Identifying genetic variants underlying phenotypic variation in plants without complete genomes

Multiple independent MGR5 alleles contribute to a clinal pattern in leaf magnesium across the distribution of Arabidopsis thaliana

Magnesium (Mg) is a crucial element in plants, particularly for photosynthesis. Mg homeostasis is influenced by environmental and genetic factors, and our understanding of its variation in natural populations remains incomplete. We examine the variation in leaf Mg accumulation across the distribution of Arabidopsis thaliana, and we investigate the environmental and genetic factors associated with Mg levels. Using genome-wide association studies in both the widespread Eurasian population and a local-scale population in Cape Verde, we identify genetic factors associated with variation in leaf Mg. We validate our main results, including effect size estimates, using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) mutagenesis. Our findings reveal a significant association between leaf Mg and latitude of origin. In Eurasia, we find a signal at the nutrient-response regulator, RAPTOR1A, and across the species range, we find that multiple alleles of the Mg transporter, MAGNESIUM RELEASE 5 (MGR5), underlie variation in leaf Mg and contribute to the observed latitudinal cline. Overall, our results indicate that the spatial distribution of leaf Mg in A. thaliana is affected by climatic and genetic factors, resulting in a latitudinal cline. Further, they show an example of allelic heterogeneity, in which multiple alleles at a single locus contribute to a trait and the formation of a phenotypic cline.

Independent domestication and cultivation histories of two West African indigenous fonio millet crops

Crop evolutionary history and domestication processes are key issues for better conservation and effective use of crop genetic diversity. Black and white fonio (Digitaria iburua and D. exilis, respectively) are two small indigenous grain cereals grown in West Africa. The relationship between these two cultivated crops and wild Digitaria species is still unclear. Here, we analyse whole genome sequences of 265 accessions comprising these two cultivated species and their close wild relatives. We show that white and black fonio were the result of two independent domestications without gene flow. We infer a cultivation expansion that began at the outset of the CE era, coinciding with the earliest discovered archaeological fonio remains in Nigeria. Fonio population sizes declined a few centuries ago, probably due to a combination of several factors, including major social and agricultural changes, intensification of the slave trade and the introduction of new, less labour-intensive crops. The key knowledge and genomic resources outlined here will help to promote and conserve these neglected climate-resilient crops and thereby provide an opportunity to tailor agriculture to the changing world.

K-mer-based Approaches to Bridging Pangenomics and Population Genetics

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes can be challenging for many species, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that k-mers are a very useful but underutilized tool for bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of k-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different k-mer-based measures of genetic variation behave in population genetic simulations according to the choice of k, depth of sequencing coverage, and degree of data compression. Overall, we find that k-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity (π) up to values of about π=0.025 (R2=0.97) for neutrally evolving populations. For populations with even more variation, using shorter k-mers will maintain the scalability up to at least π=0.1. Furthermore, in our simulated populations, k-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of k-mer-based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using k-mers.

k-mer approaches for biodiversity genomics

The wide array of currently available genomes displays a wonderful diversity in size, composition, and structure and is quickly expanding thanks to several global biodiversity genomics initiatives. However, sequencing of genomes, even with the latest technologies, can still be challenging for both technical (e.g., small physical size, contaminated samples, or access to appropriate sequencing platforms) and biological reasons (e.g., germline-restricted DNA, variable ploidy levels, sex chromosomes, or very large genomes). In recent years, k-mer-based techniques have become popular to overcome some of these challenges. They are based on the simple process of dividing the analyzed sequences (e.g., raw reads or genomes) into a set of subsequences of length k, called k-mers, and then analyzing the frequency or sequences of those k-mers. Analyses based on k-mers allow for a rapid and intuitive assessment of complex sequencing data sets. Here, we provide a comprehensive review to the theoretical properties and practical applications of k-mers in biodiversity genomics with a special focus on genome modeling.

A population genomics approach to unlock the genetic potential of lablab (Lablab purpureus (L.) Sweet), an underutilized tropical forage crop

BACKGROUND

Lablab is one of the conventionally grown multi-purpose crops that originated in Africa. It is an annual or short-lived perennial forage legume which has versatile uses (as a vegetable and dry seeds, as food or feed, or as green manure) but is yet to receive adequate research attention and hence remains underexploited. To develop new and highly productive lablab varieties, using genomics-assisted selection, the present study aimed to identify quantitative trait loci associated with agronomically important traits in lablab and to assess the stability of these traits across two different agro-ecologies in Ethiopia. Here, we resequenced one hundred and forty-two lablab accessions, utilised whole genome genotyping approaches, and conducted multi-locational phenotyping over two years.

RESULTS

The selected lablab accessions displayed significant agro-morphological variation in eight analysed traits, including plant height, total fresh weight, and total dry weight. Furthermore, the agronomic performance of the accessions was significantly different across locations and years, highlighting substantial genotype-by-environment interactions. The population genetic structure of the lablab accessions, based on ~ 500,000 informative single nucleotide polymorphisms (SNPs), revealed an independent domestication pattern for two-seeded and four-seeded lablab accessions. Finally, based on multi-environmental trial data, a genome-wide association study (GWAS) identified useful SNPs and k-mers for key traits, such as plant height and total dry weight.

CONCLUSIONS

The publicly available genomic tools and field evaluation data from this study will offer a valuable resource for plant breeders and researchers to inform a new cycle of lablab breeding. With the aid of these tools, the breeding cycle will be significantly reduced and livestock farmers will have access to improved lablab varieties in a shorter time-frame.

