Estimates Of Relatedness Research Articles

Population genetic structure in a self-compatible hermaphroditic snail is driven by drift independently of its contemporary mating system.

Genetic drift, gene flow, and natural selection commonly influence population genetic diversity. In populations of self-compatible hermaphrodites, the mating system (e.g., self-fertilization) further reduces individual heterozygosity. Furthermore, selfing, as a form of inbreeding, significantly impacts genetic drift by reducing effective population size (N e). This can potentially accelerate genetic drift, particularly in small populations where self-fertilization is likely during founder events. To investigate the roles of genetic drift and contemporary mating system in populations of the freshwater snail Lymnaea stagnalis, we examined their effective population sizes (N e) and Tajima's D values, which reflect genetic drift over extended time periods, as well as estimates of within-population selfing rates and pairwise relatedness reflecting contemporary mating system. We used 4054 SNP markers obtained using restriction site associated DNA (RAD) sequencing from individuals in five snail populations originating from geographically closely located ponds. We found strong population genetic structure and differences in genetic diversity among populations. Covariation between genetic diversity and N e estimates and Tajima's D values suggested drift being an important determinant of genetic diversity and structure in these populations. However, this effect was independent of the contemporary mating system, as indicated by the similarity of selfing rates and relatedness estimates among populations. Thus, founder events (possibly including historical inbreeding) and/or drift due to small sizes of L. stagnalis populations are likely to explain their genetic structure and limit within-population genetic diversity.

Methylomonas rivi sp. nov., Methylomonas rosea sp. nov., Methylomonas aurea sp. nov. and Methylomonas subterranea sp. nov., type I methane-oxidizing bacteria isolated from a freshwater creek and the deep terrestrial subsurface.

Four methane-oxidizing bacteria, designated as strains WSC-6T, WSC-7T, SURF-1T, and SURF-2T, were isolated from Saddle Mountain Creek in southwestern Oklahoma, USA, and the Sanford Underground Research Facility (SURF) in Lead, South Dakota, USA. The strains were Gram-negative, motile, short rods that possessed intracytoplasmic membranes characteristic of type I methanotrophs. All four strains were oxidase-negative and weakly catalase-positive. Colonies ranged from pale pink to orange in colour. Methane and methanol were the only compounds that could serve as carbon and energy sources for growth. Strains WSC-6T and WSC-7T grew optimally at lower temperatures (25 and 20 °C, respectively) compared to strains SURF-1T and SURF-2T (40 °C). Strains WSC-6T and SURF-2T were neutrophilic (optimal pH of 7.5 and 7.3, respectively), while strains WSC-7T and SURF-1T were slightly alkaliphilic, with an optimal pH of 8.8. The strains grew best in media amended with ≤0.5% NaCl. The major cellular fatty acids were C14 : 0, C16 : 1 ω8c, C16 : 1 ω7c, and C16 : 1 ω5c. The DNA G+C content ranged from 51.5 to 56.0 mol%. Phylogenetic analyses indicated that the strains belonged to the genus Methylomonas, with each exhibiting 98.6-99.6% 16S rRNA gene sequence similarity to closely related strains. Genome-wide estimates of relatedness (84.5-88.4% average nucleotide identity, 85.8-92.4% average amino acid identity and 27.4-35.0% digital DNA-DNA hybridization) fell below established thresholds for species delineation. Based on these combined results, we propose to classify these strains as representing novel species of the genus Methylomonas, for which the names Methylomonas rivi (type strain WSC-6T=ATCC TSD-251T=DSM 112293T), Methylomonas rosea (type strain WSC-7T=ATCC TSD-252T=DSM 112281T), Methylomonas aurea (type strain SURF-1T=ATCC TSD-253T=DSM 112282T), and Methylomonas subterranea (type strain SURF-2T=ATCC TSD-254T=DSM 112283T) are proposed.

