Genotype Likelihoods Research Articles

Association of Cytotoxic T-Lymphocyte Antigen-4 Gene Polymorphism with Type 1 Diabetes Mellitus: In silico Analysis of Biological Features of CTLA-4 Protein on Ethiopian Population.

BackgroundT1DM is a chronic organ-specific T-cell-mediated autoimmune disease characterized by the selective destruction of β-cells in the islets of Langerhans, resulting in insulin deficiency and hyperglycemia. Genes for cytotoxic T lymphocyte-associated antigen 4 have been hypothesized as possible contender genes for T1DM vulnerability. However, it has not been studied in the Ethiopian population yet.ObjectiveThe aim of the study was to investigate CTLA-4 exon 1 was linked to A49G polymorphism with T1DM and its biological features of CTLA-4 among T1DM patients, in Ethiopia.MethodsA case–control study was done from December 2019 to March 2020 on 210 study participants (105 T1DM patients and 105 healthy controls). Polymerase Chain Reaction amplification with forward and reverse primers was followed by restriction fragment length polymorphism and gel electrophoresis to determine gene polymorphism. Bioinformatics data of SNP was retrieved from National Centers for Biotechnology Information databases. The chi-square test and logistic regression were used. Statistical significance was defined as a P-value of less than 0.05.ResultsThe CTLA-4 (+A49G) gene polymorphism was observed on 56 (26.7%) study participants, 39 (18.57%) of T1DM patients, and 17 (0.08%) were controls. In T1DM and controls, the frequency of the A allele was 73.3% and 89.5%, while the G allele was 26.7% and 10.5%, respectively. The G allele was found to be associated with T1DM (OR=3.1; 95% CI, 1.82 −5.32; P=0.001). Statistical analysis revealed an association between the likelihood of T1DM and GG genotype of the CTLA-4 (+A49G) gene polymorphism (OR=3.11; 95% CI, 1.37–10.90; P=0.01). Further in silico analyzed the SNP to assess its biological features.ConclusionThe study showed as CTLA-4 (+A49G) gene polymorphism is linked with T1DM in the Ethiopian population.

Moment estimators of relatedness from low-depth whole-genome sequencing data

BackgroundEstimating relatedness is an important step for many genetic study designs. A variety of methods for estimating coefficients of pairwise relatedness from genotype data have been proposed. Both the kinship coefficient varphi and the fraternity coefficient psi for all pairs of individuals are of interest. However, when dealing with low-depth sequencing or imputation data, individual level genotypes cannot be confidently called. To ignore such uncertainty is known to result in biased estimates. Accordingly, methods have recently been developed to estimate kinship from uncertain genotypes.ResultsWe present new method-of-moment estimators of both the coefficients varphi and psi calculated directly from genotype likelihoods. We have simulated low-depth genetic data for a sample of individuals with extensive relatedness by using the complex pedigree of the known genetic isolates of Cilento in South Italy. Through this simulation, we explore the behaviour of our estimators, demonstrate their properties, and show advantages over alternative methods. A demonstration of our method is given for a sample of 150 French individuals with down-sampled sequencing data.ConclusionsWe find that our method can provide accurate relatedness estimates whilst holding advantages over existing methods in terms of robustness, independence from external software, and required computation time. The method presented in this paper is referred to as LowKi (Low-depth Kinship) and has been made available in an R package (https://github.com/genostats/LowKi).

Fast and accurate estimation of multidimensional site frequency spectra from low-coverage high-throughput sequencing data.