Utilizing insights of DNA repair machinery to discover MMEJ deletions and novel mechanisms

We developed Del-read, an algorithm targeting medium-sized deletions (6-100 bp) in short-reads, which are challenging for current variant callers relying on alignment. Our focus was on Micro-Homolog mediated End Joining deletions (MMEJ-dels), prevalent in myeloid malignancies. MMEJ-dels follow a distinct pattern, occurring between two homologies, allowing us to generate a comprehensive list of MMEJ-dels in the exome. Using Del-read, we identified numerous novel germline and somatic MMEJ-dels in BEAT-AML and TCGA-breast datasets. Validation in 672 healthy individuals confirmed their presence. These novel MMEJ-dels were linked to genomic features associated with replication stress, like G-quadruplexes and minisatellite. Additionally, we observed a new category of MMEJ-dels with an imperfect-match at the flanking sequences of the homologies, suggesting a mechanism involving mispairing in homology alignment. We demonstrated robustness of the repair system despite CRISPR/Cas9-induced mismatches in the homologies. Further analysis of the canonical ASXL1 deletion revealed a diverse array of these imperfect-matches. This suggests a potentially more flexible and error-prone MMEJ repair system than previously understood. Our findings highlight Del-read's potential in uncovering previously undetected deletions and deepen our understanding of repair mechanisms.

k-mer genome-wide association study for anthracnose and BCMV resistance in a Phaseolus vulgaris Andean Diversity Panel

Access to broad genomic resources and closely linked marker-trait associations for common beans (Phaseolus vulgaris L.) can facilitate development of improved varieties with increased yield, improved market quality traits, and enhanced disease resistance. The emergence of virulent races of anthracnose (caused by Colletotrichum lindemuthianum) and bean common mosaic virus (BCMV) highlight the need for improved methods to identify and incorporate pan-genomic variation in breeding for disease resistance. We sequenced the P. vulgaris Andean Diversity Panel (ADP) and performed a genome-wide association study (GWAS) to identify associations for resistance to BCMV and eight races of anthracnose. Historical single nucleotide polymorphism (SNP)-chip and phenotypic data enabled a three-way comparison between SNP-chip, reference-based whole genome shotgun sequence (WGS)-SNP, and reference-free k-mer (short nucleotide subsequence) GWAS. Across all traits, there was excellent concordance between SNP-chip, WGS-SNP, and k-mer GWAS results-albeit at a much higher marker resolution for the WGS data sets. Significant k-mer haplotype variation revealed selection of the linked I-gene and Co-u traits in North American breeding lines and cultivars. Due to structural variation, only 9.1 to 47.3% of the significantly associated k-mers could be mapped to the reference genome. Thus, to determine the genetic context of cis-associated k-mers, we generated draft whole genome assemblies of four ADP accessions and identified an expanded local repertoire of disease resistance genes associated with resistance to anthracnose and BCMV. With access to variant data in the context of a pan-genome, high resolution mapping of agronomic traits for common bean is now feasible.

Genome-wide association insights into the genomic regions controlling vegetative and oil production traits in Acrocomia aculeata

BACKGROUND

Macauba (Acrocomia aculeata) is a non-domesticated neotropical palm that has been attracting attention for economic use due to its great potential for oil production comparable to the commercially used oil palm (Elaeis guineensis). The discovery of associations between quantitative trait loci and economically important traits represents an advance toward understanding its genetic architecture and can contribute to accelerating macauba domestication. Pursuing this advance, this study performs single-trait and multi-trait GWAS models to identify candidate genes associated with vegetative and oil production traits in macauba. Eighteen phenotypic traits were evaluated from 201 palms within a native population. Genotyping was performed with SNP markers, following the protocol of genotyping-by-sequencing. Given that macauba lacks a reference genome, SNP calling was performed using three different strategies: using i) de novo sequencing, ii) the Elaeis guineenses Jacq. reference genome and iii) the macauba transcriptome sequences. After quality control, we identified a total of 27,410 SNPs in 153 individuals for the de novo genotypic dataset, 10,444 SNPs in 158 individuals using the oil palm genotypic dataset, and 4,329 SNPs in 167 individuals using the transcriptome genotypic dataset. The GWAS analysis was then performed on these three genotypic datasets.

RESULTS

Statistical phenotypic analyses revealed significant differences across all studied traits, with heritability values ranging from 63 to 95%. This indicates that the population contains promising genotypes for selection and the initiation of breeding programs. Genetic correlations between the 18 traits ranged from -0.47 to 0.99. The total number of significant SNPs in the single-trait and multi-trait GWAS was 92 and 6 using the de novo genotypic dataset, 19 and 11 using the oil palm genotypic dataset, and 1 and 2 using the transcriptome genotypic dataset, respectively. Gene annotation identified 12 candidate genes in the single-trait GWAS and four in the multi-trait GWAS, across the 18 phenotypic traits studied, in the three genotypic datasets. Gene mapping of the macauba candidate genes revealed similarities with Elaeis guineensis and Phoenix dactylifera. The candidate genes detected are responsible for metal ion binding and transport, protein transportation, DNA repair, and other cell regulation biological processes.

CONCLUSIONS

We provide new insights into genomic regions that map candidate genes associated with vegetative and oil production traits in macauba. These potential candidate genes require confirmation through targeted functional analyses in the future, and multi-trait associations need to be scrutinized to investigate the presence of pleiotropic or linked genes. Markers linked to traits of interest could serve as valuable resources for the development of marker-assisted selection in macauba for its domestication and pre-breeding.

SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data

Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In contrast, RNA-seq data, which are more accessible and less expensive, can simultaneously yield gene expressions and SNPs. Thus, eQTL analysis based on RNA-seq offers significant potential applications. Two primary strategies were employed for eQTL in this study. The first involved analyzing expression levels in relation to variant sites detected between populations from RNA-seq data. The second approach utilized kmers, which are sequences of length k derived from RNA-seq reads, to represent variant sites and associated these kmer genotypes with gene expression. We discovered 87 significant association signals involving eGene on the basis of the SNP-based eQTL analysis. These genes include DYNLT1, NMNAT1, and MRLC2, which are closely related to neurological functions such as motor coordination and homeostasis, play a role in cellular energy metabolism, and function in regulating calcium-dependent signaling in muscle contraction, respectively. This study compared the results obtained from eQTL mapping using RNA-seq identified SNPs and gene expression with those derived from kmers. We found that the vast majority (23/30) of the association signals overlapping the two methods could be verified by haplotype block analysis. This comparison elucidates the strengths and limitations of each method, providing insights into their relative efficacy for eQTL identification.

