Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach

The construction of a haplotype reference panel using extremely low coverage whole genome sequences and its application in genome-wide association studies and genomic prediction in Duroc pigs

Extremely low coverage whole genome sequencing (lcWGS) is an economical technique to obtain high-density single nucleotide polymorphisms (SNPs). Here, we explored the feasibility of constructing a haplotype reference panel (lcHRP) using lcWGS and evaluated the effects of lcHRP through a genome-wide association study (GWAS) and genomic prediction in pigs. A total of 297 and 974 Duroc pigs were genotyped using lcWGS and a 50 K SNP array, respectively. We obtained 19,306,498 SNPs using lcWGS with an accuracy of 0.984. With the help of lcHRP, the accuracy of imputation from the SNP array to lcWGS was 0.922. Compared to the SNP array findings, those from the imputation-based GWAS identified more signals across four traits. With the integration of the top 1% imputation-based GWAS findings as genomic features, the accuracies of genomic prediction was improved by 6.0% to 13.2%. This study showed the great potential of lcWGS in pigs' molecular breeding.

Genetic Diversity and Population Structure Analysis of the USDA Olive Germplasm Using Genotyping-By-Sequencing (GBS)

Olives are one of the most important fruit and woody oil trees cultivated in many parts of the world. Olive oil is a critical component of the Mediterranean diet due to its importance in heart health. Olives are believed to have been brought to the United States from the Mediterranean countries in the 18th century. Despite the increase in demand and production areas, only a few selected olive varieties are grown in most traditional or new growing regions in the US. By understanding the genetic background, new sources of genetic diversity can be incorporated into the olive breeding programs to develop regionally adapted varieties for the US market. This study aimed to explore the genetic diversity and population structure of 90 olive accessions from the USDA repository along with six popular varieties using genotyping-by-sequencing (GBS)-generated SNP markers. After quality filtering, 54,075 SNP markers were retained for the genetic diversity analysis. The average gene diversity (GD) and polymorphic information content (PIC) values of the SNPs were 0.244 and 0.206, respectively, indicating a moderate genetic diversity for the US olive germplasm evaluated in this study. The structure analysis showed that the USDA collection was distributed across seven subpopulations; 63% of the accessions were grouped into an identifiable subpopulation. The phylogenetic and principal coordinate analysis (PCoA) showed that the subpopulations did not align with the geographical origins or climatic zones. An analysis of the molecular variance revealed that the major genetic variation sources were within populations. These findings provide critical information for future olive breeding programs to select genetically distant parents and facilitate future gene identification using genome-wide association studies (GWAS) or a marker-assisted selection (MAS) to develop varieties suited to production in the US.

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

A high-resolution consensus linkage map for barley based on GBS-derived genotypes

As genotyping-by-sequencing (GBS) is widely used in barley genetic studies, the translation of the physical position of GBS-derived SNPs into accurate genetic positions has become relevant. The main aim of this study was to develop a high-resolution consensus linkage map based on GBS-derived SNPs. The construction of this integrated map involved 11 bi-parental populations composed of 3743 segregating progenies. We adopted a uniform set of SNP-calling and filtering conditions to identify 50 875 distinct SNPs segregating in at least one population. These SNPs were grouped into 18 580 non-redundant SNPs (bins). The resulting consensus linkage map spanned 1050.1 cM, providing an average density of 17.7 bins and 48.4 SNPs per cM. The consensus map is characterized by the absence of large intervals devoid of marker coverage (significant gaps), the largest interval between bins was only 3.7 cM and the mean distance between adjacent bins was 0.06 cM. This high-resolution linkage map will contribute to several applications in genomic research, such as providing useful information on the recombination landscape for QTLs/genes identified via GWAS or ensuring a uniform distribution of SNPs when developing low-cost genotyping tools offering a limited number of markers.

Mapping of a novel major effect Hessian fly field partial-resistance locus in southern soft red winter wheat line LA03136E71

Hessian fly resistance has centralized around resistance loci that are biotype specific. We show that field resistance is evident and controlled by a single locus on chromosome 7D. Hessian flies (Mayetiola destructor Say) infest and feed upon wheat (Triticum aestivum L) resulting in significant yield loss. Genetically resistant cultivars are the most effective method of Hessian fly management. Wheat breeders in the southern USA have observed cultivars exhibiting a "field resistance" to Hessian fly that is not detectable by greenhouse assay. The resistant breeding line "LA03136E71" and susceptible cultivar "Shirley" were crossed to develop a population of 200 random F_4:5 lines using single seed descent. The population was evaluated in a total of five locations in North Carolina during the 2019, 2020, and 2021 seasons. A subsample of each plot was evaluated for the total number of tillers, number of infested tillers, and total number of larvae/pupae. From these data, the percent infested tillers, number of larvae/pupae per tiller, and the number of larvae/pupae per infested tiller were estimated. In all within and across environment combinations for all traits recorded, the genotype effect was significant (p < 0.05). Interval mapping identified a single large effect QTL distally on the short arm of chromosome 7D for all environment-trait combinations. This locus was identified on a chromosome where no other Hessian fly resistance/tolerance QTL has been previously identified. This novel Hessian fly partial-resistance QTL is termed QHft.nc-7D. Fine mapping must be conducted in this region to narrow down the causal agents responsible for this trait, and investigation into the mode of action is highly suggested.

Evidence of altitudinal gradient modifying genomic and chemical diversity in populations of Lychnophora pinaster Mart

Lychnophora pinaster Mart. (Asteraceae) is endemic to the Brazilian Cerrado. It is distributed along the altitudinal gradient of the mountainous ranges of the state of Minas Gerais. This study aimed to evaluate the influence of altitude on the genetic diversity of L. pinaster populations and the effects of altitude and climatic factors on essential oil chemical composition. Essential oils from L. pinaster populations from the north (North 01, North 02, and North 03, 700-859 m) and the Metropolitan region of Belo Horizonte (MhBH 01 and MrBH 02, 1366-1498 m) were analyzed. SNP markers from L. pinaster in these regions and Campos das Vertentes (CV 01, CV 02, and CV 03, 1055-1292 m) were also analyzed. The main compounds in essential oils were 14-hydroxy-α-humulene (North 01 and North 03), cedr-8(15)-en-9-α-ol (North 02), 14-acetoxy-α-humulene (MrBH 01), and 4-oxo-15-nor-eudesman-11-ene (MrBH 02). Hierarchical cluster and heatmap analyses showed that the North and MrBH populations included five different groups, indicating the chemical composition of essential oils is distinct in each population. Furthermore, principal component analysis showed that higher altitudes (1366 m and 1498 m) in the MrBH influence the chemical composition of essential oils, and climatic factors determine the chemical composition in North region. The genetic diversity showed that most alleles are in Hardy-Weinberg equilibrium and imply high genetic variation and genetic polymorphisms between populations. Furthermore, the results of Mantel tests (R = 0.3861517; p = 0.04709529; R = 0.9423121; p = 0.02739726) also showed that higher altitude (>1360 m) shapes the genetic diversity at the MrBH. The genetic structure showed that higher altitudes (>1360 m) contribute to the structure of the MrBH populations, but not to North and CV populations. Therefore, the altitudinal ranges of Minas Gerais mountainous ranges determine the higher genetic and chemical diversity of L. pinaster populations.

Identification and fine mapping of qPBR10-1, a novel locus controlling panicle blast resistance in Pigm-containing P/TGMS line

Rice blast is one of the most widespread and devastating diseases in rice production. Tremendous success has been achieved in the identification and characterization of genes and quantitative trait loci (QTLs) conferring seedling blast resistance, however, genetic studies on panicle blast resistance have lagged far behind. In this study, two advanced backcross inbred sister lines (MSJ13 and MSJ18) were obtained in the process of introducing Pigm into C134S and showed significant differences in the panicle blast resistance. One F2 population derived from the crossing MSJ13/MSJ18 was used to QTL mapping for panicle blast resistance using genotyping by sequencing (GBS) method. A total of seven QTLs were identified, including a major QTL qPBR10-1 on chromosome 10 that explains 24.21% of phenotypic variance with LOD scores of 6.62. Furthermore, qPBR10-1 was verified using the BC1F2 and BC1F3 population and narrowed to a 60.6-kb region with six candidate genes predicted, including two genes encoding exonuclease family protein, two genes encoding hypothetical protein, and two genes encoding transposon protein. The nucleotide variations and the expression patterns of the candidate genes were identified and analyzed between MSJ13 and MSJ18 through sequence comparison and RT-PCR approach, and results indicated that ORF1 and ORF2 encoding exonuclease family protein might be the causal candidate genes for panicle blast resistance in the qPBR10-1 locus.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01268-3.

