Pittion et al. (2025) introduce an R package called hdmax2, which implements an enhanced version of the “High-Dimensional Mediation Analysis using the Max-Squared” (HDMAX2) method originally proposed by Jumentier et al. (2023) for high-dimensional mediation analysis. The goal of mediation analysis is to quantify the indirect effect of a variable M in the causal relationship between exposure X and outcome Y. The fundamental concept behind HDMAX2 methods is to use a latent factor mixed model to estimate the effects of unobserved confounders and a max-squared test to identify significant mediators. The HDMAX2 method represents a significant advancement in the case of high-dimensional mediation, such as DNA methylation or gene expression analysis, where the number of mediators often far exceeds the sample size.

The main contributions of this article are the implementation of the HDMAX2 method as an R package, and an extension of the original method to binary outcomes and to binary, categorical, and multivariate exposures, as opposed to only continuous variables. The package includes visualization tools, helper functions for mediator selection, and options for handling multivariate exposures. A key strength of the package lies in its versatility. The new package, hdmax2, accommodates a variety of data types. This flexibility makes it a valuable tool for researchers analyzing high-throughput molecular data. Finally to illustrate this flexibility, the authors present two case studies that were not described in the Jumentier et al. (2023) analysis. In the first case study, the authors employed mediation analysis to assess the potential causal role of DNA methylation in the pathway linking the HER2 status of breast cancer (a marker for an aggressive breast cancer subtype) to a survival risk score, which was derived from a six-gene expression signature and is inversely correlated with patient survival. In the second case study, the authors conducted mediation analysis to explore the role of gene expression in the pathway linking patient gender to the occurrence of multiple sclerosis specific subtypes: clinically isolated syndrome and relapsing-remitting multiple sclerosis. These illustrate the relevance of hdmax2 to study the transcriptome and the methylome.

In conclusion, the hdmax2 R package will be invaluable for handling high-dimensional molecular data in the study of the intricate pathways through which exposures influence health outcomes.

References

Jumentier B, Barrot C-C, Estavoyer M, Tost J, Heude B, François O, Lepeule J (2023) High-dimensional mediation analysis: A new method applied to maternal smoking, placental DNA methylation, and birth outcomes. Environmental Health Perspectives, 131, 047011. https://doi.org/10.1289/EHP11559

Pittion F, Jumentier B, Nakamura A, Lepeule J, Francois O, Richard M (2025) hdmax2, an R package to perform high dimension mediation analysis. HAL, ver. 4 peer-reviewed and recommended by PCI Genomics. https://hal.science/hal-04658960 

Parasitic worms infect billions of animals worldwide. While parasitism is now considered a context-dependent relation along a symbiosis continuum, most of these parasitic worms, also known as helminths, can cause diseases that have a significant impact (Hopkins et al. 2017; Selzer, Epe 2021). When considering livestock animals, these impacts have a high economic cost, and therefore, prophylactic drugs are widely used (Selzer and Epe 2021). Consequently, drug resistance has become increasingly common across all parasites and concerns about drug effects on non-target organisms have been raised (de Souza and Guimarães 2022). This is why understanding the relationship between parasitic worms and their animal hosts and the diseases they cause at the genetic and molecular level is high on the agenda of parasitologists (Doyle 2022). The development of genomics resources plays a pivotal role in this agenda and is at the origin of Sallé and colleagues' article (2025).

The most common intestinal parasites in equids are helminths of the cyathostomin nematode complex. These are the primary parasitic cause of death in young horses and also exhibit a reduced sensitivity to anthelmintic drugs. Therefore, Sallé and colleagues embarked on the arduous journey to build a reference annotated genome of the Cylicocylus nassatus nematode. They used cutting-edge molecular genetics methods to amplify and sequence the genome of a single individual and obtained chromosomal-level contiguity using Hi-C technology for six chromosomes and an assembly of 514.7 Mbp. Remarkably, transposable elements occupy more than half of the C. nassatus genome and may have led to an increase in genome size in this nematode. In parallel, the authors built a gene catalogue using transcriptomic data, reaching a BUSCO gene completion score of 94.1% with 22,718 protein-coding genes. They quantified allele frequencies based on the resequencing of nine populations, including an ancient Egyptian worm from the 19th century, indicating a recent loss of genetic diversity in European cyathostomin even if geographical sampling  was limited. They also analysed transcriptomic differences between sexes and found differences linked with drug treatment. While there may be confounding effects due to global differences between sex that could explain this finding, these results will likely fuel future transcriptomic analyses investigating the response to antiparasitic drugs.

The Cylicocylus nassatus genome assembly obtained will be invaluable for studying nematode genome evolution and analysing the genetic and molecular basis of drug resistance in these parasites. 