Prediction of resistance, virulence, and host-by-pathogen interactions using dual-genome prediction models

Key messageIntegrating disease screening data and genomic data for host and pathogen populations into prediction models provides breeders and pathologists with a unified framework to develop disease resistance.Developing disease resistance in crops typically consists of exposing breeding populations to a virulent strain of the pathogen that is causing disease. While including a diverse set of pathogens in the experiments would be desirable for developing broad and durable disease resistance, it is logistically complex and uncommon, and limits our capacity to implement dual (host-by-pathogen)-genome prediction models. Data from an alternative disease screening system that challenges a diverse sweet corn population with a diverse set of pathogen isolates are provided to demonstrate the changes in genetic parameter estimates that result from using genomic data to provide connectivity across sparsely tested experimental treatments. An inflation in genetic variance estimates was observed when among isolate relatedness estimates were included in prediction models, which was moderated when host-by-pathogen interaction effects were incorporated into models. The complete model that included genomic similarity matrices for host, pathogen, and interaction effects indicated that the proportion of phenotypic variation in lesion size that is attributable to host, pathogen, and interaction effects was similar. Estimates of the stability of lesion size predictions for host varieties inoculated with different isolates and the stability of isolates used to inoculate different hosts were also similar. In this pathosystem, genetic parameter estimates indicate that host, pathogen, and host-by-pathogen interaction predictions may be used to identify crop varieties that are resistant to specific virulence mechanisms and to guide the deployment of these sources of resistance into pathogen populations where they will be more effective.

Ecological genetics of isolated loach populations indicate compromised adaptive potential

Many endangered species live in fragmented and isolated populations with low genetic variability, signs of inbreeding, and small effective population sizes - all features elevating their extinction risk. The flat-headed loach (Oreonectes platycephalus), a small noemacheilid fish, is widely across southern China, but only in the headwaters of hillstreams; as a result, they are spatially isolated from conspecific populations. We surveyed single nucleotide polymorphisms in 16 Hong Kong populations of O. platycephalus to determine whether loach populations from different streams were genetically isolated from each other, showed low levels of genetic diversity, signs of inbreeding, and had small contemporary effective population sizes. Estimates of average observed heterozygosity (HO = 0.0473), average weighted nucleotide diversity (πw = 0.0546) and contemporary effective population sizes (Ne = 10.2 ~ 129.8) were very low, and several populations showed clear signs of inbreeding as judged from relatedness estimates. The degree of genetic differentiation among populations was very high (average FST = 0.668), even over short geographic distances (<1.5 km), with clear patterns of isolation by distance. These results suggest that Hong Kong populations of O. platycephalus have experienced strong genetic drift and loss of genetic variability because sea-level rise after the last glaciation reduced connectedness among paleodrainages, isolating populations in headwaters. All this, together with the fact that the levels of genetic diversity and contemporary effective population sizes within O. platycephalus populations are lower than most other freshwater fishes, suggests that they face high local extinction risk and have limited capacity for future adaptation.

MethylGenotyper: Accurate Estimation of SNP Genotypes and Genetic Relatedness from DNA Methylation Data.

Epigenome-wide association studies (EWAS) are susceptible to widespread confounding caused by population structure and genetic relatedness. Nevertheless, kinship estimation is challenging in EWAS without genotyping data. Here, we proposed MethylGenotyper, a method that for the first time enables accurate genotyping at thousands of single nucleotide polymorphisms (SNPs) directly from commercial DNA methylation microarrays. We modeled the intensities of methylation probes near SNPs with a mixture of three beta distributions corresponding to different genotypes and estimated parameters with an expectation-maximization algorithm. We conducted extensive simulations to demonstrate the performance of the method. When applying MethylGenotyper to the Infinium EPIC array data of 4662 Chinese samples, we obtained genotypes at 4319 SNPs with a concordance rate of 98.26%, enabling the identification of 255 pairs of close relatedness. Furthermore, we showed that MethylGenotyper allows for the estimation of both population structure and cryptic relatedness among 702 Australians of diverse ancestry. We also implemented MethylGenotyper in a publicly available R package (https://github.com/Yi-Jiang/MethylGenotyper) to facilitate future large-scale EWAS.

