Efficient genome-wide genotyping strategies and data integration in crop plants

Using genotype imputation to integrate Canola populations for genome-wide association and genomic prediction of blackleg resistance

BACKGROUND

Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10× coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated.

RESULTS

The empirical imputation accuracy (r2) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported Rsq (Rsq < 0.2) quality statistics. The r2 for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS.

CONCLUSION

It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analysis. Increasing the sample size by combining datasets showed slightly greater predictive ability and greater detection power in GWASs in the present study.

Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden)

Eucalyptus dunnii is one of the most important Eucalyptus species for short-fiber pulp production in regions where other species of the genus are affected by poor soil and climatic conditions. In this context, E. dunnii holds promise as a resource to address and adapt to the challenges of climate change. Despite its rapid growth and favorable wood properties for solid wood products, the advancement of its improvement remains in its early stages. In this work, we evaluated the performance of two single nucleotide polymorphism, (SNP), genotyping methods for population genetics analysis and Genomic Selection in E. dunnii. Double digest restriction-site associated DNA sequencing (ddRADseq) was compared with the EUChip60K array in 308 individuals from a provenance-progeny trial. The compared SNP set included 8,011 and 19,008 informative SNPs distributed along the 11 chromosomes, respectively. Although the two datasets differed in the percentage of missing data, genome coverage, minor allele frequency and estimated genetic diversity parameters, they revealed a similar genetic structure, showing two subpopulations with little differentiation between them, and low linkage disequilibrium. GS analyses were performed for eleven traits using Genomic Best Linear Unbiased Prediction (GBLUP) and a conventional pedigree-based model (ABLUP). Regardless of the SNP dataset, the predictive ability (PA) of GBLUP was better than that of ABLUP for six traits (Cellulose content, Total and Ethanolic extractives, Total and Klason lignin content and Syringyl and Guaiacyl lignin monomer ratio). When contrasting the SNP datasets used to estimate PAs, the GBLUP-EUChip60K model gave higher and significant PA values for six traits, meanwhile, the values estimated using ddRADseq gave higher values for three other traits. The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. The two genotyping methods, ddRADseq and EUChip60K, are generally comparable for population genetics and genomic prediction, demonstrating the utility of the former when subjected to rigorous SNP filtering. The results of this study provide a basis for future whole-genome studies using ddRADseq in non-model forest species for which SNP arrays have not yet been developed.

Characterization of a New Citrus Mutant Induced by Gamma Irradiation with a Unique Fruit Shape, Gwonje-Early, and Determination of Specific Selection Markers Using Allele-Specific PCR

Gamma-ray irradiation is one of the most widely used mutagens worldwide. We previously conducted mutation breeding using gamma irradiation to develop new Citrus unshiu varieties. Among these mutants, Gwonje-early had an ovate shape, a protrusion of the upper part of the fruit, and a large fruit size compared with wild-type (WT) fruits. We investigated the external/internal morphological characteristics and fruit sugar/acid content of Gwonje-early. Additionally, we investigated genome-wide single-nucleotide polymorphisms (SNPs) and insertion/deletion (InDel) variants in Gwonje-early using whole-genome re-sequencing. Functional annotation by Gene Ontology analysis confirmed that InDels were more commonly annotated than SNPs. To identify specific molecular markers for Gwonje-early, allele-specific PCR was performed using homozygous SNPs detected via Gwonje-early genome re-sequencing. The GJ-SNP1 and GJ-SNP4 primer sets were effectively able to distinguish Gwonje-early from the WT and other commercial citrus varieties, demonstrating their use as specific molecular markers for Gwonje-early. These findings also have important implications in terms of intellectual property rights and the variety protection of Gwonje-early. Our results may provide insights into the understanding of morphological traits and the molecular breeding mechanisms of citrus species.

MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison

Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.

Construction and Application of an F1-Derived Doubled-Haploid Population and High-Density Genetic Map for Ornamental Kale Breeding

Ornamental kale (Brassica oleracea var. acephala) is an attractive ornamental plant with a range of leaf colors and shapes. Breeding new varieties of ornamental kale has proven challenging due to its lengthy breeding cycle and the limited availability of genetic markers. In this study, a F1DH ornamental kale population comprising 300 DH lines was constructed using microspore culture. A high-density genetic map was developed by conducting whole-genome sequencing on 150 individuals from the F1DH population. The genetic map contained 1696 bin markers with 982,642 single-nucleotide polymorphisms (SNPs) spanning a total distance of 775.81 cM on all nine chromosomes with an average distance between markers of 0.46 cM. The ornamental kale genetic map contained substantially more SNP markers compared with published genetic maps for other B. oleracea crops. Furthermore, utilizing this high-density genetic map, we identified seven quantitative trait loci (QTLs) that significantly influence the leaf shape of ornamental kale. These findings are valuable for understanding the genetic basis of key agronomic traits in ornamental kale. The F1DH progenies provide an excellent resource for germplasm innovation and breeding new varieties of ornamental kale. Additionally, the high-density genetic map provides crucial insights for gene mapping and unraveling the molecular mechanisms behind important agronomic traits in ornamental kale.