References

Doyle SR (2022) Improving helminth genome resources in the post-genomic era. Trends in Parasitology, 38, 831–840. https://doi.org/10.1016/j.pt.2022.06.002

Hopkins SR, Wojdak JM, Belden LK (2017) Defensive symbionts mediate host–parasite interactions at multiple scales. Trends in Parasitology, 33, 53–64. https://doi.org/10.1016/j.pt.2016.10.003

Sallé G, Courtot É, Cabau C, Parrinello H, Serreau D, Reigner F, Gesbert A, Jacquinot L, Lenhof O, Aimé A, Picandet V, Kuzmina T, Holovachov O, Bellaw J, Nielsen MK, Samson-Himmelstjerna G von, Valière S, Gislard M, Lluch J, Kuchly C, Klopp C (2024) Spatio-temporal diversity and genetic architecture of pyrantel resistance in Cylicocyclus nassatus, the most abundant horse parasite. bioRxiv, ver. 2 peer-reviewed and recommended by PCI Genomics https://doi.org/10.1101/2023.07.19.549683

Selzer PM, Epe C (2021) Antiparasitics in animal health: quo vadis? Trends in Parasitology, 37, 77–89. https://doi.org/10.1016/j.pt.2020.09.004

de Souza RB, Guimarães JR (2022) Effects of avermectins on the environment based on its toxicity to plants and soil invertebrates–a review. Water, Air, and Soil Pollution, 233, 259. https://doi.org/10.1007/s11270-022-05744-0

Dating evolutionary trees is a critical task that allows us to connect biological history to ecological and geological events, helping us explore connections between environmental change and genetic innovations. The central idea behind these techniques is to link changes at the sequence level to divergence times, under the general assumption that substitutions accumulate steadily over time. So, sequences that diverged earlier are expected to be more different than sequences that diverged more recently. For a number of biological and statistical reasons, the relationship between sequence divergence and time is not linear, so it is not always the case that more divergent sequences have accumulated more substitutions than less divergent ones. In the case of organismal-level divergences, a natural approach to mitigate these challenges is to incorporate as many genes as possible into the analyses. However, this route is not available when we are focusing our interest on a single gene or a gene family. Thus, exploring how different features of single gene trees impact the accuracy and precision of divergence time estimates is of interest. In this study, Louvel and Roest Crollius (2024), select a well-studied group of mammals, primates, extract single copy genes from their genomes, and explore how different factors such as alignment size, evolutionary rate variation and discordance between the gene and species trees impact divergence time estimates. 

There are many strengths of this study. The central ones are the number of factors considered and the transparent discussion of the limitations. In this regard, the study is an elegant combination of empirical and simulated data. Some of the results match intuitive expectations. For example, the authors find that longer alignments are more informative than shorter ones, that differences in evolutionary rate among branches lead to loss in precision, and that slow-evolving genes perform worse. Intriguingly, they also find differences in performance among genes with different ontologies. The empirical data used in this study is limited to a single group, and generally considers genes that have apparently remained as single copies. Accordingly, the conclusions that can be drawn are somewhat limited, calling for future studies building on and expanding the concepts of the study by Louvel and colleagues. For example, including genes that have been lost or duplicated would be of interest because changes in gene complement are a prevalent source of variation at the genome level in mammals in general (Demuth et al. 2006), and particularly in primates (Hahn et al. 2007).

References

Demuth JP, De Bie T, Stajich JE, Cristianini N, Hahn MW (2006) The evolution of mammalian gene families. PLoS One, e85. https://doi.org/10.1371/journal.pone.0000085

Hahn MW, Demuth JP, Han SG (2007) Accelerated rate of gene gain and loss in primates. Genetics, 177,1941-1949. https://doi.org/10.1534/genetics.107.080077

Louvel, G and Roest Crollius, H (2024) Factors influencing the accuracy and precision in dating single gene trees. bioRxiv, ver. 6 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2020.08.24.264671

One way to identify all organisms at their various life stages is by their genetic signature. DNA-based taxonomy, gene tagging and barcoding are different shortcuts used to name such strategies (Lamb et al. 2019; Tautz et al. 2003). Reading and analyzing nucleic acid sequences to perform genetic inventories is now faster than ever, and the latest nucleic acid sequencing technologies reveal an impressive taxonomic, genetic, and functional diversity hidden in all ecosystems (Lamb et al. 2019; Sunagawa et al. 2015). This knowledge should enable us to evaluate biodiversity across its scales, from genetic to species to ecosystem and is sometimes referred to with the neologism of ecogenomics (Dicke et al. 2004).

