Whole-genome sequencing reveals a complex African population demographic history and signatures of local adaptation.

We conduct high coverage (>30×) whole-genome sequencing of 180 individuals from 12 indigenous African populations. We identify millions of unreported variants, many predicted to be functionally important. We observe that the ancestors of southern African San and central African rainforest hunter-gatherers (RHG) diverged from other populations >200 kya and maintained a large effective population size. We observe evidence for ancient population structure in Africa and for multiple introgression events from "ghost" populations with highly diverged genetic lineages. Although currently geographically isolated, we observe evidence for gene flow between eastern and southern Khoesan-speaking hunter-gatherer populations lasting until ∼12 kya. We identify signatures of local adaptation for traits related to skin color, immune response, height, and metabolic processes. We identify a positively selected variant in the lightly pigmented San that influences pigmentation in vitro by regulating the enhancer activity and gene expression of PDPK1.

CherryML: scalable maximum likelihood estimation of phylogenetic models.

Phylogenetic models of molecular evolution are central to numerous biological applications spanning diverse timescales, from hundreds of millions of years involving orthologous proteins to just tens of days relating to single cells within an organism. A fundamental problem in these applications is estimating model parameters, for which maximum likelihood estimation is typically employed. Unfortunately, maximum likelihood estimation is a computationally expensive task, in some cases prohibitively so. To address this challenge, we here introduce CherryML, a broadly applicable method that achieves several orders of magnitude speedup by using a quantized composite likelihood over cherries in the trees. The massive speedup offered by our method should enable researchers to consider more complex and biologically realistic models than previously possible. Here we demonstrate CherryML's utility by applying it to estimate a general 400 × 400 rate matrix for residue-residue coevolution at contact sites in three-dimensional protein structures; we estimate that using current state-of-the-art methods such as the expectation-maximization algorithm for the same task would take >100,000 times longer.

DNA language models are powerful predictors of genome-wide variant effects.

The expanding catalog of genome-wide association studies (GWAS) provides biological insights across a variety of species, but identifying the causal variants behind these associations remains a significant challenge. Experimental validation is both labor-intensive and costly, highlighting the need for accurate, scalable computational methods to predict the effects of genetic variants across the entire genome. Inspired by recent progress in natural language processing, unsupervised pretraining on large protein sequence databases has proven successful in extracting complex information related to proteins. These models showcase their ability to learn variant effects in coding regions using an unsupervised approach. Expanding on this idea, we here introduce the Genomic Pre-trained Network (GPN), a model designed to learn genome-wide variant effects through unsupervised pretraining on genomic DNA sequences. Our model also successfully learns gene structure and DNA motifs without any supervision. To demonstrate its utility, we train GPN on unaligned reference genomes of Arabidopsis thaliana and seven related species within the Brassicales order and evaluate its ability to predict the functional impact of genetic variants in A. thaliana by utilizing allele frequencies from the 1001 Genomes Project and a comprehensive database of GWAS. Notably, GPN outperforms predictors based on popular conservation scores such as phyloP and phastCons. Our predictions for A. thaliana can be visualized as sequence logos in the UCSC Genome Browser (https://genome.ucsc.edu/s/gbenegas/gpn-arabidopsis). We provide code (https://github.com/songlab-cal/gpn) to train GPN for any given species using its DNA sequence alone, enabling unsupervised prediction of variant effects across the entire genome.

Selective whole-genome amplification reveals population genetics of Leishmania braziliensis directly from patient skin biopsies.

In Brazil, Leishmania braziliensis is the main causative agent of the neglected tropical disease, cutaneous leishmaniasis (CL). CL presents on a spectrum of disease severity with a high rate of treatment failure. Yet the parasite factors that contribute to disease presentation and treatment outcome are not well understood, in part because successfully isolating and culturing parasites from patient lesions remains a major technical challenge. Here we describe the development of selective whole genome amplification (SWGA) for Leishmania and show that this method enables culture-independent analysis of parasite genomes obtained directly from primary patient skin samples, allowing us to circumvent artifacts associated with adaptation to culture. We show that SWGA can be applied to multiple Leishmania species residing in different host species, suggesting that this method is broadly useful in both experimental infection models and clinical studies. SWGA carried out directly on skin biopsies collected from patients in Corte de Pedra, Bahia, Brazil, showed extensive genomic diversity. Finally, as a proof-of-concept, we demonstrated that SWGA data can be integrated with published whole genome data from cultured parasite isolates to identify variants unique to specific geographic regions in Brazil where treatment failure rates are known to be high. SWGA provides a relatively simple method to generate Leishmania genomes directly from patient samples, unlocking the potential to link parasite genetics with host clinical phenotypes.

Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency.

Genetic variants in SLC22A5, encoding the membrane carnitine transporter OCTN2, cause the rare metabolic disorder Carnitine Transporter Deficiency (CTD). CTD is potentially lethal but actionable if detected early, with confirmatory diagnosis involving sequencing of SLC22A5. Interpretation of missense variants of uncertain significance (VUSs) is a major challenge. In this study, we sought to characterize the largest set to date (n = 150) of OCTN2 variants identified in diverse ancestral populations, with the goals of furthering our understanding of the mechanisms leading to OCTN2 loss-of-function (LOF) and creating a protein-specific variant effect prediction model for OCTN2 function. Uptake assays with 14C-carnitine revealed that 105 variants (70%) significantly reduced transport of carnitine compared to wild-type OCTN2, and 37 variants (25%) severely reduced function to less than 20%. All ancestral populations harbored LOF variants; 62% of green fluorescent protein (GFP)-tagged variants impaired OCTN2 localization to the plasma membrane of human embryonic kidney (HEK293T) cells, and subcellular localization significantly associated with function, revealing a major LOF mechanism of interest for CTD. With these data, we trained a model to classify variants as functional (>20% function) or LOF (<20% function). Our model outperformed existing state-of-the-art methods as evaluated by multiple performance metrics, with mean area under the receiver operating characteristic curve (AUROC) of 0.895 ± 0.025. In summary, in this study we generated a rich dataset of OCTN2 variant function and localization, revealed important disease-causing mechanisms, and improved upon machine learning-based prediction of OCTN2 variant function to aid in variant interpretation in the diagnosis and treatment of CTD.

Likelihood-based deconvolution of bulk gene expression data using single-cell references.

Direct comparison of bulk gene expression profiles is complicated by distinct cell type mixtures in each sample that obscure whether observed differences are actually caused by changes in the expression levels themselves or are simply a result of differing cell type compositions. Single-cell technology has made it possible to measure gene expression in individual cells, achieving higher resolution at the expense of increased noise. If carefully incorporated, such single-cell data can be used to deconvolve bulk samples to yield accurate estimates of the true cell type proportions, thus enabling one to disentangle the effects of differential expression and cell type mixtures. Here, we propose a generative model and a likelihood-based inference method that uses asymptotic statistical theory and a novel optimization procedure to perform deconvolution of bulk RNA-seq data to produce accurate cell type proportion estimates. We show the effectiveness of our method, called RNA-Sieve, across a diverse array of scenarios involving real data and discuss extensions made uniquely possible by our probabilistic framework, including a demonstration of well-calibrated confidence intervals.

Transporter genes in biosynthetic gene clusters predict metabolite characteristics and siderophore activity.

Biosynthetic gene clusters (BGCs) are operonic sets of microbial genes that synthesize specialized metabolites with diverse functions, including siderophores and antibiotics, which often require export to the extracellular environment. For this reason, genes for transport across cellular membranes are essential for the production of specialized metabolites and are often genomically colocalized with BGCs. Here, we conducted a comprehensive computational analysis of transporters associated with characterized BGCs. In addition to known exporters, in BGCs we found many importer-specific transmembrane domains that co-occur with substrate binding proteins possibly for uptake of siderophores or metabolic precursors. Machine learning models using transporter gene frequencies were predictive of known siderophore activity, molecular weights, and a measure of lipophilicity (log P) for corresponding BGC-synthesized metabolites. Transporter genes associated with BGCs were often equally or more predictive of metabolite features than biosynthetic genes. Given the importance of siderophores as pathogenicity factors, we used transporters specific for siderophore BGCs to identify both known and uncharacterized siderophore-like BGCs in genomes from metagenomes from the infant and adult gut microbiome. We find that 23% of microbial genomes from premature infant guts have siderophore-like BGCs, but only 3% of those assembled from adult gut microbiomes do. Although siderophore-like BGCs from the infant gut are predominantly associated with Enterobacteriaceae and Staphylococcus, siderophore-like BGCs can be identified from taxa in the adult gut microbiome that have rarely been recognized for siderophore production. Taken together, these results show that consideration of BGC-associated transporter genes can inform predictions of specialized metabolite structure and function.