BackgroundThe site frequency spectrum summarizes the distribution of allele frequencies throughout the genome, and it is widely used as a summary statistic to infer demographic parameters and to detect signals of natural selection. The use of high-throughput low-coverage DNA sequencing data can lead to biased estimates of the site frequency spectrum due to high levels of uncertainty in genotyping.ResultsHere we design and implement a method to efficiently and accurately estimate the multidimensional joint site frequency spectrum for large numbers of haploid or diploid individuals across an arbitrary number of populations, using low-coverage sequencing data. The method maximizes a likelihood function that represents the probability of the sequencing data observed given a multidimensional site frequency spectrum using genotype likelihoods. Notably, it uses an advanced binning heuristic paired with an accelerated expectation-maximization algorithm for a fast and memory-efficient computation, and can generate both unfolded and folded spectra and bootstrapped replicates for haploid and diploid genomes. On the basis of extensive simulations, we show that the new method requires remarkably less storage and is faster than previous implementations whilst retaining the same accuracy. When applied to low-coverage sequencing data from the fungal pathogen Neonectria neomacrospora, results recapitulate the patterns of population differentiation generated using the original high-coverage data.ConclusionThe new implementation allows for accurate estimation of population genetic parameters from arbitrarily large, low-coverage datasets, thus facilitating cost-effective sequencing experiments in model and non-model organisms.

Racial disparity in age and genotype defines urban hepatitis C patient profiles in the recent direct-acting antiviral era

Introduction: Hepatitis C virus (HCV) infections are a national health issue despite highly effective therapy and screening of the high prevalence age cohort. Since an increase in younger patients may be occurring, we evaluated HCV patients seen in our urban clinic for their demographics and treatment history. Methods:EMR charts of all 601 patients with a diagnosis of HCV seen in Wayne Health Clinics in 2019 were reviewed. Results:The HCV patients were predominantly African American (AA) (85%) with most yet to be treated (73%). AA patients were older than non-AA patients (63 vs 51years), had a greater likelihood of HCV genotype 1 (94% vs 60%) and had lower ALT values. Patients were seen and treated by both Gastroenterology (73%) and Infectious Disease (27%) physicians and SVR rates were greater than 95%. Fibrosis, as determined by serological biomarkers, was improved in AA patients who had achieved viral clearance (SVR) as compared to those who were untreated. Conclusions: AA patients were older and more likely to have genotype 1, and with less inflammation (ALT) as compared to Non-AA patients. The data confirms the continuing need to ensure linkage to all HCV patients to prevent additional HCV transmission and future liver disease manifestation. While fibrosis in our population correlates with age, there was a significant number of older AA patients with minimal fibrosis. Further evaluation of these patients may reveal a more recent acquisition versus a slower development of fibrosis over time.

LocalNgsRelate: a software tool for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data.

MotivationInference of identity-by-descent (IBD) sharing along the genome between pairs of individuals has important uses. But all existing inference methods are based on genotypes, which is not ideal for low-depth Next Generation Sequencing (NGS) data from which genotypes can only be called with high uncertainty.ResultsWe present a new probabilistic software tool, LocalNgsRelate, for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data. Its inference is based on genotype likelihoods instead of genotypes, and thereby it takes the uncertainty of the genotype calling into account. Using real data from the 1000 Genomes project, we show that LocalNgsRelate provides more accurate IBD inference for low-depth NGS data than two state-of-the-art genotype-based methods, Albrechtsen et al. (2009) and hap-IBD. We also show that the method works well for NGS data down to a depth of 2×. Availability and implementationLocalNgsRelate is freely available at https://github.com/idamoltke/LocalNgsRelate.Supplementary information Supplementary data are available at Bioinformatics online.

Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinson\u2019s disease

BackgroundLow-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson’s disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD.ResultsUsing eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered.ConclusionsOverall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD.

Scalable bias-corrected linkage disequilibrium estimation under genotype uncertainty.

