Genetic Datasets Research Articles

Tropical fossil mitogenomes: Holocene biogeography of two codistributed rodent genera from the Yucatán peninsula

• We used fossils from stratigraphic strata (2,000–12,000 years old) from Loltún cave, Yucatán, Mexico. • We successfully reconstructed partial ancient mitogenomes from tiny rodent fossil hemimandibles. • Our results provide evidence for biogeographic processes that affected Yucatán species during the Holocene. • The region was dominated by a wet and cold climate during the LGM that recently changed to warmer and much dryer. • Our results support the potential for phylogenomic research from fossils recovered from tropical caves. Integrating ancient fossil DNA with modern genetic samples is aiding in advancing the fields of ecology and biogeography. However, wide gaps in the fossil record still remain throughout the tropics, while genetic and genomic datasets from tiny (<50 mg) ancient tropical fossils recovered from small mass species (<100 g) are particularly scarce. We studied tropical fossil hemimandible samples of two rodent genera from seven stratigraphic strata from Loltún cave, Yucatán, Mexico, dated 2,000–12,000 years ago. We also selected contemporary museum skin samples of these rodents and amplified mitochondrial DNA from both sources (fossil and museum) with next generation sequencing. We recovered the first nearly complete contemporary and partial fossil mitogenomes for Ototylomys and Oryzomys . Ancient and contemporary samples of both species clustered together, whilst exhibiting a topology in which haplotypes differentiated in correspondence with biogeographic breaks. Accordingly, Ototylomys and Oryzomys distinct lineages are associated with biogeographic provinces and breaks across neotropical Mexico and Central America. Results from Loltún provide further evidence for biogeographic processes that affected Yucatán species during the Holocene. Relative abundances of the studied genera’s fossil remains differed among the stratigraphic layers, mirroring the expansion and contraction of their populations in response to changing climatic conditions and shifting plant regimes. Oryzomys fossil remains are much less abundant at the most recent and oldest layers, supporting a biogoegraphic hypothesis where this region was dominated by a wet and cold climate during the Last Glacial Maximum and which, only a few thousand years ago, changed to a warmer and much dryer climate.

Genoppi is an open-source software for robust and standardized integration of proteomic and genetic data

Combining genetic and cell-type-specific proteomic datasets can generate biological insights and therapeutic hypotheses, but a technical and statistical framework for such analyses is lacking. Here, we present an open-source computational tool called Genoppi (lagelab.org/genoppi) that enables robust, standardized, and intuitive integration of quantitative proteomic results with genetic data. We use Genoppi to analyze 16 cell-type-specific protein interaction datasets of four proteins (BCL2, TDP-43, MDM2, PTEN) involved in cancer and neurological disease. Through systematic quality control of the data and integration with published protein interactions, we show a general pattern of both cell-type-independent and cell-type-specific interactions across three cancer cell types and one human iPSC-derived neuronal cell type. Furthermore, through the integration of proteomic and genetic datasets in Genoppi, our results suggest that the neuron-specific interactions of these proteins are mediating their genetic involvement in neurodegenerative diseases. Importantly, our analyses suggest that human iPSC-derived neurons are a relevant model system for studying the involvement of BCL2 and TDP-43 in amyotrophic lateral sclerosis.