SCITO-seq: single-cell combinatorial indexed cytometry sequencing.

The development of DNA-barcoded antibodies to tag cell surface molecules has enabled the use of droplet-based single-cell sequencing (dsc-seq) to profile protein abundances from thousands of cells simultaneously. As compared to flow and mass cytometry, the high per cell cost of current dsc-seq-based workflows precludes their use in clinical applications and large-scale pooled screens. Here, we introduce SCITO-seq, a workflow that uses splint oligonucleotides (oligos) to enable combinatorially indexed dsc-seq of DNA-barcoded antibodies from over 105 cells per reaction using commercial microfluidics. By encoding sample barcodes into splint oligos, we demonstrate that multiplexed SCITO-seq produces reproducible estimates of cellular composition and surface protein expression comparable to those from mass cytometry. We further demonstrate two modified splint oligo designs that extend SCITO-seq to achieve compatibility with commercial DNA-barcoded antibodies and simultaneous expression profiling of the transcriptome and surface proteins from the same cell. These results demonstrate SCITO-seq as a flexible and ultra-high-throughput platform for sequencing-based single-cell protein and multimodal profiling.

A fast machine-learning-guided primer design pipeline for selective whole genome amplification.

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales-precisely the scales at which these processes occur-microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primer sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.

Terminal Pleistocene Alaskan genome reveals first founding population of Native Americans.

Despite broad agreement that the Americas were initially populated via Beringia, the land bridge that connected far northeast Asia with northwestern North America during the Pleistocene epoch, when and how the peopling of the Americas occurred remains unresolved. Analyses of human remains from Late Pleistocene Alaska are important to resolving the timing and dispersal of these populations. The remains of two infants were recovered at Upward Sun River (USR), and have been dated to around 11.5 thousand years ago (ka). Here, by sequencing the USR1 genome to an average coverage of approximately 17 times, we show that USR1 is most closely related to Native Americans, but falls basal to all previously sequenced contemporary and ancient Native Americans. As such, USR1 represents a distinct Ancient Beringian population. Using demographic modelling, we infer that the Ancient Beringian population and ancestors of other Native Americans descended from a single founding population that initially split from East Asians around 36 ± 1.5 ka, with gene flow persisting until around 25 ± 1.1 ka. Gene flow from ancient north Eurasians into all Native Americans took place 25-20 ka, with Ancient Beringians branching off around 22-18.1 ka. Our findings support a long-term genetic structure in ancestral Native Americans, consistent with the Beringian 'standstill model'. We show that the basal northern and southern Native American branches, to which all other Native Americans belong, diverged around 17.5-14.6 ka, and that this probably occurred south of the North American ice sheets. We also show that after 11.5 ka, some of the northern Native American populations received gene flow from a Siberian population most closely related to Koryaks, but not Palaeo-Eskimos, Inuits or Kets, and that Native American gene flow into Inuits was through northern and not southern Native American groups. Our findings further suggest that the far-northern North American presence of northern Native Americans is from a back migration that replaced or absorbed the initial founding population of Ancient Beringians.

Efficacy and safety of shunt surgery in patients with idiopathic normal-pressure hydrocephalus: can we predict shunt response by preoperative magnetic resonance imaging (MRI)?