Precise mapping of QTL for Hessian fly resistance in the hard winter wheat cultivar 'Overland'

A major QTL for Hessian fly resistance was precisely mapped to a 2.32 Mb region on chromosome 3B of the US hard winter wheat cultivar 'Overland'. The Hessian fly (HF, Mayetiola destructor) is a destructive insect pest of wheat in the USA and worldwide. Deploying HF-resistant cultivars is the most effective and economical approach to control this insect pest. A population of 186 recombinant inbred lines (RILs) was developed from 'Overland' × 'Overley' and phenotyped for responses to HF attack using the HF biotype 'Great Plains'. A high-density genetic linkage map was constructed using 1,576 single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). Two quantitative trait loci (QTLs) with a significant epistatic effect on HF resistance were mapped to chromosomes 3B (QHf.hwwg-3B) and 7A (QHf.hwwg-7A) in Overland, which are located in similar chromosome regions as found for H35 and H36 in the cultivar 'SD06165', respectively. QHf.hwwg-3B showed a much larger effect on HF resistance than QHf.hwwg-7A. Five and four GBS-SNPs, respectively, in the QHf.hwwg-3B and QHf.hwwg-7A QTL intervals were converted into Kompetitive allele specific polymerase chain reaction (KASP) markers. QHf.hwwg-3B was precisely mapped to a 2.32 Mb interval (2,479,314-4,799,538 bp) using near-isogenic lines (NILs) and RILs that have recombination within the QTL interval. The US winter wheat accessions carrying contrasting alleles at KASP markers KASP-3B4525164, KASP-7A47772047 and KASP-7A65090410 showed significant difference in HF resistance. The combination of the two KASP markers KASP-3B3797431 and KASP-3B4525164 is near-diagnostic for the detection of QHf.hwwg-3B in a US winter wheat panel and can be potentially used for screening the QTL in breeding programs.

Meta-QTLs and candidate genes for stripe rust resistance in wheat

In bread wheat, meta-QTL analysis was conducted using 353 QTLs that were available from earlier studies. When projected onto a dense consensus map comprising 76,753 markers, only 184 QTLs with the required information, could be utilized leading to identification of 61 MQTLs spread over 18 of the 21 chromosomes (barring 5D, 6D and 7D). The range for mean R2 (PVE %) was 1.9% to 48.1%, and that of CI was 0.02 to 11.47 cM; these CIs also carried 37 Yr genes. Using these MQTLs, 385 candidate genes (CGs) were also identified. Out of these CGs, 241 encoded known R proteins and 120 showed differential expression due to stripe rust infection at the seedling stage; the remaining 24 CGs were common in the sense that they encoded R proteins as well as showed differential expression. The proteins encoded by CGs carried the following widely known domains: NBS-LRR domain, WRKY domains, ankyrin repeat domains, sugar transport domains, etc. Thirteen breeders' MQTLs (PVE > 20%) including four pairs of closely linked MQTLs are recommended for use in wheat molecular breeding, for future studies to understand the molecular mechanism of stripe rust resistance and for gene cloning.

The barley immune receptor Mla recognizes multiple pathogens and contributes to host range dynamics

Crop losses caused by plant pathogens are a primary threat to stable food production. Stripe rust (Puccinia striiformis) is a fungal pathogen of cereal crops that causes significant, persistent yield loss. Stripe rust exhibits host species specificity, with lineages that have adapted to infect wheat and barley. While wheat stripe rust and barley stripe rust are commonly restricted to their corresponding hosts, the genes underlying this host specificity remain unknown. Here, we show that three resistance genes, Rps6, Rps7, and Rps8, contribute to immunity in barley to wheat stripe rust. Rps7 cosegregates with barley powdery mildew resistance at the Mla locus. Using transgenic complementation of different Mla alleles, we confirm allele-specific recognition of wheat stripe rust by Mla. Our results show that major resistance genes contribute to the host species specificity of wheat stripe rust on barley and that a shared genetic architecture underlies resistance to the adapted pathogen barley powdery mildew and non-adapted pathogen wheat stripe rust.

Population-tailored mock genome enables genomic studies in species without a reference genome

Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.

Characterization of the genetic basis of local adaptation of wheat landraces from Iran and Pakistan using genome-wide association study

Characterization of genomic regions underlying adaptation of landraces can reveal a quantitative genetics framework for local wheat (Triticum aestivum L.) adaptability. A collection of 512 wheat landraces from the eastern edge of the Fertile Crescent in Iran and Pakistan were genotyped using genome-wide single nucleotide polymorphism markers generated by genotyping-by-sequencing. The minor allele frequency (MAF) and the heterozygosity (H) of Pakistani wheat landraces (MAF = 0.19, H = 0.008) were slightly higher than the Iranian wheat landraces (MAF = 0.17, H = 0.005), indicating that Pakistani landraces were slightly more genetically diverse. Population structure analysis clearly separated the Pakistani landraces from Iranian landraces, which indicates two separate adaptability trajectories. The large-scale agro-climatic data of seven variables were quite dissimilar between Iran and Pakistan as revealed by the correlation coefficients. Genome-wide association study identified 91 and 58 loci using agroclimatic data, which likely underpin local adaptability of the wheat landraces from Iran and Pakistan, respectively. Selective sweep analysis identified significant hits on chromosomes 4A, 4B, 6B, 7B, 2D, and 6D, which were colocalized with the loci associated with local adaptability and with some known genes related to flowering time and grain size. This study provides insight into the genetic diversity with emphasis on the genetic architecture of loci involved in adaptation to local environments, which has breeding implications.

QTL for seed shattering and threshability in intermediate wheatgrass align closely with well-studied orthologs from wheat, barley, and rice

Perennial grain crops have the potential to improve agricultural sustainability but few existing species produce sufficient grain yield to be economically viable. The outcrossing, allohexaploid, and perennial forage species intermediate wheatgrass (IWG) [Thinopyrum intermedium (Host) Barkworth & D. R. Dewey] has shown promise in undergoing direct domestication as a perennial grain crop using phenotypic and genomic selection. However, decades of selection will be required to achieve yields on par with annual small-grain crops. Marker-aided selection could accelerate progress if important genomic regions associated with domestication were identified. Here we use the IWG nested association mapping (NAM) population, with 1,168 F₁ progeny across 10 families to dissect the genetic control of brittle rachis, floret shattering, and threshability. We used a genome-wide association study (GWAS) with 8,003 single nucleotide polymorphism (SNP) markers and linkage mapping-both within-family and combined across families-with a robust phenotypic dataset collected from four unique year-by-location combinations. A total of 29 quantitative trait loci (QTL) using GWAS and 20 using the combined linkage analysis were detected, and most large-effect QTL were in common across the two analysis methods. We reveal that the genetic control of these traits in IWG is complex, with significant QTL across multiple chromosomes, sometimes within and across homoeologous groups and effects that vary depending on the family. In some cases, these QTL align within 216 bp to 31 Mbp of BLAST hits for known domestication genes in related species and may serve as precise targets of selection and directions for further study to advance the domestication of IWG.

Accelerating wheat breeding for end-use quality through association mapping and multivariate genomic prediction

In hard-winter wheat (Triticum aestivum L.) breeding, the evaluation of end-use quality is expensive and time-consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance, and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome-wide association study (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard-winter wheat. Advanced breeding lines (n = 462) from 2015-2017 were genotyped using genotyping-by-sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time, most of which were within or in tight linkage to glutenin and gliadin loci and could be suitable for marker-assisted breeding. Candidate genes for newly associated loci are phosphate-dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits, thus we evaluated various GS models for end-use quality traits. As a baseline, univariate GS models had 0.25-0.55 prediction accuracy in cross-validation and from 0 to 0.41 in forward prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end-use quality.

