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Over the past decade, sequencing data generated by large microbiome projects showed that taxa exhibit patchy geographical distribution, raising questions about the geospatial dynamics that shape natural microbiomes and the spread of antimicrobial resistance genes. Answering these questions requires distinguishing between local and nonlocal microorganisms and identifying the source sites for the latter. Predicting the source sites and migration routes of microbiota has been envisioned for decades but was hampered by the lack of data, tools, and understanding of the processes governing biodiversity. State-of-the-art biogeographical tools suffer from low resolution and cannot predict biogeographical patterns at a scale relevant to ecological, medical, or epidemiological applications. Analyzing urban, soil, and marine microorganisms, we found that some taxa exhibit regional-specific composition and abundance, suggesting they can be used as biogeographical biomarkers. We developed the microbiome geographic population structure, a machine learning-based tool that utilizes microbial relative sequence abundances to yield a fine-scale source site for microorganisms. Microbiome geographic population structure predicted the source city for 92% of the samples and the within-city source for 82% of the samples, though they were often only a few hundred meters apart. Microbiome geographic population structure also predicted soil and marine sampling sites for 86% and 74% of the samples, respectively. We demonstrated that microbiome geographic population structure differentiated local from nonlocal microorganisms and used it to trace the global spread of antimicrobial resistance genes. Microbiome geographic population structure's ability to localize samples to their water body, country, city, and transit stations opens new possibilities in tracing microbiomes and has applications in forensics, medicine, and epidemiology., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Multiple myeloma (MM) is a plasma cell (PC) malignancy characterized by cytogenetic abnormalities, such as t(11;14)(q13;q32), resulting in CCND1 overexpression. The rs9344 G allele within CCND1 is the most significant susceptibility allele for t(11;14). Sequencing data from 2 independent cohorts, CoMMpass (n = 698) and Mayo Clinic (n = 661), confirm the positive association between the G allele and t(11;14). Among 80% of individuals heterozygous for rs9344 with t(11;14), the t(11;14) event occurs on the G allele, demonstrating a biological preference for the G allele in t(11;14). Within t(11;14), the G allele is associated with higher CCND1 expression and elevated H3K27ac and H3K4me3. CRISPR/Cas9 mediated A to G conversion resulted in increased H3K27ac over CCND1 and elevated CCND1 expression. ENCODE ChIP-seq data supported a PAX5 binding site within the enhancer region covering rs9344, showing preferential binding to the G allele. Overexpression of PAX5 resulted in increased CCND1 expression. These results support the importance of rs9344 G enhancer in increasing CCND1 expression in MM., (© 2024. The Author(s).)

Motivation: Computational analysis of large-scale metagenomics sequencing datasets have proven to be both incredibly valuable for extracting isolate-level taxonomic, and functional insights from complex microbial communities. However, due to an ever-expanding ecosystem of metagenomics-specific methods and file-formats, designing studies which implement seamless and scalable end-to-end workflows, and exploring the massive amounts of output data have become studies unto themselves. One-click bioinformatics pipelines have helped to organize these tools into targeted workflows, but they suffer from general compatibility and maintainability issues., Methods: To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed a module-based metagenomics analysis system: "Core Analysis Metagenomics Pipeline" (CAMP), written in Snakemake, a popular workflow management system, along with a standardized module and working directory architecture. Each module can be run independently or conjointly with a series of others to produce the target data format (ex. short-read preprocessing alone, or short-read preprocessing followed by de novo assembly), and outputs aggregated summary statistics reports and semi-guided Jupyter notebook-based visualizations., Results: We have applied CAMP to a set of ten metagenomics samples to demonstrate how a modular analysis system with built-in data visualization at intermediate steps facilitates rich and seamless inter-communication between output data from different analytic purposes., Availability: The module template as well as the modules described below can be found at https://github.com/MetaSUB-CAMP., Competing Interests: Competing Interests No competing interest is declared.