Trait association and prediction through integrative k-mer analysis

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

Simple and accurate genomic classification model for distinguishing between human and pig Staphylococcus aureus

Staphylococcus aureus (S. aureus) can cause various infections in humans and animals, contributing to high morbidity and mortality. To prevent and control cross-species transmission of S. aureus, it is necessary to understand the host-associated genetic variants. We performed a two-stage genome-wide association study (GWAS) including initial screening and further validation to compare genomic differences between human and pig S. aureus, aiming to identify host-associated determinants. Our multiple GWAS analyses found six consensus significant k-mers associated with host species, providing novel genetic evidence for distinguishing human from pig S. aureus. The best k-mer predictor achieved a high classification accuracy of 98.12% on its own and had extremely high resolution similar to the SNPs-based phylogeny, offering a very simple target for predicting the cross-species transmission risk of S. aureus. The final k-mer model revealed that 90% of S. aureus isolates from farm workers were predicted as livestock origin, suggesting a high risk of cross-species transmission. Bayesian inference revealed different cross-species transmission directions, with the human-to-pig transmission for ST5 and the pig-to-human transmission for ST398. Our findings provide a simple and accurate k-mer model for identifying and predicting the cross-species transmission risk of S. aureus.

Previously unmeasured genetic diversity explains part of Lewontin's paradox in a k -mer-based meta-analysis of 112 plant species

At the molecular level, most evolution is expected to be neutral. A key prediction of this expectation is that the level of genetic diversity in a population should scale with population size. However, as was noted by Richard Lewontin in 1974 and reaffirmed by later studies, the slope of the population size-diversity relationship in nature is much weaker than expected under neutral theory. We hypothesize that one contributor to this paradox is that current methods relying on single nucleotide polymorphisms (SNPs) called from aligning short reads to a reference genome underestimate levels of genetic diversity in many species. To test this idea, we calculated nucleotide diversity ( π ) and k -mer-based metrics of genetic diversity across 112 plant species, amounting to over 205 terabases of DNA sequencing data from 27,488 individual plants. We then compared how these different metrics correlated with proxies of population size that account for both range size and population density variation across species. We found that our population size proxies scaled anywhere from about 3 to over 20 times faster with k -mer diversity than nucleotide diversity after adjusting for evolutionary history, mating system, life cycle habit, cultivation status, and invasiveness. The relationship between k -mer diversity and population size proxies also remains significant after correcting for genome size, whereas the analogous relationship for nucleotide diversity does not. These results suggest that variation not captured by common SNP-based analyses explains part of Lewontin's paradox in plants.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

Plant pangenomes for crop improvement, biodiversity and evolution

Plant genome sequences catalogue genes and the genetic elements that regulate their expression. Such inventories further research aims as diverse as mapping the molecular basis of trait diversity in domesticated plants or inquiries into the origin of evolutionary innovations in flowering plants millions of years ago. The transformative technological progress of DNA sequencing in the past two decades has enabled researchers to sequence ever more genomes with greater ease. Pangenomes - complete sequences of multiple individuals of a species or higher taxonomic unit - have now entered the geneticists' toolkit. The genomes of crop plants and their wild relatives are being studied with translational applications in breeding in mind. But pangenomes are applicable also in ecological and evolutionary studies, as they help classify and monitor biodiversity across the tree of life, deepen our understanding of how plant species diverged and show how plants adapt to changing environments or new selection pressures exerted by human beings.

Harnessing landrace diversity empowers wheat breeding

Harnessing genetic diversity in major staple crops through the development of new breeding capabilities is essential to ensure food security1. Here we examined the genetic and phenotypic diversity of the A. E. Watkins landrace collection2 of bread wheat (Triticum aestivum), a major global cereal, by whole-genome re-sequencing of 827 Watkins landraces and 208 modern cultivars and in-depth field evaluation spanning a decade. We found that modern cultivars are derived from two of the seven ancestral groups of wheat and maintain very long-range haplotype integrity. The remaining five groups represent untapped genetic sources, providing access to landrace-specific alleles and haplotypes for breeding. Linkage disequilibrium-based haplotypes and association genetics analyses link Watkins genomes to the thousands of identified high-resolution quantitative trait loci and significant marker-trait associations. Using these structured germplasm, genotyping and informatics resources, we revealed many Watkins-unique beneficial haplotypes that can confer superior traits in modern wheat. Furthermore, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritized quantitative trait loci in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilizing genetic diversity in crop improvement to achieve sustainable food security.

A k-mer-based pangenome approach for cataloging seed-storage-protein genes in wheat to facilitate genotype-to-phenotype prediction and improvement of end-use quality

Wheat is a staple food for more than 35% of the world's population, with wheat flour used to make hundreds of baked goods. Superior end-use quality is a major breeding target; however, improving it is especially time-consuming and expensive. Furthermore, genes encoding seed-storage proteins (SSPs) form multi-gene families and are repetitive, with gaps commonplace in several genome assemblies. To overcome these barriers and efficiently identify superior wheat SSP alleles, we developed "PanSK" (Pan-SSP k-mer) for genotype-to-phenotype prediction based on an SSP-based pangenome resource. PanSK uses 29-mer sequences that represent each SSP gene at the pangenomic level to reveal untapped diversity across landraces and modern cultivars. Genome-wide association studies with k-mers identified 23 SSP genes associated with end-use quality that represent novel targets for improvement. We evaluated the effect of rye secalin genes on end-use quality and found that removal of ω-secalins from 1BL/1RS wheat translocation lines is associated with enhanced end-use quality. Finally, using machine-learning-based prediction inspired by PanSK, we predicted the quality phenotypes with high accuracy from genotypes alone. This study provides an effective approach for genome design based on SSP genes, enabling the breeding of wheat varieties with superior processing capabilities and improved end-use quality.

An alignment- and reference-free strategy using k-mer present pattern for population genomic analyses

Pangenomes are replacing single reference genomes to capture all variants within a species or clade, but their analysis predominantly leverages graph-based methods that require multiple high-quality genomes and computationally intensive multiple-genome alignments. K-mer decomposition is an alternative to graph-based pangenomes. However, how to directly use k-mers for the population genetic analyses is unknown. Here, we developed a novel strategy that uses the variants of k-mer count in the genome for population analyses. To test the effectivity of this method, we compared it directly to the SNP-based method on the analysis of population structure and genetic diversity of 267 Saccharomyces cerevisiae strains within two simulated datasets and a real sequence dataset. The population structure identified with k-mers recapitulates that obtained using SNPs, indicating the effectiveness of k-mer-based approach, and higher genetic diversity within real dataset supported k-mers contained more genetic variants. Based on k-mer frequency, we found not only SNP but also some insertion/deletion and horizontal gene transfer (HGT) fragments related to the adaptive evolution of S. cerevisiae. Our study creates a framework for the alignment- and reference-free (ARF) method in population genetic analyses, which will be more pronounced in the species with no complete genome or highly diverged species.