AIM: The aim of this study was to identify preoperative magnetic resonance imaging (MRI) findings that can predict the shunt responsiveness in idiopathic normal-pressure hydrocephalus (iNPH) patients and to investigate postoperative outcome and complications. MATERIALS AND METHODS: A total of 192 patients with iNPH who underwent shunt at our hospital between 2000 and 2021 were included to investigate complications. Of these, after exclusion, 127 (1-month postoperative follow-up) and 77 (1-year postoperative follow-up) patients were evaluated. The preoperative MRI features (the presence of tightness of the high-convexity subarachnoid space, Sylvian fissure enlargement, Evans' index, and callosal angle) of the shunt-response and nonresponse groups were compared, and a systematic review was conducted to evaluate whether preoperative MRI findings could predict shunt response. RESULTS: Postoperative complications within one month after surgery were observed in 6.8% (13/192), and the most common complication was hemorrhage. Changes in corpus callosum were observed in 4.2% (8/192). The shunt-response rates were 83.5% (106/127) in the 1-month follow-up group and 70.1% (54/77) in 1-year follow-up group. In the logistic regression analysis, only Evans' index measuring >0.4 had a significant negative relationship with shunt response at 1-month follow-up; however, no significant relationship was observed at 1-year follow-up. According to our systematic review, it is still controversial whether preoperative MRI findings could predict shunt response. CONCLUSION: Evans' index measure of >0.4 had a significant relationship with the shunt response in the 1-month follow-up group. In systematic reviews, there is ongoing debate about whether preoperative MRI findings can accurately predict responses to shunt surgery. Postoperative corpus callosal change was observed in 4.2% of iNPH patients.

Reproducibility and efficiency of liver volumetry using manual method and liver analysis software.

BACKGROUND: For liver volumetry, manual tracing on computed tomography (CT) images is time-consuming and operator dependent. To overcome these disadvantages, several three-dimensional simulation software programs have been developed; however, their efficacy has not fully been evaluated. METHODS: Three physicians performed liver volumetry on preoperative CT images on 30 patients who underwent formal right hepatectomy, using manual tracing volumetry and two simulation software programs, SYNAPSE and syngo.via. The future liver remnant (FLR) was calculated using each method of volumetry. The primary endpoint was reproducibility and secondary outcomes were calculation time and learning curve. RESULTS: The mean FLR was significantly lower for manual volumetry than for SYNAPSE or syngo.via; there was no significant difference in mean FLR between the two software-based methods. Reproducibility was lower for the manual method than for the software-based methods. Mean calculation time was shortest for SYNAPSE. For the two physicians unfamiliar with the software, no obvious learning curve was observed for using SYNAPSE, whereas learning curves were observed for using syngo.via. CONCLUSIONS: Liver volumetry was more reproducible and faster with three-dimensional simulation software, especially SYNAPSE software, than with the conventional manual tracing method. Software can help even inexperienced physicians learn quickly how to perform liver volumetry.

Image findings of anti-neutrophil cytoplasmic antibody-associated vasculitis involving the skull base.

AIM: To investigate computed tomography (CT) and magnetic resonance imaging (MRI) features of skull bases involving anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV). MATERIALS AND METHODS: A retrospective review was undertaken to identify an institutional historical cohort of 17 patients with confirmed AAV who underwent CT or MRI and had skull base involvement between 2002 and 2021. Two radiologists reviewed the extent and features of the lesions, bone changes, and other MRI findings. RESULTS: A total of 17 patients (12 men; mean age ± standard deviation, 46.5 ± 17.1 years) were selected. AAV presented as infiltrative lesions with involvement at various sites. Most cases involved the paranasal sinuses (PNS; 88%, 15/17), nasopharynx (88%, 15/17), pterygopalatine fossa (82%, 14/17), and parapharyngeal space (82%, 14/17), frequently accompanied by mucosal irregularity of the PNS and nasopharynx (71%, 12/17). Central skull base and temporal bone involvement were seen in 53% (9/17) and 38% (6/16) of cases, respectively. On T1-weighted imaging (WI) and T2WI MRI, all lesions (15/15) showed predominant signal iso-intensity to grey matter. CONCLUSIONS: Although radiological findings of AAV are non-specific and skull base involvement is less common, AAV may be considered if infiltrative lesions predominantly involving the PNS, nasopharynx, pterygopalatine fossa, and parapharyngeal space with combined bone changes of skull base are seen.

Evaluating the benefit of surgical resection for hepatocellular carcinoma with multifocality or intrahepatic vascular invasion.

