Phylodynamic approaches enable viral genetic variation to be tracked over time, providing insight into pathogen phylogenetic relationships and epidemiological dynamics. These are important methods for monitoring viral spread, and identifying important parameters such as transmission rate, geographic origin and duration of infection [1]. This knowledge makes it possible to adjust public health measures in real-time and was important in the case of the COVID-19 pandemic [2]. However, these approaches can be complicated to use when combining a very large number of sequences. This was particularly true during the COVID-19 pandemic, when sequencing data representing millions of entire viral genomes was generated, with associated metadata enabling their precise identification.

Danesh et al. [3] present a bioinformatics pipeline, CovFlow, for selecting relevant sequences according to user-defined criteria to produce files that can be used directly for phylodynamic analyses. The selection of sequences first involves a quality filter on the size of the sequences and the absence of unresolved bases before being able to make choices based on the associated metadata. Once the sequences are selected, they are aligned and a time-scaled phylogenetic tree is inferred. An output file in a format directly usable by BEAST 2 [4] is finally generated.

To illustrate the use of the pipeline, Danesh et al. [3] present an analysis of the Delta variant in two regions of France. They observed a delay in the start of the epidemic depending on the region. In addition, they identified genetic variation linked to the start of the school year and the extension of vaccination, as well as the arrival of a new variant. This tool will be of major interest to researchers analysing SARS-CoV-2 sequencing data, and a number of future developments are planned by the authors.

References

[1] Baele G, Dellicour S, Suchard MA, Lemey P, Vrancken B. 2018. Recent advances in computational phylodynamics. Curr Opin Virol. 31:24-32. https://doi.org/10.1016/j.coviro.2018.08.009

[2] Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. 2022. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet. 23:547-562. https://doi.org/10.1038/s41576-022-00483-8

[3] Danesh G, Boennec C, Verdurme L, Roussel M, Trombert-Paolantoni S, Visseaux B, Haim-Boukobza S, Alizon S. 2023. COVFlow: phylodynamics analyses of viruses from selected SARS-CoV-2 genome sequences. bioRxiv, ver. 7 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.06.17.496544

[4] Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H et al. 2014. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol 10: e1003537. https://doi.org/10.1371/journal.pcbi.1003537

Contamination, the presence of foreign DNA sequences in a sample of interest, is currently a major problem in genomics. Because contamination is often unavoidable at the experimental stage, it is increasingly recognized that the processing of high-throughput sequencing data must include a decontamination step. This is usually performed after the many sequence reads have been assembled into a relatively small number of contigs. Dubious contigs are then discarded based on their composition (e.g. GC-content) or because they are highly similar to a known piece of DNA from a foreign species.

Here [1], Mathieu Gautier explores a novel strategy consisting in decontaminating reads, not contigs. Why is this promising? Assembly programs and algorithms are complex, and it is not easy to predict, or monitor, how they handle contaminant reads. Ideally, contaminant reads will be assembled into obvious contaminant contigs. However, there might be more complex situations, such as chimeric contigs with alternating genuine and contaminant segments. Decontaminating at the read level, if possible, should eliminate such unfavorable situations where sequence information from contaminant and target samples are intimately intertwined by an assembler.

To achieve this aim, Gautier proposes to use methods initially designed for the analysis of metagenomic data. This is pertinent since the decontamination process involves considering a sample as a mixture of different sources of DNA. The programs used here, CLARK and CLARK-L, are based on so-called k-mer analysis, meaning that the similarity between a read to annotate and a reference sequence is measured by how many sub-sequences (of length 31 base pairs for CLARK and 27 base pairs for CLARK-L) they share. This is notoriously more efficient than traditional sequence alignment algorithms when it comes to comparing a very large number of (most often unrelated) sequences. This is, therefore, a reference-based approach, in which the reads from a sample are assigned to previously sequenced genomes based on k-mer content.