Racial Disparities Among Clinical Trials for Inherited Forms of Lipodystrophy

Background: There has been renewed interest in understanding how medical research serves minority communities disproportionally affected by disease. A recent study in a predominantly white population identified 12 subjects with partial lipodystrophy by genetics without clinical diagnosis of lipodystrophy (Gonzaga-Jauregui et al., 2020). Partial lipodystrophies are rare monogenic disorders leading to diabetes that can be challenging to diagnosis due to their similarity with common obesity-associated metabolic syndrome. We hypothesize minority populations may be underdiagnosed with lipodystrophy, and thus underrepresented in clinical trials. Methods: We compared racial demographics of lipodystrophic subjects participating in clinical trials to subjects with predicted loss-of-function (pLOF) mutations in 4 genes associated with lipodystrophy in the GnomAD dataset (>140K exome sequences): LMNA & PPARG (causing dominantly inherited partial lipodystrophy), and AGPAT2 & BSCL2 (causing recessively inherited generalized lipodystrophy, which is more phenotypically apparent, as an internal control for the study design). We also compared rates of synonymous mutations in these 4 genes among races to test if subjects of different ethnicities may be more genetically predisposed to developing inherited forms of lipodystrophy. Comparisons were done using chi-square analysis. Results: We identified 322 subjects with pLOF mutations in genes associated with lipodystrophy in the GnomAD dataset. The racial composition of GnomAD subjects with pLOF mutations in each gene was different than GnomAD subjects without pLOF mutations (p<0.001). 144 lipodystrophic subjects with known pathogenic variants in these genes participated in clinical trials. The racial composition of GnomAD and clinical trial subjects with LMNA mutations were significantly different (p=0.024) with the clinical trial cohort being more enriched for white patients (78 vs 21%) and less enriched for Latino patients (7 vs. 21%). There were no differences for other genes. The rates of synonymous mutations were different among patients of different ethnicities, p<0.001 for all genes. Discussion: Partial lipodystrophy due to LMNA mutations may be underdiagnosed in Latinos, leading to reduced participation in clinical trials. The lack of differences in other genes suggests there is no overall cohort bias. Different rates of synonymous mutations suggest there may be evolutionary drivers to racial differences in inherited forms of lipodystrophy, such as founder mutations or heterozygote advantage. Future work will determine prevalence of pLOF variants in lipodystrophy-associated genes in other genetic data sets enriched for minority subjects.

Large‐scale diversity reassessment, evolutionary history, and taxonomic revision of the green macroalgae family Udoteaceae (Bryopsidales, Chlorophyta)

AbstractUdoteaceae is a morphologically diverse family of the order Bryopsidales. Despite being very widespread geographically, this family is little known as compared with the closely related Halimedaceae or Caulerpaceae. Using the most extensive Udoteaceae collection to date and a multilocus genetic data set (tufA, rbcL, and 18S rDNA), we reassessed the species diversity of the family, as well as the phylogenetic relationships, the diagnostic morphoanatomical characters, and evolutionary history of its genera, toward a proposed taxonomic revision. Our approach included a combination of molecular and morphological criteria, including species delimitation methods, phylogenetic reconstruction, and mapping of trait evolution. We successfully delimited 62 species hypotheses, of which 29 were assigned (existing) species names and 13 represent putative new species. Our results also led us to revise the genera Udotea s.s., Rhipidosiphon s.s., and Chlorodesmis s.s., to validate the genus Rhipidodesmis, and to propose three new genera: Glaukea gen. nov., Ventalia gen. nov., and Udoteopsis gen. nov. We also identified two large species complexes, which we refer to as the “Penicillus–Rhipidosiphon–Rhipocephalus–Udotea complex” and the “Poropsis–Penicillus–Rhipidodesmis complex”. Using a time‐calibrated phylogeny, we estimated the origin of the family Udoteaceae at Late Triassic (ca. 216 Ma), whereas most of the genera originated during Paleogene. Our morphological inference results indicated that the thallus of the Udoteaceae ancestor was likely entirely corticated and calcified, composed of a creeping axis with a multisiphonous stipe and a pluristromatic flabellate frond. The frond shape, cortication, and calcification are still symplesiomorphies for most extant Udoteaceae genera and represent useful diagnostic characters.

Global wind patterns shape genetic differentiation, asymmetric gene flow, and genetic diversity in trees

Wind disperses the pollen and seeds of many plants, but little is known about whether and how it shapes large-scale landscape genetic patterns. We address this question by a synthesis and reanalysis of genetic data from more than 1,900 populations of 97 tree and shrub species around the world, using a newly developed framework for modeling long-term landscape connectivity by wind currents. We show that wind shapes three independent aspects of landscape genetics in plants with wind pollination or seed dispersal: populations linked by stronger winds are more genetically similar, populations linked by directionally imbalanced winds exhibit asymmetric gene flow ratios, and downwind populations have higher genetic diversity. For each of these distinct hypotheses, partial correlations between the respective wind and genetic metrics (controlling for distance and climate) are positive for a significant majority of wind-dispersed or wind-pollinated genetic data sets and increase significantly across functional groups expected to be increasingly influenced by wind. Together, these results indicate that the geography of both wind strength and wind direction play important roles in shaping large-scale genetic patterns across the world's forests. These findings have implications for various aspects of basic plant ecology and evolution, as well as the response of biodiversity to future global change.