BACKGROUND: The role of hepatectomy for hepatocellular carcinoma (HCC) with multifocality or intrahepatic vascular involvement remains ill-defined. Our objective was to evaluate benefits of surgical resection for patients with these high-risk features. METHODS: The National Cancer Database was used to identify HCC patients with vascular involvement and/or multifocality (T2/T3, N-/M-) from 2011 to 2015. Propensity score matching (k-nearest neighbors, no replacement, 1:1) grouped patients by treatment: surgical resection versus non-surgical modalities. Groups were matched using patient, clinical, and liver-specific characteristics. Median overall survival (OS) was calculated using Kaplan-Meier, and adjusted analyses were performed using shared frailty models. RESULTS: 14,557 patients met inclusion criteria, including 1892 (9.4%) treated with surgical resection. Median cohort OS was 20.5 months. After adjustment, surgical resection was associated with survival advantage compared to non-surgical treatment (37.8 versus 15.7 months, log-rank P < .001; adjusted hazard ratio 0.49, 95% confidence interval, 0.45-0.54). Patients with minimal comorbidity, unifocal disease, and age <54 had highest probability of survival one year post-surgery. CONCLUSIONS: Surgical resection is associated with a survival advantage in HCC with multifocality and/or intrahepatic vascular involvement. The presence of these features should not contraindicate consideration of hepatectomy in suitable surgical candidates.

Impact of cumulative operative time on postoperative complication risk in simultaneous resections of colorectal liver metastases and primary tumors.

BACKGROUND: Simultaneous resection of colorectal liver metastases (CLM) and primary colorectal cancers (CRC) is nuanced without firm rules for selection. This study aimed to identify factors associated with morbidity after simultaneous resection. METHODS: Using a prospective database, patients undergoing simultaneous CLM-CRC resection from 1/1/2017-7/1/2020 were analyzed. Regression modeling estimated impact of colorectal resection type, Kawaguchi-Gayet (KG) hepatectomy complexity, and perioperative factors on 90-day complications. RESULTS: Overall, 120 patients underwent simultaneous CLM-CRC resection. Grade≥2 complications occurred in 38.3% (n = 46); these patients experienced longer length of stay (median LOS 7.5 vs. 4, p < 0.001) and increased readmission (39% vs. 1.4%, p < 0.001) compared to patients with zero or Grade 1 complications. Median OR time was 298 min. Patients within highest operative time quartile (>506 min) had higher grade≥2 complications (57%vs. 23%, p = 0.04) and greater than 4-fold increased odds of grade≥2 morbidity (OR 4.3, 95% CI (Confidence Interval) 1.41-13.1, p = 0.01). After adjusting for Pringle time, KG complexity and colorectal resection type, increasing operative time was associated with grade≥2 complications, especially for resections in highest quartile of operative time (OR 7.28, 95% CI 1.73-30.6, p = 0.007). CONCLUSION: In patients undergoing simultaneous CLM-CRC resection, prolonged operative time is independently associated with grade≥2 complications. Awareness of cumulative operative time may inform intraoperative decision-making by surgical teams.

Chromosomally normal miscarriage is associated with vaginal dysbiosis and local inflammation.

BACKGROUND: Emerging evidence supports an association between vaginal microbiota composition and risk of miscarriage; however, the underlying mechanisms are poorly understood. We aim to investigate the vaginal microbial composition and the local immune response in chromosomally normal and abnormal miscarriages and compare this to uncomplicated pregnancies delivering at term. METHODS: We used 16S rRNA gene based metataxonomics to interrogate the vaginal microbiota in a cohort of 167 women, 93 miscarriages (54 euploid and 39 aneuploid using molecular cytogenetics) and 74 women who delivered at term and correlate this with the aneuploidy status of the miscarriages. We also measured the concentrations of IL-2, IL-4, IL-6, IL-8, TNF-α, IFN-γ, IL-1ß, IL-18 and IL-10 in cervical vaginal fluid. RESULTS: We show that euploid miscarriage is associated with a significantly higher prevalence of Lactobacillus spp. deplete vaginal microbial communities compared to aneuploid miscarriage (P = 0.01). Integration of matched cervicovaginal fluid immune-profiles showed that Lactobacillus spp. depleted vaginal microbiota associated with pro-inflammatory cytokine levels most strongly in euploid miscarriage compared to viable term pregnancy (IL-1ß; P < 0.001, IL-8; P = 0.01, IL-6; P < 0.001). CONCLUSIONS: Our data suggest the vaginal microbiota plays an important aetiological role in euploid miscarriage and may represent a target to modify risk of pregnancy loss.