This original approach is here specifically applied to the case of Drosophila suzukii, an invasive pest damaging fruit production in Europe and America. Fortunately, Drosophila is a genus of insects with abundant genomic resources, including high-quality reference genomes in dozens of species. Having calibrated and validated his pipeline using data sets of known origins, Gautier quantifies in each of 258 presumed D. suzukii samples the proportion of reads that likely belong to other species of fruit flies, or to fruit fly-associated microbes. This proportion is close to one in 16 samples, which clearly correspond to mis-labelled individuals. It is non-negligible in another ~10 samples, which really correspond to D. suzukii individuals. Most of these reads of unexpected origin are contaminants and should be filtered out. Interestingly, one D. suzukii sample contains a substantial proportion of reads from the closely related D. subpulchera, which might instead reflect a recent episode of gene flow between these two species. The approach, therefore, not only serves as a crucial technical step, but also has the potential to reveal biological processes.

Gautier's thorough, well-documented work will clearly benefit the ongoing and future research on D. suzuki, and Drosophila genomics in general. The author and reviewers rightfully note that, like any reference-based approach, this method is heavily dependent on the availability and quality of reference genomes - Drosophila being a favorable case. Building the reference database is a key step, and the interpretation of the output can only be made in the light of its content and gaps, as illustrated by Gautier's careful and detailed discussion of his numerous results. 

This pioneering study is a striking demonstration of the potential of metagenomic methods for the decontamination of high-throughput sequence data at the read level. The pipeline requires remarkably few computing resources, ensuring low carbon emission. I am looking forward to seeing it applied to a wide range of taxa and samples.

Reference

[1] Gautier Mathieu. Efficient k-mer based curation of raw sequence data: application in Drosophila suzukii. bioRxiv, 2023.04.18.537389​, ver. 2, peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2023.04.18.537389​

One important goal of modern genetics is to establish functional associations between genotype and phenotype. Single nucleotide polymorphisms (SNPs) are numerous and widely distributed in the genome and can be obtained from nucleic acid sequencing (1). SNPs allow for the investigation of genetic diversity, which is critical for increasing crop resilience to the challenges posed by global climate change. The associations between SNPs and phenotypes can be captured in genome-wide association studies. SNPs can also be used in combination with machine learning, which is becoming more popular for predicting complex phenotypic traits like yield and biotic and abiotic stress tolerance from genotypic data (2). The availability of many SNP datasets is important in machine learning predictions because this approach requires big data to build a comprehensive model of the association between genotype and phenotype.

Aubert and colleagues have studied, as part of the PeaMUST project, the genetic diversity of 240 Pisum accessions (3). They sequenced exome-enriched genomic libraries, a technique that enables the identification of high-density, high-quality SNPs at a low cost (4). This technique involves capturing and sequencing only the exonic regions of the genome, which are the protein-coding regions. A total of 2,285,342 SNPs were obtained in this study. The analysis of these SNPs with the annotations of the genome sequence of one of the studied pea accessions (5) identified a number of SNPs that could have an impact on gene activity. Additional analyses revealed 647,220 SNPs that were unique to individual pea accessions, which might contribute to the fitness and diversity of accessions in different habitats. Phylogenetic and clustering analyses demonstrated that the SNPs could distinguish Pisum germplasms based on their agronomic and evolutionary histories. These results point out the power of selected SNPs as markers for identifying Pisum individuals.

Overall, this study found high-quality SNPs that are meaningful in a biological context. This dataset was derived from a large set of germplasm and is thus particularly useful for studying genotype-phenotype associations, as well as the diversity within Pisum species. These SNPs could also be used in breeding programs to develop new pea varieties that are resilient to abiotic and biotic stressors.  

References

1.         Fallah M, Jean M, Boucher St-Amour VT, O’Donoughue L, Belzile F. The construction of a high-density consensus genetic map for soybean based on SNP markers derived from genotyping-by-sequencing. Genome. 2022 Aug;65(8):413–25.

https://doi.org/10.1139/gen-2021-005

2.         Gill M, Anderson R, Hu H, Bennamoun M, Petereit J, Valliyodan B, et al. Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC Plant Biology. 2022 Apr 8;22(1):180.