Detecting inbreeding depression in structured populations

Measuring inbreeding and its consequences on fitness is central for many areas in biology including human genetics and the conservation of endangered species. However, there is no consensus on the best method, neither for quantification of inbreeding itself nor for the model to estimate its effect on specific traits. We simulated traits based on simulated genomes from a large pedigree and empirical whole-genome sequences of human data from populations with various sizes and structures (from the 1,000 Genomes project). We compare the ability of various inbreeding coefficients ([Formula: see text]) to quantify the strength of inbreeding depression: allele-sharing, two versions of the correlation of uniting gametes which differ in the weight they attribute to each locus and two identical-by-descent segments-based estimators. We also compare two models: the standard linear model and a linear mixed model (LMM) including a genetic relatedness matrix (GRM) as random effect to account for the nonindependence of observations. We find LMMs give better results in scenarios with population or family structure. Within the LMM, we compare three different GRMs and show that in homogeneous populations, there is little difference among the different [Formula: see text] and GRM for inbreeding depression quantification. However, as soon as a strong population or family structure is present, the strength of inbreeding depression can be most efficiently estimated only if i) the phenotypes are regressed on [Formula: see text] based on a weighted version of the correlation of uniting gametes, giving more weight to common alleles and ii) with the GRM obtained from an allele-sharing relatedness estimator.

Relatedness-based mate choice and female philopatry: inbreeding trends of wolf packs in a human-dominated landscape

Inbreeding can reduce offspring fitness and has substantial implications for the genetic diversity and long-term viability of populations. In social cooperative canids, inbreeding is conditioned by the geographic proximity between opposite-sex kin outside natal groups and the presence of related individuals in neighbouring groups. Consequently, challenges in moving into other regions where the species is present can also affect inbreeding rates. These can be particularly problematic in areas of high human density, where movement can be restricted, even for highly vagile species. In this study, we investigate the socio-ecological dynamics of Iberian wolf packs in the human-dominated landscape of Alto Minho, in northwest Portugal, where wolves exhibit a high prevalence of short-distance dispersal and limited gene flow with neighbouring regions. We hypothesise that mating occurs regardless of relatedness, resulting in recurrent inbreeding due to high kin encounter rates. Using data from a 10-year non-invasive genetic monitoring programme and a combination of relatedness estimates and genealogical reconstructions, we describe genetic diversity, mate choice, and dispersal strategies among Alto Minho packs. In contrast with expectations, our findings reveal relatedness-based mate choice, low kin encounter rates, and a reduced number of inbreeding events. We observed a high prevalence of philopatry, particularly among female breeders, with the most common breeding strategy involving the pairing of a philopatric female with an unrelated immigrant male. Overall, wolves were not inbred, and temporal changes in genetic diversity were not significant. Our findings are discussed, considering the demographic trend of wolves in Alto Minho and its human-dominated landscape.

Construction of relatedness matrices in autopolyploid populations using low-depth high-throughput sequencing data

Key messageAn improved estimator of genomic relatedness using low-depth high-throughput sequencing data for autopolyploids is developed. Its outputs strongly correlate with SNP array-based estimates and are available in the package GUSrelate.High-throughput sequencing (HTS) methods have reduced sequencing costs and resources compared to array-based tools, facilitating the investigation of many non-model polyploid species. One important quantity that can be computed from HTS data is the genetic relatedness between all individuals in a population. However, HTS data are often messy, with multiple sources of errors (i.e. sequencing errors or missing parental alleles) which, if not accounted for, can lead to bias in genomic relatedness estimates. We derive a new estimator for constructing a genomic relationship matrix (GRM) from HTS data for autopolyploid species that accounts for errors associated with low sequencing depths, implemented in the R package GUSrelate. Simulations revealed that GUSrelate performed similarly to existing GRM methods at high depth but reduced bias in self-relatedness estimates when the sequencing depth was low. Using a panel consisting of 351 tetraploid potato genotypes, we found that GUSrelate produced GRMs from genotyping-by-sequencing (GBS) data that were highly correlated with a GRM computed from SNP array data, and less biased than existing methods when benchmarking against the array-based GRM estimates. GUSrelate provides researchers with a tool to reliably construct GRMs from low-depth HTS data.

Relatedness coefficients and their applications for triplets and quartets of genetic markers.

Relatedness coefficients which seek the identity-by-descent of genetic markers are described. The markers are in groups of two, three or four, and if four, can consist of two pairs. It is essential to use cumulants (not moments) for four-marker-gene probabilities, as the covariance of homozygosity, used in four-marker applications, can only be described with cumulants. A covariance of homozygosity between pairs of markers arises when populations follow a mixture distribution. Also, the probability of four markers all identical-by-descent equals the normalized fourth cumulant. In this article, a "genetic marker" generally represents either a gene locus or an allele at a locus. Applications of three marker coefficients mainly involve conditional regression, and applications of four marker coefficients can involve identity disequilibrium. Estimation of relatedness using genetic marker data is discussed. However, three- and four-marker estimators suffer from statistical and numerical problems, including higher statistical variance, complexity of estimation formula, and singularity at some intermediate allele frequencies.