Covering the bases: Population genomic structure of Lemna minor and the cryptic species L. japonica in Switzerland

Duckweeds, including the common duckweed Lemna minor, are increasingly used to test eco-evolutionary theories. Yet, despite its popularity and near-global distribution, the understanding of its population structure (and genetic variation therein) is still limited. It is essential that this is resolved, because of the impact genetic diversity has on experimental responses and scientific understanding. Through whole-genome sequencing, we assessed the genetic diversity and population genomic structure of 23 natural Lemna spp. populations from their natural range in Switzerland. We used two distinct analytical approaches, a reference-free kmer approach and the classical reference-based one. Two genetic clusters were identified across the described species distribution of L. minor, surprisingly corresponding to species-level divisions. The first cluster contained the targeted L. minor individuals and the second contained individuals from a cryptic species: Lemna japonica. Within the L. minor cluster, we identified a well-defined population structure with little intra-population genetic diversity (i.e., within ponds) but high inter-population diversity (i.e., between ponds). In L. japonica, the population structure was significantly weaker and genetic variation between a subset of populations was as low as within populations. This study revealed that L. japonica is more widespread than previously thought. Our findings signify that thorough genotype-to-phenotype analyses are needed in duckweed experimental ecology and evolution.

A k-mer-based bulked segregant analysis approach to map seed traits in unphased heterozygous potato genomes

High-throughput sequencing-based methods for bulked segregant analysis (BSA) allow for the rapid identification of genetic markers associated with traits of interest. BSA studies have successfully identified qualitative (binary) and quantitative trait loci (QTLs) using QTL mapping. However, most require population structures that fit the models available and a reference genome. Instead, high-throughput short-read sequencing can be combined with BSA of k-mers (BSA-k-mer) to map traits that appear refractory to standard approaches. This method can be applied to any organism and is particularly useful for species with genomes diverged from the closest sequenced genome. It is also instrumental when dealing with highly heterozygous and potentially polyploid genomes without phased haplotype assemblies and for which a single haplotype can control a trait. Finally, it is flexible in terms of population structure. Here, we apply the BSA-k-mer method for the rapid identification of candidate regions related to seed spot and seed size in diploid potato. Using a mixture of F1 and F2 individuals from a cross between 2 highly heterozygous parents, candidate sequences were identified for each trait using the BSA-k-mer approach. Using parental reads, we were able to determine the parental origin of the loci. Finally, we mapped the identified k-mers to a closely related potato genome to validate the method and determine the genomic loci underlying these sequences. The location identified for the seed spot matches with previously identified loci associated with pigmentation in potato. The loci associated with seed size are novel. Both loci are relevant in future breeding toward true seeds in potato.

The genomics and evolution of inter-sexual mimicry and female-limited polymorphisms in damselflies

Sex-limited morphs can provide profound insights into the evolution and genomic architecture of complex phenotypes. Inter-sexual mimicry is one particular type of sex-limited polymorphism in which a novel morph resembles the opposite sex. While inter-sexual mimics are known in both sexes and a diverse range of animals, their evolutionary origin is poorly understood. Here, we investigated the genomic basis of female-limited morphs and male mimicry in the common bluetail damselfly. Differential gene expression between morphs has been documented in damselflies, but no causal locus has been previously identified. We found that male mimicry originated in an ancestrally sexually dimorphic lineage in association with multiple structural changes, probably driven by transposable element activity. These changes resulted in ~900 kb of novel genomic content that is partly shared by male mimics in a close relative, indicating that male mimicry is a trans-species polymorphism. More recently, a third morph originated following the translocation of part of the male-mimicry sequence into a genomic position ~3.5 mb apart. We provide evidence of balancing selection maintaining male mimicry, in line with previous field population studies. Our results underscore how structural variants affecting a handful of potentially regulatory genes and morph-specific genes can give rise to novel and complex phenotypic polymorphisms.

kGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS

Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).

k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean

Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a given trait in crop species. Assessing genetic variants other than single-nucleotide polymorphisms (SNPs) could alleviate this problem. In this study, we tested the potential of structural variant (SV)- and k-mer-based GWAS in soybean by applying these methods as well as conventional SNP/indel-based GWAS to 13 traits. We assessed the performance of each GWAS approach based on loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources invested. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Our results suggest that coupling k-mer- and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.

Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen

Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., k-mer) approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.

Genome-wide artificial introgressions of Gossypium barbadense into G. hirsutum reveal superior loci for simultaneous improvement of cotton fiber quality and yield traits

INTRODUCTION

The simultaneous improvement of fiber quality and yield for cotton is strongly limited by the narrow genetic backgrounds of Gossypium hirsutum (Gh) and the negative genetic correlations among traits. An effective way to overcome the bottlenecks is to introgress the favorable alleles of Gossypium barbadense (Gb) for fiber quality into Gh with high yield.

OBJECTIVES

This study was to identify superior loci for the improvement of fiber quality and yield.

METHODS

Two sets of chromosome segment substitution lines (CSSLs) were generated by crossing Hai1 (Gb, donor-parent) with cultivar CCRI36 (Gh) and CCRI45 (Gh) as genetic backgrounds, and cultivated in 6 and 8 environments, respectively. The kmer genotyping strategy was improved and applied to the population genetic analysis of 743 genomic sequencing data. A progeny segregating population was constructed to validate genetic effects of the candidate loci.

