity in determining floral morphology and pigmentation.

Statistical Approach for Improving Genomic Prediction Accuracy through Efficient Diagnostic Measure of Influential Observation

It is expected the predictive performance of genomic prediction methods may be adversely affected in the presence of outliers. In agriculture science an outlier may arise due to wrong data imputation, outlying response, and in a series of trials over the time or location. Although several statistical procedures are already there in literature for identification of outlier but identification of true outlier is still a challenge especially in case of high dimensional genomic data. Here we have proposed an efficient approach for detecting outlier in high dimensional genomic data, our approach is p-value based combination methods to produce single p-value for detecting the outliers. Robustness of our approach has been tested using simulated data through the evaluation measures like precision, recall etc. It has been observed that significant improvement in the performance of genomic prediction has been obtained by detecting the outliers and handling them accordingly through our proposed approach using real data.

Improving root characterisation for genomic prediction in cassava

Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava.

Genome-Wide Association Study for Maize Leaf Cuticular Conductance Identifies Candidate Genes Involved in the Regulation of Cuticle Development

The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed at night and under water-limited conditions. Elucidating the genetic architecture of natural variation for leaf cuticular conductance (g_c) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we conducted a genome-wide association study of g_c of adult leaves in a maize inbred association panel that was evaluated in four environments (Maricopa, AZ, and San Diego, CA, in 2016 and 2017). Five genomic regions significantly associated with g_c were resolved to seven plausible candidate genes (ISTL1, two SEC14 homologs, cyclase-associated protein, a CER7 homolog, GDSL lipase, and β-D-XYLOSIDASE 4). These candidates are potentially involved in cuticle biosynthesis, trafficking and deposition of cuticle lipids, cutin polymerization, and cell wall modification. Laser microdissection RNA sequencing revealed that all these candidate genes, with the exception of the CER7 homolog, were expressed in the zone of the expanding adult maize leaf where cuticle maturation occurs. With direct application to genetic improvement, moderately high average predictive abilities were observed for whole-genome prediction of g_c in locations (0.46 and 0.45) and across all environments (0.52). The findings of this study provide novel insights into the genetic control of g_c and have the potential to help breeders more effectively develop drought-tolerant maize for target environments.

Dominance Effects and Functional Enrichments Improve Prediction of Agronomic Traits in Hybrid Maize

Single-cross hybrids have been critical to the improvement of maize (Zea mays L.), but the characterization of their genetic architectures remains challenging. Previous studies of hybrid maize have shown the contribution of within-locus complementation effects (dominance) and their differential importance across functional classes of loci. However, they have generally considered panels of limited genetic diversity, and have shown little benefit from genomic prediction based on dominance or functional enrichments. This study investigates the relevance of dominance and functional classes of variants in genomic models for agronomic traits in diverse populations of hybrid maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the U.S. (1106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1640 hybrids). We investigated three agronomic traits: days to silking (DTS), plant height (PH), and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results suggest that dominance and gene annotations improve genomic prediction across diverse populations in hybrid maize.

The genetics of exapted resistance to two exotic pathogens in pedunculate oak

Exotic pathogens cause severe damage in natural populations in the absence of coevolutionary dynamics with their hosts. However, some resistance to such pathogens may occur in naive populations. The objective of this study was to investigate the genetics of this so-called 'exapted' resistance to two pathogens of Asian origin (Erysiphe alphitoides and Phytophthora cinnamomi) in European oak. Host-pathogen compatibility was assessed by recording infection success and pathogen growth in a full-sib family of Quercus robur under controlled and natural conditions. Two high-resolution genetic maps anchored on the reference genome were used to study the genetic architecture of resistance and to identify positional candidate genes. Two genomic regions, each containing six strong and stable quantitative trait loci (QTLs) accounting for 12-19% of the phenotypic variation, were mainly associated with E. alphitoides infection. Candidate genes, especially genes encoding receptor-like-kinases and galactinol synthases, were identified in these regions. The three QTLs associated with P. cinnamomi infection did not colocate with QTLs found for E. alphitoides. These findings provide evidence that exapted resistance to E. alphitoides and P. cinnamomi is present in Q. robur and suggest that the underlying molecular mechanisms involve genes encoding proteins with extracellular signaling functions.

