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

Somatic Mutation Accumulations in Micropropagated Cannabis Are Proportional to the Number of Subcultures

Advancements in micropropagation techniques have made it easier to produce large numbers of cannabis clones, but these methods may also introduce genetic instability over successive generations. This instability often manifests as somaclonal variation, characterized by the progressive accumulation of genetic mutations or epigenetic alterations with each subculture. In this study, we examined how mutations accumulate in cannabis clones subjected to 6-11 subcultures. Using genotyping-by-sequencing, we identified 9405 polymorphic variants across 70 clones. The analysis revealed a correlation between the number of subcultures and the frequency of these mutations, revealing that genetic changes accumulate over successive subcultures despite clones sharing the same chronological age. Furthermore, we evaluated the functional impacts of accumulated mutations, with particular attention to implications on gene function and overall plant health. While rare, 14 high-impact variants were identified in genes that are important for plant development. Notably, six variants were also found in genes related to cannabinoid and terpene synthesis pathways, potentially affecting the plant's biochemical composition. These findings highlight the need for genetic assessments in micropropagation protocols, impacting plant breeding and conservation. Understanding genetic variations in clonally propagated plants optimizes practices for stability. Crucial for cannabis and horticultural plants, it emphasizes techniques to prevent genetic decay and ensure viability.

Genetic insights into agronomic and morphological traits of drug-type cannabis revealed by genome-wide association studies

Cannabis sativa L., previously concealed by prohibition, is now a versatile and promising plant, thanks to recent legalization, opening doors for medical research and industry growth. However, years of prohibition have left the Cannabis research community lagging behind in understanding Cannabis genetics and trait inheritance compared to other major crops. To address this gap, we conducted a comprehensive genome-wide association study (GWAS) of nine key agronomic and morphological traits, using a panel of 176 drug-type Cannabis accessions from the Canadian legal market. Utilizing high-density genotyping-by-sequencing (HD-GBS), we successfully generated dense genotyping data in Cannabis, resulting in a catalog of 800 K genetic variants, of which 282 K common variants were retained for GWAS analysis. Through GWAS analysis, we identified 18 markers significantly associated with agronomic and morphological traits. Several identified markers exert a substantial phenotypic impact, guided us to putative candidate genes that reside in high linkage-disequilibrium (LD) with the markers. These findings lay a solid foundation for an innovative cannabis research, leveraging genetic markers to inform breeding programs aimed at meeting diverse needs in the industry.

The Effect of Genome Parametrization and SNP Marker Subsetting on Genomic Selection in Autotetraploid Alfalfa

BACKGROUND

Alfalfa, the most economically important forage legume worldwide, features modest genetic progress due to long selection cycles and the extent of the non-additive genetic variance associated with its autotetraploid genome.

METHODS

To improve the efficiency of genomic selection in alfalfa, we explored the effects of genome parametrization (as tetraploid and diploid dosages, plus allele ratios) and SNP marker subsetting (all available SNPs, only genic regions, and only non-genic regions) on genomic regressions, together with various levels of filtering on reading depth and missing rates. We used genotyping by sequencing-generated data and focused on traits of different genetic complexity, i.e., dry biomass yield in moisture-favorable (FE) and drought stress (SE) environments, leaf size, and the onset of flowering, which were assessed in 143 genotyped plants from a genetically broad European reference population and their phenotyped half-sib progenies.

RESULTS

On average, the allele ratio improved the predictive ability compared with other genome parametrizations (+7.9% vs. tetraploid dosage, +12.6% vs. diploid dosage), while using all the SNPs offered an advantage compared with any specific SNP subsetting (+3.7% vs. genic regions, +7.6% vs. non-genic regions). However, when focusing on specific traits, different combinations of genome parametrization and subsetting achieved better performances. We also released Legpipe2, an SNP calling pipeline tailored for reduced representation (GBS, RAD) in medium-sized genotyping experiments.