Identification of loci associated with water use efficiency and symbiotic nitrogen fixation in soybean

Soybean (Glycine max) production is greatly affected by persistent and/or intermittent droughts in rainfed soybean-growing regions worldwide. Symbiotic N2 fixation (SNF) in soybean can also be significantly hampered even under moderate drought stress. The objective of this study was to identify genomic regions associated with shoot carbon isotope ratio (δ13C) as a surrogate measure for water use efficiency (WUE), nitrogen isotope ratio (δ15N) to assess relative SNF, N concentration ([N]), and carbon/nitrogen ratio (C/N). Genome-wide association mapping was performed with 105 genotypes and approximately 4 million single-nucleotide polymorphism markers derived from whole-genome resequencing information. A total of 11, 21, 22, and 22 genomic loci associated with δ13C, δ15N, [N], and C/N, respectively, were identified in two environments. Nine of these 76 loci were stable across environments, as they were detected in both environments. In addition to the 62 novel loci identified, 14 loci aligned with previously reported quantitative trait loci for different C and N traits related to drought, WUE, and N2 fixation in soybean. A total of 58 Glyma gene models encoding for different genes related to the four traits were identified in the vicinity of the genomic loci.

Identification of Late Ripening Citrus Mutant, Ara-unshiu (Citrus unshiu), and Its Selectable Marker

'Miyagawa-wase' mandarin (Citrus unshiu Marc. cv. Miyagawa-wase early) is one of the most widely cultivated citrus varieties on Jeju Island in Korea. Mutation breeding is a useful tool for the induction of genetic diversity for the rapid creation of new plant variants. We previously reported the use of gamma irradiation for the development of new citrus varieties. Here, we report a new mutant, Ara-unshiu, with a unique late fruit ripening phenotype. We investigated the fruit morphological characteristics including weight, vertical/transverse diameter, peel thickness, hardness, and color difference, as well as sugar and acid contents of the Ara-unshiu compared to wild-type controls. We then used whole genome re-sequencing and functional annotation by gene ontology to identify and characterize single nucleotide polymorphism (SNP) and insertion/deletion (InDel) variants in the Ara-unshiu, finding a greater abundance of annotated genes containing InDels compared to SNPs. Finally, we used allele-specific PCR to identify molecular markers among the homozygous SNPs detected from the Ara-unshiu genome sequencing. We report a primer set that effectively distinguishes the Ara-unshiu from the wild-type control and other citrus varieties. Our findings provide insights into the mechanisms controlling the timing of fruit ripening and tools for the molecular breeding of citrus varieties.

QTL Mapping and Transcriptome Analysis Reveal Candidate Genes Regulating Seed Color in Brassica napus

Yellow seeds are desirable in rapeseed breeding because of their higher oil content and better nutritional quality than black seeds. However, the underlying genes and formation mechanism of yellow seeds remain unclear. Here, a novel yellow-seeded rapeseed line (Huangaizao, HAZ) was crossed with a black-seeded rapeseed line (Zhongshuang11, ZS11) to construct a mapping population of 196 F₂ individuals, based on which, a high-density genetic linkage map was constructed. This map, comprising 4174 bin markers, was 1618.33 cM in length and had an average distance of 0.39 cM between its adjacent markers. To assess the seed color of the F₂ population, three methods (imaging, spectrophotometry, and visual scoring) were used and a common major quantitative trait locus (QTL) on chromosome A09, explaining 10.91-21.83% of the phenotypic variance, was detected. Another minor QTL, accounting for 6.19-6.69% of the phenotypic variance, was detected on chromosome C03, only by means of imaging and spectrophotometry. Furthermore, a dynamic analysis of the differential expressions between the parental lines showed that flavonoid biosynthesis-related genes were down-regulated in the yellow seed coats at 25 and 35 days after flowering. A coexpression network between the differentially expressed genes identified 17 candidate genes for the QTL intervals, including a flavonoid structure gene, novel4557 (BnaC03.TT4), and two transcription factor genes, namely, BnaA09G0616800ZS (BnaA09.NFYA8) and BnaC03G0060200ZS (BnaC03.NAC083), that may regulate flavonoid biosynthesis. Our study lays a foundation for further identifying the genes responsible for and understanding the regulatory mechanism of yellow seed formation in Brassica napus.

Semi-Thermal Asymmetric Reverse PCR (STARP) Genotyping

PCR-based individual Single nucleotide polymorphism (SNP) genotyping methods are preferred due to their flexibility, high-throughput, and improved accuracy. Semi-thermal asymmetric reverse PCR (STARP) is one of the SNP genotyping methods developed to reduce operational cost with improved platform compatibility. STARP is a unique method which can be used either as a gel-free SNP genotyping by detection of fluorescent signals or polyacrylamide gel-based size separation. SNP assay designing using sequence information and detection methods of STARP are described in detail.