Investigating pedigree- and SNP-associated components of heritability in a wild population of Soay sheep

Estimates of narrow sense heritability derived from genomic data that contain related individuals may be biased due to the within-family effects such as dominance, epistasis and common environmental factors. However, for many wild populations, removal of related individuals from the data would result in small sample sizes. In 2013, Zaitlen et al. proposed a method to estimate heritability in populations that include close relatives by simultaneously fitting an identity-by-state (IBS) genomic relatedness matrix (GRM) and an identity-by-descent (IBD) GRM. The IBD GRM is identical to the IBS GRM, except relatedness estimates below a specified threshold are set to 0. We applied this method to a sample of 8557 wild Soay sheep from St. Kilda, with genotypic information for 419,281 single nucleotide polymorphisms. We aimed to see how this method would partition heritability into population-level (IBS) and family-associated (IBD) variance for a range of genetic architectures, and so we focused on a mixture of polygenic and monogenic traits. We also implemented a variant of the model in which the IBD GRM was replaced by a GRM constructed from SNPs with low minor allele frequency to examine whether any additive genetic variance is captured by rare alleles. Whilst the inclusion of the IBD GRM did not significantly improve the fit of the model for the monogenic traits, it improved the fit for some of the polygenic traits, suggesting that dominance, epistasis and/or common environment not already captured by the non-genetic random effects fitted in our models may influence these traits.

Sequence-based population structure, relatedness, and inbreeding estimates for forensic autosomal STR markers

Population data have become available for sequence data to aid forensic investigations and prepare the forensic community in the move towards implementing NGS methods. This comes with a need for updated population genetic parameters estimates to allow DNA evidence evaluations using sequence data. Initial work has been done on a small sample and here we expand this work by providing estimates of population structure and relatedness for autosomal STR data generated by sequencing technologies. We also discuss the effect of inbreeding on forensic calculations and discuss why the use of genotypic-based estimates may be preferred over allelic-based estimates.

GRUPS-rs, a high-performance ancient DNA genetic relatedness estimation software relying on pedigree simulations

Background: The study of fine-grain genetic kinship ties (parents, siblings, cousins, etc.) from ancient remains is now gaining significant interest within the field of paleogenetics, as a means of deciphering the social organization of past societies. However, kinship analyses are in practice often quite difficult to apply within paleogenetic studies, and may carry a high degree of uncertainty in the results they provide, especially when applied on low coverage and/or highly degraded samples, or when studying poorly characterized populations. To overcome these challenges, most of the available kinship estimation methods either refrain from inferring ties beyond the second degree (e.g., half-siblings), and/or rely on the use of a cohort of individuals to obtain a satisfactory statistical significance. Thus, the current state of the art remains intrinsically limited when attempting to estimate kinship on a small number of individuals, or when trying to detect more distant relationships (e.g., cousins). Methods:Here, we present GRUPS-rs:an update and complete reimplementation of GRUPS (Get Relatedness Using Pedigree Simulations), an ancient DNA kinship estimation software based on the methods originally developed in (Martin et al. 2017).GRUPS-rs both computes an estimate of relatedness from randomly sampled pseudo-haploidized variant calls, and leverages high-definition pedigree simulations to bypass the use of a cohort of individuals. Results: We highlight that GRUPS and GRUPS-rs are especially suitable to perform kinship analysis on a restricted number of ancient samples, and can provide a sufficient statistical significance to estimate genetic relatedness past the second degree, while taking into account user-defined contamination and sequencing error estimates. Importantly, GRUPS-rs offers an estimated 14000-fold speed-up in runtime performance compared to its predecessor — allowing the joint estimation of kinship between dozens of individuals in a matter of minutes — and is now bundled with a user-friendly Shiny interface, in which users can interactively visualize their results. Conclusions: The GRUPS kinship estimation method is now fully operational in its "GRUPS-rs" implementation, whose use is particularly recommended when analyzing a restricted number of low coverage DNA samples.

A purebred South American breed showing high effective population size and independent breed ancestry: The Chilean Terrier.