Linkage disequilibrium (LD) estimates are often calculated genome-wide for use in many tasks, such as SNP pruning and LD decay estimation. However, in the presence of genotype uncertainty, naive approaches to calculating LD have extreme attenuation biases, incorrectly suggesting that SNPs are less dependent than in reality. These biases are particularly strong in polyploid organisms, which often exhibit greater levels of genotype uncertainty than diploids. A principled approach using maximum likelihood estimation with genotype likelihoods can reduce this bias, but is prohibitively slow for genome-wide applications. Here, we present scalable moment-based adjustments to LD estimates based on the marginal posterior distributions of the genotypes. We demonstrate, on both simulated and real data, that these moment-based estimators are as accurate as maximum likelihood estimators, but are almost as fast as naive approaches based only on posterior mean genotypes. This opens up bias-corrected LD estimation to genome-wide applications. In addition, we provide standard errors for these moment-based estimators. All methods discussed in this manuscript are implemented in the ldsep package, available on the Comprehensive R Archive Network ( https://cran.r-project.org/package=ldsep ).

NGSremix: a software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data.

Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here, we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

Pairwise linkage disequilibrium estimation for polyploids.

Many tasks in statistical genetics involve pairwise estimation of linkage disequilibrium (LD). The study of LD in diploids is mature. However, in polyploids, the field lacks a comprehensive characterization of LD. Polyploids also exhibit greater levels of genotype uncertainty than diploids, yet no methods currently exist to estimate LD in polyploids in the presence of such genotype uncertainty. Furthermore, most LD estimation methods do not quantify the level of uncertainty in their LD estimates. Our study contains three major contributions. (i) We characterize haplotypic and composite measures of LD in polyploids. These composite measures of LD turn out to be functions of common statistical measures of association. (ii) We derive procedures to estimate haplotypic and composite LD in polyploids in the presence of genotype uncertainty. We do this by estimating LD directly from genotype likelihoods, which may be obtained from many genotyping platforms. (iii) We derive standard errors of all LD estimators that we discuss. We validate our methods on both real and simulated data. Our methods are implemented in the R package ldsep, available on the Comprehensive R Archive Network https://cran.r-project.org/package=ldsep.

A reference-free approach to analyse RADseq data using standard next generation sequencing toolkits.

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.

Evaluating genotype imputation pipeline for ultra-low coverage ancient genomes

Although ancient DNA data have become increasingly more important in studies about past populations, it is often not feasible or practical to obtain high coverage genomes from poorly preserved samples. While methods of accurate genotype imputation from > 1 × coverage data have recently become a routine, a large proportion of ancient samples remain unusable for downstream analyses due to their low coverage. Here, we evaluate a two-step pipeline for the imputation of common variants in ancient genomes at 0.05–1 × coverage. We use the genotype likelihood input mode in Beagle and filter for confident genotypes as the input to impute missing genotypes. This procedure, when tested on ancient genomes, outperforms a single-step imputation from genotype likelihoods, suggesting that current genotype callers do not fully account for errors in ancient sequences and additional quality controls can be beneficial. We compared the effect of various genotype likelihood calling methods, post-calling, pre-imputation and post-imputation filters, different reference panels, as well as different imputation tools. In a Neolithic Hungarian genome, we obtain ~ 90% imputation accuracy for heterozygous common variants at coverage 0.05 × and > 97% accuracy at coverage 0.5 ×. We show that imputation can mitigate, though not eliminate reference bias in ultra-low coverage ancient genomes.

Estimating and accounting for genotyping errors in RAD‐seq experiments

In non-model organisms, evolutionary questions are frequently addressed using reduced representation sequencing techniques due to their low cost, ease of use, and because they do not require genomic resources such as a reference genome. However, evidence is accumulating that such techniques may be affected by specific biases, questioning the accuracy of obtained genotypes, and as a consequence, their usefulness in evolutionary studies. Here, we introduce three strategies to estimate genotyping error rates from such data: through the comparison to high quality genotypes obtained with a different technique, from individual replicates, or from a population sample when assuming Hardy-Weinberg equilibrium. Applying these strategies to data obtained with Restriction site Associated DNA sequencing (RAD-seq), arguably the most popular reduced representation sequencing technique, revealed per-allele genotyping error rates that were much higher than sequencing error rates, particularly at heterozygous sites that were wrongly inferred as homozygous. As we exemplify through the inference of genome-wide and local ancestry of well characterized hybrids of two Eurasian poplar (Populus) species, such high error rates may lead to wrong biological conclusions. By properly accounting for these error rates in downstream analyses, either by incorporating genotyping errors directly or by recalibrating genotype likelihoods, we were nevertheless able to use the RAD-seq data to support biologically meaningful and robust inferences of ancestry among Populus hybrids. Based on these findings, we strongly recommend carefully assessing genotyping error rates in reduced representation sequencing experiments, and to properly account for these in downstream analyses, for instance using the tools presented here.