Environment characterization and genomic prediction for end-use quality traits in soft white winter wheat

End-use quality phenotyping is laborious and expensive, thus, testing may not occur until later generations in wheat breeding programs. We investigated the pattern of genotype × environment (G × E) interaction for end-use quality traits in soft white wheat (Triticum aestivum L.) and tested the effectiveness of implementing genomic selection to optimize breeding for these traits. We used a multi-environment unbalanced dataset comprised of 672 breeding lines and cultivars adapted to the Pacific Northwest region of the United States, which were evaluated for 14 end-use quality traits. Genetic correlations between environments based on factor analytic models showed low-to-moderate G × E interaction for most traits but high G × E interaction for grain and flour protein. A total of 40,518 single-nucleotide polymorphism markers were used for genomic prediction. Genomic prediction accuracies were high for most traits thereby justifying the use of genomic selection to assist breeding for superior end-use quality in soft white wheat. Excluding outlier environments based on genetic correlations between environments was more effective in increasing genomic prediction accuracies compared with that based on environment clustering analysis. For kernel size, kernel weight, milling score, ash, and flour swelling volume, excluding outlier environments increased prediction accuracies by 1-11%. However, for grain and flour protein, flour yield, and cookie diameter, excluding outlier environments did not improve genomic prediction performance.

Highly Replicated Evolution of Parapatric Ecotypes

Parallel evolution of ecotypes occurs when selection independently drives the evolution of similar traits across similar environments. The multiple origins of ecotypes are often inferred based on a phylogeny that clusters populations according to geographic location and not by the environment they occupy. However, the use of phylogenies to infer parallel evolution in closely related populations is problematic because gene flow and incomplete lineage sorting can uncouple the genetic structure at neutral markers from the colonization history of populations. Here, we demonstrate multiple origins within ecotypes of an Australian wildflower, Senecio lautus. We observed strong genetic structure as well as phylogenetic clustering by geography and show that this is unlikely due to gene flow between parapatric ecotypes, which was surprisingly low. We further confirm this analytically by demonstrating that phylogenetic distortion due to gene flow often requires higher levels of migration than those observed in S. lautus. Our results imply that selection can repeatedly create similar phenotypes despite the perceived homogenizing effects of gene flow.

Identification and Validation of High LD Hotspot Genomic Regions Harboring Stem Rust Resistant Genes on 1B, 2A (Sr38), and 7B Chromosomes in Wheat

Stem rust caused by Puccinia graminis f. sp. tritici Eriks. is an important disease of common wheat globally. The production and cultivation of genetically resistant cultivars are one of the most successful and environmentally friendly ways to protect wheat against fungal pathogens. Seedling screening and genome-wide association study (GWAS) were used to determine the genetic diversity of wheat genotypes obtained on stem rust resistance loci. At the seedling stage, the reaction of the common stem rust race QFCSC in Nebraska was measured in a set of 212 genotypes from F_3:6 lines. The results indicated that 184 genotypes (86.8%) had different degrees of resistance to this common race. While 28 genotypes (13.2%) were susceptible to stem rust. A set of 11,911 single-nucleotide polymorphism (SNP) markers was used to perform GWAS which detected 84 significant marker-trait associations (MTAs) with SNPs located on chromosomes 1B, 2A, 2B, 7B and an unknown chromosome. Promising high linkage disequilibrium (LD) genomic regions were found in all chromosomes except 2B which suggested they include candidate genes controlling stem rust resistance. Highly significant LD was found among these 59 significant SNPs on chromosome 2A and 12 significant SNPs with an unknown chromosomal position. The LD analysis between SNPs located on 2A and Sr38 gene reveal high significant LD genomic regions which was previously reported. To select the most promising stem rust resistant genotypes, a new approach was suggested based on four criteria including, phenotypic selection, number of resistant allele(s), the genetic distance among the selected parents, and number of the different resistant allele(s) in the candidate crosses. As a result, 23 genotypes were considered as the most suitable parents for crossing to produce highly resistant stem rust genotypes against the QFCSC.

Evaluations of Genomic Prediction and Identification of New Loci for Resistance to Stripe Rust Disease in Wheat (Triticum aestivum L.)

Stripe rust is one of the most destructive diseases of wheat (Triticum aestivum L.), caused by Puccinia striiformis f. sp. tritici (Pst), and responsible for significant yield losses worldwide. Single-nucleotide polymorphism (SNP) diagnostic markers were used to identify new sources of resistance at adult plant stage to wheat stripe rust (YR) in 141 CIMMYT advanced bread wheat lines over 3 years in replicated trials at Borlaug Institute for South Asia (BISA), Ludhiana. We performed a genome-wide association study and genomic prediction to aid the genetic gain by accumulating disease resistance alleles. The responses to YR in 141 advanced wheat breeding lines at adult plant stage were used to generate G × E (genotype × environment)-dependent rust scores for prediction and genome-wide association study (GWAS), eliminating variation due to climate and disease pressure changes. The lowest mean prediction accuracies were 0.59 for genomic best linear unbiased prediction (GBLUP) and ridge-regression BLUP (RRBLUP), while the highest mean was 0.63 for extended GBLUP (EGBLUP) and random forest (RF), using 14,563 SNPs and the G × E rust score results. RF and EGBLUP predicted higher accuracies (∼3%) than did GBLUP and RRBLUP. Promising genomic prediction demonstrates the viability and efficacy of improving quantitative rust tolerance. The resistance to YR in these lines was attributed to eight quantitative trait loci (QTLs) using the FarmCPU algorithm. Four (Q.Yr.bisa-2A.1, Q.Yr.bisa-2D, Q.Yr.bisa-5B.2, and Q.Yr.bisa-7A) of eight QTLs linked to the diagnostic markers were mapped at unique loci (previously unidentified for Pst resistance) and possibly new loci. The statistical evidence of effectiveness and distribution of the new diagnostic markers for the resistance loci would help to develop new stripe rust resistance sources. These diagnostic markers along with previously established markers would be used to create novel DNA biosensor-based microarrays for rapid detection of the resistance loci on large panels upon functional validation of the candidate genes identified in the present study to aid in rapid genetic gain in the future breeding programs.

Application of Restriction Site-Associated DNA Sequencing (RAD-Seq) for Copy Number Variation and Triploidy Detection in Human

At present, low-pass whole-genome sequencing (WGS) is frequently used in clinical research and in the screening of copy number variations (CNVs). However, there are still some challenges in the detection of triploids. Restriction site-associated DNA sequencing (RAD-Seq) technology is a reduced-representation genome sequencing technology developed based on next-generation sequencing. Here, we verified whether RAD-Seq could be employed to detect CNVs and triploids. In this study, genomic DNA of 11 samples was extracted employing a routine method and used to build libraries. Five cell lines of known karyotypes and 6 triploid abortion tissue samples were included for RAD-Seq testing. The triploid samples were confirmed by STR analysis and also tested by low-pass WGS. The accuracy and efficiency of detecting CNVs and triploids by RAD-Seq were then assessed, compared with low-pass WGS. In our results, RAD-Seq detected 11 out of 11 (100%) chromosomal abnormalities, including 4 deletions and 1 aneuploidy in the purchased cell lines and all triploid samples. By contrast, these triploids were missed by low-pass WGS. Furthermore, RAD-Seq showed a higher resolution and more accurate allele frequency in the detection of triploids than low-pass WGS. Our study shows that, compared with low-pass WGS, RAD-Seq has relatively higher accuracy in CNV detection at a similar cost and is capable of identifying triploids. Therefore, the application of this technique in medical genetics has a significant potential value.

Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.)

Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2-3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.