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable 'app' and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

Introduction: The ancient city of Tel Megiddo in the Jezreel Valley (Israel), which lasted from the Neolithic to the Iron Age, has been continuously excavated since 1903 and is now recognized as a World Heritage Site. The site features multiple ruins in various areas, including temples and stables, alongside modern constructions, and public access is allowed in designated areas. The site has been studied extensively since the last century; however, its microbiome has never been studied. We carried out the first survey of the microbiomes in Tel Megiddo. Our objectives were to study (i) the unique microbial community structure of the site, (ii) the variation in the microbial communities across areas, (iii) the similarity of the microbiomes to urban and archeological microbes, (iv) the presence and abundance of potential bio-corroding microbes, and (v) the presence and abundance of potentially pathogenic microbes., Methods: We collected 40 swab samples from ten major areas and identified microbial taxa using next-generation sequencing of microbial genomes. These genomes were annotated and classified taxonomically and pathogenetically., Results: We found that eight phyla, six of which exist in all ten areas, dominated the site (>99%). The relative sequence abundance of taxa varied between the ruins and the sampled materials and was assessed using all metagenomic reads mapping to a respective taxon. The site hosted unique taxa characteristic of the built environment and exhibited high similarity to the microbiome of other monuments. We identified acid-producing bacteria that may pose a risk to the site through biocorrosion and staining and thus pose a danger to the site's preservation. Differences in the microbiomes of the publicly accessible or inaccessible areas were insignificant; however, pathogens were more abundant in the former., Discussion: We found that Tel Megiddo combines microbiomes of arid regions and monuments with human pathogens. The findings shed light on the microbial community structures and have relevance for bio-conservation efforts and visitor health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Ruff, Oskolkov, Tierney, Ryon, Danko, Mason and Elhaik.)

Glucocorticoid resistance is commonly observed in depression, and has been linked to reduced expression and/or function of the glucocorticoid receptor (NR3C1 in human, hereafter referred to as GR). Previous studies have shown that GR-mutant zebrafish exhibit behavioural abnormalities that are indicative of an affective disorder, suggesting that GR plays a role in brain function. We compared the brain methylomes and brain transcriptomes of adult wild-type and GR-mutant zebrafish, and identified 249 differentially methylated regions (DMRs) that are regulated by GR. These include a cluster of CpG sites within the first intron of fkbp5, the gene encoding the glucocorticoid-inducible heat shock protein co-chaperone Fkbp5. RNA-sequencing analysis revealed that genes associated with chaperone-mediated protein folding, the regulation of circadian rhythm and the regulation of metabolism are particularly sensitive to loss of GR function. In addition, we identified subsets of genes exhibiting GR-regulated transcription that are known to regulate behaviour, and are linked to unipolar depression and anxiety. Taken together, our results identify key biological processes and novel molecular mechanisms through which the GR is likely to mediate responses to stress in the adult zebrafish brain, and they provide further support for the zebrafish GR mutant as a model for the study of affective disorders., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2023. Published by The Company of Biologists Ltd.)

Variability in disease severity caused by a microbial pathogen is impacted by each infection representing a unique combination of host and pathogen genomes. Here, we show that the outcome of invasive Streptococcus pyogenes infection is regulated by an interplay between human STING genotype and bacterial NADase activity. S. pyogenes-derived c-di-AMP diffuses via streptolysin O pores into macrophages where it activates STING and the ensuing type I IFN response. However, the enzymatic activity of the NADase variants expressed by invasive strains suppresses STING-mediated type I IFN production. Analysis of patients with necrotizing S. pyogenes soft tissue infection indicates that a STING genotype associated with reduced c-di-AMP-binding capacity combined with high bacterial NADase activity promotes a 'perfect storm' manifested in poor outcome, whereas proficient and uninhibited STING-mediated type I IFN production correlates with protection against host-detrimental inflammation. These results reveal an immune-regulating function for bacterial NADase and provide insight regarding the host-pathogen genotype interplay underlying invasive infection and interindividual disease variability., (© 2023. The Author(s).)

Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed., (© 2022. The Author(s).)

Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. We developed the temporal population structure (TPS), a DNA-based dating method for genomes ranging from the Late Mesolithic to today, and applied it to 3,591 ancient and 1,307 modern Eurasians. TPS predictions aligned with the known dates and correctly accounted for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We also demonstrated how TPS improved the ability to study phenotypic traits over time. TPS can be used when radiocarbon dating is unfeasible or uncertain or to develop alternative hypotheses for samples younger than 10,000 years ago, a limitation that may be resolved over time as ancient data accumulate., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases., Competing Interests: Declaration of interests M.P.S. is a co-founder and member of the scientific advisory board of Personalis, Qbio, January, SensOmics, Protos, Mirvie, NiMo, Onza, and Oralome. He is also on the scientific advisory board of Danaher, Genapsys, and Jupiter. J.L. is a co-founder of SensOmics. No other authors have competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance, and environmental influences that hamper genome-wide association studies approaches to untargeted discovery. Thus, SB genetic studies may suffer from population substructure and/or selection bias introduced by typical candidate gene searches. We report a population based, ancestry-matched whole-genome sequence analysis of SB genetic predisposition using a systems biology strategy to interrogate 298 case-control subject genomes (149 pairs). Genes that were enriched in likely gene disrupting (LGD), rare protein-coding variants were subjected to machine learning analysis to identify genes in which LGD variants occur with a different frequency in cases versus controls and so discriminate between these groups. Those genes with high discriminatory potential for SB significantly enriched pathways pertaining to carbon metabolism, inflammation, innate immunity, cytoskeletal regulation, and essential transcriptional regulation consistent with their having impact on the pathogenesis of human SB. Additionally, an interrogation of conserved noncoding sequences identified robust variant enrichment in regulatory regions of several transcription factors critical to embryonic development. This genome-wide perspective offers an effective approach to the interrogation of coding and noncoding sequence variant contributions to rare complex genetic disorders., Competing Interests: Competing interest statement. R.H.F. formerly held a leadership position with the now dissolved TeratOmic Consulting LLC. He also receives travel funds to attend editorial board meetings of the Journal of Reproductive and Developmental Medicine published out of the Red Hospital of Fudan University. E.E. consults for the DNA Diagnostics Center. P.S. and R.H.F. are coauthors on a 2020 paper resulting from an NIH workshop: Maruvada P et al., Knowledge gaps in understanding the metabolic and clinical effects of excess folates/folic acid: a summary, and perspectives, from an NIH workshop. Am J Clin Nutr. 2020 Nov 11;112(5):1390-1403. doi: 10.1093/ajcn/nqaa259. PMID: 33022704; PMCID: PMC7657327., (Copyright © 2021 the Author(s). Published by PNAS.)

As the second year of the COVID-19 pandemic begins, it remains clear that a massive increase in the ability to test for SARS-CoV-2 infections in a myriad of settings is critical to controlling the pandemic and to preparing for future outbreaks. The current gold standard for molecular diagnostics is the polymerase chain reaction (PCR), but the extraordinary and unmet demand for testing in a variety of environments means that both complementary and supplementary testing solutions are still needed. This review highlights the role that loop-mediated isothermal amplification (LAMP) has had in filling this global testing need, providing a faster and easier means of testing, and what it can do for future applications, pathogens, and the preparation for future outbreaks. This review describes the current state of the art for research of LAMP-based SARS-CoV-2 testing, as well as its implications for other pathogens and testing. The authors represent the global LAMP (gLAMP) Consortium, an international research collective, which has regularly met to share their experiences on LAMP deployment and best practices; sections are devoted to all aspects of LAMP testing, including preanalytic sample processing, target amplification, and amplicon detection, then the hardware and software required for deployment are discussed, and finally, a summary of the current regulatory landscape is provided. Included as well are a series of first-person accounts of LAMP method development and deployment. The final discussion section provides the reader with a distillation of the most validated testing methods and their paths to implementation. This review also aims to provide practical information and insight for a range of audiences: for a research audience, to help accelerate research through sharing of best practices; for an implementation audience, to help get testing up and running quickly; and for a public health, clinical, and policy audience, to help convey the breadth of the effect that LAMP methods have to offer., (© 2021 ABRF.)

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities., Competing Interests: Declaration of interests C.E.M. is co-founder of Biotia and Onegevity Health. D.B. is co-founder and CSO of Poppy Health Inc. The other authors declare they have no competing interests that impacted this study., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks (locally and internationally), increase the accuracy of association analyses and inform developmental efforts.