The metabarcoding approach is a key workhorse of ecogenomics. At the core of metabarcoding strategies lies the sequencing of amplicons obtained from so-called multi-template PCR, a formidable and potent experiment with the potential to unravel hidden biosphere components from different samples obtained from organisms or the environment (Kalle et al. 2014; Rodríguez-Ezpeleta et al. 2021). Next to this core approach, and equally important, lies the bioinformatic analysis to convert the raw sequencing data into amplicon sequence variants or operational taxonomic units and interpretable abundance tables.

Methodologically, the analysis of sequences obtained from metabarcoding projects is replete with devilish details. This is why different pipelines and tools have been developed, starting with mothur (Schloss et al. 2009) and QIIME 2 (Bolyen et al. 2019), but including more user friendly tools such as FROGS (Escudié et al. 2018). Yet, across all available tools, scientists must choose the optimal algorithms and parameter values to filter raw reads, trim primers, identify chimeras and cluster reads into operational taxonomic units. In addition, the number of genetic markers used to characterize a sample using metabarcoding has increased as  sequencing methods are now less costly and more efficient. In such cases, results and interpretations may become limited or confounded. This is where the novel tools proposed by Barnabé and colleagues (2024), mbctools, will benefit researchers in this field.

The authors provide a detailed description with a walk-through of the mbctools pipeline to analyse raw reads obtained in a metabarcoding project. The mbctools pipeline can be installed under different computing environments, requires only VSEARCH and a few Python dependencies, and is easy to use with a menu-driven interface. Users need to prepare their data following simple rules, providing single or paired-end reads, primer and target database sequences. An interesting feature of mbctools output is the possibility of integration with the metaXplor visualization tool developed by the authors (Sempéré et al. 2021). As it stands, mbctools should be used for short-read sequences. The taxonomy assignment module has the advantage to enable parameters exploration in an easy way, but it may be oversimplistic for specific taxa.

The lightweight aspect of mbctools and its overall simplicity are appealing. These features will make it a useful pipeline for training workshops and to help disseminate the use of metabarcoding. It also holds the potential for further improvement, by the developers or by others. In the end, mbctools will support study reproducibility by enabling a streamlined analysis of raw reads, and like many useful tools, only time will tell whether it is widely adopted.

         
References

Barnabé C, Sempéré G, Manzanilla V, Millan JM, Amblard-Rambert A, Waleckx E (2024) mbctools: A user-friendly metabarcoding and cross-platform pipeline for analyzing multiple amplicon sequencing data across a large diversity of organisms. bioRxiv, ver. 2 peer-reviewed and recommended by PCI Genomics https://doi.org/10.1101/2024.02.08.579441

Bolyen E, Rideout JR, Dillon MR, Bokulich NA, et al. (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9

Dicke M, van Loon JJA, de Jong PW (2004) Ecogenomics benefits community ecology. Science, 305, 618–619. https://doi.org/10.1126/science.1101788

Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, Maman S, Hernandez-Raquet G, Combes S, Pascal G (2018) FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics, 34, 1287-1294. https://doi.org/10.1093/bioinformatics/btx791

Kalle E, Kubista M, Rensing C (2014) Multi-template polymerase chain reaction. Biomolecular Detection and Quantification, 2, 11–29. https://doi.org/10.1016/j.bdq.2014.11.002

Lamb CT, Ford AT, Proctor MF, Royle JA, Mowat G, Boutin S (2019) Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems. Ecological Applications, 29, e01876. https://doi.org/10.1002/eap.1876

Rodríguez-Ezpeleta N, Zinger L, Kinziger A, Bik HM, Bonin A, Coissac E, Emerson BC, Lopes CM, Pelletier TA, Taberlet P, Narum S (2021) Biodiversity monitoring using environmental DNA. Molecular Ecology Resources, 21, 1405–1409. https://doi.org/10.1111/1755-0998.13399

Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75, 7537-41. https://doi.org/10.1128/AEM.01541-09

Sempéré G, Pétel A, Abbé M, Lefeuvre P, Roumagnac P, Mahé F, Baurens G, Filloux D 2021 metaXplor: an interactive viral and microbial metagenomic data manager. Gigascience, 10, https://doi.org/10.1093/gigascience/giab001

Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, Djahanschiri B, Zeller G, Mende DR, Alberti A, Cornejo-Castillo FM, Costea PI, Cruaud C, d’Ovidio F, Engelen S, Ferrera I, Gasol JM, Guidi L, Hildebrand F, Kokoszka F, Lepoivre C, Lima-Mendez G, Poulain J, Poulos BT, Royo-Llonch M, Sarmento H, Vieira-Silva S, Dimier C, Picheral M, Searson S, Kandels-Lewis S, Tara Oceans coordinators, Bowler C, de Vargas C, Gorsky G, Grimsley N, Hingamp P, Iudicone D, Jaillon O, Not F, Ogata H, Pesant S, Speich S, Stemmann L, Sullivan MB, Weissenbach J, Wincker P, Karsenti E, Raes J, Acinas SG, Bork P (2015) Structure and function of the global ocean microbiome. Science, 348, 1261359. https://doi.org/10.1126/science.1261359

