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bioRxiv2017

Efficient strategies for screening large-scale genetic interaction networks

Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.\n\nTo address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

systems biology↗

bioRxiv2017

Context-Dependent Genetic Regulation

Cells process extra-cellular signals with multiple layers of complex biological networks. Due to the stochastic nature of the networks, the signals become significantly noisy within the cells and in addition, due to the nonlinear nature of the networks, the signals become distorted, shifted, and (de-)amplified. Such nonlinear signal processing can lead to non-trivial cellular phenotypes such as cell cycles, differentiation, cell-to-cell communication, and homeostasis. These nonlinear pheno-types, when observed at the cell population levels, can be quite different from the single-cell level observation. As one of the underlying mechanisms behind this difference, we report the interplay between nonlinearity and stochasticity in genetic regulation. Here we show that nonlinear genetic regulation, characterized at the cellular population level, can be affected by cell-to-cell variability in the regulatory factor concentrations. The observed genetic regulation at the cell population is shown to be significantly dependent on the upstream DNA sequences of the regulator, in particular, 5 untranslated region. This indicates that genetic regulation observed at the cell population level can be significantly dependent on its genetic context, and that its characterization needs a careful attention on noise propagation.\n\nOne Sentence SummaryGenetic regulation observed at the cell population level can be significantly affected by cell-to-cell variability in the regulatory factor copy numbers, indicating that the observed regulation is dependent on 5 UTR of the regulator coding gene.

synthetic biology↗

bioRxiv2017

The functional and genetic associations of neuroimaging data: a toolbox

Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and to identify the roots of psychiatric diseases. However, the results generated by most neuroimaging studies, i.e., activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. We describe a Brain Annotation Toolbox (BAT), a toolbox that helps to generate functional and genetic annotations for neuroimaging results. The toolbox can take data from brain regions identified with an atlas, or from brain regions identified as activated in tasks, or from functional connectivity links or networks of links. Then, the voxel-level functional description from the Neurosynth database and the gene expression profile from the Allen Brain Atlas are used to generate functional and genetic knowledge for such region-level data. Parametric (Fishers exact test) or non-parametric (permutation test) statistical tests are adopted to identify significantly related functional descriptors and genes for the neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and gene co-expression networks are highly correlated for many major brain atlases. One application of BAT is to help provide functional and genetic annotations for the newly discovered regions with unknown functions, e.g., the 97 new regions identified in the Human Connectome Project. Importantly too, this toolbox can help understand differences between patients with psychiatric disorders and controls, and this is demonstrated using data for schizophrenia and autism, for which the functional and genetic annotations for the neuroimaging data differences between patients and controls are consistent with each other and help with the interpretation of the differences.

neuroscience↗

bioRxiv2017

Identifying the genetic determinants of particular phenotypes in microbial genomes with very small training sets

Machine learning (ML) encompasses numerous algorithms that aim at discovering complex patterns between elements within large data using limited prior assumptions or modeling. However, some scientific disciplines still produce small data sets: in particular, empirical studies that try to find the mutations responsible for complex phenotypes are often limited to very small sample sizes (n), while scanning a large number of amino acid sites (p) in a proteome. To date, little is known on how ML performs in this type of so-called \"large p, small n\" problem. To address this question, we evaluated the performance of two general ML classifiers, adaptive boosting (AB) and random forest, on two data sets. To assess the impact of proteome size, we contrasted a small (viral) genome with a larger (bacterial) one. To analyze large proteomes, we further developed a chunking algorithm, and introduce a repeated random forest (RRF) algorithm that stabilizes model predictions. With the influenza data, we were able to rediscover amino acid sites experimentally implicated in three different complex phenotypes (infectivity, transmissibility, and pathogenicity). Results for the larger proteome, pertaining to three types of drug resistance (Ciprofloxacin, Ceftazidime, and Gentamicin), were more nuanced, with RRF making more sensible pre-dictions, with smaller errors rates, than AB. Furthermore, we show that chunking improved runtimes by an order of magnitude and may increase sensitivity of the predictions. Altogether, we demonstrate that ML algorithms can be used to identify genetic determinants in small proteomes (viruses), even with small numbers of individuals. We further show that even if the size of bacterial proteomes pushes AB to its limits in the context of small n, RRF may deserve more scrutiny, which should be facilitated by the plummeting costs of sequencing and, more critically, by phenotyping large cohorts of individuals.\n\nAuthor SummaryFinding the genetic determinants of a phenotype is typically performed by testing for an association between a particular allele and a trait, carrying out the testing over a large number of loci in a large cohort of individuals, itself divided into two subsets of individuals: those who have the trait (cases), and those who do not (controls). However, recruiting large cohorts can be problematic in some experimental fields, while using genotypic information rather than complete genomes can miss some mutations. To address these issues, we implemented two machine learning (ML) algorithms, tweaked for analyzing large genomes and providing stable results. The analysis of a small viral genome, for which genetic determinants of three phenotypes are already known, showed that our approach can rediscover known mutations, almost irrespective of the ML algorithm used. However, the analysis of a larger bacterial genome, for which genetic determinants of three phenotypes are unknown, suggested that the simpler of our modified algorithms performed better, returning more sensitive predictions with lower error rates. This work demonstrates the feasibility of finding genetic determinants of complex phenotypes based on a small number of complete genomes.