Community phylogeographic patterns reveal how a barrier filters and structures taxa in North American warm deserts

AbstractAimThe study of biogeographic barriers is instrumental in understanding the evolution and distribution of taxa. With the increasing availability of empirical datasets, emergent patterns can be inferred from communities by synthesizing how barriers filter and structure populations across species. We assemble phylogeographic data across a barrier and perform spatially explicit simulations, quantifying spatiotemporal patterns of divergence, the influence of traits on these patterns, and the statistical power needed to differentiate diversification modes.TaxonVertebrates, Invertebrates, PlantsLocationNorth AmericaMethodsWe incorporate published datasets, from papers that match relevant keywords, to examine taxa around the Cochise Filter Barrier, separating the Sonoran and Chihuahuan Deserts of North America, to synthesize phylogeographic structuring across the communities with respect to organismal functional traits. We then use simulation and machine learning to assess the power of phylogeographic model selection.ResultsTaxa distributed across the Cochise Filter Barrier show heterogeneous responses to the barrier in levels of gene flow, phylogeographic structure, divergence timing, barrier width, and divergence mechanism. These responses correlate with locomotor and thermoregulatory traits. Many taxa show a Pleistocene population genetic break, often with introgression after divergence. Allopatric isolation and isolation by environment are the primary mechanisms structuring genetic divergence within taxa. Simulations reveal that in spatially explicit isolation with migration models across the barrier, age of divergence, presence of gene flow, and presence of isolation by distance can confound the interpretation of evolutionary history and model selection by producing easily confusable results. We re‐analyze five empirical genetic datasets to illustrate the utility of these simulations despite these constraints.Main ConclusionsBy synthesizing phylogeographic data for the Cochise Filter Barrier, we show that barriers interact with species traits to differentiate taxa in communities over millions of years. Identifying diversification modes across the barrier for these taxa remains challenging because commonly invoked demographic models may not be identifiable across a range of likely parameter space.

Genetic, Morphological, and Environmental Differentiation of an Arid-Adapted Oak with a Disjunct Distribution

The patterns of genetic and morphological diversity of a widespread species can be influenced by environmental heterogeneity and the degree of connectivity across its geographic distribution. Here, we studied Quercus havardii Rydb., a uniquely adapted desert oak endemic to the Southwest region of the United States, using genetic, morphometric, and environmental datasets over various geographic scales to quantify differentiation and understand forces influencing population divergence. First, we quantified variation by analyzing 10 eastern and 13 western populations from the disjunct distribution of Q. havardii using 11 microsatellite loci, 17 morphological variables, and 19 bioclimatic variables. We then used regressions to examine local and regional correlations of climate with genetic variation. We found strong genetic, morphological and environmental differences corresponding with the large-scale disjunction of populations. Additionally, western populations had higher genetic diversity and lower relatedness than eastern populations. Levels of genetic variation in the eastern populations were found to be primarily associated with precipitation seasonality, while levels of genetic variation in western populations were associated with lower daily temperature fluctuations and higher winter precipitation. Finally, we found little to no observed environmental niche overlap between regions. Our results suggest that eastern and western populations likely represent two distinct taxonomic entities, each associated with a unique set of climatic variables potentially influencing local patterns of diversity.

Complex reticulate evolution of speckled brush-furred rats (Lophuromys) in the Ethiopian centre of endemism.

The Ethiopian highlands represent a remarkable biodiversity 'hot spot' with a very high number of endemic species, even among vertebrates. Ethiopian representatives of a species complex of speckled brush-furred rats (Lophuromys flavopunctatus sensu lato) inhabit highland habitats ranging from low-elevation forests to Afroalpine grasslands. These may serve as a suitable model for understanding evolutionary processes leading to high genetic and ecological diversity in montane biodiversity hot spots. Here, we analyse the most comprehensive genetic data set of this group, comprising 315 specimens (all nine putative Ethiopian Lophuromys taxa sampled across most of their distribution ranges) genotyped at one mitochondrial and four nuclear markers, and thousands of SNPs from ddRAD sequencing. We performed phylogenetic analyses, delimited species and mapped their distribution and estimated divergence time between species (under the species-tree framework) and mitochondrial lineages. We found significant incongruence between mitochondrial and nuclear phylogenies, most probably caused by multiple interspecific introgression events. We discuss alternative scenarios of Ethiopian Lophuromys evolution, from retention of ancestral polymorphism to hybridization upon secondary contact of partially reproductively isolated lineages leading to reticulate evolution. Finally, we use the diversity of the speckled brush-furred rats for the description of the main biogeographic patterns in the fauna of the Ethiopian highlands.

Ageing genetic signature of hypersomatotropism.