Bringing Barley Back: Analysis of Heritage Varieties for Use as Germplasm Sources to Improve Resistance against the Most Devastating, Contemporary Disease in Canada, Fusarium Head Blight (Fusarium graminearum)

Fusarium head blight (FHB), caused by Fusarium graminearum, is currently the most devastating disease for barley (Hordeum vulgare) in Canada. Associated mycotoxins can compromise grain quality, where deoxynivalenol (DON) is considered particularly damaging due to its frequency of detection. Breeding barley with a lower DON content is difficult, due to the poor adaptation and malt quality of resistance sources. A set of European-derived heritage varieties were screened in an FHB nursery in Charlottetown, PE, with selections tested at Brandon, MB, between 2018-2022. Genetic evaluation demonstrated a distinct clustering of Canadian varieties from the heritage set. At Brandon, 72% of the heritage varieties ranked lower for DON content than did the moderately resistant Canadian check 'AAC Goldman', but resistance was associated with later heading and taller stature. In contrast with Canadian modern malting variety 'AAC Synergy', general deficiencies were observed in yield, enzyme activity, and extract, along with higher protein content. Nonetheless, several resistant varieties were identified with reasonable a heading date and yield, including 'Chevallier Chile', 'Domen', 'Djugay', 'Hannchen', 'Heils Franken', 'Moravian Barley', 'Loosdorfer' with 'Golden Melon', 'Nutans Moskva', and 'Vellavia', these being some of the most promising varieties when malting quality characteristics were also considered. These heritage resources could be used as parents in breeding to develop FHB-resistant malting barley varieties.

Population structure analysis to explore genetic diversity and geographical distribution characteristics of wild tea plant in Guizhou Plateau

BACKGROUND

Tea, the second largest consumer beverage in the world after water, is widely cultivated in tropical and subtropical areas. However, the effect of environmental factors on the distribution of wild tea plants is unclear.

RESULTS

A total of 159 wild tea plants were collected from different altitudes and geological types of the Guizhou Plateau. Using the genotyping-by-sequencing method, 98,241 high-quality single nucleotide polymorphisms were identified. Genetic diversity, population structure analysis, principal component analysis, phylogenetic analysis, and linkage disequilibrium were performed. The genetic diversity of the wild tea plant population from the Silicate Rock Classes of Camellia gymnogyna was higher than that from the Carbonate Rock Classes of Camellia tachangensis. In addition, the genetic diversity of wild tea plants from the second altitude gradient was significantly higher than that of wild tea plants from the third and first altitude gradients. Two inferred pure groups (GP01 and GP02) and one inferred admixture group (GP03) were identified by population structure analysis and were verified by principal component and phylogenetic analyses. The highest differentiation coefficients were determined for GP01 vs. GP02, while the lowest differentiation coefficients were determined for GP01 vs. GP03.

CONCLUSIONS

This study revealed the genetic diversity and geographical distribution characteristics of wild tea plants in the Guizhou Plateau. There are significant differences in genetic diversity and evolutionary direction between Camellia tachangensis with Carbonate Rock Classes at the first altitude gradient and Camellia gymnogyna with Silicate Rock Classes at the third altitude gradient. Geological environment, soil mineral element content, soil pH, and altitude markedly contributed to the genetic differentiation between Camellia tachangensis and Camellia gymnogyna.

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.

Optimizing genomic selection in soybean: An important improvement in agricultural genomics

Fast-paced yield improvement in strategic crops such as soybean is pivotal for achieving sustainable global food security. Precise genomic selection (GS), as one of the most effective genomic tools for recognizing superior genotypes, can accelerate the efficiency of breeding programs through shortening the breeding cycle, resulting in significant increases in annual yield improvement. In this study, we investigated the possible use of haplotype-based GS to increase the prediction accuracy of soybean yield and its component traits through augmenting the models by using sophisticated machine learning algorithms and optimized genetic information. The results demonstrated up to a 7% increase in the prediction accuracy when using haplotype-based GS over the full single nucleotide polymorphisms-based GS methods. In addition, we discover an auspicious haplotype block on chromosome 19 with significant impacts on yield and its components, which can be used for screening climate-resilient soybean genotypes with improved yield in large breeding populations.