bioinformatics↗

bioRxiv2017

Genetic and metabolic regulation of Mycobacterium tuberculosis acid growth arrest

Mycobacterium tuberculosis (Mtb) senses and adapts to acidic environments during the course of infection. Acidic pH-dependent adaptations include the induction of metabolic genes associated with anaplerosis and growth arrest on specific carbon sources. In this study, reverse and forward genetic studies were undertaken to define new mechanisms underlying pH-dependent adaptations. Here we report that deletion of isocitrate lyase (icl1/2) or phosphoenolpyruvate carboxykinase (pckA) results in reduced growth at acidic pH and altered metabolite profiles, supporting that remodeling of anaplerotic metabolism is required for pH-dependent adaptation. Mtb cultured at pH 5.7 in minimal medium containing glycerol as a single carbon source exhibits an acid growth arrest phenotype, where the bacterium is non-replicating but viable and metabolically active. The bacterium uptakes and metabolizes glycerol and maintains ATP pools during acid growth arrest and becomes tolerant to detergent stress and the antibiotics isoniazid and rifampin. A forward genetic screen identified mutants that do not arrest their growth at acidic pH, including four enhanced acid growth (eag) mutants with three distinct mutations in the PPE gene MT3221. Overexpression of the MT3221(S211R) variant protein in wild type Mtb results in enhanced acid growth and reduced drug tolerance. Together, these findings provide new evidence for a genetic and physiological basis for acid growth arrest and support that growth arrest is an adaptive process and not simply a physiological limitation associated with acidic pH.\n\nAuthor SummaryThe bacterium Mycobacterium tuberculosis (Mtb) causes the disease tuberculosis in humans. During infection Mtb colonizes a variety of environments that have acidic environments and Mtb must adapt to these environments to cause disease. One of these adaptations is that Mtb slows and arrests its growth at acidic pH, and the goal of this study was to examine the genetics and physiology of these pH-dependent adaptations. We found that Mtb modifies its metabolism at acidic pH and that these adaptations are required for optimal growth. We also found that acidic pH and specific nutrient sources can promote the bacterium to enter a state of dormancy, called acid growth arrest, where the bacterium becomes tolerant to antibiotics. Mutants were identified that do not arrest their growth at acidic, revealing that acid growth arrest is a genetically controlled process. Overall, understanding how Mtb adapts to acidic pH has revealed pathway that are required for virulence and drug tolerance and thus may identify new targets for drug development that may function to shorten the course of TB therapy.