In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transformation was aided by a series of articles by Daniel Schrider and Andrew Kern. Within this series, a paper entitled "Soft sweeps are the dominant mode of adaptation in the human genome" (Schrider and Kern, Mol. Biol. Evolut . 2017 , 34 (8), 1863-1877) attracted a great deal of attention, in particular in conjunction with another paper (Kern and Hahn, Mol. Biol. Evolut . 2018 , 35 (6), 1366-1371), for purporting to discredit the Neutral Theory of Molecular Evolution (Kimura 1968). Here, we address an alleged novelty in Schrider and Kern's paper, i.e., the claim that their study involved an artificial intelligence technique called supervised machine learning (SML). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known empirically to be true. Curiously, Schrider and Kern did not possess a training dataset of genomic segments known a priori to have evolved either neutrally or through soft or hard selective sweeps. Thus, their claim of using SML is thoroughly and utterly misleading. In the absence of legitimate training datasets, Schrider and Kern used: (1) simulations that employ many manipulatable variables and (2) a system of data cherry-picking rivaling the worst excesses in the literature. These two factors, in addition to the lack of negative controls and the irreproducibility of their results due to incomplete methodological detail, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S/HIC) should be taken with a huge shovel of salt.

Sudden infant death syndrome (SIDS) is the unexpected death of an infant under one year of age that remains unexplained after a thorough investigation. Despite SIDS remaining a diagnosis of exclusion with an unexplained etiology, it is widely accepted that SIDS can be caused by environmental and/or biological factors, with multiple underlying candidate genes. However, the lack of biomarkers raises questions as to why genetic studies on SIDS to date are unable to provide a clearer understanding of the disease etiology. We sought to improve the identification of SIDS-associated genes by reviewing the SIDS genetic literature and objectively categorizing and scoring the reported genes based on the strength of evidence (from C1 (high) to C5 (low)). This was followed by analyses of function, associations between genes, the enrichment of gene ontology (GO) terms, and pathways and gender difference in tissue gene expression. We constructed a curated database for SIDS gene candidates consisting of 109 genes, 14 of which received a category 4 (C4) and 95 genes received the lowest category of C5. That none of the genes was classified into the higher categories indicates the low level of supporting evidence. We found that genes of both scoring categories show distinct networks and are highly diverse in function and involved in many GO terms and pathways, in agreement with the perception of SIDS as a heterogeneous syndrome. Genes of both scoring categories are part of the cardiac system, muscle, and ion channels, whereas immune-related functions showed enrichment for C4 genes. A limited association was found with neural development. Overall, inconsistent reports and missing metadata contribute to the ambiguity of genetic studies. Considering those parameters could help improve the identification of at-risk SIDS genes. However, the field is still far from offering a full-pledged genetic test to identify at-risk infants and is still hampered with methodological challenges and misunderstandings of the vulnerabilities of vital biological mechanisms.

The rise of microbiomics and metagenomics has been driven by advances in genomic sequencing technology, improved microbial sampling methods, and fast-evolving approaches in bioinformatics. Humans are a host to diverse microbial communities in and on their bodies, which continuously interact with and alter the surrounding environments. Since information relating to these interactions can be extracted by analyzing human and environmental microbial profiles, they have the potential to be relevant to forensics. In this review, we analyzed over 100 papers describing forensic microbiome applications with emphasis on geolocation, personal identification, trace evidence, manner and cause of death, and inference of the postmortem interval (PMI). We found that although the field is in its infancy, utilizing microbiome and metagenome signatures has the potential to enhance the forensic toolkit. However, many of the studies suffer from limited sample sizes and model accuracies, and unrealistic environmental settings, leaving the full potential of microbiomics to forensics unexplored. It is unlikely that the information that can currently be elucidated from microbiomics can be used by law enforcement. Nonetheless, the research to overcome these challenges is ongoing, and it is foreseeable that microbiome-based evidence could contribute to forensic investigations in the future., Competing Interests: EE consults the DNA Diagnostics Center. CM is a co-founder of Biotia, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Robinson, Pasternak, Mason and Elhaik.)