3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes

Despite the increased efficiency of sequencing technologies and the development of reduced-representation sequencing (RRS) approaches allowing high-throughput sequencing (HTS) of multiplexed samples, the per-sample genotyping cost remains the most limiting factor in the context of large-scale studies. For example, in the context of genomic selection (GS), breeders need genome-wide markers to predict the breeding value of large cohorts of progenies, requiring the genotyping of thousands candidates. Here, we introduce 3D-GBS, an optimized GBS procedure, to provide an ultra-high-throughput and ultra-low-cost genotyping solution for species with small to medium-sized genome and illustrate its use in soybean. Using a combination of three restriction enzymes (PstI/NsiI/MspI), the portion of the genome that is captured was reduced fourfold (compared to a "standard" ApeKI-based protocol) while reducing the number of markers by only 40%. By better focusing the sequencing effort on limited set of restriction fragments, fourfold more samples can be genotyped at the same minimal depth of coverage. This GBS protocol also resulted in a lower proportion of missing data and provided a more uniform distribution of SNPs across the genome. Moreover, we investigated the optimal number of reads per sample needed to obtain an adequate number of markers for GS and QTL mapping (500-1000 markers per biparental cross). This optimization allows sequencing costs to be decreased by ~ 92% and ~ 86% for GS and QTL mapping studies, respectively, compared to previously published work. Overall, 3D-GBS represents a unique and affordable solution for applications requiring extremely high-throughput genotyping where cost remains the most limiting factor.

Chile Pepper (Capsicum) Breeding and Improvement in the "Multi-Omics" Era

Chile pepper (Capsicum spp.) is a major culinary, medicinal, and economic crop in most areas of the world. For more than hundreds of years, chile peppers have "defined" the state of New Mexico, USA. The official state question, "Red or Green?" refers to the preference for either red or the green stage of chile pepper, respectively, reflects the value of these important commodities. The presence of major diseases, low yields, decreased acreages, and costs associated with manual labor limit production in all growing regions of the world. The New Mexico State University (NMSU) Chile Pepper Breeding Program continues to serve as a key player in the development of improved chile pepper varieties for growers and in discoveries that assist plant breeders worldwide. Among the traits of interest for genetic improvement include yield, disease resistance, flavor, and mechanical harvestability. While progress has been made, the use of conventional breeding approaches has yet to fully address producer and consumer demand for these traits in available cultivars. Recent developments in "multi-omics," that is, the simultaneous application of multiple omics approaches to study biological systems, have allowed the genetic dissection of important phenotypes. Given the current needs and production constraints, and the availability of multi-omics tools, it would be relevant to examine the application of these approaches in chile pepper breeding and improvement. In this review, we summarize the major developments in chile pepper breeding and present novel tools that can be implemented to facilitate genetic improvement. In the future, chile pepper improvement is anticipated to be more data and multi-omics driven as more advanced genetics, breeding, and phenotyping tools are developed.

Genetic analysis of sucrose concentration in soybean seeds using a historical soybean genomic panel

KEY MESSAGE

Significant QTL for sucrose concentration have been identified using a historical soybean genomic panel, which could aid in the development of food-grade soybean cultivars. Soybean (Glycine max (L.) Merr) is a crop of global importance for both human and animal consumption, which was domesticated in China more than 6000 years ago. A concern about losing genetic diversity as a result of decades of breeding has been expressed by soybean researchers. In order to develop new cultivars, it is critical for breeders to understand the genetic variability present for traits of interest in their program germplasm. Sucrose concentration is becoming an increasingly important trait for the production of soy-food products. The objective of this study was to use a genome-wide association study (GWAS) to identify putative QTL for sucrose concentration in soybean seed. A GWAS panel consisting of 266 historic and current soybean accessions was genotyped with 76 k genotype-by-sequencing (GBS) SNP data and phenotyped in four field locations in Ontario (Canada) from 2015 to 2017. Seven putative QTL were identified on chromosomes 1, 6, 8, 9, 10, 13 and 14. A key gene related to sucrose synthase (Glyma.06g182700) was found to be associated with the QTL located on chromosome 6. This information will facilitate efforts to increase the available genetic variability for sucrose concentration in soybean breeding programs and develop new and improved high-sucrose soybean cultivars suitable for the soy-food industry.

Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies

Growing genomic and phenotypic datasets require different groups around the world to collaborate and integrate these valuable resources to maximize their benefit and increase reference population sizes for genomic prediction and genome-wide association studies (GWAS). However, different studies use different genotyping techniques which requires a synchronizing step for the genotyped variants called "imputation" before combining them. Optimally, different GWAS datasets can be analysed within a meta-analysis, which recruits summary statistics instead of actual data. This chapter describes the general principles for genotypic imputation and meta-GWAS analysis with a description of study designs and command lines required for such analyses.

Genome-Wide Association Study Statistical Models: A Review

Statistical models are at the core of the genome-wide association study (GWAS). In this chapter, we provide an overview of single- and multilocus statistical models, Bayesian, and machine learning approaches for association studies in plants. These models are discussed based on their basic methodology, cofactors adjustment accounted for, statistical power and computational efficiency. New statistical models and machine learning algorithms are both showing improved performance in detecting missed signals, rare mutations and prioritizing causal genetic variants; nevertheless, further optimization and validation studies are required to maximize the power of GWAS.