microbiology↗

bioRxiv2017

Biological Insights Into Muscular Strength: Genetic Findings in the UK Biobank

BackgroundHand grip strength, a simple indicator of muscular strength, has been associated with a range of health conditions, including fractures, disability, cardiovascular disease and premature death risk. Twin studies have suggested a high (50-60%) heritability, but genetic determinants are largely unknown.\n\nAimsIn this study, our aim was to study genetic variation associated with muscular strength in a large sample of 334,925 individuals of European descent from the UK Biobank, and to evaluate shared genetic aetiology with and causal effects of grip strength on physical and cognitive health.\n\nMethods and ResultsIn our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength at genome-wide significance (P<5x10-8). Of these, 64 were associated (P<0.01 and consistent direction) also in the replication dataset (N=111,610). Many of the lead SNPs were located in or near genes known to have a function in developmental disorders (FTO, SLC39A8, TFAP2B, TGFA, CELF1, TCF4, BDNF, FOXP1, KIF1B, ANTXR2), and one of the most significant genes based on a gene-based analysis (ATP2A1) encodes SERCA1, the critical enzyme in calcium uptake to the sarcoplasmic reticulum, which plays a major role in muscle contraction and relaxation. Further, we demonstrated a significant enrichment of gene expression in brain-related transcripts among grip strength associations. Finally, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents age of death and education; and showed that grip strength was causally related to fitness, physical activity and other indicators of frailty, including cognitive performance scores.\n\nConclusionsIn our study of over 330,000 individuals from the general population, the genetic findings for hand grip strength suggest an important role of the central nervous system in strength performance. Further, our results indicate that maintaining good muscular strength is important for physical and cognitive performance and healthy aging.

genomics↗

bioRxiv2019

Diverse genetic origins of medieval steppe nomad conquerors

Over millennia, steppe nomadic tribes raided and sometimes overran settled Eurasian civilizations. Most polities formed by steppe nomads were ephemeral, making it difficult to ascertain their genetic roots or what present-day populations, if any, have descended from them. Exceptionally, the Khazar Khaganate controlled the trade artery between the Black and Caspian Seas in VIII-IX centuries, acting as one of the major conduits between East and West. However, the genetic identity of the ruling elite within the polyglot and polyethnic Khaganate has been a much-debated mystery; a controversial hypothesis posits that post-conversion to Judaism the Khazars gave rise to modern Ashkenazim. We analyzed whole-genome sequences of eight men and one woman buried within the distinctive kurgans of the Khazar upper (warrior) class. After comparing them with reference panels of present-day Eurasian and Iron Age populations, we found that the Khazar political organization relied on a polyethnic elite. It was predominantly descended from Central Asian tribes but incorporated genetic admixture from populations conquered by Khazars. Thus, the Khazar ruling class was likely relatively small and able to maintain a genetic identity distinct from their subjugated populations over the course of centuries. Yet, men of mixed ancestry could also rise into the warrior class, possibly providing troop numbers necessary to maintain control of their large territory. However, when the Khaganate collapsed it left few persistent genetic traces in Europe. Our data confirm the Turkic roots of the Khazars, but also highlight their ethnic diversity and some integration of conquered populations.View Full Text

paleontology↗

bioRxiv2019

Explaining the genetic causality for complex diseases via deep association kernel learning

The genetic effect explains the causality from genetic mutation to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a Deep Association Kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects that existing approaches fail. When applied to real-world GWAS data, our approach discovered potential casual pathways that could be explained by alternative biological studies.

bioinformatics↗

bioRxiv2020

Overcoming Genetic Drop-outs in Variants-based Lineage Tracing from Single-cell RNA Sequencing Data

Lineage tracing provides key insights into the fates of individual cells in complex tissues. Recent works on lineage reconstruction based on the single-cell expression data are suitable for short time frames while tracing lineage based on more stable genetic markers is needed for studies that span time scales over months or years. However, variant calling from the single-cell RNA sequencing (scRNA-Seq) data suffers from "genetic drop-outs", including low coverage and allelic bias, which presents significant obstacles for lineage reconstruction. Prior studies focused only on mitochondrial (chrM) variants and need to be expanded to the whole genome to capture more variants with clearer physiological meaning. However, non-chrM variants suffer even more severe drop-outs than chrM variants, although drop-outs affect all variants. We developed strategies to overcome genetic drop-outs in scRNA-Seq-derived whole genomic variants for accurate lineage tracing, and we developed SClineger, a Bayesian Hierarchical model, to implement our approach. Our validation analyses on a series of sequencing protocols demonstrated the necessity of correction for genetic drop-outs and consideration of variants in the whole genome, and also showed the improvement that our approach provided. We showed that genetic-based lineage tracing is applicable for single-cell studies of both tumors and non-tumor tissues using our approach, and can reveal novel biological insights not afforded by expressional analyses. Interestingly, we showed that cells of various lineages grew under the spatial constraints of their respective organs during the developmental process. Overall, our work provides a powerful tool that can be applied to the large amounts of already existing scRNA-Seq data to construct the lineage histories of cells and derive new knowledge.