Tautz D, Arctander P, Minelli A, Thomas RH, Vogler AP (2003) A plea for DNA taxonomy. Trends in Ecology & Evolution, 18, 70–74. https://doi.org/10.1016/S0169-5347(02)00041-1

While it is widely agreed that the conservation of local breeds is key to the maintenance of livestock biodiversity, the implementation of such programmes is often carried out by amateur breeders and may be inadequate due to a lack of knowledge and financial resources. Bortoluzzi et al. (2024) clearly demonstrate the utility of whole-genome sequencing (WGS) data for this purpose, compare two scenarios that differ in the consistency of conservation efforts, and provide valuable recommendations for conservation programmes.

Genetic diversity in livestock is generally considered to be crucial to maintaining food security and ensuring the provision of necessary nutrients to humans (Godde et al. 2021). It is also important to recognise that the preservation of local breeds is a matter of cultural identity for certain regions, and that the products of these breeds are niche products which are in high demand. Especially today, as we face extreme weather conditions, drought and other consequences of global warming, modern breeds selected to perform under constant and temperate conditions are being challenged. The possibility of tapping into the reservoir of genetic variation held by traditional, locally adapted breeds offers an important option for breeding robust livestock. The best way to characterise genetic diversity is through modern molecular methods, based on whole genome sequencing and subsequent advanced population analyses, which has been demonstrated for domesticated and wild chicken (Qanbari et al. 2019).

But are local breed conservation programmes up to the task? In their article, Bortoluzzi and colleagues show that well-designed and professionally managed conservation programmes for local chicken breeds are effective in maintaining genetic diversity. Their article is based on a comparison of two examples of conservation programmes for local chicken breeds: the Barbezieux and the Gasconne, which originated from comparably sized founder populations and for which WGS data were available in a biobank at two timepoints, 2003 and 2013, representing 10 generations. While the conservation programme for the former was continuous, that for the latter was interrupted and later started from scratch with a small number of sires and dams.

The greater loss of genomic diversity in the Gasconne than in the Barbezieux shown in this article may therefore be unsurprising, but the authors provide a range of evidence for this using their population genomics toolbox. The less well-managed breed, Gasconne, shows a lower genome-wide heterozygosity, higher lengths of runs of homozygosity, higher levels of genomic inbreeding, a smaller effective population size and a higher genetic load in terms of predicted deleterious mutations. 

The sample sizes available for population genetic analyses are typically small for local breeds, which is difficult to change as the populations are very small at any given time. It is therefore all the more important to make the most out of it, and Bortoluzzi and co-authors approach the issue from several angles that help support their claim, using WGS data and the latest genomic resources.

In addition to their analyses, the authors provide clear and valuable advice for the management of such conservation programmes. Their analysis of signatures of selection suggests that, apart from adult fertility, not much selection has been taking place. However, the authors emphasise that clear selection objectives other than maintaining the breed, such as production and product quality, can help conservation efforts by providing better guidelines for managing the programme and increasing the availability of resources for conservation programmes when the products of these local breeds become successful. 

In summary, Bortoluzzi et al. (2024) have provided a clear, well-written account of the impact of conservation programme management on the genetic diversity of local chicken breeds, using the most up-to-date genomic resources and analysis methods. As such, this article makes a significant and valuable contribution to the maintenance of genomic resources in livestock, providing approaches and lessons that will hopefully be adopted by other such initiatives.

            
References

Bortoluzzi C, Restoux G, Rouger R, Desnoues B, Petitjean F, Bosse M, Tixier-Boichard M (2024) Trends in genome diversity of small populations under a conservation program: a case study of two French chicken breeds. bioRxiv, ver. 2 peer-reviewed and recommended by PCI Genomics. https://doi.org/10.1101/2024.02.22.581528

Godde CM, Mason-D’Croz D, Mayberry DE, Thornton PK, Herrero M (2021) Impacts of climate change on the livestock food supply chain; a review of the evidence. Global Food Security 28:100488. https://doi.org/10.1016/j.gfs.2020.100488

Qanbari S, Rubin C-J, Maqbool K, Weigend S, Weigend A, Geibel J, Kerje S, Wurmser C, Peterson AT, IL Brisbin Jr., Preisinger R, Fries R, Simianer H, Andersson L (2019) Genetics of adaptation in modern chicken. PLOS Genetics, 15, e1007989. https://doi.org/10.1371/journal.pgen.1007989