Genome-wide Imputation Using the Practical Haplotype Graph in the Heterozygous Crop Cassava

Genomic applications such as genomic selection and genome-wide association have become increasingly common since the advent of genome sequencing. The cost of sequencing has decreased in the past two decades; however, genotyping costs are still prohibitive to gathering large datasets for these genomic applications, especially in nonmodel species where resources are less abundant. Genotype imputation makes it possible to infer whole-genome information from limited input data, making large sampling for genomic applications more feasible. Imputation becomes increasingly difficult in heterozygous species where haplotypes must be phased. The practical haplotype graph (PHG) is a recently developed tool that can accurately impute genotypes, using a reference panel of haplotypes. We showcase the ability of the PHG to impute genomic information in the highly heterozygous crop cassava (Manihot esculenta). Accurately phased haplotypes were sampled from runs of homozygosity across a diverse panel of individuals to populate PHG, which proved more accurate than relying on computational phasing methods. The PHG achieved high imputation accuracy, using sparse skim-sequencing input, which translated to substantial genomic prediction accuracy in cross-validation testing. The PHG showed improved imputation accuracy, compared to a standard imputation tool Beagle, especially in predicting rare alleles.

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.

Conservation implications of genetic structure in the narrowest endemic quillwort from the Eastern Amazon

The quillwort Isoëtes cangae is a critically endangered species occurring in a single lake in Serra dos Carajás, Eastern Amazon. Low genetic diversity and small effective population sizes (N e) are expected for narrow endemic species (NES). Conservation biology studies centered in a single species show some limitations, but they are still useful considering the limited time and resources available for protection of species at risk of extinction. Here, we evaluated the genetic diversity, population structure, N e, and minimum viable population (MVP) of I. cangae to provide information for effective conservation programs. Our analyses were based on 55 individuals collected from the Amendoim Lake and 35,638 neutral SNPs. Our results indicated a single panmictic population, moderate levels of genetic diversity, and N e in the order of thousands, contrasting the expected for NES. Negative FIS values were also found, suggesting that I. cangae is not under risk of inbreeding depression. Our findings imply that I. cangae contains enough genetic diversity to ensure evolutionary potential and that all individuals should be treated as one demographic unit. These results provide essential information to optimize ex situ conservation efforts and genetic diversity monitoring, which are currently applied to guide I. cangae conservation plans.

Natural Genetic Diversity in Tomato Flavor Genes

Fruit flavor is defined as the perception of the food by the olfactory and gustatory systems, and is one of the main determinants of fruit quality. Tomato flavor is largely determined by the balance of sugars, acids and volatile compounds. Several genes controlling the levels of these metabolites in tomato fruit have been cloned, including LIN5, ALMT9, AAT1, CXE1, and LoxC. The aim of this study was to identify any association of these genes with trait variation and to describe the genetic diversity at these loci in the red-fruited tomato clade comprised of the wild ancestor Solanum pimpinellifolium, the semi-domesticated species Solanum lycopersicum cerasiforme and early domesticated Solanum lycopersicum. High genetic diversity was observed at these five loci, including novel haplotypes that could be incorporated into breeding programs to improve fruit quality of modern tomatoes. Using newly available high-quality genome assemblies, we assayed each gene for potential functional causative polymorphisms and resolved a duplication at the LoxC locus found in several wild and semi-domesticated accessions which caused lower accumulation of lipid derived volatiles. In addition, we explored gene expression of the five genes in nine phylogenetically diverse tomato accessions. In general, the expression patterns of these genes increased during fruit ripening but diverged between accessions without clear relationship between expression and metabolite levels.

Advances and Perspectives in Tissue Culture and Genetic Engineering of Cannabis

For a long time, Cannabis sativa has been used for therapeutic and industrial purposes. Due to its increasing demand in medicine, recreation, and industry, there is a dire need to apply new biotechnological tools to introduce new genotypes with desirable traits and enhanced secondary metabolite production. Micropropagation, conservation, cell suspension culture, hairy root culture, polyploidy manipulation, and Agrobacterium-mediated gene transformation have been studied and used in cannabis. However, some obstacles such as the low rate of transgenic plant regeneration and low efficiency of secondary metabolite production in hairy root culture and cell suspension culture have restricted the application of these approaches in cannabis. In the current review, in vitro culture and genetic engineering methods in cannabis along with other promising techniques such as morphogenic genes, new computational approaches, clustered regularly interspaced short palindromic repeats (CRISPR), CRISPR/Cas9-equipped Agrobacterium-mediated genome editing, and hairy root culture, that can help improve gene transformation and plant regeneration, as well as enhance secondary metabolite production, have been highlighted and discussed.

Genome-Wide Identification of Barley Long Noncoding RNAs and Analysis of Their Regulatory Interactions during Shoot and Grain Development

Long noncoding RNAs (lncRNAs) are a class of RNA molecules with gene regulatory functions in plant development and the stress response. Although the number of lncRNAs identified in plants is rapidly increasing, very little is known about their role in barley development. In this study, we performed global identification of barley lncRNAs based on 53 RNAseq libraries derived from nine different barley tissues and organs. In total, 17,250 lncRNAs derived from 10,883 loci were identified, including 8954 novel lncRNAs. Differential expression of lncRNAs was observed in the developing shoot apices and grains, the two organs that have a direct influence on the final yield. The regulatory interaction of differentially expressed lncRNAs with the potential target genes was evaluated. We identified 176 cis-acting lncRNAs in shoot apices and 424 in grains, while the number of trans-acting lncRNAs in these organs was 1736 and 540, respectively. The potential target protein-coding genes were identified, and their biological function was annotated using MapMan ontology. This is the first insight into the roles of lncRNAs in barley development on the genome-wide scale, and our results provide a solid background for future functional studies.