The Chilean Terrier is a known breed in Chile that has not been genetically assessed despite its distinctive color patterns, agility, and hardiness across the diversity of climates encountered within the Chilean landscape. The population structure and its relatedness with other breeds, as well as the actual origin of the breed, remain unknown. We estimated several population parameters using samples from individuals representing the distribution of the Chilean Terrier across the country. By utilizing the Illumina HD canine genotyping array, we computed the effective population size (Ne ), individual inbreeding, and relatedness to evaluate the genetic diversity of the breed. The results show that linkage disequilibrium was relatively low and decayed rapidly; in fact, Ne was very high when compared to other breeds, and similar to other American indigenous breeds (such as the Chihuahua with values of Ne near 500). These results are in line with the low estimates of genomic inbreeding and relatedness and the relatively large number of effective chromosome segments (Me = 2467) obtained using the properties of the genomic relationship matrix. Between population analysis (cross-population extended haplotype homozygosity, di ) with other breeds such as the Jack Russell Terrier, the Peruvian-Inca Orchid, and the Chihuahua suggested that candidate regions harboring FGF5, PAX3, and ASIP, probably explained some morphological traits, such as the distinctive color pattern characteristic of the breed. When considering Admixture estimates and phylogenetic analysis, together with other breeds of American and European origin, the Chilean Terrier does not have a recent European ancestry. Overall, the results suggest that the breed has evolved independently in Chile from other terrier breeds, from an unknown European terrier ancestor.

Genetic mixing in conservation translocations increases diversity of a keystone threatened species, Bettongia lesueur.

Translocation programmes are increasingly being informed by genetic data to monitor and enhance conservation outcomes for both natural and established populations. These data provide a window into contemporary patterns of genetic diversity, structure and relatedness that can guide managers in how to best source animals for their translocation programmes. The inclusion of historical samples, where possible, strengthens monitoring by allowing assessment of changes in genetic diversity over time and by providing a benchmark for future improvements in diversity via management practices. Here, we used reduced representation sequencing (ddRADseq) data to report on the current genetic health of three remnant and seven translocated boodie (Bettongia lesueur) populations, now extinct on the Australian mainland. In addition, we used exon capture data from seven historical mainland specimens and a subset of contemporary samples to compare pre-decline and current diversity. Both data sets showed the significant impact of population founder source (whether multiple or single) on the genetic diversity of translocated populations. Populations founded by animals from multiple sources showed significantly higher genetic diversity than the natural remnant and single-source translocation populations, and we show that by mixing the most divergent populations, exon capture heterozygosity was restored to levels close to that observed in pre-decline mainland samples. Relatedness estimates were surprisingly low across all contemporary populations and there was limited evidence of inbreeding. Our results show that a strategy of genetic mixing has led to successful conservation outcomes for the species in terms of increasing genetic diversity and provides strong rationale for mixing as a management strategy.

Estimates of the Effective Population Size and Genetic Structure of the Critically Endangered Ship Sturgeon (Acipenser nudiventris) in the Chinese Section of the Ili River

Ship sturgeon (Acipenser nudiventris) is a critically endangered fish that is listed on the International Union for Conservation of Nature’s Red List of Threatened Species. Sixteen individuals from the Chinese section of the Ili River were genotyped using genome re-sequencing technology. By applying a genomic relatedness estimation with 1,527,694 genome-wide SNP markers, we found that the coancestry coefficients showed a high level of relatedness between individuals. The effective population sizes over 500 generations were estimated, and this showed that the effective population sizes began to dramatically decline from about 14,840 to 171 individuals when going back four generations from the current population. Artificial reproduction techniques guided by genomic relatedness may be a valuable approach to the conservation of this critically endangered fish species.

Graph Component Contrastive Learning for Concept Relatedness Estimation

Concept relatedness estimation (CRE) aims to determine whether two given concepts are related. Existing methods only consider the pairwise relationship between concepts, while overlooking the higher-order relationship that could be encoded in a concept-level graph structure. We discover that this underlying graph satisfies a set of intrinsic properties of CRE, including reflexivity, commutativity, and transitivity. In this paper, we formalize the CRE properties and introduce a graph structure named ConcreteGraph. To address the data scarcity issue in CRE, we introduce a novel data augmentation approach to sample new concept pairs from the graph. As it is intractable for data augmentation to fully capture the structural information of the ConcreteGraph due to a large amount of potential concept pairs, we further introduce a novel Graph Component Contrastive Learning framework to implicitly learn the complete structure of the ConcreteGraph. Empirical results on three datasets show significant improvement over the state-of-the-art model. Detailed ablation studies demonstrate that our proposed approach can effectively capture the high-order relationship among concepts.