bioinformatics↗

bioRxiv2020

Exploring the potential genetic heterogeneity in the incidence of hoof and leg disorders in Austrian Fleckvieh and Braunvieh cattle

BackgroundGenetic heterogeneity denotes the situation when different genetic architectures underlying diverse populations result in the same phenotype. In this study, we explore the nature of differences in the incidence of the number of hoof and leg disorders between Braunvieh and Fleckvieh cattle in the context of genetic heterogeneity between the breeds. ResultsDespite potentially higher power of testing due to twice as large sample size, none of the SNPs was significantly associated with the number of hoof and leg disorders in Fleckvieh, while 16 SNPs were significant in Braunvieh. The most promising candidate genes in Braunvieh are: CBLB on BTA01, which causes arthritis in rats; CAV2 on BTA04, which in effects mouse skeletal muscles; PTHLH on BTA05, which causes disease phenotypes related to the skeleton in humans, mice and zebrafish; SORCS2 on BTA06, which causes decreased susceptibility to injury in the mouse. Some of the significant SNPs (BTA01, BTA04, BTA05, BTA13, BTA16) reveal allelic heterogeneity - i.e. differences due to different allele frequencies between Fleckvieh and Braunvieh. Some of the significant regions (BTA01, BTA05, BTA13, BTA16) correlate to inter-breed differences in LD structure and may thus represent false-positive heterogeneity. However, positions on BTA06 (SORCS2), BTA14 and BTA24 mark Braunvieh-specific regions. ConclusionsWe hypothesise that the observed genetic heterogeneity of hoof and leg disorders is a by-product of multigenerational differential selection of the breeds - towards dairy production in the case of Braunvieh and towards beef production in the case of Fleckvieh. Based on the current data set it is no possibly to unequivocally confirm/exclude the hypothesis of genetic heterogeneity in the susceptibility to leg disorders between Fleckvieh and Braunvieh because only explore it through associations and not the causal mutations. Rationales against genetic heterogeneity comprise a limited power of detection of true associations as well as differences in the length of LD blocks and in linkage phase between breeds. On the other hand, multigenerational differential selection of the breeds and no systematic differences in LD structure between the breeds favour the heterogeneity hypothesis at some of the significant sites.

zoology↗

bioRxiv2020

Unidirectional response to bidirectional selection on body size. II Quantitative genetics.

Anticipating the genetic and phenotypic changes induced by natural or artificial selection requires reliable estimates of trait evolvabilities (genetic variances and covariances). However, whether or not multivariate quantitative genetics models are able to predict precisely the evolution of traits of interest, especially fitness-related, life-history traits, remains an open empirical question. Here, we assessed to what extent the response to bivariate artificial selection on both body size and maturity in the medaka Oryzias latipes, a model fish species, fits the theoretical predictions. Three lines (Large, Small, and Control lines) were differentially selected for body length at 75 days of age, conditional on maturity. As maturity and body size were phenotypically correlated, this selection procedure generated a bi-dimensional selection pattern on two life history traits. After removal of non-heritable trends and noise with a random effect (animal) model, the observed selection response did not match the expected bidirectional response. For body size, Large and Control lines responded along selection gradients (larger body size and stasis, respectively), but, surprisingly, the Small did not evolve a smaller body length, and remained identical to the Control line throughout the experiment. The magnitude of the empirical response was smaller than the theoretical prediction in both selected directions. For maturity, the response was opposite to the expectation (the Large line evolved late maturity compared to the Control line, while the Small line evolved early maturity, while the opposite pattern was predicted due to the strong positive genetic correlation between both traits). The mismatch between predicted and observed response was substantial and could not be explained by usual sources of uncertainties (including sampling effects, genetic drift, and error in G matrix estimates).