Accurate Imputation of Untyped Variants from Deep Sequencing Data

The quality, statistical power, and resolution of genome-wide association studies (GWAS) are largely dependent on the comprehensiveness of genotypic data. Over the last few years, despite the constant decrease in the price of sequencing, whole-genome sequencing (WGS) of association panels comprising a large number of samples remains cost-prohibitive. Therefore, most GWAS populations are still genotyped using low-coverage genotyping methods resulting in incomplete datasets. Imputation of untyped variants is a powerful method to maximize the number of SNPs identified in study samples, it increases the power and resolution of GWAS and allows to integrate genotyping datasets obtained from various sources. Here, we describe the key concepts underlying imputation of untyped variants, including the architecture of reference panels, and review some of the associated challenges and how these can be addressed. We also discuss the need and available methods to rigorously assess the accuracy of imputed data prior to their use in any genetic study.

Machine learning in plant science and plant breeding

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

Genome-wide association identifies several QTLs controlling cysteine and methionine content in soybean seed including some promising candidate genes

Soybean is an important source of protein, oil and carbohydrates, as well as other beneficial nutrients. A major function of proteins in nutrition is to supply adequate amounts of amino acids. Although they are essential for human nutrition, the sulfur-containing amino acids cysteine (Cys) and methionine (Met) are often limited and the genetic control of their content in soybean seeds is poorly characterized. This study aimed to characterize the phenotypic variation and identify quantitative trait loci (QTL) associated with Cys and Met content in a core set of 137 soybean lines, representative of the genetic diversity among Canadian short-season soybean, spanning maturity groups 000-II (MG000-II). Significant phenotypic differences were found among these lines for Cys, Met and Cys + Met content. Using both a mixed linear model and six multi-locus methods with a catalogue of 2.18 M SNPs, we report a total of nine QTLs and seventeen QTNs of which seven comprise promising candidate genes. This work allowed us to reproducibly detect multiple novel loci associated with sulfur-containing amino acid content. The markers and genes identified in this study may be useful for soybean genetic improvement aiming to increase Cys and Met content.

Population structure of Nepali spring wheat (Triticum aestivum L.) germplasm

BACKGROUND

Appropriate information about genetic diversity and population structure of germplasm improves the efficiency of plant breeding. The low productivity of Nepali bread wheat (Triticum aestivum L.) is a major concern particularly since Nepal is ranked the 4th most vulnerable nation globally to climate change. The genetic diversity and population structure of Nepali spring wheat have not been reported. This study aims to improve the exploitation of more diverse and under-utilized genetic resources to contribute to current and future breeding efforts for global food security.

RESULTS

We used genotyping-by-sequencing (GBS) to characterize a panel of 318 spring wheat accessions from Nepal including 166 landraces, 115 CIMMYT advanced lines, and 34 Nepali released varieties. We identified 95 K high-quality SNPs. The greatest genetic diversity was observed among the landraces, followed by CIMMYT lines, and released varieties. Though we expected only 3 groupings corresponding to these 3 seed origins, the population structure revealed two large, distinct subpopulations along with two smaller and scattered subpopulations in between, with significant admixture. This result was confirmed by principal component analysis (PCA) and UPGMA distance-based clustering. The pattern of LD decay differed between subpopulations, ranging from 60 to 150 Kb. We discuss the possibility that germplasm explorations during the 1970s-1990s may have mistakenly collected exotic germplasm instead of local landraces and/or collected materials that had already cross-hybridized since exotic germplasm was introduced starting in the 1950s.

CONCLUSION

We suggest that only a subset of wheat "landraces" in Nepal are authentic which this study has identified. Targeting these authentic landraces may accelerate local breeding programs to improve the food security of this climate-vulnerable nation. Overall, this study provides a novel understanding of the genetic diversity of wheat in Nepal and this may contribute to global wheat breeding initiatives.

Fast-GBS v2.0: an analysis toolkit for genotyping-by-sequencing data

Genotyping-by-sequencing (GBS) is a rapid, flexible, low-cost, and robust genotyping method that simultaneously discovers variants and calls genotypes within a broad range of samples. These characteristics make GBS an excellent tool for many applications and research questions from conservation biology to functional genomics in both model and non-model species. Continued improvement of GBS relies on a more comprehensive understanding of data analysis, development of fast and efficient bioinformatics pipelines, accurate missing data imputation, and active post-release support. Here, we present the second generation of Fast-GBS (v2.0) that offers several new options (e.g., processing paired-end reads and imputation of missing data) and features (e.g., summary statistics of genotypes) to improve the GBS data analysis process. The performance assessment analysis showed that Fast-GBS v2.0 outperformed other available analytical pipelines, such as GBS-SNP-CROP and Gb-eaSy. Fast-GBS v2.0 provides an analysis platform that can be run with different types of sequencing data, modest computational resources, and allows for missing-data imputation for various species in different contexts.