RESULTS

A total of 68,912 and 83,352 genome-wide introgressed kmers were identified in the CCRI36 and CCRI45 populations, respectively. Over 90 % introgressions were homologous exchanges and about 21 % were reverse insertions. In total, 291 major introgressed segments were identified with stable genetic effects, of which 66(22.98 %), 64(21.99 %), 35(12.03 %), 31(10.65 %) and 18(6.19 %) were beneficial for the improvement of fiber length (FL), strength (FS), micronaire, lint-percentage (LP) and boll-weight, respectively. Thirty-nine introgression segments were detected with stable favorable additive effects for simultaneous improvement of 2 or more traits in Gh genetic background, including 6 could increase FL/FS and LP. The pyramiding effects of 3 pleiotropic segments (A07:C45Clu-081, D06:C45Clu-218, D02:C45Clu-193) were further validated in the segregating population.

CONCLUSION

The combining of genome-wide introgressions and kmer genotyping strategy showed significant advantages in exploring genetic resources. Through the genome-wide comprehensive mining, a total of 11 clusters (segments) were discovered for the stable simultaneous improvement of FL/FS and LP, which should be paid more attention in the future.

PanKmer: k-mer-based and reference-free pangenome analysis

SUMMARY

Pangenomes are replacing single reference genomes as the definitive representation of DNA sequence within a species or clade. Pangenome analysis predominantly leverages graph-based methods that require computationally intensive multiple genome alignments, do not scale to highly complex eukaryotic genomes, limit their scope to identifying structural variants (SVs), or incur bias by relying on a reference genome. Here, we present PanKmer, a toolkit designed for reference-free analysis of pangenome datasets consisting of dozens to thousands of individual genomes. PanKmer decomposes a set of input genomes into a table of observed k-mers and their presence-absence values in each genome. These are stored in an efficient k-mer index data format that encodes SNPs, INDELs, and SVs. It also includes functions for downstream analysis of the k-mer index, such as calculating sequence similarity statistics between individuals at whole-genome or local scales. For example, k-mers can be "anchored" in any individual genome to quantify sequence variability or conservation at a specific locus. This facilitates workflows with various biological applications, e.g. identifying cases of hybridization between plant species. PanKmer provides researchers with a valuable and convenient means to explore the full scope of genetic variation in a population, without reference bias.

AVAILABILITY AND IMPLEMENTATION

PanKmer is implemented as a Python package with components written in Rust, released under a BSD license. The source code is available from the Python Package Index (PyPI) at https://pypi.org/project/pankmer/ as well as Gitlab at https://gitlab.com/salk-tm/pankmer. Full documentation is available at https://salk-tm.gitlab.io/pankmer/.

Natural variations in the Sl-AKR9 aldo/keto reductase gene impact fruit flavor volatile and sugar contents

The unique flavors of different fruits depend upon complex blends of soluble sugars, organic acids, and volatile organic compounds. 2-Phenylethanol and phenylacetaldehyde are major contributors to flavor in many foods, including tomato. In the tomato fruit, glucose, and fructose are the chemicals that most positively contribute to human flavor preferences. We identified a gene encoding a tomato aldo/keto reductase, Sl-AKR9, that is associated with phenylacetaldehyde and 2-phenylethanol contents in fruits. Two distinct haplotypes were identified; one encodes a chloroplast-targeted protein while the other encodes a transit peptide-less protein that accumulates in the cytoplasm. Sl-AKR9 effectively catalyzes reduction of phenylacetaldehyde to 2-phenylethanol. The enzyme can also metabolize sugar-derived reactive carbonyls, including glyceraldehyde and methylglyoxal. CRISPR-Cas9-induced loss-of-function mutations in Sl-AKR9 significantly increased phenylacetaldehyde and lowered 2-phenylethanol content in ripe fruit. Reduced fruit weight and increased soluble solids, glucose, and fructose contents were observed in the loss-of-function fruits. These results reveal a previously unidentified mechanism affecting two flavor-associated phenylalanine-derived volatile organic compounds, sugar content, and fruit weight. Modern varieties of tomato almost universally contain the haplotype associated with larger fruit, lower sugar content, and lower phenylacetaldehyde and 2-phenylethanol, likely leading to flavor deterioration in modern varieties.

Transposons are important contributors to gene expression variability under selection in rice populations

Transposable elements (TEs) are an important source of genome variability. Here, we analyze their contribution to gene expression variability in rice by performing a TE insertion polymorphism expression quantitative trait locus mapping using expression data from 208 varieties from the Oryza sativa ssp. indica and O. sativa ssp. japonica subspecies. Our data show that TE insertions are associated with changes of expression of many genes known to be targets of rice domestication and breeding. An important fraction of these insertions were already present in the rice wild ancestors, and have been differentially selected in indica and japonica rice populations. Taken together, our results show that small changes of expression in signal transduction genes induced by TE insertions accompany the domestication and adaptation of rice populations.

k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives

Genome-wide association studies (GWAS) have allowed the discovery of marker-trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, k-mer-based GWAS approaches have recently been developed. k-mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, k-mer-based analyses can be used in species that lack a reference genome. However, the use of k-mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for k-mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant k-mers to sequence variation.

A High-Continuity Genome Assembly of Chinese Flowering Cabbage (Brassica rapa var. parachinensis) Provides New Insights into Brassica Genome Structure Evolution

Chinese flowering cabbage (Brassica rapa var. parachinensis) is a popular and widely cultivated leaf vegetable crop in Asia. Here, we performed a high quality de novo assembly of the 384 Mb genome of 10 chromosomes of a typical cultivar of Chinese flowering cabbage with an integrated approach using PacBio, Illumina, and Hi-C technology. We modeled 47,598 protein-coding genes in this analysis and annotated 52% (205.9/384) of its genome as repetitive sequences including 17% in DNA transposons and 22% in long terminal retrotransposons (LTRs). Phylogenetic analysis reveals the genome of the Chinese flowering cabbage has a closer evolutionary relationship with the AA diploid progenitor of the allotetraploid species, Brassica juncea. Comparative genomic analysis of Brassica species with different subgenome types (A, B and C) reveals that the pericentromeric regions on chromosome 5 and 6 of the AA genome have been significantly expanded compared to the orthologous genomic regions in the BB and CC genomes, largely driven by LTR-retrotransposon amplification. Furthermore, we identified a large number of structural variations (SVs) within the B. rapa lines that could impact coding genes, suggesting the functional significance of SVs on Brassica genome evolution. Overall, our high-quality genome assembly of the Chinese flowering cabbage provides a valuable genetic resource for deciphering the genome evolution of Brassica species and it can potentially serve as the reference genome guiding the molecular breeding practice of B. rapa crops.