TypeTE: a tool to genotype mobile element insertions from whole genome resequencing data.

Alu retrotransposons account for more than 10% of the human genome, and insertions of these elements create structural variants segregating in human populations. Such polymorphic Alus are powerful markers to understand population structure, and they represent variants that can greatly impact genome function, including gene expression. Accurate genotyping of Alus and other mobile elements has been challenging. Indeed, we found that Alu genotypes previously called for the 1000 Genomes Project are sometimes erroneous, which poses significant problems for phasing these insertions with other variants that comprise the haplotype. To ameliorate this issue, we introduce a new pipeline – TypeTE – which genotypes Alu insertions from whole-genome sequencing data. Starting from a list of polymorphic Alus, TypeTE identifies the hallmarks (poly-A tail and target site duplication) and orientation of Alu insertions using local re-assembly to reconstruct presence and absence alleles. Genotype likelihoods are then computed after re-mapping sequencing reads to the reconstructed alleles. Using a high-quality set of PCR-based genotyping of >200 loci, we show that TypeTE improves genotype accuracy from 83% to 92% in the 1000 Genomes dataset. TypeTE can be readily adapted to other retrotransposon families and brings a valuable toolbox addition for population genomics.

CellCoal: Coalescent Simulation of Single-Cell Sequencing Samples.

Our capacity to study individual cells has enabled a new level of resolution for understanding complex biological systems such as multicellular organisms or microbial communities. Not surprisingly, several methods have been developed in recent years with a formidable potential to investigate the somatic evolution of single cells in both healthy and pathological tissues. However, single-cell sequencing data can be quite noisy due to different technical biases, so inferences resulting from these new methods need to be carefully contrasted. Here, I introduce CellCoal, a software tool for the coalescent simulation of single-cell sequencing genotypes. CellCoal simulates the history of single-cell samples obtained from somatic cell populations with different demographic histories and produces single-nucleotide variants under a variety of mutation models, sequencing read counts, and genotype likelihoods, considering allelic imbalance, allelic dropout, amplification, and sequencing errors, typical of this type of data. CellCoal is a flexible tool that can be used to understand the implications of different somatic evolutionary processes at the single-cell level, and to benchmark dedicated bioinformatic tools for the analysis of single-cell sequencing data. CellCoal is available at https://github.com/dapogon/cellcoal.

Distinctive targetable genotypes of younger patients with lung adenocarcinoma: a cBioPortal for cancer genomics data base analysis

ABSTRACTThere is still limited comprehensive genotyping data about young patients with lung adenocarcinoma. Herein, next generation sequencing (NGS) data of lung adenocarcinoma patients was retrospectively analyzed to evaluate the relationship between young age at diagnosis and the comprehensive molecular characteristics. The cBioPortal for Cancer Genomics database was queried for cancer genomic studies of lung adenocarcinoma and a cohort of 773 patients with complete cancer genomics data was selected from 2 of 11 studies. The relationship between age at diagnosis and frequency of targetable genotypes was analyzed and verified in another cohort composed of 177 Chinese lung adenocarcinoma patients undergoing NGS assay. Of the 773 eligible lung adenocarcinoma patients, younger age was associated with an increased likelihood of a targetable genotype (P < .001). Specifically, a higher prevalence of EGFR mutations (P = .005), ALK arrangements, ROS1 arrangements (P = .035) and RET arrangements (P < .001) were identified in younger patients. The frequency of KRAS mutations (P < .001) was significantly associated with older age at diagnosis and a similar trend existed for MET (P = .057) but not BRAF-V600E (P = .686) and ERBB2 (P = .083). Additionally, an age at diagnosis of 45 years was found to be a feasible cutoff point to differentiate the younger from the older patients by comprehensive molecular characteristics. These results indicated that younger patients with lung adenocarcinoma were associated with an increased likelihood of harboring a targetable genotype. Distinctive molecular characteristics were identified in patients younger than 45 years with lung adenocarcinoma, which highlights the importance of the NGS assay and personalized therapy in this subpopulation.