Genetic Architecture of Early Vigor Traits in Wild Soybean

A worldwide food shortage has been projected as a result of the current increase in global population and climate change. In order to provide sufficient food to feed more people, we must develop crops that can produce higher yields. Plant early vigor traits, early growth rate (EGR), early plant height (EPH), inter-node length, and node count are important traits that are related to crop yield. Glycine soja, the wild counterpart to cultivated soybean, Glycine max, harbors much higher genetic diversity and can grow in diverse environments. It can also cross easily with cultivated soybean. Thus, it holds a great potential in developing soybean cultivars with beneficial agronomic traits. In this study, we used 225 wild soybean accessions originally from diverse environments across its geographic distribution in East Asia. We quantified the natural variation of several early vigor traits, investigated the relationships among them, and dissected the genetic basis of these traits by applying a Genome-Wide Association Study (GWAS) with genome-wide single nucleotide polymorphism (SNP) data. Our results showed positive correlation between all early vigor traits studied. A total of 12 SNPs significantly associated with EPH were identified with 4 shared with EGR. We also identified two candidate genes, Glyma.07G055800.1 and Glyma.07G055900.1, playing important roles in influencing trait variation in both EGR and EPH in G. soja.

BWGS: A R package for genomic selection and its application to a wheat breeding programme

We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric. The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.

Effect of salt tolerance on biomass production in a large population of sorghum accessions

Salinity causes major reductions in cultivated land area, crop productivity, and crop quality, and salt-tolerant crops have been required to sustain agriculture in salinized areas. The annual C₄ crop plant Sorghum bicolor (L.) Moench is salt tolerant, with large variation among accessions. Sorghum's salt tolerance is often evaluated during early growth, but such evaluations are weakly related to overall performance. Here, we evaluated salt tolerance of 415 sorghum accessions grown in saline soil (0, 50, 100, and 150 mM NaCl) for 3 months. Some accessions produced up to 400 g per plant of biomass and showed no growth inhibition at 50 mM NaCl. Our analysis indicated that the genetic factors that affected biomass production under 100 mM salt stress were more different from those without salt stress, comparing to the differences between those under 50 mM and 100 mM salt stress. A genome-wide association study for salt tolerance identified two single-nucleotide polymorphisms (SNPs) that were significantly associated with biomass production, only at 50 mM NaCl. Additionally, two SNPs were significantly associated with salt tolerance index as an indicator for growth response of each accession to salt stress. Our results offer candidate genetic resources and SNP markers for breeding salt-tolerant sorghum.

Enhancing Crop Domestication Through Genomic Selection, a Case Study of Intermediate Wheatgrass

Perennial grains could simultaneously provide food for humans and a host of ecosystem services, including reduced erosion, minimized nitrate leaching, and increased carbon capture. Yet most of the world's food and feed is supplied by annual grains. Efforts to domesticate intermediate wheatgrass (Thinopyrumn intermedium, IWG) as a perennial grain crop have been ongoing since the 1980's. Currently, there are several breeding programs within North America and Europe working toward developing IWG into a viable crop. As new breeding efforts are established to provide a widely adapted crop, questions of how genomic and phenotypic data can be used among sites and breeding programs have emerged. Utilizing five cycles of breeding data that span 8 years and two breeding programs, University of Minnesota, St. Paul, MN, and The Land Institute, Salina, KS, we developed genomic selection (GS) models to predict IWG traits. Seven traits were evaluated with free-threshing seed, seed mass, and non-shattering being considered domestication traits while agronomic traits included spike yield, spikelets per inflorescence, plant height, and spike length. We used 6,199 genets - unique, heterozygous, individual plants - that had been profiled with genotyping-by-sequencing, resulting in 23,495 SNP markers to develop GS models. Within cycles, the predictive ability of GS was high, ranging from 0.11 to 0.97. Across-cycle predictions were generally much lower, ranging from -0.22 to 0.76. The prediction ability for domestication traits was higher than agronomic traits, with non-shattering and free threshing prediction abilities ranging from 0.27 to 0.75 whereas spike yield had prediction abilities ranging from -0.22 to 0.26. These results suggest that progress to reduce shattering and increase the percent free-threshing grain can be made irrespective of the location and breeding program. While site-specific programs may be required for agronomic traits, synergies can be achieved in rapidly improving key domestication traits for IWG. As other species are targeted for domestication, these results will aid in rapidly domesticating new crops.