On the genetic architecture in a public tropical maize panel of the symbiosis between corn and plant growth-promoting bacteria aiming to improve plant resilience

Exploring the symbiosis between plants and plant growth-promoting bacteria (PGPB) is a new challenge for sustainable agriculture. Even though many works have reported the beneficial effects of PGPB in increasing plant resilience for several stresses, its potential is not yet widely explored. One of the many reasons is the differential symbiosis performance depending on the host genotype. This opens doors to plant breeding programs to explore the genetic variability and develop new cultivars with higher responses to PGPB interaction and, therefore, have higher resilience to stress. Hence, we aimed to study the genetic architecture of the symbiosis between PGPB and tropical maize germplasm, using a public association panel and its impact on plant resilience. Our findings reveal that the synthetic PGPB population can modulate and impact root architecture traits and improve resilience to nitrogen stress, and 37 regions were significant for controlling the symbiosis between PGPB and tropical maize. In addition, we found two overlapping SNPs in the GWAS analysis indicating strong candidates for further investigations. Furthermore, genomic prediction analysis with genomic relationship matrix computed using only significant SNPs obtained from GWAS analysis substantially increased the predictive ability for several traits endorsing the importance of these genomic regions for the response of PGPB. Finally, the public tropical panel reveals a significant genetic variability to the symbiosis with the PGPB and can be a source of alleles to improve plant resilience.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01257-6.

Identifying the genetic control of salinity tolerance in the bread wheat landrace Mocho de Espiga Branca

Salinity tolerance in bread wheat is frequently reported to be associated with low leaf sodium (Na+) concentrations. However, the Portuguese landrace, Mocho de Espiga Branca, accumulates significantly higher leaf Na+ but has comparable salinity tolerance to commercial bread wheat cultivars. To determine the genetic loci associated with the salinity tolerance of this landrace, an F2 mapping population was developed by crossing Mocho de Espiga Branca with the Australian cultivar Gladius. The population was phenotyped for 19 salinity tolerance subtraits using both non-destructive and destructive techniques. Genotyping was performed using genotyping-by-sequencing (GBS). Genomic regions associated with salinity tolerance were detected on chromosomes 1A, 1D, 4B and 5A for the subtraits of relative and absolute growth rate (RGR, AGR respectively), and on chromosome 2A, 2B, 4D and 5D for Na+, potassium (K+) and chloride (Cl-) accumulation. Candidate genes that encode proteins associated with salinity tolerance were identified within the loci including Na+/H+ antiporters, K+ channels, H+-ATPase, calcineurin B-like proteins (CBLs), CBL-interacting protein kinases (CIPKs), calcium dependent protein kinases (CDPKs) and calcium-transporting ATPase. This study provides a new insight into the genetic control of salinity tolerance in a Na+ accumulating bread wheat to assist with the future development of salt tolerant cultivars.

GWAS identifies an ortholog of the rice D11 gene as a candidate gene for grain size in an international collection of hexaploid wheat

Grain size is a key agronomic trait that contributes to grain yield in hexaploid wheat. Grain length and width were evaluated in an international collection of 157 wheat accessions. These accessions were genetically characterized using a genotyping-by-sequencing (GBS) protocol that produced 73,784 single nucleotide polymorphism (SNP) markers. GBS-derived genotype calls obtained on Chinese Spring proved extremely accurate when compared to the reference (> 99.9%) and showed > 95% agreement with calls made at SNP loci shared with the 90 K SNP array on a subset of 71 Canadian wheat accessions for which both types of data were available. This indicates that GBS can yield a large amount of highly accurate SNP data in hexaploid wheat. The genetic diversity analysis performed using this set of SNP markers revealed the presence of six distinct groups within this collection. A GWAS was conducted to uncover genomic regions controlling variation for grain length and width. In total, seven SNPs were found to be associated with one or both traits, identifying three quantitative trait loci (QTLs) located on chromosomes 1D, 2D and 4A. In the vicinity of the peak SNP on chromosome 2D, we found a promising candidate gene (TraesCS2D01G331100), whose rice ortholog (D11) had previously been reported to be involved in the regulation of grain size. These markers will be useful in breeding for enhanced wheat productivity.

Toward identification of a putative candidate gene for nutrient mineral accumulation in wheat grains for human nutrition purposes

A multilocus genome-wide association study of a panel of 369 diverse wheat (Triticum aestivum) genotypes was carried out in order to examine the genetic basis of variations in nutrient mineral concentrations in the grains. The panel was grown under field conditions for three consecutive years and the concentrations of Ca, K, Mg, Mn, P, and S were determined. Wide ranges of natural variation were detected among the genotypes. Strong positive correlations were found among the minerals except for K, which showed negative correlation trends with the other minerals. Genetic association analysis detected 86 significant marker-trait associations (MTAs) underlying the natural variations in mineral concentrations in grains. The major MTA was detected on the long arm of chromosome 5A and showed a pleiotropic effect on Ca, K, Mg, Mn, and S. Further significant MTAs were distributed among the whole genome except for chromosomes 3D and 6D. We identified putative candidate genes that are potentially involved in metal uptake, transport, and assimilation, including TraesCS5A02G542600 on chromosome 5A, which was annotated as a Major Facilitator Superfamily transporter and acted on all the minerals except K. TraesCS5A02G542600 was highly expressed in seed coat, and to a lesser extent in the peduncle, awns, and lemma. Our results provide important insights into the genetic basis of enhancement of nutrient mineral concentrations that can help to inform future breeding studies in order to improve human nutrition.

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson’s correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson’s correlation calculated by fitting a bivariate model was higher than the division of the Pearson’s correlation by the squared root of the heritability across traits, by 2.53–11.46%. Across the datasets, the corrected Pearson’s correlation was higher than the uncorrected by 5.80–14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

Genomic Selection in Sugarcane: Current Status and Future Prospects

Sugarcane is a C4 and agro-industry-based crop with a high potential for biomass production. It serves as raw material for the production of sugar, ethanol, and electricity. Modern sugarcane varieties are derived from the interspecific and intergeneric hybridization between Saccharum officinarum, Saccharum spontaneum, and other wild relatives. Sugarcane breeding programmes are broadly categorized into germplasm collection and characterization, pre-breeding and genetic base-broadening, and varietal development programmes. The varietal identification through the classic breeding programme requires a minimum of 12-14 years. The precise phenotyping in sugarcane is extremely tedious due to the high propensity of lodging and suckering owing to the influence of environmental factors and crop management practices. This kind of phenotyping requires data from both plant crop and ratoon experiments conducted over locations and seasons. In this review, we explored the feasibility of genomic selection schemes for various breeding programmes in sugarcane. The genetic diversity analysis using genome-wide markers helps in the formation of core set germplasm representing the total genomic diversity present in the Saccharum gene bank. The genome-wide association studies and genomic prediction in the Saccharum gene bank are helpful to identify the complete genomic resources for cane yield, commercial cane sugar, tolerances to biotic and abiotic stresses, and other agronomic traits. The implementation of genomic selection in pre-breeding, genetic base-broadening programmes assist in precise introgression of specific genes and recurrent selection schemes enhance the higher frequency of favorable alleles in the population with a considerable reduction in breeding cycles and population size. The integration of environmental covariates and genomic prediction in multi-environment trials assists in the prediction of varietal performance for different agro-climatic zones. This review also directed its focus on enhancing the genetic gain over time, cost, and resource allocation at various stages of breeding programmes.

Landscape-scale population connectivity in two parasitoid species associated with the spruce budworm: Testing the birdfeeder effect using genetic data

Periodic and spatially synchronous outbreaks of insect pests have dramatic consequences for boreal and sub-boreal forests. Within these multitrophic systems, parasitoids can be stabilizing agents by dispersing toward patches containing higher host density (the so-called birdfeeder effect). However, we know little about the dispersal abilities of parasitoids in continuous forested landscapes, limiting our understanding of the spatiotemporal dynamics of host-parasitoid systems, and constraining our ability to predict forest resilience in the context of global changes. In this study, we investigate the spatial genetic structure and spatial variation in genetic diversity of two important species of spruce budworm larval parasitoids during outbreaks: Apanteles fumiferanae Viereck (Braconidae) and Glypta fumiferanae (Viereck) (Ichneumonidae). Using parasitoids sampled in 2014 from 26 and 29 locations across a study area of 350,000 km² , we identified 1,012 and 992 neutral SNP loci for A. fumiferanae (N = 279 individuals) and G. fumiferanae (N = 382), respectively. Using DAPC, PCA, AMOVA, and IBD analyses, we found evidence for panmixia and high genetic connectivity for both species, matching the previously described genetic structure of the spruce budworm within the same context, suggesting similar effective dispersal during outbreaks and high parasitoid population densities between outbreaks. We also found a significant negative relationship between genetic diversity and latitude for A. fumiferanae but not for G. fumiferanae, suggesting that northern range limits may vary by species within the spruce budworm parasitoid community. These spatial dynamics should be considered when predicting future insect outbreak severities in boreal landscapes.