Novel signals of adaptive genetic variation in northwestern Atlantic cod revealed by whole-genome sequencing.

Selection can create complex patterns of adaptive differentiation among populations in the wild that may be relevant to management. Atlantic cod in the Northwest Atlantic are at a fraction of their historical abundance and a lack of recovery within the Gulf of Maine has created concern regarding the misalignment of fisheries management structures with biological population structure. To address this and investigate genome‐wide patterns of variation, we used low‐coverage sequencing to perform a region‐wide, whole‐genome analysis of fine‐scale population structure. We sequenced 306 individuals from 20 sampling locations in U.S. and Canadian waters, including the major spawning aggregations in the Gulf of Maine in addition to spawning aggregations from Georges Bank, southern New England, the eastern Scotian Shelf, and St. Pierre Bank. With genotype likelihoods estimated at almost 11 million loci, we found large differences in haplotype frequencies of previously described chromosomal inversions between Canadian and U.S. sampling locations and also among U.S. sampling locations. Our whole‐genome resolution also revealed novel outlier peaks, some of which showed significant genetic differentiation among sampling locations. Comparisons between allochronic winter‐ and spring‐spawning populations revealed highly elevated relative (FST) and absolute (dxy) genetic differentiation near genes involved in reproduction, particularly genes associated with the brain‐pituitary‐gonadal axis, which likely control timing of spawning, contributing to prezygotic isolation. We also found genetic differentiation associated with heat shock proteins and other genes of functional relevance, with complex patterns that may point to multifaceted selection pressures and local adaptation among spawning populations. We provide a high‐resolution picture of U.S. Atlantic cod population structure, revealing greater complexity than is currently recognized in management. Our genome‐scan approach likely underestimates the full suite of adaptive differentiation among sampling locations. Nevertheless, it should inform the revision of stock boundaries to preserve adaptive genetic diversity and evolutionary potential of cod populations.

An evaluation of sequencing coverage and genotyping strategies to assess neutral and adaptive diversity.

Whole genome sequences (WGS) greatly increase our ability to precisely infer population genetic parameters, demographic processes, and selection signatures. However, WGS may still be not affordable for a representative number of individuals/populations. In this context, our goal was to assess the efficiency of several SNP genotyping strategies by testing their ability to accurately estimate parameters describing neutral diversity and to detect signatures of selection. We analysed 110 WGS at 12× coverage for four different species, i.e., sheep, goats and their wild counterparts. From these data we generated 946 data sets corresponding to random panels of 1K to 5M variants, commercial SNP chips and exome capture, for sample sizes of five to 48 individuals. We also extracted low-coverage genome resequencing of 1×, 2× and 5× by randomly subsampling reads from the 12× resequencing data. Globally, 5K to 10K random variants were enough for an accurate estimation of genome diversity. Conversely, commercial panels and exome capture displayed strong ascertainment biases. Besides the characterization of neutral diversity, the detection of the signature of selection and the accurate estimation of linkage disequilibrium (LD) required high-density panels of at least 1M variants. Finally, genotype likelihoods increased the quality of variant calling from low coverage resequencing but proportions of incorrect genotypes remained substantial, especially for heterozygote sites. Whole genome resequencing coverage of at least 5× appeared to be necessary for accurate assessment of genomic variations. These results have implications for studies seeking to deploy low-density SNP collections or genome scans across genetically diverse populations/species showing similar genetic characteristics and patterns of LD decay for a wide variety of purposes.