Choosing the optimal population for a genome-wide association study: A simulation of whole-genome sequences from rice

A genome-wide association study (GWAS) needs to have a suitable population. The factors that affect a GWAS (e.g. population structure, sample size, and sequence analysis and field testing costs) need to be considered. Mixed populations containing subpopulations of different genetic backgrounds may be suitable populations. We conducted simulation experiments to see if a population with high genetic diversity, such as a diversity panel, should be added to a target population, especially when the target population harbors small genetic diversity. The target population was 112 accessions of Oryza sativa L. subsp. japonica, mainly developed in Japan. We combined the target population with three populations that had higher genetic diversity. These were 100 indica accessions, 100 japonica accessions, and 100 accessions with various genetic backgrounds. The results showed that the GWAS's power with a mixed population was generally higher than with a separate population. Also, the optimal GWAS populations varied depending on the fixation index (F_ST ) of the quantitative trait nucleotides (QTNs) and the polymorphism of QTNs in each population. When a QTN was polymorphic in a target population, a target population combined with a higher diversity population improved the QTN's detection power. By investigating F_ST and the expected heterozygosity (H_e ) as factors influencing the detection power, we showed that single nucleotide polymorphisms with high F_ST or low H_e are less likely to be detected by GWAS with mixed populations. Sequenced or genotyped germplasm collections can improve the GWAS's detection power by using a subset of the collections with a target population.

Genomics-assisted breeding in minor and pseudo-cereals

Minor and pseudo-cereals, which can grow with lower input and often produce specific nutrients compared to major cereal crops, are attracting worldwide attention. Since these crops generally have a large genetic diversity in a breeding population, rapid genetic improvement can be possible by the application of genomics-assisted breeding methods. In this review, we discuss studies related to biparental quantitative trait locus (QTL) mapping, genome-wide association study, and genomic selection for minor and pseudo-cereals. Especially, we focus on the current progress in a pseudo-cereal, buckwheat. Prospects for the practical utilization of genomics-assisted breeding in minor and pseudo-cereals are discussed including the issues to overcome especially for these crops.

RAINBOW: Haplotype-based genome-wide association study using a novel SNP-set method

Difficulty in detecting rare variants is one of the problems in conventional genome-wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set method named "RAINBOW" and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes. We prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. Our proposed method was shown to be superior to these in three aspects: (1) controlling false positives; (2) in detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset; and (3) it showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, primarily when the causal variants were located very close to each other, and the directions of their effects were opposite. By using the SNP-set approach as in this study, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as an R package named "RAINBOWR" and is available from CRAN (https://cran.r-project.org/web/packages/RAINBOWR/index.html) and GitHub (https://github.com/KosukeHamazaki/RAINBOWR).

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

Genome-Wide Association Study Uncovers Novel Genomic Regions Associated With Coleoptile Length in Hard Winter Wheat

Successful seedling establishment depends on the optimum depth of seed placement especially in drought-prone conditions, providing an opportunity to exploit subsoil water and increase winter survival in winter wheat. Coleoptile length is a key determinant for the appropriate depth at which seed can be sown. Thus, understanding the genetic basis of coleoptile length is necessary and important for wheat breeding. We conducted a genome-wide association study (GWAS) using a diverse panel of 298 winter wheat genotypes to dissect the genetic architecture of coleoptile length. We identified nine genomic regions associated with the coleoptile length on seven different chromosomes. Of the nine genomic regions, five have been previously reported in various studies, including one mapped to previously known Rht-B1 region. Three novel quantitative trait loci (QTLs), QCL.sdsu-2AS, QCL.sdsu-4BL, and QCL.sdsu-5BL were identified in our study. QCL.sdsu-5BL has a large substitution effect which is comparable to Rht-B1's effect and could be used to compensate for the negative effect of Rht-B1 on coleoptile length. In total, the nine QTLs explained 59% of the total phenotypic variation. Cultivars 'Agate' and 'MT06103' have the longest coleoptile length and interestingly, have favorable alleles at nine and eight coleoptile loci, respectively. These lines could be a valuable germplasm for longer coleoptile breeding. Gene annotations in the candidate regions revealed several putative proteins of specific interest including cytochrome P450-like, expansins, and phytochrome A. The QTLs for coleoptile length linked to single-nucleotide polymorphism (SNP) markers reported in this study could be employed in marker-assisted breeding for longer coleoptile in wheat. Thus, our study provides valuable insights into the genetic and molecular regulation of the coleoptile length in winter wheat.