Acromegaly is a pathological condition that is caused by over-secretion of growth hormone (GH) and develops primarily from a pituitary adenoma. Excess GH exposure over a prolonged period of time leads to a wide range of systemic manifestations and comorbidities. Studying the effect of excess GH on the cellular level could help to understand the underlying causes of acromegaly health complications and comorbidities. In our previous publications, we have shown that excess GH reduces body side population (SP) stem cells and induces signs of premature ageing in an acromegaly zebrafish model. Here, we study acromegaly ageing in greater depth at the level of gene expression. We investigated whether acromegaly induces an ageing genetic signature in different organs. Using the GenAge database, our acromegaly model showed a significant enrichment of ageing genetic datasets in the muscle but not in other organs. Likewise, the hierarchical clustering of wild type (WT), acromegaly and aged RNA data from various organs revealed the similarity of gene expression profiles between the acromegaly and the aged muscles. We therefore identified overlapping differentially expressed genes (DEGs) in different organs between acromegaly and aged zebrafish. Importantly, about half of the muscle, liver and brain acromegaly DEGs overlapped with aged zebrafish DEGs. Interestingly, overlapping was observed in the same way; acromegaly-up DEGs overlapped with aged zebrafish up DEGs, not down DEGs, and vice versa. We then identified the biological functions of overlapping DEGs. Enrichment database analysis and gene ontology showed that most overlapping muscle genes were involved in ageing metabolism, while overlapping liver DEGs were involved in metabolic pathways, response to hypoxia and endoplasmic reticulum stress. Thus, this study provides a full ageing genetic signature of acromegaly at the gene expression level.

The evolutionary dynamics of biological invasions: A multi-approach perspective.

Biological invasions, the establishment and spread of non‐native species in new regions, can have extensive economic and environmental consequences. Increased global connectivity accelerates introduction rates, while climate and land‐cover changes may decrease the barriers to invasive populations spread. A detailed knowledge of the invasion history, including assessing source populations, routes of spread, number of independent introductions, and the effects of genetic bottlenecks and admixture on the establishment success, adaptive potential, and further spread, is crucial from an applied perspective to mitigate socioeconomic impacts of invasive species, as well as for addressing fundamental questions on the evolutionary dynamics of the invasion process. Recent advances in genomics together with the development of geographic information systems provide unprecedented large genetic and environmental datasets at global and local scales to link population genomics, landscape ecology, and species distribution modeling into a common framework to study the invasion process. Although the factors underlying population invasiveness have been extensively reviewed, analytical methods currently available to optimally combine molecular and environmental data for inferring invasive population demographic parameters and predicting further spreading are still under development. In this review, we focus on the few recent insect invasion studies that combine different datasets and approaches to show how integrating genetic, observational, ecological, and environmental data pave the way to a more integrative biological invasion science. We provide guidelines to study the evolutionary dynamics of invasions at each step of the invasion process, and conclude on the benefits of including all types of information and up‐to‐date analytical tools from different research areas into a single framework.

Come together: calibration of tiger shark (Galeocerdo cuvier) microsatellite databases for investigating global population structure and assignment of historical specimens

This study provides the first standardized global microsatellite database for a shark species, the tiger shark (Galeocerdo cuvier). Genotyping of reference individuals was used to develop and apply a calibration key for data from eight microsatellite loci data produced by three different laboratories, thereby allowing merging of genotypes into a single dataset. The unified data helped to elucidate the global population structure of the species and provided improved statistical power, through allowing a higher number of samples per location compared to the original studies from which the samples were obtained. Pairwise FST estimates and PCA plots showed significant genetic differentiation between Atlantic and Indo-Pacific samples, confirming previous findings by identifying the presence of a strong genetic break between tiger sharks inhabiting the two ocean basins. In turn, the standardized database (n = 799) also allowed archived historical samples to be genotyped and assigned back to their population (ocean basin) of origin. We demonstrate how calibration tests in population structure studies using microsatellite data is important as it simply provides more data to single studies. Importance factors for successful assignment analysis is discussed, as well as how the possibility of assigning historical samples of unknown origin back to the population, increases sample value. Our results demonstrate that global calibration of microsatellite and other genetic datasets can improve the statistical power and resolution of population structure analysis; an approach applicable not only when working with highly mobile globally distributed species such as the tiger shark, but with any species for which multiple genetic datasets exist.