Identification of quantitative trait loci associated with seed quality traits between Canadian and Ukrainian mega-environments using genome-wide association study

KEY MESSAGE

Identifying QTL associated with soybean seed quality traits from a diverse GWAS panel cultivated in Canadian and Ukrainian mega-environments may facilitate future cultivar development for foreign markets. Understanding the complex genetic basis of seed quality traits for soybean in the mega-environments (MEs) is critical for developing a marker-assisted selection program that will lead to breeding superior cultivars adapted to specific regions. This study aimed to analyze the accumulation of 14 soybean seed quality traits in Canadian ME and two seed quality traits in Ukrainian ME and identify associated ME specific quantitative trait loci (QTLSP) and ME universal QTL (QTLU) for protein and oil using a genome-wide association study (GWAS) panel consisting of 184 soybean genotypes. The panel was planted in three locations in Canada and two locations in Ukraine in 2018 and 2019. Genotype plus genotype-by-environment biplot analysis was conducted to assess the accumulation of individual seed compounds across different locations. The protein accumulation was high in the Canadian ME and low in the Ukrainian ME, whereas the oil concentration showed the opposite trends between the two MEs. No QTLU were identified across the MEs for protein and oil concentrations. In contrast, nine Canadian QTLSP for protein were identified on various chromosomes, which were co-located with QTL controlling other traits identified in the Canadian ME. The lack of common QTLU for protein and oil suggests that it may be necessary to use QTLSP associated with these traits separately for the Canadian and Ukrainian ME. Additional Ukrainian data for seed compounds other than oil and protein are required to identify novel QTLSP and QTLU for such traits for the individual or combined Canadian and Ukrainian MEs.

Molecular and archaeological evidence on the geographical origin of domestication for Camelina sativa

PREMISE

Camelina (gold-of-pleasure or false flax) is an ancient oilseed crop with emerging applications in the production of sustainable, low-input biofuels. Previous domestication hypotheses suggested a European or western Asian origin, yet little genetic evidence has existed to assess the geographical origin for this crop, and archaeological data have not been systematically surveyed.

METHODS

We utilized genotyping-by-sequencing of 185 accessions of C. sativa and its wild relatives to examine population structure within the crop species and its relationship to populations of its wild progenitor, C. microcarpa; cytotype variation was also assessed in both species. In a complementary analysis, we surveyed the archaeological literature to identify sites with archaeobotanical camelina remains and assess the timing and prevalence of usage across Europe and western Asia.

RESULTS

The majority of C. microcarpa sampled in Europe and the United States belongs to a variant cytotype (2n = 38) with a distinct evolutionary origin from that of the crop lineage (2n = 40). Populations of C. microcarpa from Transcaucasia (South Caucasus) are most closely related to C. sativa based on cytotype and population structure; in combination with archaeological insights, these data refute prior hypotheses of a European domestication origin.

CONCLUSIONS

Our findings support a Caucasus, potentially Armenian, origin of C. sativa domestication. We cannot definitively determine whether C. sativa was intentionally targeted for domestication in its own right or instead arose secondarily through selection for agricultural traits in weedy C. sativa, as originally proposed by Vavilov for this species.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

Genome-Wide Association Study of Soybean Germplasm Derived From Canadian × Chinese Crosses to Mine for Novel Alleles to Improve Seed Yield and Seed Quality Traits

Genome-wide association study (GWAS) has emerged in the past decade as a viable tool for identifying beneficial alleles from a genomic diversity panel. In an ongoing effort to improve soybean [Glycine max (L.) Merr.], which is the third largest field crop in Canada, a GWAS was conducted to identify novel alleles underlying seed yield and seed quality and agronomic traits. The genomic panel consisted of 200 genotypes including lines derived from several generations of bi-parental crosses between modern Canadian × Chinese cultivars (CD-CH). The genomic diversity panel was field evaluated at two field locations in Ontario in 2019 and 2020. Genotyping-by-sequencing (GBS) was conducted and yielded almost 32 K high-quality SNPs. GWAS was conducted using Fixed and random model Circulating Probability Unification (FarmCPU) model on the following traits: seed yield, seed protein concentration, seed oil concentration, plant height, 100 seed weight, days to maturity, and lodging score that allowed to identify five QTL regions controlling seed yield and seed oil and protein content. A candidate gene search identified a putative gene for each of the three traits. The results of this GWAS study provide insight into potentially valuable genetic resources residing in Chinese modern cultivars that breeders may use to further improve soybean seed yield and seed quality traits.

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.