Molecular tracking of multiple disease resistance in a winter wheat diversity panel

About 10% of cultivars possessed superior resistance to four fungal diseases and association mapping for multiple disease resistance identified loci which are not detected by analyzing individual disease resistances. Multiple disease resistance (MDR) aims for cultivars that are resistant to more than one disease which is an important prerequisite for the registration of commercial cultivars. We analyzed a European winter wheat diversity panel of 158 old and new cultivars for four diseases by natural (powdery mildew) and artificial inoculation (yellow rust, stem rust, Fusarium head blight) observed on the same plot in a multilocation trial. Genotypic analyses were based on 21,543 genotype-by-sequencing markers. By association mapping, eight to 18 quantitative-trait loci (QTL) were detected for individual disease resistances, explaining in total 67-90% of the total genotypic variation. For MDR, nine QTL could be found explaining 62% of the total genotypic variation. Only three of them were also found as QTL for a single disease resistance illustrating that mapping of MDR-associated QTL can be regarded as a complementary approach. The high prediction ability obtained for MDR (> 0.9) implies that genomic prediction could be used in future, thereby eliminating the necessity to separately screen large numbers of lines in breeding programs for several diseases.

Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: an example from white spruce

Genomic selection (GS) has a large potential for improving the prediction accuracy of breeding values and significantly reducing the length of breeding cycles. In this context, the choice of mating designs becomes critical to improve the efficiency of breeding operations and to obtain the largest genetic gains per time unit. Polycross mating designs have been traditionally used in tree and plant breeding to perform backward selection of the female parents. The possibility to use genetic markers for paternity identification and for building genomic prediction models should allow for a broader use of polycross tests in forward selection schemes. We compared the accuracies of genomic predictions of offspring's breeding values from a polycross and a full-sib (partial diallel) mating design with similar genetic background in white spruce (Picea glauca). Trees were phenotyped for growth and wood quality traits, and genotyped for 4092 SNPs representing as many gene loci distributed across the 12 spruce chromosomes. For the polycross progeny test, heritability estimates were smaller, but more precise using the genomic BLUP (GBLUP) model as compared with pedigree-based models accounting for the maternal pedigree or for the reconstructed full pedigree. Cross-validations showed that GBLUP predictions were 22-52% more accurate than predictions based on the maternal pedigree, and 5-7% more accurate than predictions using the reconstructed full pedigree. The accuracies of GBLUP predictions were high and in the same range for most traits between the polycross (0.61-0.70) and full-sib progeny tests (0.61-0.74). However, higher genetic gains per time unit were expected from the polycross mating design given the shorter time needed to conduct crosses. Considering the operational advantages of the polycross design in terms of easier handling of crosses and lower associated costs for test establishment, we believe that this mating scheme offers great opportunities for the development and operational application of forward GS.

Transcriptome-Based Prediction of Complex Traits in Maize

The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.

Genome-Wide Association Study of Topsoil Root System Architecture in Field-Grown Soybean [Glycine max (L.) Merr.]

Water and nutrient acquisition is a critical function of plant root systems. Root system architecture (RSA) traits are often complex and controlled by many genes. This is the first genome-wide association study reporting genetic loci for RSA traits for field-grown soybean (Glycine max). A collection of 289 soybean genotypes was grown in three environments, root crowns were excavated, and 12 RSA traits assessed. The first two components of a principal component analysis of these 12 traits were used as additional aggregate traits for a total of 14 traits. Marker-trait association for RSA traits were identified using 31,807 single-nucleotide polymorphisms (SNPs) by a genome-wide association analysis. In total, 283 (non-unique) SNPs were significantly associated with one or more of the 14 root traits. Of these, 246 were unique SNPs and 215 SNPs were associated with a single root trait, while 26, four, and one SNPs were associated with two, three, and four root traits, respectively. The 246 SNPs marked 67 loci associated with at least one of the 14 root traits. Seventeen loci on 13 chromosomes were identified by SNPs associated with more than one root trait. Several genes with annotation related to processes that could affect root architecture were identified near these 67 loci. Additional follow-up studies will be needed to confirm the markers and candidate genes identified for RSA traits and to examine the importance of the different root characteristics for soybean productivity under a range of soil and environmental conditions.