https://doi.org/10.1186/s12870-022-03559-z

3.         Aubert G, Kreplak J, Leveugle M, Duborjal H, Klein A, Boucherot K, et al. SNP discovery by exome capture and resequencing in a pea genetic resource collection., biorxiv, ver. 4, peer-reviewed and recommended by Peer Community in Genomics.

https://doi.org/10.1101/2022.08.03.502586 

4.         Warr A, Robert C, Hume D, Archibald A, Deeb N, Watson M. Exome sequencing: current and future perspectives. G3 Genes|Genomes|Genetics. 2015 Aug 1;5(8):1543–50.

https://doi.org/10.1534/g3.115.018564

5.         Kreplak J, Madoui MA, Cápal P, Novák P, Labadie K, Aubert G, et al. A reference genome for pea provides insight into legume genome evolution. Nat Genet. 2019 Sep;51(9):1411–22.

https://doi.org/10.1038/s41588-019-0480-1

The paper from Silva et al. (2023) provides new insights into the genetic bases of natural resistance of rice to the Rice Hoja Blanca (RHB) disease, one of its most serious diseases in tropical countries of the American continent and the Caribbean. This disease is caused by the Rice Hoja Blanca Virus, or RHBV, the vector of which is the planthopper insect Tagosodes orizicolus Müir. It is responsible for serious damage to the rice crop (Morales and Jennings 2010). The authors take a Quantitative Trait Loci (QTL) detection approach to find genomic regions statistically associated with the resistant phenotype. To this aim, they use four resistant x susceptible crosses (the susceptible parent being the same in all four crosses) to maximize the chances to find new QTLs. The F2 populations derived from the crosses are genotyped using Single Nucleotide Polymorphisms (SNPs) extracted from whole-genome sequencing (WGS) data of the resistant parents, and the F3 families derived from the F2 individuals are scored for disease symptoms. For this, they use a computer-aided image analysis protocol that they designed so they can estimate the severity of the damages in the plant. They find several new QTLs, some being apparently more associated with disease severity, others with disease incidence. They also find that a previously identified QTL of Oryza sativa ssp. japonica origin is also present in the indica cluster (Romero et al. 2014). Finally, they discuss the candidate genes that could underlie the QTLs and provide a simple model for resistance.

It has to be noted that scoring symptoms of a viral disease such as RHB is very challenging. It requires maintaining populations of viruliferous insect vectors, mastering times and conditions for infestation by nymphs, and precise symptom scoring. It also requires the preparation of segregating populations, their genotyping with enough genetic markers, and mastering QTL detection methods. All these aspects are present in this work. In particular, the phenotyping of symptom severity implemented using computer-aided image processing represents an impressive, enormous amount of work.

From the genomics side, the fine-scale genotyping is based on the WGS of the parental lines (resistant and susceptible), followed by the application of suitable bioinformatic tools for SNP extraction and primers prediction that can be used on their Fluidigm platform. It also required implementing data correction algorithms to achieve precise genetic maps in the four crosses. The QTL detection itself required careful statistical pre-processing of phenotypic data. The authors then used a combination of several QTL detection methods, including an original meta-QTL method they developed in the software MapDisto. 

The authors then perform a very complete and convincing analysis of candidate genes, which includes genes already identified for a similar disease (RSV) on chromosome 11 of rice. What remains to elucidate is whether the candidate genes are actually involved or not in the disease resistance process. The team has already started implementing gene knockout strategies to study some of them in more detail. It will be interesting to see whether those genes act against the virus itself, or against the insect vector. 

Overall the work is of high quality and represents an important advance in the knowledge of disease resistance. In addition, it has many implications for crop breeding, allowing the setup of large-scale, marker-assisted strategies, for new resistant elite varieties of rice.