Identification of loci controlling mineral element concentration in soybean seeds

BACKGROUND

Mineral nutrients play a crucial role in the biochemical and physiological functions of biological systems. The enhancement of seed mineral content via genetic improvement is considered as the most promising and cost-effective approach compared alternative means for meeting the dietary needs. The overall objective of this study was to perform a GWAS of mineral content (Ca, K, P and S) in seeds of a core set of 137 soybean lines that are representative of the diversity of early maturing soybeans cultivated in Canada (maturity groups 000-II).

RESULTS

This panel of 137 soybean lines was grown in five environments (in total) and the seed mineral content was measured using a portable x-ray fluorescence (XRF) spectrometer. The association analyses were carried out using three statistical models and a set of 2.2 million SNPs obtained from a combined dataset of genotyping-by-sequencing and whole-genome sequencing. Eight QTLs significantly associated with the Ca, K, P and S content were identified by at least two of the three statistical models used (in two environments) contributing each from 17 to 31% of the phenotypic variation. A strong reproducibility of the effect of seven out these eight QTLs was observed in three other environments. In total, three candidate genes were identified involved in transport and assimilation of these mineral elements.

CONCLUSIONS

There have been very few GWAS studies to identify QTLs associated with the mineral element content of soybean seeds. In addition to being new, the QTLs identified in this study and candidate genes will be useful for the genetic improvement of soybean nutritional quality through marker-assisted selection. Moreover, this study also provides details on the range of phenotypic variation encountered within the Canadian soybean germplasm.

DepthFinder: a tool to determine the optimal read depth for reduced-representation sequencing

MOTIVATION

Identification of DNA sequence variations such as single nucleotide polymorphisms (SNPs) is a fundamental step toward genetic studies. Reduced-representation sequencing methods have been developed as alternatives to whole genome sequencing to reduce costs and enable the analysis of many more individual. Amongst these methods, restriction site associated sequencing (RSAS) methodologies have been widely used for rapid and cost-effective discovery of SNPs and for high-throughput genotyping in a wide range of species. Despite the extensive improvements of the RSAS methods in the last decade, the estimation of the number of reads (i.e. read depth) required per sample for an efficient and effective genotyping remains mostly based on trial and error.

RESULTS

Herein we describe a bioinformatics tool, DepthFinder, designed to estimate the required read counts for RSAS methods. To illustrate its performance, we estimated required read counts in six different species (human, cattle, spruce budworm, salmon, barley and soybean) that cover a range of different biological (genome size, level of genome complexity, level of DNA methylation and ploidy) and technical (library preparation protocol and sequencing platform) factors. To assess the prediction accuracy of DepthFinder, we compared DepthFinder-derived results with independent datasets obtained from an RSAS experiment. This analysis yielded estimated accuracies of nearly 94%. Moreover, we present DepthFinder as a powerful tool to predict the most effective size selection interval in RSAS work. We conclude that DepthFinder constitutes an efficient, reliable and useful tool for a broad array of users in different research communities.

AVAILABILITY AND IMPLEMENTATION

https://bitbucket.org/jerlar73/DepthFinder.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

NanoGBS: A Miniaturized Procedure for GBS Library Preparation

High-throughput reduced-representation sequencing (RRS)-based genotyping methods, such as genotyping-by-sequencing (GBS), have provided attractive genotyping solutions in numerous species. Here, we present NanoGBS, a miniaturized and eco-friendly method for GBS library construction. Using acoustic droplet ejection (ADE) technology, NanoGBS libraries were constructed in tenfold smaller volumes compared to standard methods (StdGBS) and leading to a reduced use of plastics of up to 90%. A high-quality DNA library and SNP catalogue were obtained with extensive overlap (96%) in SNP loci and 100% agreement in genotype calls compared to the StdGBS dataset with a high level of accuracy (98.5%). A highly multiplexed pool of GBS libraries (768-plex) was sequenced on a single Ion Proton PI chip and yielded enough SNPs (~4K SNPs; 1.5 SNP per cM, on average) for many high-volume applications. Combining NanoGBS library preparation and increased multiplexing can dramatically reduce (72%) genotyping cost per sample. We believe that this approach will greatly facilitate the adoption of marker applications where extremely high throughputs are required and cost is still currently limiting.

Comprehensive Genome-Wide Association Analysis Reveals the Genetic Basis of Root System Architecture in Soybean

Increasing the understanding genetic basis of the variability in root system architecture (RSA) is essential to improve resource-use efficiency in agriculture systems and to develop climate-resilient crop cultivars. Roots being underground, their direct observation and detailed characterization are challenging. Here, were characterized twelve RSA-related traits in a panel of 137 early maturing soybean lines (Canadian soybean core collection) using rhizoboxes and two-dimensional imaging. Significant phenotypic variation (P < 0.001) was observed among these lines for different RSA-related traits. This panel was genotyped with 2.18 million genome-wide single-nucleotide polymorphisms (SNPs) using a combination of genotyping-by-sequencing and whole-genome sequencing. A total of 10 quantitative trait locus (QTL) regions were detected for root total length and primary root diameter through a comprehensive genome-wide association study. These QTL regions explained from 15 to 25% of the phenotypic variation and contained two putative candidate genes with homology to genes previously reported to play a role in RSA in other species. These genes can serve to accelerate future efforts aimed to dissect genetic architecture of RSA and breed more resilient varieties.