evolutionary biology↗

bioRxiv2020

Genetics survey reveals colonization and philopatry of the loggerhead sea turtles across geographic scales

AimUnderstanding the processes that underlie the distribution of genetic diversity in endangered species is a goal of modern conservation biology. Specifically, how population structure affects genetic diversity and contributes to a species adaptive potential remain elusive. The loggerhead sea turtle (Caretta caretta) faces multiple conservation challenges due to its migratory nature and philopatric behaviour. LocationsAtlantic Ocean, Cabo Verde, island of Boavista MethodsHere, using 4207 mtDNA sequences, we analysed the colonisation patterns and distribution of genetic diversity within a major ocean basin (the Atlantic), a regional rookery (Cabo Verde Archipelago) and a local island (Island of Boavista, Cabo Verde). ResultsHypothesis-driven population genetic models suggest the colonization of the Atlantic has occurred in two distinct waves, each corresponding to major mtDNA lineages. We propose the oldest lineage entered the basin via the isthmus of Panama and sequentially established aggregations in Brazil, Cabo Verde and in the area of USA and Mexico. The second lineage entered the Atlantic via the Cape of Good Hope, establishing colonies in the Mediterranean Sea, and from then on, re-colonized the already existing rookeries of the Atlantic. At the Cabo Verde level, we reveal an asymmetric gene flow maintaining links across nesting groups despite significant genetic structure amongst nesting groups. This structure stems from female philopatric behaviour which could further be detected by weak but significant structure amongst beaches separated by only a few kilometres on the island of Boavista. Main conclusionTo explore demographic processes at diverse geographic scales improves understanding the complex evolutionary history of highly migratory philopatric species. Unveiling the past facilitates the design of conservation programmes targeting the right management scale to maintain a species adaptive potential and putative response to human-induced selection.

evolutionary biology↗

bioRxiv2020

BiGen: Integrative Clinical and Brain-Imaging Genetics Analysis Using Structural Equation Model

The identification of genetic variants associated with complex brain diseases has evolved in the past decades. Studies in the field have taken different approaches and study designs including genome-wide association studies. Neuroimaging and connectomics have also improved our understanding of the structural connectivity of the human brain and produced reliable measurements. Combining both neuroimaging and genetic characteristics significantly contributes to understand their complex relationship in affecting behaviour and cognition. Throughout this thesis we proposed analysis pipeline to study the association between imaging and genetics of two different types of brain disease, which is, Alzheimers disease and glioblastoma. We observe the need for a unified model to study the complex interplay between genetic, environmental and clinical, neuroimaging and phenotype features. In this chapter, we developed BiGen, a mathematical model to measure the inter-correlation structure through the integration of genetic, environmental, neuroimaging and disease measurements. We utilised the structural equation model and used a path construct of latent variables to study the hidden association between genes and brain-related diseases, mediated by connectivity characteristics. We applied BiGen to simulated data and to a dataset from the Alzheimers Disease Neuroimaging Initiative.

bioinformatics↗

bioRxiv2020

Genetic variation and sex differences are missed opportunities for addiction biology.

Concerns about external validity of rodent models and translation of findings across species are often based on narrow investigations of populations with limited diversity. Sources of individual variation - including genetics and sex - are only infrequently encompassed in model organism studies. As with most complex diseases, risk for cocaine use disorder is subject to considerable inter-individual variation. Explicit inclusion of individual differences in rodent research may reveal conserved phenotypes and molecular systems relevant to human addiction. We surveyed cocaine-related traits in both males and females of eight inbred mouse strains whose genomes collectively capture 90% of the genetic diversity of the mouse species. Across these strains, individual differences explained a substantial proportion of variance in cocaine-responsive or cocaine response-predictive behavioral and physiological phenotypes. Wild-derived mouse strains often extended the phenotypic ranges of these behaviors beyond what is observed in conventional laboratory strains. Striatum transcriptional responses to cocaine were also highly dependent upon strain and sex differences; most cocaine-responsive genes were differentially expressed in a manner moderated by strain, sex, or their combination. We compared the strain- and sex-mediated transcriptional responses to cocaine in mice to transcriptomic analysis of people with cocaine use disorder and found that mouse similarity to humans was highly dependent upon mouse genetic background and sex. Specifically, male WSB/EiJ mice and female NOD/ShiLtJ mice exhibited the greatest degree of neural transcriptional consilience with humans with cocaine use disorder. Model organism diversity thus represents a crucial source of biological information that can substantially improve external validity of neuropsychiatric research. Significance StatementLaboratory mice are widely used in research on neurobiological mechanisms of addiction, but most studies use a single strain and often sex of mice. To assess how individual differences in mice modulate addiction-related traits and how this impacts comparative analysis with humans, we studied cocaine-relevant behaviors and brain molecular correlates in both males and females of genetically diverse mouse strains. In this population, individual differences related to sex and/or genetics explain large proportions of differences in cocaine-related traits. Importantly, brain gene expression data demonstrated that some strains mimic human genomic states more readily than others. Individual differences thus represent a crucial and underdeveloped source of biological information about addiction mechanisms that may influence the translational utility of such studies.