Discrepant serological findings in SARS-CoV-2 PCR-negative hospitalized patients with fever and acute respiratory symptoms during the pandemic.

Coronavirus Disease 2019 (COVID-19) serology has an evolving role in the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, its use in hospitalized patients with acute respiratory symptoms remains unclear. Hospitalized patients with acute respiratory illness admitted to an isolation ward were recruited. All patients had negative nasopharyngeal swab polymerase chain reaction (PCR) for SARS-CoV-2. Serological studies using four separate assays (cPass: surrogate neutralizing enzyme-linked immunosorbent assay [ELISA]; Elecsys: N-antigen based chemiluminescent assay; SFB: S protein flow-based; epitope peptide-based ELISA) were performed on stored plasma collected from patients during the initial hospital stay, and a convalescent visit 4-12 weeks later. Of the 51 patients studied (aged 54, interquartile range 21-84; 62.7% male), no patients tested positive on the Elecsys or cPass assays. Out of 51 patients, 5 had antibodies detected on B-cell Epitope Assay and 3/51 had antibodies detected on SFB assay. These 8 patients with positive serological test to COVID-19 were more likely to have a high-risk occupation (p = 0.039), bacterial infection (p = 0.028), and neutrophilia (p = 0.013) during their initial hospital admission. Discrepant COVID-19 serological findings were observed among those with recent hospital admissions and bacterial infections. The positive serological findings within our cohort raise important questions about the interpretation of sero-epidemiology during the current pandemic.

The Simons Genome Diversity Project: 300 genomes from 142 diverse populations.

Here we report the Simons Genome Diversity Project data set: high quality genomes from 300 individuals from 142 diverse populations. These genomes include at least 5.8 million base pairs that are not present in the human reference genome. Our analysis reveals key features of the landscape of human genome variation, including that the rate of accumulation of mutations has accelerated by about 5% in non-Africans compared to Africans since divergence. We show that the ancestors of some pairs of present-day human populations were substantially separated by 100,000 years ago, well before the archaeologically attested onset of behavioural modernity. We also demonstrate that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that of other non-Africans.

Clinical characteristics and differences of 802 acral tumours, categorized by anatomical sites.

BACKGROUND: Acral skin tumours are common, but information in the literature regarding their incidence is scarce. AIM: To investigate the clinical characteristics and differences in incidence of benign and malignant acral tumours by anatomical site. METHODS: A retrospective review was conducted of 802 patients with acral skin tumours confirmed by skin biopsy between January 2010 and December 2019. Age, sex, duration, symptoms and sites were obtained from medical records and photographs. RESULTS: The mean age of onset was 43.8 years, the male/female ratio was 1 : 1.41, and the mean duration was 68.8 months. Most tumours were asymptomatic (66.7%). In total, 802 acral tumours were identified: 512 (63.8%) were benign and 290 (36.2%) were malignant. The most common benign tumours were benign melanocytic lesions (n = 239), and the most common malignant tumours were melanoma (n = 234). The most common site was the sole (n = 408). Benign melanocytic lesions, melanoma and epidermal cysts were more frequent on the foot, whereas pyogenic granuloma, glomus tumours, haemangiomas and mucous cysts were more frequent on the hand. Glomus tumours, fibromas, mucous cysts and osteomas were more frequent on the nail portion, and benign melanocytic lesions and epidermal cysts were more frequent on the non-nail portion. CONCLUSION: This study reports the incidence of various benign and malignant acral tumours according to site, and we believe the results will be helpful in making a clinical diagnosis.

Inference of complex population histories using whole-genome sequences from multiple populations.

There has been much interest in analyzing genome-scale DNA sequence data to infer population histories, but inference methods developed hitherto are limited in model complexity and computational scalability. Here we present an efficient, flexible statistical method, diCal2, that can use whole-genome sequence data from multiple populations to infer complex demographic models involving population size changes, population splits, admixture, and migration. Applying our method to data from Australian, East Asian, European, and Papuan populations, we find that the population ancestral to Australians and Papuans started separating from East Asians and Europeans about 100,000 y ago, and that the separation of East Asians and Europeans started about 50,000 y ago, with pervasive gene flow between all pairs of populations.