Basis exchange and learning algorithms for extracting collinear patterns

ABSTRACT Understanding large data sets is one of the most important and challenging problems in the modern days. Exploration of genetic data sets composed of high dimensional feature vectors can be treated as a leading example in this context. A better understanding of large, multivariate data sets can be achieved through exploration and extraction of their structure. Collinear patterns can be an important part of a given data set structure. Collinear (flat) pattern exists in a given set of feature vectors when many of these vectors are located on (or near) some plane in the feature space. Discovered flat patterns can reflect various types of interaction in an explored data set. The presented paper compares basis exchange algorithms with learning algorithms in the task of flat patterns extraction.

Integrated cross-study datasets of genetic dependencies in cancer

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

Species limits, patterns of secondary contact and a new species in theTrogon rufuscomplex (Aves: Trogonidae)

AbstractThe black-throated trogon, Trogon rufus, is a widespread, polytypic species-complex with a convoluted taxonomic history. Here, we integrated morphological, vocal and genetic datasets, including spectral data and digital quantification of barred plumage, to assess and redefine its species limits according to the foremost species concepts. We suggest the recognition of four named and one new species. Trogon tenellus and T. cupreicauda are divergent across Central and South America without geographic overlap or intermediates. Trogon chrysochloros in the Atlantic Forests of Brazil is phenotypically, genetically and ecologically distinct. In Amazonia, Trogon rufus consists of three phenotypically distinct subspecies intergrading with each other in a ring-like formation around central Amazonian rivers. Trogon rufus rufus in the Guiana Shield, Trogon rufus amazonicus in south-eastern Amazonia and Trogon rufus sulphureus in western Amazonia, with contact across the Lower Amazon and Madeira rivers, likely due to secondary contact between incompletely diverged lineages. The unique combination of song, morphology and mtDNA features of an unnamed, isolated population in the Atlantic Forest of north-eastern Brazil resulted in its description as a new species, known only from the type locality and considered here as Critically Endangered, requiring urgent conservation actions.

Welfare Genome Project: A Participatory Korean Personal Genome Project With Free Health Check-Up and Genetic Report Followed by Counseling.

The Welfare Genome Project (WGP) provided 1,000 healthy Korean volunteers with detailed genetic and health reports to test the social perception of integrating personal genetic and healthcare data at a large-scale. WGP was launched in 2016 in the Ulsan Metropolitan City as the first large-scale genome project with public participation in Korea. The project produced a set of genetic materials, genotype information, clinical data, and lifestyle survey answers from participants aged 20–96. As compensation, the participants received a free general health check-up on 110 clinical traits, accompanied by a genetic report of their genotypes followed by genetic counseling. In a follow-up survey, 91.0% of the participants indicated that their genetic reports motivated them to improve their health. Overall, WGP expanded not only the general awareness of genomics, DNA sequencing technologies, bioinformatics, and bioethics regulations among all the parties involved, but also the general public’s understanding of how genome projects can indirectly benefit their health and lifestyle management. WGP established a data construction framework for not only scientific research but also the welfare of participants. In the future, the WGP framework can help lay the groundwork for a new personalized healthcare system that is seamlessly integrated with existing public medical infrastructure.

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the "gold standard". The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76±23.14 ms). Similarly, within the prospective, genetic heart disease-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Ancient mitochondrial genomes from Chinese cave hyenas provide insights into the evolutionary history of the genus Crocuta.

Cave hyenas (genus Crocuta) are extinct bone-cracking carnivores from the family Hyaenidae and are generally split into two taxa that correspond to a European/Eurasian and an (East) Asian lineage. They are close relatives of the extant African spotted hyenas, the only extant member of the genus Crocuta. Cave hyenas inhabited a wide range across Eurasia during the Pleistocene, but became extinct at the end of the Late Pleistocene. Using genetic and genomic datasets, previous studies have proposed different scenarios about the evolutionary history of Crocuta. However, causes of the extinction of cave hyenas are widely speculative and samples from China are severely understudied. In this study, we assembled near-complete mitochondrial genomes from two cave hyenas from northeastern China dating to 20 240 and 20 253 calBP, representing the youngest directly dated fossils of Crocuta in Asia. Phylogenetic analyses suggest a monophyletic clade of these two samples within a deeply diverging mitochondrial haplogroup of Crocuta. Bayesian analyses suggest that the split of this Asian cave hyena mitochondrial lineage from their European and African relatives occurred approximately 1.85 Ma (95% CI 1.62-2.09 Ma), which is broadly concordant with the earliest Eurasian Crocuta fossil dating to approximately 2 Ma. Comparisons of mean genetic distance indicate that cave hyenas harboured higher genetic diversity than extant spotted hyenas, brown hyenas and aardwolves, but this is probably at least partially due to the fact that their mitochondrial lineages do not represent a monophyletic group, although this is also true for extant spotted hyenas. Moreover, the joint female effective population size of Crocuta (both cave hyenas and extant spotted hyenas) has sustained two declines during the Late Pleistocene. Combining this mitochondrial phylogeny, previous nuclear findings and fossil records, we discuss the possible relationship of fossil Crocuta in China and the extinction of cave hyenas.