Single-nucleotide polymorphism (SNP) genotyping assays for the varietal authentication of 'Nebbiolo' musts and wines

'Nebbiolo' (Vitis vinifera L.) is renowned for its use in producing monovarietal high-quality red wines, such Barolo and Barbaresco. The fight against fraud to safeguard high-quality productions requires an effective varietal identification system applicable in musts and wines. 'Nebbiolo'-specific single-nucleotide polymorphisms (SNPs) were identified starting from available databases and 260 genotypes analysed by Vitis18kSNP array. Two SNPs were sufficient to identify 'Nebbiolo' from 1157 genotypes. The SNP TaqMan® genotyping assays developed in this work successfully identified 'Nebbiolo' in all musts and wines collected at different experimental wine-making steps. The high sensitivity of the assays allowed identification of must mixtures at 1% and wine mixtures at 10-20% with non-'Nebbiolo' genotypes. In commercial wines, the amplification efficiency was limited by the low amount of grapevine DNA and the presence of PCR inhibitors. The TaqMan® genotyping assay is a rapid, highly sensitive and specific methodology with remarkable potential for varietal identification in wines.

LD-annot: A Bioinformatics Tool to Automatically Provide Candidate SNPs With Annotations for Genetically Linked Genes

A multitude of model and non-model species studies have now taken full advantage of powerful high-throughput genotyping advances such as SNP arrays and genotyping-by-sequencing (GBS) technology to investigate the genetic basis of trait variation. However, due to incomplete genome coverage by these technologies, the identified SNPs are likely in linkage disequilibrium (LD) with the causal polymorphisms, rather than be causal themselves. In addition, researchers could benefit from annotations for the identified candidate SNPs and, simultaneously, for all neighboring genes in genetic linkage. In such case, LD extent estimation surrounding the candidate SNPs is required to determine the regions encompassing genes of interest. We describe here an automated pipeline, “LD-annot,” designed to delineate specific regions of interest for a given experiment and candidate polymorphisms on the basis of LD extent, and furthermore, provide annotations for all genes within such regions. LD-annot uses standard file formats, bioinformatics tools, and languages to provide identifiers, coordinates, and annotations for genes in genetic linkage with each candidate polymorphism. Although the focus lies upon SNP arrays and GBS data as they are being routinely deployed, this pipeline can be applied to a variety of datasets as long as genotypic data are available for a high number of polymorphisms and formatted into a vcf file. A checkpoint procedure in the pipeline allows to test several threshold values for linkage without having to rerun the entire pipeline, thus saving the user computational time and resources. We applied this new pipeline to four different sample sets: two breeding populations GBS datasets, one within-pedigree SNP set coming from whole genome sequencing (WGS), and a very large multi-varieties SNP dataset obtained from WGS, representing variable sample sizes, and numbers of polymorphisms. LD-annot performed within minutes, even when very high numbers of polymorphisms are investigated and thus will efficiently assist research efforts aimed at identifying biologically meaningful genetic polymorphisms underlying phenotypic variation. LD-annot tool is available under a GPL license from https://github.com/ArnaudDroitLab/LD-annot.

Genome-wide genetic diversity is maintained through decades of soybean breeding in Canada

KEY MESSAGE

Genetic diversity in Canadian soybean is maintained over decades of selection in two public breeding programs. Breeders have used a portion of the genetic diversity available in germplasm collections. Both public and private breeding efforts have been critical for the development of soybean cultivars grown around the world. Global genetic diversity of soybean has been well characterized; however, this diversity is not well studied at the breeding program scale. The objective of this study was to characterize genetic diversity over decades of breeding in two public soybean breeding programs at the University of Guelph, Canada. To address this objective, a pedigree-related panel combining 296 soybean accessions from the Ridgetown and Guelph Campus breeding programs was studied. The accessions were genotyped using genotyping-by-sequencing, imputed using the GmHapMap reference genotypes resulting in more than 3.8M SNPs, further filtered to 77k SNPs. Population structure analysis did not identify structure between the breeding programs and historical germplasm. The linkage disequilibrium decay ranged from 400 to 600 kb on average in euchromatic regions. Nucleotide diversity over decades of breeding shows that historical accessions had the highest nucleotide diversity, with significant decreases corresponding to the initial breeding activity in Canada; however, genetic diversity has increased in the last 20 years in both breeding programs. Maturity gene E2 was nearly fixed for e2 in Ridgetown accessions, while unfixed in Guelph accessions. Comparison of the breeding programs to the USDA germplasm collection reveals that breeders have only used a portion of the available genetic diversity, allowing future breeders to exploit this untapped resource. The approach used in this study may be of interest to other breeding programs for evaluating changes in genetic diversity resulting from breeding activities.