Genome-Wide Association Studies for Fusarium Head Blight Resistance and Its Trade-Off With Grain Yield in Soft Red Winter Wheat

Identification of quantitative trait loci for Fusarium head blight (FHB) resistance from different sources and pyramiding them into cultivars could provide effective protection against FHB. The objective of this study was to characterize a soft red winter wheat (SRWW) breeding population that has been subjected to intense germplasm introduction and alien introgression for FHB resistance in the past. The population was evaluated under misted FHB nurseries inoculated with Fusarium graminearum-infested corn spawn for two years. Phenotypic data included disease incidence (INC), disease severity (SEV), Fusarium damaged kernels (FDK), FHB index (FHBdx), and deoxynivalenol concentration (DON). Genome-wide association studies using 13,784 SNP markers identified 25 genomic regions at -logP ≥ 4.0 that were associated with five FHB-related traits. Of these 25, the marker trait associations that explained more than 5% phenotypic variation were localized on chromosomes 1A, 2B, 3B, 5A, 7A, 7B, and 7D, and from diverse sources including adapted SRWW lines such as Truman and Bess, and unadapted common wheat lines such as Ning7840 and Fundulea 201R. Furthermore, individuals with favorable alleles at the four loci Fhb1, Qfhb.nc-2B.1 (Q_2B.1), Q_7D.1, and Q_7D.2 showed better FDK and DON scores (but not INC, SEV, and FHBdx) compared with other allelic combinations. Our data also showed while pyramiding multiple loci provides protection against FHB disease, it has a significant trade-off with grain yield.

Pleistocene dynamics of the Eurasian steppe as a driving force of evolution: Phylogenetic history of the genus Capsella (Brassicaceae)

Capsella is a model plant genus of the Brassicaceae closely related to Arabidopsis. To disentangle its biogeographical history and intrageneric phylogenetic relationships, 282 individuals of all five currently recognized Capsella species were genotyped using a restriction digest-based next-generation sequencing method. Our analysis retrieved two main lineages within Capsella that split c. one million years ago, with western C. grandiflora and C. rubella forming a sister lineage to the eastern lineage consisting of C. orientalis. The split was attributed to continuous latitudinal displacements of the Eurasian steppe belt to the south during Early Pleistocene glacial cycles. During the interglacial cycles of the Late Pleistocene, hybridization of the two lineages took place in the southwestern East European Plain, leading to the allotetraploid C. bursa-pastoris. Extant genetic variation within C. orientalis postdated any extensive glacial events. Ecological niche modeling showed that suitable habitat for C. orientalis existed during the Last Glacial Maximum around the north coast of the Black Sea and in southern Kazakhstan. Such a scenario is also supported by population genomic data that uncovered the highest genetic diversity in the south Kazakhstan cluster, suggesting that C. orientalis originated in continental Asia and migrated north- and possibly eastwards after the last ice age. Post-glacial hybridization events between C. bursa-pastoris and C. grandiflora/rubella in the southwestern East European Plain and the Mediterranean gave rise to C. thracica. Introgression of C. grandiflora/rubella into C. bursa-pastoris resulted in a new Mediterranean cluster within the already existing Eurasian C. bursa-pastoris cluster. This study shows that the continuous displacement and disruption of the Eurasian steppe belt during the Pleistocene was the driving force in the evolution of Capsella.

Barley Viridis-k links an evolutionarily conserved C-type ferredoxin to chlorophyll biosynthesis

Ferredoxins are single-electron carrier proteins involved in various cellular reactions. In chloroplasts, the most abundant ferredoxin accepts electrons from photosystem I and shuttles electrons via ferredoxin NADP+ oxidoreductase to generate NADPH or directly to ferredoxin dependent enzymes. In addition, plants contain other isoforms of ferredoxins. Two of these, named FdC1 and FdC2 in Arabidopsis thaliana, have C-terminal extensions and functions that are poorly understood. Here we identified disruption of the orthologous FdC2 gene in barley (Hordeum vulgare L.) mutants at the Viridis-k locus; these mutants are deficient in the aerobic cyclase reaction of chlorophyll biosynthesis. The magnesium-protoporphyrin IX monomethyl ester cyclase is one of the least characterized enzymes of the chlorophyll biosynthetic pathway and its electron donor has long been sought. Agroinfiltrations showed that the viridis-k phenotype could be complemented in vivo by Viridis-k but not by canonical ferredoxin. VirK could drive the cyclase reaction in vitro and analysis of cyclase mutants showed that in vivo accumulation of VirK is dependent on cyclase enzyme levels. The chlorophyll deficient phenotype of viridis-k mutants suggests that VirK plays an essential role in chlorophyll biosynthesis that cannot be replaced by other ferredoxins, thus assigning a specific function to this isoform of C-type ferredoxins.

Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing

BACKGROUND

Genome-wide data are invaluable to characterize differentiation and adaptation of natural populations. Reduced representation sequencing (RRS) subsamples a genome repeatedly across many individuals. However, RRS requires careful optimization and fine-tuning to deliver high marker density while being cost-efficient. The number of genomic fragments created through restriction enzyme digestion and the sequencing library setup must match to achieve sufficient sequencing coverage per locus. Here, we present a workflow based on published information and computational and experimental procedures to investigate and streamline the applicability of RRS.

RESULTS

In an iterative process genome size estimates, restriction enzymes and size selection windows were tested and scaled in six classes of Antarctic animals (Ostracoda, Malacostraca, Bivalvia, Asteroidea, Actinopterygii, Aves). Achieving high marker density would be expensive in amphipods, the malacostracan target taxon, due to the large genome size. We propose alternative approaches such as mitogenome or target capture sequencing for this group. Pilot libraries were sequenced for all other target taxa. Ostracods, bivalves, sea stars, and fish showed overall good coverage and marker numbers for downstream population genomic analyses. In contrast, the bird test library produced low coverage and few polymorphic loci, likely due to degraded DNA.

CONCLUSIONS

Prior testing and optimization are important to identify which groups are amenable for RRS and where alternative methods may currently offer better cost-benefit ratios. The steps outlined here are easy to follow for other non-model taxa with little genomic resources, thus stimulating efficient resource use for the many pressing research questions in molecular ecology.

Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Global range expansion history of pepper (Capsicum spp.) revealed by over 10,000 genebank accessions

This study provides a deep population genomic analysis of 10,000 Capsicum accessions held in genebanks and representing a frame of the global diversity of the genus. By combining single nucleotide polymorphisms (SNPs) based data and passport information, we investigated the genomic diversity and population structure of wild and domesticated peppers, tracing back to routes of evolution and providing a model of Capsicum annuum distribution, which reflects human trade and historical/cultural influences. Our results highlight west–east routes of expansion, shedding light on the links between South and Mesoamerica, Africa, and East/South Asia, the latter two constituting important diversification centers of pepper diversity. Finally, we outline a roadmap for genebank management and future direction for better exploitation of germplasm resources.

Genebanks collect and preserve vast collections of plants and detailed passport information, with the aim of preserving genetic diversity for conservation and breeding. Genetic characterization of such collections has the potential to elucidate the genetic histories of important crops, use marker–trait associations to identify loci controlling traits of interest, search for loci undergoing selection, and contribute to genebank management by identifying taxonomic misassignments and duplicates. We conducted a genomic scan with genotyping by sequencing (GBS) derived single nucleotide polymorphisms (SNPs) of 10,038 pepper (Capsicum spp.) accessions from worldwide genebanks and investigated the recent history of this iconic staple. Genomic data detected up to 1,618 duplicate accessions within and between genebanks and showed that taxonomic ambiguity and misclassification often involve interspecific hybrids that are difficult to classify morphologically. We deeply interrogated the genetic diversity of the commonly consumed Capsicum annuum to investigate its history, finding that the kinds of peppers collected in broad regions across the globe overlap considerably. The method ReMIXTURE—using genetic data to quantify the similarity between the complement of peppers from a focal region and those from other regions—was developed to supplement traditional population genetic analyses. The results reflect a vision of pepper as a highly desirable and tradable cultural commodity, spreading rapidly throughout the globe along major maritime and terrestrial trade routes. Marker associations and possible selective sweeps affecting traits such as pungency were observed, and these traits were shown to be distributed nonuniformly across the globe, suggesting that human preferences exerted a primary influence over domesticated pepper genetic structure.