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease. CAV1 and CAV2 organize membrane lipid rafts (MLRs) important for cell signaling and neuronal survival, and overexpression of CAV1 ameliorates ALS phenotypes in vivo. Genome-wide association studies localize a large proportion of ALS risk variants within the non-coding genome, but further characterization has been limited by lack of appropriate tools. By designing and applying a pipeline to identify pathogenic genetic variation within enhancer elements responsible for regulating gene expression, we identify disease-associated variation within CAV1/CAV2 enhancers, which replicate in an independent cohort. Discovered enhancer mutations reduce CAV1/CAV2 expression and disrupt MLRs in patient-derived cells, and CRISPR-Cas9 perturbation proximate to a patient mutation is sufficient to reduce CAV1/CAV2 expression in neurons. Additional enrichment of ALS-associated mutations within CAV1 exons positions CAV1 as an ALS risk gene. We propose CAV1/CAV2 overexpression as a personalized medicine target for ALS., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Ancient Y-Chromosomal DNA is an invaluable tool for dating and discerning the origins of migration routes and demographic processes that occurred thousands of years ago. Driven by the adoption of high-throughput sequencing and capture enrichment methods in paleogenomics, the number of published ancient genomes has nearly quadrupled within the last three years (2018-2020). Whereas ancient mtDNA haplogroup repositories are available, no similar resource exists for ancient Y-Chromosomal haplogroups. Here, we present aYChr-DB-a comprehensive collection of 1797 ancient Eurasian human Y-Chromosome haplogroups ranging from 44 930 BC to 1945 AD. We include descriptors of age, location, genomic coverage and associated archaeological cultures. We also produced a visualization of ancient Y haplogroup distribution over time. The aYChr-DB database is a valuable resource for population genomic and paleogenomic studies., (© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has rapidly caused a global pandemic associated with a novel respiratory infection: coronavirus disease-19 (COVID-19). Angiotensin-converting enzyme-2 (ACE2) is necessary to facilitate SARS-CoV-2 infection, but-owing to its essential metabolic roles-it may be difficult to target it in therapies. Transmembrane protease serine 2 (TMPRSS2), which interacts with ACE2, may be a better candidate for targeted therapies. Using publicly available expression data, we show that both ACE2 and TMPRSS2 are expressed in many host tissues, including lung. The highest expression of ACE2 is found in the testes, whereas the prostate displays the highest expression of TMPRSS2. Given the increased severity of disease among older men with SARS-CoV-2 infection, we address the potential roles of ACE2 and TMPRSS2 in their contribution to the sex differences in severity of disease. We show that expression levels of ACE2 and TMPRSS2 are overall comparable between men and women in multiple tissues, suggesting that differences in the expression levels of TMPRSS2 and ACE2 in the lung and other non-sex-specific tissues may not explain the gender disparities in severity of SARS CoV-2. However, given their instrumental roles for SARS-CoV-2 infection and their pleiotropic expression, targeting the activity and expression levels of TMPRSS2 is a rational approach to treat COVID-19., (Copyright © 2020. Published by Elsevier Inc.)

Motivation: In clinical trials, individuals are matched using demographic criteria, paired and then randomly assigned to treatment and control groups to determine a drug's efficacy. A chief cause for the irreproducibility of results across pilot to Phase-III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups., Results: Pair Matcher (PaM) addresses stratification bias by optimizing pairing assignments a priori and/or a posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on competing methods, including the commonly used principal component analysis (PCA). Homogenizing the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the trial or genetic study. PaM's ancestral inferences also allow characterizing responders and developing a precision medicine approach to treatment., Availability and Implementation: PaM is freely available via Rhttps://github.com/eelhaik/PAM and a web-interface at http://elhaik-matcher.sheffield.ac.uk/ElhaikLab/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press.)

Background: Sudden infant death syndrome (SIDS) is the most common cause of postneonatal unexplained infant death. The allostatic load hypothesis posits that SIDS is the result of cumulative perinatal painful, stressful, or traumatic exposures that tax neonatal regulatory systems., Aims: To test the predictions of the allostatic load hypothesis we explored the relationships between SIDS and two common phenotypes, male neonatal circumcision (MNC) and prematurity., Methods: We collated latitudinal data from 15 countries and 40 US states sampled during 2009 and 2013. We used linear regression analyses and likelihood ratio tests to calculate the association between SIDS and the phenotypes., Results: SIDS mortality rate was significantly and positively correlated with MNC. Globally (weighted): Increase of 0.06 (95% CI: 0.01-0.1, t = 2.86, p = 0.01) per 1000 SIDS mortality per 10% increase in circumcision rate. US (weighted): Increase of 0.1 (95% CI: 0.03-0.16, t = 2.81, p = 0.01) per 1000 unexplained mortality per 10% increase in circumcision rate. US states in which Medicaid covers MNC had significantly higher MNC rates ( χ̄ = 0.72 vs 0.49, p = 0.007) and male/female ratio of SIDS deaths ( χ̄ = 1.48 vs 1.125, p = 0.015) than other US states. Prematurity was also significantly and positively correlated with MNC. Globally: Increase of 0.5 (weighted: 95% CI: 0.02-0.086, t = 3.37, p = 0.004) per 1000 SIDS mortality per 10% increase in the prematurity rates. US: Increase of 1.9 (weighted: 95% CI: 0.06-0.32, t = 3.13, p = 0.004) per 1000 unexplained mortalities per 10% increase in the prematurity rates. Combined, the phenotypes increased the likelihood of SIDS., Conclusions: Epidemiological analyses are useful to generate hypotheses but cannot provide strong evidence of causality. Biological plausibility is provided by a growing body of experimental and clinical evidence linking aversive preterm and early-life SIDS events. Together with historical and anthropological evidence, our findings emphasize the necessity of cohort studies that consider these phenotypes with the aim of improving the identification of at-risk infants and reducing infant mortality., Relevance for Patients: Preterm birth and neonatal circumcision are associated with a greater risk of SIDS, and efforts should be focused on reducing their rates.