A high-quality cucumber genome assembly enhances computational comparative genomics

Genetic variation is expressed by the presence of polymorphisms in compared genomes of individuals that can be transferred to next generations. The aim of this work was to reveal genome dynamics by predicting polymorphisms among the genomes of three individuals of the highly inbred B10 cucumber (Cucumis sativus L.) line. In this study, bioinformatic comparative genomics was used to uncover cucumber genome dynamics (also called real-time evolution). We obtained a new genome draft assembly from long single molecule real-time (SMRT) sequencing reads and used short paired-end read data from three individuals to analyse the polymorphisms. Using this approach, we uncovered differentiation aspects in the genomes of the inbred B10 line. The newly assembled genome sequence (B10v3) has the highest contiguity and quality characteristics among the currently available cucumber genome draft sequences. Standard and newly designed approaches were used to predict single nucleotide and structural variants that were unique among the three individual genomes. Some of the variant predictions spanned protein-coding genes and their promoters, and some were in the neighbourhood of annotated interspersed repetitive elements, indicating that the highly inbred homozygous plants remained genetically dynamic. This is the first bioinformatic comparative genomics study of a single highly inbred plant line. For this project, we developed a polymorphism prediction method with optimized precision parameters, which allowed the effective detection of small nucleotide variants (SNVs). This methodology could significantly improve bioinformatic pipelines for comparative genomics and thus has great practical potential in genomic metadata handling.

Optimizing ddRADseq in Non-Model Species: A Case Study in Eucalyptus dunnii Maiden

Restriction site-associated DNA sequencing (RADseq) and its derived protocols, such as double digest RADseq (ddRADseq), offer a flexible and highly cost-effective strategy for efficient plant genome sampling. This has become one of the most popular genotyping approaches for breeding, conservation, and evolution studies in model and non-model plant species. However, universal protocols do not always adapt well to non-model species. Herein, this study reports the development of an optimized and detailed ddRADseq protocol in Eucalyptus dunnii, a non-model species, which combines different aspects of published methodologies. The initial protocol was established using only two samples by selecting the best combination of enzymes and through optimal size selection and simplifying lab procedures. Both single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) were determined with high accuracy after applying stringent bioinformatics settings and quality filters, with and without a reference genome. To scale it up to 24 samples, we added barcoded adapters. We also applied automatic size selection, and therefore obtained an optimal number of loci, the expected SNP locus density, and genome-wide distribution. Reliability and cross-sequencing platform compatibility were verified through dissimilarity coefficients of 0.05 between replicates. To our knowledge, this optimized ddRADseq protocol will allow users to go from the DNA sample to genotyping data in a highly accessible and reproducible way.

Next-Generation Sequencing Accelerates Crop Gene Discovery

The identification and isolation of genes underlying quantitative trait loci (QTLs) associated with agronomic traits in crops have been recently accelerated thanks to next-generation sequencing (NGS)-based technologies combined with plant genetics. With NGS, various revisited genetic approaches, which benefited from higher marker density, have been elaborated. These approaches improved resolution in QTL position and assisted in determining functional causative variations in genes. Examples of QTLs/genes associated with agronomic traits in crops and identified using different strategies based on whole-genome sequencing (WGS)/whole-genome resequencing (WGR) or RNA-seq are presented and discussed in this review. More specifically, we summarize and illustrate how NGS boosted bulk-segregant analysis (BSA), expression profiling, and the construction of polymorphism databases to facilitate the detection of QTLs and causative genes.

SRG extractor: a skinny reference genome approach for reduced-representation sequencing

Motivation

Reduced-representation sequencing is a genome-wide scanning method for simultaneous discovery and genotyping of thousands to millions of single nucleotide polymorphisms that is used across a wide range of species. However, in this method a reproducible but very small fraction of the genome is captured for sequencing, while the resulting reads are typically aligned against the entire reference genome.

Results

Here we present a skinny reference genome approach in which a simplified reference genome is used to decrease computing time for data processing and to increase single nucleotide polymorphism counts and accuracy. A skinny reference genome can be integrated into any reduced-representation sequencing analytical pipeline.

Availability and implementation

https://bitbucket.org/jerlar73/SRG-Extractor.

Supplementary information

Supplementary data are available at Bioinformatics online.

Improving and Maintaining Winter Hardiness and Frost Tolerance in Bread Wheat by Genomic Selection

Winter hardiness is a major constraint for autumn sown crops in temperate regions, and thus an important breeding goal in the development of new winter wheat varieties. Winter hardiness is though influenced by many environmental factors rendering phenotypic selection under field conditions a difficult task due to irregular occurrence or absence of winter damage in field trials. Controlled frost tolerance tests in growth chamber experiments are, on the other hand, even with few genotypes, often costly and laborious, which makes a genomic breeding strategy for early generation selection an attractive alternative. The aims of this study were thus to compare the merit of marker-assisted selection using the major frost tolerance QTL Fr-A2 with genomic prediction for winter hardiness and frost tolerance, and to assess the potential of combining both measures with a genomic selection index using a high density marker map or a reduced set of pre-selected markers. Cross-validation within two training populations phenotyped for frost tolerance and winter hardiness underpinned the importance of Fr-A2 for frost tolerance especially when upweighting its effect in genomic prediction models, while a combined genomic selection index increased the prediction accuracy for an independent validation population in comparison to training with winter hardiness data alone. The prediction accuracy could moreover be maintained with pre-selected marker sets, which is highly relevant when employing cost reducing fingerprinting techniques such as targeted genotyping-by-sequencing. Genomic selection showed thus large potential to improve or maintain the performance of winter wheat for these difficult, costly, and laborious to phenotype traits.