Biology subjects

de Jong, M. J.

Publications and source records attributed to de Jong, M. J..

5 recordsLinked to original sources

bioRxiv2025

Species delimitation using genome-wide estimates of Dxy and Fst

Accurate and stable species delimitation is essential for effective nature conservation and management. Advances in genomics now offer the potential for consistent decision-making. We performed a meta-analysis of genomic data from hundreds of sister lineages of large mammals to test whether species boundaries can be identified using genome-wide sequence dissimilarity estimates. We find that a combined threshold of absolute (Dxy = 0.225% [0.20%-0.27%]) and relative genetic distance (Fst = 0.26 [0.19-0.54]) predicts current taxonomic status with over 90% accuracy. This predictive power indicates that mammalian taxonomists managed to maintain consistency across taxa despite having to rely on disparate morphological traits. Lineage pairs exhibiting Haldanes Rule exceed far higher thresholds (Dxy = 0.4%; Fst = 0.55), implying that mammalian taxonomists adhere to species concepts which allow for interbreeding. Our findings challenge the significance of fast-track bottleneck speciation and suggest instead that deep divergences of up to 100,000 generations are usually required for mammalian sister lineages to remain genetically isolated upon secondary contact. We discuss taxonomic revisions to improve temporal banding, including three potential cases of cryptic speciation: southern versus eastern aardwolves, Asiatic black bears versus Japanese black bears, and European versus Asian wild boar. In summary, our meta-analysis provides simple rules for species delimitation and offers new insights into the taxonomy and speciation dynamics of large mammals.

genomics↗

bioRxiv2025

DIST: Distance-based Inference of Species Trees

Inferring species trees from concatenated loci is often criticised for failing to account for gene tree discordance - particularly when using character-based methods. However, this criticism does not apply to distance-based concatenation trees, which can be shown to be statistically consistent even in anomaly zones. Building on this insight, we introduce DIST (Distance-based Inference of Species Trees), an intuitive and scalable method that infers species trees from population-level distance matrices containing multi-locus estimates of Dxy, FST or coalescence units ({tau}). DIST derives these values from between-individual sequence dissimilarity estimates, E(p), using basic equations from coalescence theory. Under certain conditions, DIST can also quantify gene tree discordance and distinguish whether it arises from gene flow or incomplete lineage sorting alone. While conceptually related to more sophisticated summary methods, DIST differs in that it does not seek the species tree which best explains a set of gene trees. Instead, it searches for the species tree which best explains an average gene tree, of which all branch lengths reflect mean coalescence time, E(t). Although this average gene tree is rarely observed empirically, it is approximated by an individual-level distance-based tree, traditionally referred to as a tree of individuals. The DIST algorithm is implemented in the R package SambaR, which now accepts input in the form of pairwise E(p) estimates.

bioinformatics↗

bioRxiv2025

Chromosome-level genome assembly of the lemon sole Microstomus kitt (Pleuronectiformes: Pleuronectidae)

BackgroundThe lemon sole (Microstomus kitt) is a culinary fish from the family of righteye flounders (Pleuronectidae) inhabiting sandy and shallow offshore grounds of the North Sea, the western Baltic Sea, the English Channel, the shallow waters of Great Britain and Ireland as well as the Bay of Biscay and the coastal waters of Norway. FindingsHere, we present the chromosome-level genome assembly of the lemon sole. We applied PacBio HiFi sequencing on the PacBio Revio system to generate a highly complete and contiguous reference genome. The resulting assembly has a contig N50 of 17.2 Mbp and a scaffold N50 of 27.2 Mbp. The total assembly length is 628 Mbp, of which 616 Mbp were scaffolded into 24 chromosome-length scaffolds. The identification of 99.7% complete BUSCO genes indicates a high assembly completeness. ConclusionsThe chromosome-level genome assembly of the lemon sole provides a high-quality reference genome for future population genomic analyses of a commercially valuable edible fish.

evolutionary biology↗

bioRxiv2024

Calculating and interpreting FST in the genomics era

The relative genetic distance between populations is commonly measured using the fixation index (FST). Traditionally inferred from allele frequency differences, the question arises how FST can be estimated and interpreted when analysing genomic datasets with low sample sizes. Here, we advocate an elegant solution first put forward by Hudson et al. (1992): FST = (Dxy -{pi} xy)/Dxy, where Dxy and{pi} xy denote mean sequence dissimilarity between and within populations, respectively. This multi-locus FST-metric can be derived from allele frequency data, but also from sequence alignment data alone, even when sample sizes are low and/or unequal. As with other FST-metrices, the numerator denotes net divergence (Da), which is equivalent to the f2-statistic and Neis D (for realistic estimates of Dxy and{pi} xy). In terms of demographic inference, net divergence measures the difference in increase of Dxy and{pi} xy since the population split, owing to a reduction of coalescence times within populations as a result of genetic drift. Because different combinations of{Delta} Dxy and{Delta}{pi} xy can produce identical FST-estimates, no universal relationship exists between FST and population split time. Still, in case of recent population splits, when novel mutations are negligible, FST-estimates can be accurately converted into coalescent units ({tau}. i.e., split time in multiples of 2Ne). This then allows to quantify gene tree discordance, without the need for multispecies coalescent based analyses, using the formula: Pdiscordance = [2/3]{middle dot}(1 - FST). To facilitate the use of the Hudson FST-metric, we implemented new utilities in the R package SambaR.

genomics↗

bioRxiv2020

SambaR: an R package for fast, easy and reproducible population-genetic analyses of biallelic SNP datasets

BackgroundSNP datasets can be used to infer a wealth of information about natural populations, including information about their structure, genetic diversity, and the presence of loci under selection. However, SNP data analysis can be a time-consuming and challenging process, not in the least because at present many different software packages are needed to execute and depict the wide variety of mainstream population-genetic analyses. Here we present SambaR, an integrative and user-friendly R package which automates and simplifies quality control and population-genetic analyses of biallelic SNP datasets. SambaR allows users to perform mainstream population-genetic analyses and to generate a wide variety of ready to publish graphs with a minimum number of commands (less than ten). These wrapper commands call functions of existing packages (including adegenet, ape, LEA, poppr, pcadapt and StAMPP) as well as new tools uniquely implemented in SambaR. ResultsWe tested SambaR on online available SNP datasets and found that SambaR can process datasets of millions of SNPs and hundreds of individuals within hours, given sufficient computing power. Newly developed tools implemented in SambaR facilitate optimization of filter settings, objective interpretation of ordination analyses, enhance comparability of diversity estimates from reduced representation library SNP datasets, and generate reduced SNP panels and structure-like plots with Bayesian population assignment probabilities. ConclusionSambaR facilitates rapid population genetic analyses on biallelic SNP datasets by removing three major time sinks: file handling, software learning, and data plotting. In addition, SambaR provides a convenient platform for SNP data storage and management, as well as several new utilities, including guidance in setting appropriate data filters. Availability and implementationThe SambaR source script, manual and example datasets are distributed through GitHub: https://github.com/mennodejong1986/SambaR

genetics↗