Multigenic resistance to Xylella fastidiosa in wild grapes (Vitis sps.) and its implications within a changing climate

Xylella fastidiosa is a bacterium that infects crops like grapevines, coffee, almonds, citrus and olives. There is little understanding of the genes that contribute to plant resistance, the genomic architecture of resistance, and the potential role of climate in shaping resistance, in part because major crops like grapevines (Vitis vinifera) are not resistant to the bacterium. Here we study a wild grapevine species, V. arizonica, that segregates for resistance. Using genome-wide association, we identify candidate resistance genes. Resistance-associated kmers are shared with a sister species of V. arizonica but not with more distant species, suggesting that resistance evolved more than once. Finally, resistance is climate dependent, because individuals from low ( < 10 °C) temperature locations in the wettest quarter were typically susceptible to infection, likely reflecting a lack of pathogen pressure in colder climates. In fact, climate is as effective a predictor of resistance phenotypes as some genetic markers. We extend our climate observations to additional crops, predicting that increased pathogen pressure is more likely for grapevines and almonds than some other susceptible crops.

Identifying genetic variants associated with amphotericin B (AMB) resistance in Aspergillus fumigatus via k-mer-based GWAS

Aspergillus fumigatus is one of the most common pathogenic fungi, which results in high morbidity and mortality in immunocompromised patients. Amphotericin B (AMB) is used as the core drug for the treatment of triazole-resistant A. fumigatus. Following the usage of amphotericin B drugs, the number of amphotericin B-resistant A. fumigatus isolates showed an increasing trend over the years, but the mechanism and mutations associated with amphotericin B sensitivity are not fully understood. In this study, we performed a k-mer-based genome-wide association study (GWAS) in 98 A. fumigatus isolates from public databases. Associations identified with k-mers not only recapitulate those with SNPs but also discover new associations with insertion/deletion (indel). Compared to SNP sites, the indel showed a stronger association with amphotericin B resistance, and a significant correlated indel is present in the exon region of AFUA_7G05160, encoding a fumarylacetoacetate hydrolase (FAH) family protein. Enrichment analysis revealed sphingolipid synthesis and transmembrane transport may be related to the resistance of A. fumigatus to amphotericin B. The expansion of variant types detected by the k-mer method increases opportunities to identify and exploit complex genetic variants that drive amphotericin B resistance, and these candidate variants help accelerate the selection of prospective gene markers for amphotericin B resistance screening in A. fumigatus.

Integrating multiomics data accelerates elucidation of plant primary and secondary metabolic pathways

Plants are the most important sources of food for humans, as well as supplying many ingredients that are of great importance for human health. Developing an understanding of the functional components of plant metabolism has attracted considerable attention. The rapid development of liquid chromatography and gas chromatography, coupled with mass spectrometry, has allowed the detection and characterization of many thousands of metabolites of plant origin. Nowadays, elucidating the detailed biosynthesis and degradation pathways of these metabolites represents a major bottleneck in our understanding. Recently, the decreased cost of genome and transcriptome sequencing rendered it possible to identify the genes involving in metabolic pathways. Here, we review the recent research which integrates metabolomic with different omics methods, to comprehensively identify structural and regulatory genes of the primary and secondary metabolic pathways. Finally, we discuss other novel methods that can accelerate the process of identification of metabolic pathways and, ultimately, identify metabolite function(s).

Machine learning for predicting phenotype from genotype and environment

Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

Plant pan-genomics and its applications

Plant genomes are so highly diverse that a substantial proportion of genomic sequences are not shared among individuals. The variable DNA sequences, along with the conserved core sequences, compose the more sophisticated pan-genome that represents the collection of all non-redundant DNA in a species. With rapid progress in genome sequencing technologies, pan-genome research in plants is now accelerating. Here we review recent advances in plant pan-genomics, including major driving forces of structural variations that constitute the variable sequences, methodological innovations for representing the pan-genome, and major successes in constructing plant pan-genomes. We also summarize recent efforts toward decoding the remaining dark matter in telomere-to-telomere or gapless plant genomes. These new genome resources, which have remarkable advantages over numerous previously assembled less-than-perfect genomes, are expected to become new references for genetic studies and plant breeding.

Predicting Gene Regulatory Interactions Using Natural Genetic Variation

Genome-wide association studies (GWAS) are a powerful tool to elucidate the genotype-phenotype map. Although GWAS are usually used to assess simple univariate associations between genetic markers and traits of interest, it is also possible to infer the underlying genetic architecture and to predict gene regulatory interactions. In this chapter, we describe the latest methods and tools to perform GWAS by calculating permutation-based significance thresholds. For this purpose, we first provide guidelines on univariate GWAS analyses that are extended in the second part of this chapter to more complex models that enable the inference of gene regulatory networks and how these networks vary.

k mdiff, large-scale and user-friendly differential k-mer analyses

SUMMARY

Genome wide association studies elucidate links between genotypes and phenotypes. Recent studies point out the interest of conducting such experiments using k-mers as the base signal instead of single-nucleotide polymorphisms. We propose a tool, kmdiff, that performs differential k-mer analyses on large sequencing cohorts in an order of magnitude less time and memory than previously possible.

AVAILABILITYAND IMPLEMENTATION

https://github.com/tlemane/kmdiff.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sex chromosomes in the tribe Cyprichromini (Teleostei: Cichlidae) of Lake Tanganyika

Sex determining loci have been described on at least 12 of 22 chromosomes in East African cichlid fishes, indicating a high rate of sex chromosome turnover. To better understand the rates and patterns of sex chromosome replacement, we used new methods to characterize the sex chromosomes of the cichlid tribe Cyprichromini from Lake Tanganyika. Our k-mer based methods successfully identified sex-linked polymorphisms without the need for a reference genome. We confirm the three previously reported sex chromosomes in this group. We determined the polarity of the sex chromosome turnover on LG05 in Cyprichromis as ZW to XY. We identified a new ZW locus on LG04 in Paracyprichromis brieni. The LG15 XY locus in Paracyprichromis nigripinnis was not found in other Paracyprichromis species, and the sample of Paracyprichromis sp. "tembwe" is likely to be of hybrid origin. Although highly divergent sex chromosomes are thought to develop in a stepwise manner, we show two cases (LG05-ZW and LG05-XY) in which the region of differentiation encompasses most of the chromosome, but appears to have arisen in a single step. This study expands our understanding of sex chromosome evolution in the Cyprichromini, and indicates an even higher level of sex chromosome turnover than previously thought.