Genomic Variation Shaped by Environmental and Geographical Factors in Prairie Cordgrass Natural Populations Collected across Its Native Range in the USA

Prairie cordgrass (Spartina pectinata Link) is a native perennial warm-season (C4) grass common in North American prairies. With its high biomass yield and abiotic stress tolerance, there is a high potential of developing prairie cordgrass for conservation practices and as a dedicated bioenergy crop for sustainable cellulosic biofuel production. However, as with many other undomesticated grass species, little information is known about the genetic diversity or population structure of prairie cordgrass natural populations as compared to their ecotypic and geographic adaptation in North America. In this study, we sampled and characterized a total of 96 prairie cordgrass natural populations with 9315 high quality SNPs from a genotyping-by-sequencing (GBS) approach. The natural populations were collected from putative remnant prairie sites throughout the Midwest and Eastern USA, which are the major habitats for prairie cordgrass. Partitioning of genetic variance using SNP marker data revealed significant variance among and within populations. Two potential gene pools were identified as being associated with ploidy levels, geographical separation, and climatic separation. Geographical factors such as longitude and altitude, and environmental factors such as annual temperature, annual precipitation, temperature of the warmest month, precipitation of the wettest month, precipitation of Spring, and precipitation of the wettest month are important in affecting the intraspecific distribution of prairie cordgrass. The divergence of prairie cordgrass natural populations also provides opportunities to increase breeding value of prairie cordgrass as a bioenergy and conservation crop.

Evidence for genetic isolation and local adaptation in the field cricket Gryllus campestris

Understanding how species can thrive in a range of environments is a central challenge for evolutionary ecology. There is strong evidence for local adaptation along large-scale ecological clines in insects. However, potential adaptation among neighbouring populations differing in their environment has been studied much less. We used RAD sequencing to quantify genetic divergence and clustering of ten populations of the field cricket Gryllus campestris in the Cantabrian Mountains of northern Spain, and an outgroup on the inland plain. Our populations were chosen to represent replicate high and low altitude habitats. We identified genetic clusters that include both high and low altitude populations indicating that the two habitat types do not hold ancestrally distinct lineages. Using common-garden rearing experiments to remove environmental effects, we found evidence for differences between high and low altitude populations in physiological and life-history traits. As predicted by the local adaptation hypothesis, crickets with parents from cooler (high altitude) populations recovered from periods of extreme cooling more rapidly than those with parents from warmer (low altitude) populations. Growth rates also differed between offspring from high and low altitude populations. However, contrary to our prediction that crickets from high altitudes would grow faster, the most striking difference was that at high temperatures, growth was fastest in individuals from low altitudes. Our findings reveal that populations a few tens of kilometres apart have independently evolved adaptations to their environment. This suggests that local adaptation in a range of traits may be commonplace even in mobile invertebrates at scales of a small fraction of species' distributions.

Global multi-environment resistance QTL for foliar late blight resistance in tetraploid potato with tropical adaptation

The identification of environmentally stable and globally predictable resistance to potato late blight is challenged by the clonal and polyploid nature of the crop and the rapid evolution of the pathogen. A diversity panel of tetraploid potato germplasm bred for multiple resistance and quality traits was genotyped by genotyping by sequencing (GBS) and evaluated for late blight resistance in three countries where the International Potato Center (CIP) has established breeding work. Health-indexed, in vitro plants of 380 clones and varieties were distributed from CIP headquarters and tuber seed was produced centrally in Peru, China, and Ethiopia. Phenotypes were recorded following field exposure to local isolates of Phytophthora infestans. QTL explaining resistance in four experiments conducted across the three countries were identified in chromosome IX, and environment-specific QTL were found in chromosomes III, V, and X. Different genetic models were evaluated for prediction ability to identify best performing germplasm in each and all environments. The best prediction ability (0.868) was identified with the genomic best linear unbiased predictors (GBLUPs) when using the diploid marker data and QTL-linked markers as fixed effects. Genotypes with high levels of resistance in all environments were identified from the B3, LBHT, and B3-LTVR populations. The results show that many of the advanced clones bred in Peru for high levels of late blight resistance maintain their resistance in Ethiopia and China, suggesting that the centralized selection strategy has been largely successful.

Genome-wide approaches for the identification of markers and genes associated with sugarcane yellow leaf virus resistance

Sugarcane yellow leaf (SCYL), caused by the sugarcane yellow leaf virus (SCYLV) is a major disease affecting sugarcane, a leading sugar and energy crop. Despite damages caused by SCYLV, the genetic base of resistance to this virus remains largely unknown. Several methodologies have arisen to identify molecular markers associated with SCYLV resistance, which are crucial for marker-assisted selection and understanding response mechanisms to this virus. We investigated the genetic base of SCYLV resistance using dominant and codominant markers and genotypes of interest for sugarcane breeding. A sugarcane panel inoculated with SCYLV was analyzed for SCYL symptoms, and viral titer was estimated by RT-qPCR. This panel was genotyped with 662 dominant markers and 70,888 SNPs and indels with allele proportion information. We used polyploid-adapted genome-wide association analyses and machine-learning algorithms coupled with feature selection methods to establish marker-trait associations. While each approach identified unique marker sets associated with phenotypes, convergences were observed between them and demonstrated their complementarity. Lastly, we annotated these markers, identifying genes encoding emblematic participants in virus resistance mechanisms and previously unreported candidates involved in viral responses. Our approach could accelerate sugarcane breeding targeting SCYLV resistance and facilitate studies on biological processes leading to this trait.

Cryptic introgressions contribute to transgressive segregation for early blight resistance in tomato

We identified cryptic early blight resistance introgressions in tomato breeding lines and demonstrated efficient genotypic selection for resistance in the context of a tomato breeding program. Early blight is a widespread and problematic disease affecting tomatoes (Solanum lycopersicum). Caused by the fungal pathogen Alternaria linariae (syn. A. tomatophila), symptoms include lesions on tomato stems, fruit, and foliage, often resulting in yield losses. Breeding tomatoes with genetic resistance would enhance production sustainability. Using cross-market breeding populations, we identified several quantitative trait loci (QTL) associated with early blight resistance. Early blight resistance putatively derived from 'Campbell 1943' was confirmed in modern fresh market tomato breeding lines. This resistance offered substantial protection against early blight stem lesions (collar rot) and moderate protection from defoliation. A distinctive and potentially novel form of early blight foliar resistance was discovered in a processing tomato breeding line and is probably derived from S. pimpinellifolium via 'Hawaii 7998'. Additional field trials validated the three most promising large-effect QTL, EB-1.2, EB-5, and EB-9. Resistance effects for EB-5 and EB-9 were consistent across breeding populations and environments, while EB-1.2's effect was population specific. Using genome-wide marker-assisted backcrossing, we developed fresh market tomato lines that were near-isogenic for early blight QTL. Resistance in these lines was largely mediated by just two QTL, EB-5 and EB-9, that together captured 49.0 and 68.7% of the defoliation and stem lesion variance, respectively. Our work showcases the value of mining cryptic introgressions in tomato lines, and across market classes, for use as additional sources of disease resistance.