References

Morales F and Jennings P (2010) Rice hoja blanca: a complex plant-virus-vector pathosystem. CAB Reviews. https://doi.org/10.1079/PAVSNNR20105043

Romero LE, Lozano I, Garavito A, et al (2014) Major QTLs control resistance to Rice hoja blanca virus and its vector Tagosodes orizicolus. G3 | Genes, Genomes, Genetics 4:133–142. https://doi.org/10.1534/g3.113.009373

Silva A, Montoya ME, Quintero C, Cuasquer J, Tohme J, Graterol E, Cruz M, Lorieux M (2023) Genetic bases of resistance to the rice hoja blanca disease deciphered by a QTL approach. bioRxiv, 2022.11.07.515427, ver. 2 peer-reviewed and recommended by Peer Community in Genomics https://doi.org/10.1101/2022.11.07.515427

Data interpretation depends on previously established and validated tools, designed for a specific type of data. These methods, however, are usually based on simple models with validity subject to a set of theoretical parameterized conditions and data types. Accordingly, the tool developers provide the potential users with guidelines for data interpretations within the tools’ limitation. Nevertheless, once the methodology is accepted by the community, it is employed in a large variety of empirical studies outside of the method’s original scope or that typically depart from the standard models used for its design, thus potentially leading to the wrong interpretation of the results.

Numerous empirical studies inferred recombination rates across genomes, detecting hotspots of recombination and comparing related species (e.g., Shanfelter et al. 2019, Spence and Song 2019). These studies used indirect methodologies based on the signals that recombination left in the genome, such as linkage disequilibrium and the patterns of haplotype segregation (e.g.,Chan et al. 2012). The conclusions from these analyses have been used, for example, to interpret the evolution of the chromosomal structure or the evolution of recombination among closely related species.

Indirect methods have the advantage of collecting a large quantity of recombination events, and thus have a better resolution than direct methods (which only detect the few recombination events occurring at that time). On the other hand, indirect methods are affected by many different evolutionary events, such as demographic changes and selection. Indeed, the inference of recombination levels across the genome has not been studied accurately in non-standard conditions. Linkage disequilibrium is affected by several factors that can modify the recombination inference, such as demographic history, events of selection, population size, and mutation rate, but is also related to the size of the studied sample, and other technical parameters defined for each specific methodology.

Raynaud et al (2023) analyzed the reliability of the recombination rate inference when considering the violation of several standard assumptions (evolutionary and methodological) in one of the most popular families of methods based on LDhat (McVean et al. 2004), specifically its improved version, LDhelmet (Chan et al. 2012). These methods cover around 70 % of the studies that infer recombination rates. The authors used recombination maps, obtained from empirical studies on humans, and included hotspots, to perform a detailed simulation study of the capacity of this methodology to correctly infer the pattern of recombination and the location of these hotspots. Correlations between the real, and inferred values from simulations were obtained, as well as several rates, such as the true positive and false discovery rate to detect hotspots.

The authors of this work send a message of caution to researchers that are applying this methodology to interpret data from the inference of recombination landscapes and the location of hotspots. The inference of recombination landscapes and hotspots can differ considerably even in standard model conditions. In addition, demographic processes, like bottleneck or admixture, but also the level of population size and mutation rates, can substantially affect the estimation accuracy of the level of recombination and the location of hotspots. Indeed, the inference of the location of hotspots in simulated data with the same landscape, can be very imprecise when standard assumptions are violated or not considered. These effects may lead to incorrect interpretations, for example about the conservation of recombination maps between closely related species. Finally, Raynaud et al (2023) included a useful guide with advice on how to obtain accurate recombination estimations with methods based on linkage disequilibrium, also emphasizing the limitations of such approaches.

REFERENCES

Chan AH, Jenkins PA, Song YS (2012) Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster. PLOS Genetics, 8, e1003090. https://doi.org/10.1371/journal.pgen.1003090

McVean GAT, Myers SR, Hunt S, Deloukas P, Bentley DR, Donnelly P (2004) The Fine-Scale Structure of Recombination Rate Variation in the Human Genome. Science, 304, 581–584. https://doi.org/10.1126/science.1092500

Raynaud M, Gagnaire P-A, Galtier N (2023) Performance and limitations of linkage-disequilibrium-based methods for inferring the genomic landscape of recombination and detecting hotspots: a simulation study. bioRxiv, 2022.03.30.486352, ver. 2 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2022.03.30.486352

Spence JP, Song YS (2019) Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations. Science Advances, 5, eaaw9206. https://doi.org/10.1126/sciadv.aaw9206