The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments

The Genome Analysis Toolkit (GATK) is a popular set of programs for discovering and genotyping variants from next-generation sequencing data. The current GATK recommendation for RNA sequencing (RNA-seq) is to perform variant calling from individual samples, with the drawback that only variable positions are reported. Versions 3.0 and above of GATK offer the possibility of calling DNA variants on cohorts of samples using the HaplotypeCaller algorithm in Genomic Variant Call Format (GVCF) mode. Using this approach, variants are called individually on each sample, generating one GVCF file per sample that lists genotype likelihoods and their genome annotations. In a second step, variants are called from the GVCF files through a joint genotyping analysis. This strategy is more flexible and reduces computational challenges in comparison to the traditional joint discovery workflow. Using a GVCF workflow for mining SNP in RNA-seq data provides substantial advantages, including reporting homozygous genotypes for the reference allele as well as missing data. Taking advantage of RNA-seq data derived from primary macrophages isolated from 50 cows, the GATK joint genotyping method for calling variants on RNA-seq data was validated by comparing this approach to a so-called “per-sample” method. In addition, pair-wise comparisons of the two methods were performed to evaluate their respective sensitivity, precision and accuracy using DNA genotypes from a companion study including the same 50 cows genotyped using either genotyping-by-sequencing or with the Bovine SNP50 Beadchip (imputed to the Bovine high density). Results indicate that both approaches are very close in their capacity of detecting reference variants and that the joint genotyping method is more sensitive than the per-sample method. Given that the joint genotyping method is more flexible and technically easier, we recommend this approach for variant calling in RNA-seq experiments.

Hybridization between two high Arctic cetaceans confirmed by genomic analysis

In 1990, a skull from a morphologically unusual Monodontid was found in West Greenland and collected for the Natural History Museum of Denmark, University of Copenhagen. From its intermediate morphology, the skull was hypothesized to be a beluga/narwhal hybrid. If confirmed, the specimen would, to our knowledge, represent the sole evidence of hybridization between the only two toothed whale species endemic to the Arctic. Here we present genome-wide DNA sequence data from the specimen and investigate its origin using a genomic reference panel of eight belugas and eight narwhals. Our analyses reveal that the specimen is a male, first-generation hybrid between a female narwhal and a male beluga. We use stable carbon and nitrogen isotope analysis to investigate the dietary niche of the hybrid and find a higher δ13C value than in both belugas and narwhals, suggesting a foraging strategy unlike either parental species. These results further our understanding of the interaction between belugas and narwhals, and underscore the importance of natural history collections in monitoring changes in biodiversity. In addition, our study exemplifies how recent major advances in population genomic analyses using genotype likelihoods can provide key biological and ecological insights from low-coverage data (down to 0.05x).

Testing for Hardy-Weinberg equilibrium in structured populations using genotype or low-depth next generation sequencing data.

Testing for deviations from Hardy-Weinberg equilibrium (HWE) is a common practice for quality control in genetic studies. Variable sites violating HWE may be identified as technical errors in the sequencing or genotyping process, or they may be of particular evolutionary interest. Large-scale genetic studies based on next-generation sequencing (NGS) methods have become more prevalent as cost is decreasing but these methods are still associated with statistical uncertainty. The large-scale studies usually consist of samples from diverse ancestries that make the existence of some degree of population structure almost inevitable. Precautions are therefore needed when analysing these data set, as population structure causes deviations from HWE. Here we propose a method that takes population structure into account in the testing for HWE, such that other factors causing deviations from HWE can be detected. We show the effectiveness of PCAngsd in low-depth NGS data, as well as in genotype data, for both simulated and real data set, where the use of genotype likelihoods enables us to model the uncertainty.