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Attempts to identify a 'homeland' for our species from genetic data are widespread in the academic literature. However, even when putting aside the question of whether a 'homeland' is a useful concept, there are a number of inferential pitfalls in attempting to identify the geographic origin of a species from contemporary patterns of genetic variation. These include making strong claims from weakly informative data, treating genetic lineages as representative of populations, assuming a high degree of regional population continuity over hundreds of thousands of years, and using circumstantial observations as corroborating evidence without considering alternative hypotheses on an equal footing, or formally evaluating any hypothesis. In this commentary we review the recent publication that claims to pinpoint the origins of 'modern humans' to a very specific region in Africa (Chan et al., 2019), demonstrate how it fell into these inferential pitfalls, and discuss how this can be avoided.

Attempts to identify a 'homeland' for our species from genetic data are widespread in the academic literature. However, even when putting aside the question of whether a 'homeland' is a useful concept, there are a number of inferential pitfalls in attempting to identify the geographic origin of a species from contemporary patterns of genetic variation. These include making strong claims from weakly informative data, treating genetic lineages as representative of populations, assuming a high degree of regional population continuity over hundreds of thousands of years, and using circumstantial observations as corroborating evidence without considering alternative hypotheses on an equal footing, or formally evaluating any hypothesis. In this commentary we review the recent publication that claims to pinpoint the origins of 'modern humans' to a very specific region in Africa (Chan et al., 2019), demonstrate how it fell into these inferential pitfalls, and discuss how this can be avoided.

Genotyping-by-sequencing methods such as RADseq are popular for generating genomic and population-scale data sets from a diverse range of organisms. These often lack a usable reference genome, restricting users to RADseq specific software for processing. However, these come with limitations compared to generic next generation sequencing (NGS) toolkits. Here, we describe and test a simple pipeline for reference-free RADseq data processing that blends de novo elements from STACKS with the full suite of state-of-the art NGS tools. Specifically, we use the de novo RADseq assembly employed by STACKS to create a catalogue of RAD loci that serves as a reference for read mapping, variant calling and site filters. Using RADseq data from 28 zebra sequenced to ~8x depth-of-coverage we evaluate our approach by comparing the site frequency spectra (SFS) to those from alternative pipelines. Most pipelines yielded similar SFS at 8x depth, but only a genotype likelihood based pipeline performed similarly at low sequencing depth (2–4x). We compared the RADseq SFS with medium-depth (~13x) shotgun sequencing of eight overlapping samples, revealing that the RADseq SFS was persistently slightly skewed towards rare and invariant alleles. Using simulations and human data we confirm that this is expected when there is allelic dropout (AD) in the RADseq data. AD in the RADseq data caused a heterozygosity deficit of ~16%, which dropped to ~5% after filtering AD. Hence, AD was the most important source of bias in our RADseq data.

We investigated the genetic composition of six Canis remains from western Iberia, directly radiocarbon dated to 7,903-7,570 years (cal BP). They were identified as dogs via their archaeological and depositional context, osteometry, and a high percentage of aquatic diet shared with humans. For comparison, genetic data were obtained from an additional 37 Iberian dog remains from the Neolithic to Late Antiquity, as well as two Palaeolithic and a Chalcolithic Canis identified as wolves. Previous data indicated that dog mtDNA haplogroup A (HgA) is prevalent in extant European dogs ( > 50%), in the Near East and Asia, but rare or absent ( < 10%) in European Canis older than 3,000 years (cal BP). We found a high frequency (83%) of dog HgA in Mesolithic Iberian dog remains. This is the first report of a high frequency of dog HgA in pre-Neolithic Europe. We show that, contrary to the current view, Canis with HgA did not necessarily arrive in Europe from East-Asia. This phylogeographical difference in HgA frequency demonstrates that genetic differentiation was high prior to, or as a consequence of, domestication which may be linked with pre-Neolithic local processes for Iberian wolf domestication. Our results emphasize that knowledge of both ancient wolves' and early dogs' genetic profiles from the European periphery should improve our understanding of the evolution of the European dog.

We investigated the genetic composition of six Canis remains from western Iberia, directly radiocarbon dated to 7,903–7,570 years (cal BP). They were identified as dogs via their archaeological and depositional context, osteometry, and a high percentage of aquatic diet shared with humans. For comparison, genetic data were obtained from an additional 37 Iberian dog remains from the Neolithic to Late Antiquity, as well as two Palaeolithic and a Chalcolithic Canis identified as wolves. Previous data indicated that dog mtDNA haplogroup A (HgA) is prevalent in extant European dogs (>50%), in the Near East and Asia, but rare or absent (<10%) in European Canis older than 3,000 years (cal BP). We found a high frequency (83%) of dog HgA in Mesolithic Iberian dog remains. This is the first report of a high frequency of dog HgA in pre-Neolithic Europe. We show that, contrary to the current view, Canis with HgA did not necessarily arrive in Europe from East-Asia. This phylogeographical difference in HgA frequency demonstrates that genetic differentiation was high prior to, or as a consequence of, domestication which may be linked with pre-Neolithic local processes for Iberian wolf domestication. Our results emphasize that knowledge of both ancient wolves' and early dogs’ genetic profiles from the European periphery should improve our understanding of the evolution of the European dog.