A catalogue of resistance gene homologs and a chromosome-scale reference sequence support resistance gene mapping in winter wheat

A resistance gene atlas is an integral component of the breeder's arsenal in the fight against evolving pathogens. Thanks to high-throughput sequencing, catalogues of resistance genes can be assembled even in crop species with large and polyploid genomes. Here, we report on capture sequencing and assembly of resistance gene homologs in a diversity panel of 907 winter wheat genotypes comprising ex situ genebank accessions and current elite cultivars. In addition, we use accurate long-read sequencing and chromosome conformation capture sequencing to construct a chromosome-scale genome sequence assembly of cv. Attraktion, an elite variety representative of European winter wheat. We illustrate the value of our resource for breeders and geneticists by (i) comparing the resistance gene complements in plant genetic resources and elite varieties and (ii) conducting genome-wide associations scans (GWAS) for the fungal diseases yellow rust and leaf rust using reference-based and reference-free GWAS approaches. The gene content under GWAS peaks was scrutinized in the assembly of cv. Attraktion.

Open problems in human trait genetics

Genetic studies of human traits have revolutionized our understanding of the variation between individuals, and yet, the genetics of most traits is still poorly understood. In this review, we highlight the major open problems that need to be solved, and by discussing these challenges provide a primer to the field. We cover general issues such as population structure, epistasis and gene-environment interactions, data-related issues such as ancestry diversity and rare genetic variants, and specific challenges related to heritability estimates, genetic association studies, and polygenic risk scores. We emphasize the interconnectedness of these problems and suggest promising avenues to address them.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

Genetic diversity analysis and marker-trait associations in Amaranthus species

Amaranth (Amaranthus spp.) is a highly nutritious, underutilized vegetable and pseudo-cereal crop. It possesses diverse abiotic stress tolerance traits, is genetically diverse and highly phenotypically plastic, making it an ideal crop to thrive in a rapidly changing climate. Despite considerable genetic diversity there is a lack of detailed characterization of germplasm or population structures. The present study utilized the DArTSeq platform to determine the genetic relationships and population structure between 188 amaranth accessions from 18 agronomically important vegetable, grain, and weedy species. A total of 74, 303 SNP alleles were generated of which 63, 821 were physically mapped to the genome of the grain species A. hypochondriacus. Population structure was inferred in two steps. First, all 188 amaranth accessions comprised of 18 species and second, only 120 A. tricolor accessions. After SNP filtering, a total of 8,688 SNPs were generated on 181 amaranth accessions of 16 species and 9,789 SNPs generated on 118 A. tricolor accessions. Both SNP datasets produced three major sub-populations (K = 3) and generate consistent taxonomic classification of the amaranth sub-genera (Amaranthus Amaranthus, Amaranthus Acnida and Amaranthus albersia), although the accessions were poorly demarcated by geographical origin and morphological traits. A. tricolor accessions were well discriminated from other amaranth species. A genome-wide association study (GWAS) of 10 qualitative traits revealed an association between specific phenotypes and genetic variants within the genome and identified 22 marker trait associations (MTAs) and 100 MTAs (P≤0.01, P≤0.001) on 16 amaranth species and 118 A.tricolor datasets, respectively. The release of SNP markers from this panel has produced invaluable preliminary genetic information for phenotyping and cultivar improvement in amaranth species.

Oxford Nanopore and Bionano Genomics technologies evaluation for plant structural variation detection

BACKGROUND

Structural Variations (SVs) are genomic rearrangements derived from duplication, deletion, insertion, inversion, and translocation events. In the past, SVs detection was limited to cytological approaches, then to Next-Generation Sequencing (NGS) short reads and partitioned assemblies. Nowadays, technologies such as DNA long read sequencing and optical mapping have revolutionized the understanding of SVs in genomes, due to the enhancement of the power of SVs detection. This study aims to investigate performance of two techniques, 1) long-read sequencing obtained with the MinION device (Oxford Nanopore Technologies) and 2) optical mapping obtained with Saphyr device (Bionano Genomics) to detect and characterize SVs in the genomes of the two ecotypes of Arabidopsis thaliana, Columbia-0 (Col-0) and Landsberg erecta 1 (Ler-1).

RESULTS

We described the SVs detected from the alignment of the best ONT assembly and DLE-1 optical maps of A. thaliana Ler-1 against the public reference genome Col-0 TAIR10.1. After filtering (SV > 1 kb), 1184 and 591 Ler-1 SVs were retained from ONT and Bionano technologies respectively. A total of 948 Ler-1 ONT SVs (80.1%) corresponded to 563 Bionano SVs (95.3%) leading to 563 common locations. The specific locations were scrutinized to assess improvement in SV detection by either technology. The ONT SVs were mostly detected near TE and gene features, and resistance genes seemed particularly impacted.

CONCLUSIONS

Structural variations linked to ONT sequencing error were removed and false positives limited, with high quality Bionano SVs being conserved. When compared with the Col-0 TAIR10.1 reference genome, most of the detected SVs discovered by both technologies were found in the same locations. ONT assembly sequence leads to more specific SVs than Bionano one, the latter being more efficient to characterize large SVs. Even if both technologies are complementary approaches, ONT data appears to be more adapted to large scale populations studies, while Bionano performs better in improving assembly and describing specificity of a genome compared to a reference.