Community-curated and standardised metadata of published ancient metagenomic samples with AncientMetagenomeDir

Ancient DNA and RNA are valuable data sources for a wide range of disciplines. Within the field of ancient metagenomics, the number of published genetic datasets has risen dramatically in recent years, and tracking this data for reuse is particularly important for large-scale ecological and evolutionary studies of individual taxa and communities of both microbes and eukaryotes. AncientMetagenomeDir (archived at https://doi.org/10.5281/zenodo.3980833) is a collection of annotated metagenomic sample lists derived from published studies that provide basic, standardised metadata and accession numbers to allow rapid data retrieval from online repositories. These tables are community-curated and span multiple sub-disciplines to ensure adequate breadth and consensus in metadata definitions, as well as longevity of the database. Internal guidelines and automated checks facilitate compatibility with established sequence-read archives and term-ontologies, and ensure consistency and interoperability for future meta-analyses. This collection will also assist in standardising metadata reporting for future ancient metagenomic studies.

Large-scale inference of population structure in presence of missingness using PCA.

Principal component analysis (PCA) is a commonly used tool in genetics to capture and visualize population structure. Due to technological advances in sequencing, such as the widely used non-invasive prenatal test, massive datasets of ultra-low coverage sequencing are being generated. These datasets are characterized by having a large amount of missing genotype information. We present EMU, a method for inferring population structure in the presence of rampant non-random missingness. We show through simulations that several commonly used PCA methods cannot handle missing data arisen from various sources, which leads to biased results as individuals are projected into the PC space based on their amount of missingness. In terms of accuracy, EMU outperforms an existing method that also accommodates missingness while being competitively fast. We further tested EMU on around 100K individuals of the Phase 1 dataset of the Chinese Millionome Project, that were shallowly sequenced to around 0.08×. From this data we are able to capture the population structure of the Han Chinese and to reproduce previous analysis in a matter of CPU hours instead of CPU years. EMU's capability to accurately infer population structure in the presence of missingness will be of increasing importance with the rising number of large-scale genetic datasets. EMU is written in Python and is freely available at https://github.com/rosemeis/emu. Supplementary data are available at Bioinformatics online.

Insight on the Genetics of Atrial Fibrillation in Puerto Rican Hispanics.

Non-Hispanic whites present with higher atrial fibrillation (AF) prevalence than other racial minorities living in the mainland USA. In two hospital-based studies, Puerto Rican Hispanics had a lower prevalence of atrial fibrillation of 2.5% than non-Hispanic Whites with 5.7%. This data is particularly controversial because Hispanics possess a higher prevalence of traditional risk factors for developing AF yet have a lower AF prevalence. This phenomenon is known as the atrial fibrillation paradox. Despite recent advancements in understanding AF, its pathogenesis remains unclear. In this study, we compared a genetic dataset of Puerto Rican Hispanics to 111 SNP known to be associated with AF in a large European cohort and determine if they are associated with AF susceptibility in our cohort. To achieve this aim, we performed a secondary analysis of existing data using the following two studies: (1) The Pharmacogenetics of Warfarin in Puerto Ricans study and the (2) A Genomic Approach for Clopidogrel in Caribbean Hispanics, and assess for the presence of European SNPs associated with AF from the genome-wide association study of 1 million people identifies 111 loci for atrial fibrillation. We used data from 555 cardiovascular Puerto Rican Hispanic patients, consisting of 486 control and 69 cases. We found that the following SNPs showed significant association with AF in PHR: rs2834618, rs6462079, rs7508, rs2040862, and rs10458660. Some of these SNPs are proteins involved in lysosomal activities responsible for breaking ceramides to sphingosines and collagen deposition around atrial cardiomyocytes. Furthermore, we performed a machine learning analysis and determined that Native American admixture and heart failure were strongly predictive of AF in PHR. For the first time, this study provides some genetic insight into AF's mechanisms in a Puerto Rican Hispanic cohort.