Genome-wide association mapping of the 'super-soft' kernel texture in white winter wheat

The novel super-soft kernel phenotype has the potential to improve wheat processing and flour quality. We identified genomic regions associated with this kernel texture in white winter wheat. Grain hardness is a key determinant of wheat milling and baking quality. The recently discovered 'super-soft' kernel phenotype has the potential to improve wheat processing and flour quality. However, the genetic basis underlying the super-soft trait in wheat is not yet well understood. In this study, we investigated the phenotypic and genotypic structure of the super-soft trait in a collection of 172 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the USA. This collection had a continuous distribution for grain hardness index (single-kernel characterization system). Ten super-soft genotypes showed hardness index ≤ 12 including the cultivar Jasper. Over 98,000 SNP markers from genotyping-by-sequencing were used for association mapping (GWAS). The GWAS identified 20 significant markers associated with grain hardness. These significant SNPs corresponded to seven QTL on chromosomes 2B, 3A, 3B, 5A, 6B,7A, and one unaligned chromosome. Two of these QTL, QSKhard.wql-3A and QSKhard.wql-5A, had large effects and distinguished between the normal soft and the super-soft classes. QSKhard.wql-3A and QSKhard.wql-5A reduced the hardness index by 11.7 and 13.1 on average, respectively. The remaining QTL had small effects and reduced grain hardness within the normal soft range. QSKhard.wql-2B, QSKhard.wql-3A, QSKhard.wql-3B, and QSKhard.wql-6B were not previously reported to be in genomic regions of grain hardness-related genes/QTL. The identified super-soft genotypes as well as the SNPs associated with lower grain hardness will be useful to assist breeding for this grain texture trait.

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year's dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.

Genome-wide SNP analysis to assess the genetic population structure and diversity of Acrocomia species

Acrocomia (Arecaceae) is a genus widely distributed in tropical and subtropical America that has been achieving economic interest due to the great potential of oil production of some of its species. In particular A. aculeata, due to its vocation to supply oil with the same productive capacity as the oil palm (Elaeis guineenses) even in areas with water deficit. Although eight species are recognized in the genus, the taxonomic classification based on morphology and geographic distribution is still controversial. Knowledge about the genetic diversity and population structure of the species is limited, which has limited the understanding of the genetic relationships and the orientation of management, conservation, and genetic improvement activities of species of the genus. In the present study, we analyzed the genomic diversity and population structure of Acrocomia genus, including 172 samples from seven species, with a focus on A. aculeata with 117 samples covering a wide geographical area of occurrence of the species, using Single Nucleotide Polymorphism (SNP) markers originated from Genotyping By Sequencing (GBS).The genetic structure of the Acrocomia species were partially congruent with the current taxonomic classification based on morphological characters, recovering the separation of the species A. aculeata, A. totai, A. crispa and A. intumescens as distinct taxonomic groups. However, the species A. media was attributed to the cluster of A. aculeata while A. hassleri and A. glauscescens were grouped together with A. totai. The species that showed the highest and lowest genetic diversity were A. totai and A. media, respectively. When analyzed separately, the species A. aculeata showed a strong genetic structure, forming two genetic groups, the first represented mainly by genotypes from Brazil and the second by accessions from Central and North American countries. Greater genetic diversity was found in Brazil when compared to the other countries. Our results on the genetic diversity of the genus are unprecedented, as is also establishes new insights on the genomic relationships between Acrocomia species. It is also the first study to provide a more global view of the genomic diversity of A. aculeata. We also highlight the applicability of genomic data as a reference for future studies on genetic diversity, taxonomy, evolution and phylogeny of the Acrocomia genus, as well as to support strategies for the conservation, exploration and breeding of Acrocomia species and in particular A. aculeata.

Single Nucleotide Polymorphism Discovery and Genetic Differentiation Analysis of Geese Bred in Poland, Using Genotyping-by-Sequencing (GBS)

Poland is the largest European producer of goose, while goose breeding has become an essential and still increasing branch of the poultry industry. The most frequently bred goose is the White Kołuda^® breed, constituting 95% of the country’s population, whereas geese of regional varieties are bred in smaller, conservation flocks. However, a goose’s genetic diversity is inaccurately explored, mainly because the advantages of the most commonly used tools are strongly limited in non-model organisms. One of the most accurate used markers for population genetics is single nucleotide polymorphisms (SNP). A highly efficient strategy for genome-wide SNP detection is genotyping-by-sequencing (GBS), which has been already widely applied in many organisms. This study attempts to use GBS in 12 conservative goose breeds and the White Kołuda^® breed maintained in Poland. The GBS method allowed for the detection of 3833 common raw SNPs. Nevertheless, after filtering for read depth and alleles characters, we obtained the final markers panel used for a differentiation analysis that comprised 791 SNPs. These variants were located within 11 different genes, and one of the most diversified variants was associated with the EDAR gene, which is especially interesting as it participates in the plumage development, which plays a crucial role in goose breeding.

Genetic characterization of a Sorghum bicolor multiparent mapping population emphasizing carbon-partitioning dynamics

Sorghum bicolor, a photosynthetically efficient C₄ grass, represents an important source of grain, forage, fermentable sugars, and cellulosic fibers that can be utilized in myriad applications ranging from bioenergy to bioindustrial feedstocks. Sorghum’s efficient fixation of carbon per unit time per unit area per unit input has led to its classification as a preferred biomass crop highlighted by its designation as an advanced biofuel by the U.S. Department of Energy. Due to its extensive genetic diversity and worldwide colonization, sorghum has considerable diversity for a range of phenotypes influencing productivity, composition, and sink/source dynamics. To dissect the genetic basis of these key traits, we present a sorghum carbon-partitioning nested association mapping (NAM) population generated by crossing 11 diverse founder lines with Grassl as the single recurrent female. By exploiting existing variation among cellulosic, forage, sweet, and grain sorghum carbon partitioning regimes, the sorghum carbon-partitioning NAM population will allow the identification of important biomass-associated traits, elucidate the genetic architecture underlying carbon partitioning and improve our understanding of the genetic determinants affecting unique phenotypes within Poaceae. We contrast this NAM population with an existing grain population generated using Tx430 as the recurrent female. Genotypic data are assessed for quality by examining variant density, nucleotide diversity, linkage decay, and are validated using pericarp and testa phenotypes to map known genes affecting these phenotypes. We release the 11-family NAM population along with corresponding genomic data for use in genetic, genomic, and agronomic studies with a focus on carbon-partitioning regimes.

Nested association mapping reveals the genetic architecture of spike emergence and anthesis timing in intermediate wheatgrass

Intermediate wheatgrass (Thinopyrum intermedium) is an outcrossing, cool season grass species currently undergoing direct domestication as a perennial grain crop. Though many traits are selection targets, understanding the genetic architecture of those important for local adaptation may accelerate the domestication process. Nested association mapping (NAM) has proven useful in dissecting the genetic control of agronomic traits many crop species, but its utility in primarily outcrossing, perennial species has yet to be demonstrated. Here, we introduce an intermediate wheatgrass NAM population developed by crossing ten phenotypically divergent donor parents to an adapted common parent in a reciprocal manner, yielding 1,168 F₁ progeny from 10 families. Using genotyping by sequencing, we identified 8,003 SNP markers and developed a population-specific consensus genetic map with 3,144 markers across 21 linkage groups. Using both genomewide association mapping and linkage mapping combined across and within families, we characterized the genetic control of flowering time. In the analysis of two measures of maturity across four separate environments, we detected as many as 75 significant QTL, many of which correspond to the same regions in both analysis methods across 11 chromosomes. The results demonstrate a complex genetic control that is variable across years, locations, traits, and within families. The methods were effective at detecting previously identified QTL, as well as new QTL that align closely to the well-characterized flowering time orthologs from barley, including Ppd-H1 and Constans. Our results demonstrate the utility of the NAM population for understanding the genetic control of flowering time and its potential for application to other traits of interest.

Parentage Analysis in Giant Grouper (Epinephelus lanceolatus) Using Microsatellite and SNP Markers from Genotyping-by-Sequencing Data

Pedigree information is necessary for the maintenance of diversity for wild and captive populations. Accurate pedigree is determined by molecular marker-based parentage analysis, which may be influenced by the polymorphism and number of markers, integrity of samples, relatedness of parents, or different analysis programs. Here, we described the first development of 208 single nucleotide polymorphisms (SNPs) and 11 microsatellites for giant grouper (Epinephelus lanceolatus) taking advantage of Genotyping-by-sequencing (GBS), and compared the power of SNPs and microsatellites for parentage and relatedness analysis, based on a mixed family composed of 4 candidate females, 4 candidate males and 289 offspring. CERVUS, PAPA and COLONY were used for mutually verification. We found that SNPs had a better potential for relatedness estimation, exclusion of non-parentage and individual identification than microsatellites, and > 98% accuracy of parentage assignment could be achieved by 100 polymorphic SNPs (MAF cut-off < 0.4) or 10 polymorphic microsatellites (mean H_o = 0.821, mean PIC = 0.651). This study provides a reference for the development of molecular markers for parentage analysis taking advantage of next-generation sequencing, and contributes to the molecular breeding, fishery management and population conservation.