Are we there yet? Driving the road to evolutionary graph-pangenomics

With increase in the number of sequenced genomes, it is now recognized that graph-based pangenomes can provide a comprehensive platform to study diversity in a population or species, from point mutations to large chromosomal rearrangements. By incorporating concepts from graph theory, a graph-pangenome can be studied directly to identify genomic regions and genes that underlie important evolutionary processes and traits. Here, I discuss how basic concepts in graph theory can be implemented to address questions in evolutionary genomics and guide future breeding efforts. Despite its compelling versatility, a graph-pangenome assembly is still challenging especially in species with large complex genomes. As technology is rapidly improving, the graph-pangenome is expected to become a central platform in genomics studies and applications. Thus, development of tools and methods that exploit the graph structure are urged to pave the route to evolutionary graph-pangenomics.

Population genomics of the grapevine pathogen Eutypa lata reveals evidence for population expansion and intraspecific differences in secondary metabolite gene clusters

Eutypa dieback of grapevine is an important disease caused by the generalist Ascomycete fungus Eutypa lata. Despite the relevance of this species to the global wine industry, its genomic diversity remains unknown, with only a single publicly available genome assembly. Whole-genome sequencing and comparative genomics was performed on forty Australian E. lata isolates to understand the genome evolution, adaptation, population size and structure of these isolates. Phylogenetic and linkage disequilibrium decay analyses provided evidence of extensive gene flow through sexual recombination between isolates obtained from different geographic locations and hosts. Investigation of the genetic diversity of these isolates suggested rapid population expansion, likely as a consequence of the recent growth of the Australian wine industry. Genomic regions affected by selective sweeps were shown to be enriched for genes associated with secondary metabolite clusters and included genes encoding proteins with a role in nutrient acquisition, degradation of host cell wall and metal and drug resistance, suggesting recent adaptation to both abiotic factors and potentially host genotypes. Genome synteny analysis using long-read genome assemblies showed significant intraspecific genomic plasticity with extensive chromosomal rearrangements impacting the secondary metabolite production potential of this species. Finally, k-mer based GWAS analysis identified a potential locus associated with mycelia recovery in canes of Vitis vinifera that will require further investigations.

Eutypa dieback of grapevine, caused by the Ascomycete fungus Eutypa lata, is responsible for significant economic losses to the wine industry. Despite the worldwide prevalence of this pathogen, its genomic diversity remains unknown, with only a single publicly available genome assembly. This knowledge gap was addressed by performing whole-genome sequencing of 40 E. lata isolates sourced from different hosts and geographical locations around Australia. Investigation of the genetic diversity of this population showed a high degree of gene-flow and sexual recombination as well as demographic expansion. Through the inspection of signatures of selective sweeps, repeat-mediated chromosomal rearrangements, and pan-genomic elements, it was shown that this species has a highly dynamic secondary metabolite production potential that could have important implications for its pathogenicity and lifestyle. In addition, application of a k-mer based GWAS methodology, identified a potential locus associated with the growth of this species within canes of Vitis vinifera.

Bringing more players into play: Leveraging stress in genome wide association studies

In order to meet the demand of the burgeoning human population as well as to adapt crops to the enhanced abiotic and biotic stress caused by the global climatic change, breeders focus on identifying valuable genes to improve both crop stress tolerance and crop quality. Recently, with the development of next-generation sequencing methods, millions of high quality single-nucleotide polymorphisms (SNPs) have been made available and genome-wide association studies (GWAS) are widely used in crop improvement studies to identify the associations between genetic variants of genomes and relevant crop agronomic traits. Here, we review classic cases of use of GWAS to identify genetic variants associated with valuable traits such as geographic adaptation, crop quality and metabolites. We discuss the power of stress GWAS to identify further associations including those with genes that are not, or only lowly, expressed during optimal growth conditions. Finally, we emphasize recent demonstrations of the efficiency and accuracy of time-resolved dynamic stress GWAS and GWAS based on genomic gene expression and structural variations, which can be applied to resolve more comprehensively the genetic regulation mechanisms of complex traits.

A review on the application of bioinformatics tools in food microbiome studies

There is currently a transformed interest toward understanding the impact of fermentation on functional food development due to growing consumer interest on modified health benefits of sustainable foods. In this review, we attempt to summarize recent findings regarding the impact of Next-generation sequencing and other bioinformatics methods in the food microbiome and use prediction software to understand the critical role of microbes in producing fermented foods. Traditionally, fermentation methods and starter culture development were considered conventional methods needing optimization to eliminate errors in technique and were influenced by technical knowledge of fermentation. Recent advances in high-output omics innovations permit the implementation of additional logical tactics for developing fermentation methods. Further, the review describes the multiple functions of the predictions based on docking studies and the correlation of genomic and metabolomic analysis to develop trends to understand the potential food microbiome interactions and associated products to become a part of a healthy diet.

Broadening the horizon of crop research: a decade of advancements in plant molecular genetics to divulge phenotype governing genes

Advancements in sequencing, genotyping, and computational technologies during the last decade (2011-2020) enabled new forward-genetic approaches, which subdue the impediments of precise gene mapping in varied crops. The modern crop improvement programs rely heavily on two major steps-trait-associated QTL/gene/marker's identification and molecular breeding. Thus, it is vital for basic and translational crop research to identify genomic regions that govern the phenotype of interest. Until the advent of next-generation sequencing, the forward-genetic techniques were laborious and time-consuming. Over the last 10 years, advancements in the area of genome assembly, genotyping, large-scale data analysis, and statistical algorithms have led faster identification of genomic variations regulating the complex agronomic traits and pathogen resistance. In this review, we describe the latest developments in genome sequencing and genotyping along with a comprehensive evaluation of the last 10-year headways in forward-genetic techniques that have shifted the focus of plant research from model plants to diverse crops. We have classified the available molecular genetic methods under bulk-segregant analysis-based (QTL-seq, GradedPool-Seq, QTG-Seq, Exome QTL-seq, and RapMap), target sequence enrichment-based (RenSeq, AgRenSeq, and TACCA), and mutation-based groups (MutMap, NIKS algorithm, MutRenSeq, MutChromSeq), alongside improvements in classical mapping and genome-wide association analyses. Newer methods for outcrossing, heterozygous, and polyploid plant genetics have also been discussed. The use of k-mers has enriched the nature of genetic variants which can be utilized to identify the phenotype-causing genes, independent of reference genomes. We envisage that the recent methods discussed herein will expand the repertoire of useful alleles and help in developing high-yielding and climate-resilient crops.