The plains zebra (Equus quagga) is an ecologically important species of the African savannah. It is also one of the most numerous and widely distributed ungulates, and six subspecies have been described based on morphological variation. However, the within-species evolutionary processes have been difficult to resolve due to its high mobility and a lack of consensus regarding the population structure. We obtained genome-wide DNA polymorphism data from more than 167,000 loci for 59 plains zebras from across the species range, encompassing all recognized extant subspecies, as well as three mountain zebras (Equus zebra) and three Grevy's zebras (Equus grevyi). Surprisingly, the population genetic structure does not mirror the morphology-based subspecies delineation, underlining the dangers of basing management units exclusively on morphological variation. We use demographic modelling to provide insights into the past phylogeography of the species. The results identify a southern African location as the most likely source region from which all extant populations expanded around 370,000 years ago. We show evidence for inclusion of the extinct and phenotypically divergent quagga (Equus quagga quagga) in the plains zebra variation and reveal that it was less divergent from the other subspecies than the northernmost (Ugandan) extant population.

Background: High-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales. One approach to reduce genome complexity, i.e. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. Like similar approaches, RRL sequencing reduces ascertainment bias due to simultaneous discovery and genotyping of single-nucleotide polymorphisms (SNPs) and does not require reference genomes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. From the bioinformatical perspective, the reliance of most studies on a single SNP caller disregards the possibility that different algorithms may produce disparate SNP datasets.Results: We present an improved RRL (iRRL) protocol that maximizes the generation of homologous DNA sequences, thus achieving improved genotyping-by-sequencing efficiency. Our modifications facilitate generation of single-sample libraries, enabling individual genotype assignments instead of pooled-sample analysis. We sequenced ~1% of the orangutan genome with 41-fold median coverage in 31 wild-born individuals from two populations. SNPs and genotypes were called using three different algorithms. We obtained substantially different SNP datasets depending on the SNP caller. Genotype validations revealed that the Unified Genotyper of the Genome Analysis Toolkit and SAMtools performed significantly better than a caller from CLC Genomics Workbench (CLC). Of all conflicting genotype calls, CLC was only correct in 17% of the cases. Furthermore, conflicting genotypes between two algorithms showed a systematic bias in that one caller almost exclusively assigned heterozygotes, while the other one almost exclusively assigned homozygotes.Conclusions: Ou

Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences have so far not been addressed and quantified. We simulated DNA sequence data under a variety of scenarios involving structured populations with variable levels of gene flow and analysed them using BSPs as implemented in the software package BEAST. Results revealed that BSPs can show false signals of population decline under biologically plausible combinations of population structure and sampling strategy, suggesting that the interpretation of several previous studies may need to be re-evaluated. We found that a balanced sampling strategy whereby samples are distributed on several populations provides the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure previous to BSP analyses, and that alternative scenarios should be evaluated when interpreting signals of population size change.

Background: High-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales. One approach to reduce genome complexity, i.e. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. Like similar approaches, RRL sequencing reduces ascertainment bias due to simultaneous discovery and genotyping of single-nucleotide polymorphisms (SNPs) and does not require reference genomes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. From the bioinformatical perspective, the reliance of most studies on a single SNP caller disregards the possibility that different algorithms may produce disparate SNP datasets.Results: We present an improved RRL (iRRL) protocol that maximizes the generation of homologous DNA sequences, thus achieving improved genotyping-by-sequencing efficiency. Our modifications facilitate generation of single-sample libraries, enabling individual genotype assignments instead of pooled-sample analysis. We sequenced ~1% of the orangutan genome with 41-fold median coverage in 31 wild-born individuals from two populations. SNPs and genotypes were called using three different algorithms. We obtained substantially different SNP datasets depending on the SNP caller. Genotype validations revealed that the Unified Genotyper of the Genome Analysis Toolkit and SAMtools performed significantly better than a caller from CLC Genomics Workbench (CLC). Of all conflicting genotype calls, CLC was only correct in 17% of the cases. Furthermore, conflicting genotypes between two algorithms showed a systematic bias in that one caller almost exclusively assigned heterozygotes, while the other one almost exclusively assigned homozygotes.Conclusions: Ou