Annual and perennial Medicago show signatures of parallel adaptation to climate and soil in highly conserved genes

Human induced environmental change may require rapid adaptation of plant populations and crops, but the genomic basis of environmental adaptation remain poorly understood. We analysed polymorphic loci from the perennial crop Medicago sativa (alfalfa or lucerne) and the annual legume model species M. truncatula to search for a common set of candidate genes that might contribute to adaptation to abiotic stress in both annual and perennial Medicago species. We identified a set of candidate genes of adaptation associated with environmental gradients along the distribution of the two Medicago species. Candidate genes for each species were detected in homologous genomic linkage blocks using genome-environment (GEA) and genome-phenotype association analyses. Hundreds of GEA candidate genes were species-specific, of these, 13.4% (M. sativa) and 24% (M. truncatula) were also significantly associated with phenotypic traits. A set of 168 GEA candidates were shared by both species, which was 25.4% more than expected by chance. When combined, they explained a high proportion of variance for certain phenotypic traits associated with adaptation. Genes with highly conserved functions dominated among the shared candidates and were enriched in gene ontology terms that have shown to play a central role in drought avoidance and tolerance mechanisms by means of cellular shape modifications and other functions associated with cell homeostasis. Our results point to the existence of a molecular basis of adaptation to abiotic stress in Medicago determined by highly conserved genes and gene functions. We discuss these results in light of the recently proposed omnigenic model of complex traits.

A robust KASP marker for selection of four pairs of linked leaf rust and stripe rust resistance genes introgressed on chromosome arm 5DS from different wheat genomes

Stripe rust and leaf rust are among the most devastating diseases of wheat, limiting its production globally. Wheat wild relatives harbour genetic diversity for new genes and alleles for all major wheat diseases. However, the use of this genetic variation from wild progenitor and non-progenitor species has been limited in the breeding programs. Reasons include limited recombination of donor and recipient genomes and the lack of tertiary gene pool markers. Here, we describe the development of a SNP based marker from the flow-sorted and sequenced Aegilops umbellulata chromosome 5U which can be used for marker assisted selection of four pair of alien leaf rust and stripe rust resistance genes. Lr57-Yr40_CAPS16 marker was reported earlier to be linked with alien leaf and stripe rust resistance genes introgressed on wheat chromosome 5DS. Due to its dominant nature and laborious to work with, a new SNP-based KASP marker, XTa5DS-2754099_kasp23, was developed from the same CAPS marker contig. XTa5DS-2754099_kasp23 was tested in Aegilops umbellulata, Ae. geniculata, Ae. peregrina and Ae. caudata derived alien introgression lines, which harbour four pairs of linked leaf and stripe rust genes; Lr76-Yr70, Lr57-Yr40, LrP- YrP, LrAc-YrAc, respectively. This KASP marker was found to be effective for the selection of the aforesaid four pairs of leaf rust and stripe rust resistance genes. Further, we tested and validated XTa5DS-2754099_kasp23 on commercial varieties and advanced breeding lines from four countries (India, Egypt, Australia and UK) including hexaploid and durum wheat. Our results provide evidence that KASP marker, XTa5DS-2754099_kasp23 can be used in marker-assisted selection of the four pairs of rust resistance alien genes in wheat breeding programmes.

Genome-wide association mapping identifies novel loci underlying fire blight resistance in apple

Fire blight, caused by epiphytotic gram-negative bacteria Erwinia amylovora, is the most destructive bacterial disease of apple (Malus spp.). Genetic mechanisms of fire blight resistance have mainly been studied using traditional biparental quantitative trait loci (QTL) mapping approaches. Here, we use large-scale historic shoot and blossom fire blight data collected over multiple years and genotyping-by-sequencing (GBS) markers to identify significant marker-trait associations in a diverse set of 566 apple [Malus domestica (Suckow) Borkh.] accessions. There was large variation in fire blight resistance and susceptibility in these accessions. We identified 23 and 38 QTL significantly (p < .001) associated with shoot and blossom blight resistance, respectively. The QTL are distributed across all 17 chromosomes of apple. Four shoot blight and 19 blossom blight QTL identified in this study colocalized with previously identified QTL associated with resistance to fire blight or apple scab. Using transcriptomics data of two apple cultivars with contrasting fire blight responses, we also identified candidate genes for fire blight resistance that are differentially expressed between resistant and susceptible cultivars and located within QTL intervals for fire blight resistance. However, further experiments are needed to confirm and validate these marker-trait associations and develop diagnostic markers before use in marker-assisted breeding to develop apple cultivars with decreased fire blight susceptibility.

Positional-based cloning 'fail-safe' approach is overpowered by wheat chromosome structural variation

Positional-based cloning is a foundational method for understanding the genes and gene networks that control valuable agronomic traits such as grain yield components. In this study, we sought to positionally clone the causal genetic variant of a 1000-grain weight (TGW) quantitative trait loci (QTL) on wheat (Triticum aestivum L.) chromosome arm 5AL. We developed heterogenous inbred families (HIFs) (>5,000 plants) for enhanced genotypic resolution and fine-mapped the QTL to a 10-Mbp region. The transcriptome of developing grains from positive and negative control HIF haplotypes revealed presence-absence chromosome arm 5AS structural variation and unexpectedly no differential expression of genes within the chromosome arm 5AL candidate region. Evaluation of genomic, transcriptomic, and phenotypic data, and predicted function of genes, identified that the 5AL QTL was the result of strong linkage disequilibrium (LD) with chromosome arm 5AS presence or absence (HIF r² = 0.91). Structural variation is common in wheat, and our results highlight that the redundant polyploid genome's masking of such variation is a significant barrier to positional cloning. We propose recommendations for more efficient and robust detection of structural variation, including transitioning from a single nucleotide polymorphism (SNP) to a haplotype-based approach to identify positional cloning targets. We also present nine candidate genes for grain yield components based on chromosome arm 5AS presence or absence, which may unveil hidden variation of homoeolog dosage-dependent genes across the group five chromosome short arms. Taken together, our discovery demonstrates the phenotypic resiliency of polyploid genomic structural variation and highlights a considerable challenge to routine positional cloning in wheat.

A genomics resource for genetics, physiology, and breeding of West African sorghum

Local landrace and breeding germplasm is a useful source of genetic diversity for regional and global crop improvement initiatives. Sorghum (Sorghum bicolor L. Moench) in western Africa (WA) has diversified across a mosaic of cultures and end uses and along steep precipitation and photoperiod gradients. To facilitate germplasm utilization, a West African sorghum association panel (WASAP) of 756 accessions from national breeding programs of Niger, Mali, Senegal, and Togo was assembled and characterized. Genotyping-by-sequencing (GBS) was used to generate 159,101 high-quality biallelic single nucleotide polymorphisms (SNPs), with 43% in intergenic regions and 13% in genic regions. High genetic diversity was observed within the WASAP (π = .00045), only slightly less than in a global diversity panel (GDP) (π = .00055). Linkage disequilibrium (LD) decayed to background level (r² < .1) by ∼50 kb in the WASAP. Genome-wide diversity was structured both by botanical type and by populations within botanical type with eight ancestral populations identified. Most populations were distributed across multiple countries, suggesting several potential common gene pools across the national programs. Genome-wide association studies (GWAS) of days to flowering (DFLo) and plant height (PH) revealed eight and three significant quantitative trait loci (QTL), respectively, with major height QTL at canonical height loci Dw3 and SbHT7.1. Colocalization of two of eight major flowering time QTL with flowering genes previously described in U.S. germplasm (Ma6 and SbCN8) suggests that photoperiodic flowering in West African sorghum is conditioned by both known and novel genes. This genomic resource provides a foundation for genomics-enabled breeding of climate-resilient varieties in WA.