Fine‐scale spatial genetic structure in a locally abundant native bunchgrass (Achnatherum thurberianum) including distinct lineages revealed within seed transfer zones

AbstractAnalyses of the factors shaping genetic variation in widespread plant species are important for understanding the evolutionary history and local adaptation and have applied significance for guiding conservation and restoration decisions. Thurber's needlegrass (Achnatherum thurberianum) is a widespread, locally abundant grass that inhabits heterogeneous arid environments of western North America and is of restoration significance. It is a common component of shrubland steppe communities in the Great Basin Desert, where drought, fire, and invasive grasses have degraded natural communities. Using a reduced representation sequencing approach, we generated SNP data at 5677 loci across 246 individuals from 17 A. thurberianum populations spanning five previously delineated seed zones from the western Great Basin. Analyses revealed a pronounced population genetic structure, with individuals forming consistent geographical clusters across a variety of population genetic analyses and spatial scales. Low levels of genetic diversity within populations, as well as high population estimates of linkage disequilibrium and relatedness, were consistent with self‐fertilization as a contributor to population differentiation. Variance partitioning and partial redundancy analysis (pRDA) indicated local adaptation to environment as additionally influencing the spatial distribution of genetic variation. The environmental variables driving these results were similar to those implicated in recent genecological work which inferred local adaptation for seed zone delineation. Our analyses also revealed a complex evolutionary history of A. thurberianum in the Great Basin, where previously delineated seed zones contain distantly related populations. Our results indicate evolutionary history, mating system, and differentiation across distinct geographic and environmental scales have shaped genetic variation in A. thurberianum and illustrate how numerous aspects of population genetic variation might require consideration for restoration planning.

Relatedness within colonies of three North American species of carpenter ants (Subgenus: Camponotus) and a comparison with relatedness estimates across Formicinae

Relatedness within colonies of three North American species of carpenter ants (Subgenus: Camponotus) and a comparison with relatedness estimates across Formicinae

Inferring biological kinship in ancient datasets: comparing the response of ancient DNA-specific software packages to low coverage data

BackgroundThe inference of biological relations between individuals is fundamental to understanding past human societies. Caregiving, resource sharing and sexual behaviours are often mediated by biological kinship and yet the identification and interpretation of kin relationships in prehistoric human groups is difficult. In recent years, the advent of archaeogenetic techniques have offered a fresh approach, and when combined with more traditional osteological and interpretive archaeological methods, allows for improved interpretation of the burial practices, cultural behaviours, and societal stratification in ancient societies. Although archaeogenetic techniques are developing at pace, questions remain as to their accuracy, particularly when applied to the low coverage datasets that results from the sequencing of DNA derived from highly degraded ancient material.ResultsThe performance of six of the most commonly used kinship identifcation software methods was explored at a range of low and ultra low genome coverages. An asymmetrical response was observed across packages, with decreased genome coverage resulting in differences in both direction and degree of change of calculated kinship scores and thus pairwise relatedness estimates are dependant on both package used and genome coverage. Methods reliant upon genotype likelihoods methods (lcMLkin, NGSrelate and NGSremix) show a decreased level of prediction at coverage below 1x, although were consistent in the particular relationships identified at these coverages when compared to the pseudohaploid reliant methods tested (READ, the Kennett 2017 method and TKGWV2.0). The three pseudohaploid methods show predictive potential at coverages as low as 0.05x, although the accuracy of the relationships identified is questionable given the increase in the number of relationships identifIed at the low coverage (type I errors).ConclusionTwo pseudohaploid methods (READ and Kennett 2017) show relatively consistent inference of kin relationships at low coverage (0.5x), with READ only showing a significant performance drop off at ultralow coverages (< 0.2x). More generally, our results reveal asymmetrical kinship classifications in some software packages even at high coverages, highlighting the importance of applying multiple methods to authenticate kin relationships in ancient material, along with the continuing need to develop laboratory methods that maximise data output for downstream analyses.

Molecular identification and mapping of mixed root samples to species and individuals.

This article is a Commentary on Osborne et al. (2023), 238: 1305–1317.