The rapid accumulation of ancient human genomes from various areas and time periods potentially enables the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day populations, which are known to misrepresent the population structure. Past studies addressed these problems by using ancestry informative markers (AIMs). It is, thereby, unclear whether AIMs derived from contemporary human genomes can capture ancient population structures, and whether AIM-finding methods are applicable to aDNA, provided that the high missingness rates in ancient-and oftentimes haploid-DNA can also distort the population structure. Here, we define ancient AIMs (aAIMs) and develop a framework to evaluate established and novel AIM-finding methods in identifying the most informative markers. We show that aAIMs identified by a novel principal component analysis (PCA)-based method outperform all of the competing methods in classifying ancient individuals into populations and identifying admixed individuals. In some cases, predictions made using the aAIMs were more accurate than those made with a complete marker set. We discuss the features of the ancient Eurasian population structure and strategies to identify aAIMs. This work informs the design of single nucleotide polymorphism (SNP) microarrays and the interpretation of aDNA results, which enables a population-wide testing of primordialist theories., Competing Interests: EE is a consultant to DNA Diagnostic Centre and DNA Consultants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Multiple myeloma (MM) is two- to three-fold more common in African Americans (AAs) compared to European Americans (EAs). This striking disparity, one of the highest of any cancer, may be due to underlying genetic predisposition between these groups. There are multiple unique cytogenetic subtypes of MM, and it is likely that the disparity is associated with only certain subtypes. Previous efforts to understand this disparity have relied on self-reported race rather than genetic ancestry, which may result in bias. To mitigate these difficulties, we studied 881 patients with monoclonal gammopathies who had undergone uniform testing to identify primary cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was quantitatively assessed using the Geographic Population Structure Origins tool. The probability of having one of three specific subtypes, namely t(11;14), t(14;16), or t(14;20) was significantly higher in the 120 individuals with highest African ancestry (≥80%) compared with the 235 individuals with lowest African ancestry (<0.1%) (51% vs. 33%, respectively, p value = 0.008). Using quantitatively measured African ancestry, we demonstrate a major proportion of the racial disparity in MM is driven by disparity in the occurrence of the t(11;14), t(14;16), and t(14;20) types of MM.

The Druze are an aggregate of communities in the Levant and Near East living almost exclusively in the mountains of Syria, Lebanon and Israel whose ~1000 year old religion formally opposes mixed marriages and conversions. Despite increasing interest in genetics of the population structure of the Druze, their population history remains unknown. We investigated the genetic relationships between Israeli Druze and both modern and ancient populations. We evaluated our findings in light of three hypotheses purporting to explain Druze history that posit Arabian, Persian or mixed Near Eastern-Levantine roots. The biogeographical analysis localised proto-Druze to the mountainous regions of southeastern Turkey, northern Iraq and southeast Syria and their descendants clustered along a trajectory between these two regions. The mixed Near Eastern-Middle Eastern localisation of the Druze, shown using both modern and ancient DNA data, is distinct from that of neighbouring Syrians, Palestinians and most of the Lebanese, who exhibit a high affinity to the Levant. Druze biogeographic affinity, migration patterns, time of emergence and genetic similarity to Near Eastern populations are highly suggestive of Armenian-Turkish ancestries for the proto-Druze., Competing Interests: E.E. is a consultant to DNA Diagnostic Centre. S.M., R.D. and M.P. declare no competing financial interests.