neuroscience↗

bioRxiv2020

Assessment of Potential Health and Genetic Impacts in Releasing Confiscated Paroaria coronata and Saltator similis

Illegal capture and trade of wild birds has long been a threat to biodiversity. Translocation—the release of individuals from one location into another—is a useful conservation tool in the management of species. However, both health (such as different pathogens) and adaptive (such as local adaptation), differences among populations must be taken into account, as both can impact the recipient population negatively. Here, we provide health and genetic information to support release planning for two of the most trafficked Brazilian wild bird species (Paroaria coronata and Saltator similis). We focused on two fundamental questions: Are there significant differences in pathogen load between wild and captive populations? Is there significant genetic structure among populations? In total, 223 free-living birds were captured, sampled, and released at the same site. Devices and live decoys characteristics were top factors influencing captures. We tested blood, feces, and oropharyngeal swabs from free-ranging (n=101) and confiscated (n=92) birds for Newcastle disease virus, Salmonella spp., and Mycoplasma gallisepticum. Genetic structure among populations was investigated using mtDNA in a subsample of these birds. We found no evidence for Newcastle disease virus and Salmonella spp. in seized and free-living birds from both species. However, seized P. coronata and S. similis may be potential sources of M. gallisepticum. We found significant but low genetic structure among populations occurring in different Biomes (ΦCT=0.26 for P. coronata; ΦCT=0.13 for S. similis) and no significant structure among populations occurring in the Pampa Biome. These results suggest that while it may be important to screen seized birds for avian pathogens, genetic structure among populations seems to be of lesser concern when planning the release of seized songbirds in the wild.View Full Text

zoology↗

bioRxiv2020

Genetic variability in the expression of the SARS-CoV-2 host cell entry factors across populations

The entry of SARS-CoV-2 into host cells is dependent upon angiotensin-converting enzyme 2 (ACE2), which serves as a functional attachment receptor for the viral spike glycoprotein, and the serine protease TMPRSS2 which allows fusion of the viral and host cell membranes. We devised a quantitative measure to estimate genetic determinants of ACE2 and TMPRSS2 expression and applied this measure to >2,500 individuals. Our data show significant variability in genetic determinants of ACE2 and TMPRSS2 expression among individuals and between populations, and demonstrate a genetic predisposition for lower expression levels of both key viral entry genes in African populations. These data suggest that genetic factors might lead to lower susceptibility for SARS-CoV-2 infection in African populations and that host genetics might help explain inter-individual variability in disease susceptibility and severity of COVID-19.

genomics↗

bioRxiv2020

Genetic, morphological, and niche variation in the widely hybridizing Rhus integrifolia-Rhus ovata species complex.

Hybridization and introgression are common processes among numerous plant species that present both challenges and opportunities for studies of species delimitation, phylogenetics, taxonomy, and adaptation. Rhus integrifolia and R. ovata are two ecologically important shrubs native to the southwestern USA and Mexico, and are known to hybridize frequently, but the morphological, genetic, and ecological implications of hybridization in these species are poorly studied on a broad geographic scale. Analyses were conducted using leaf morphology, genetic variation of plastid and nuclear loci, and species distribution models for both species and their putative hybrid introgressants across 19 localities in California and Arizona, USA. These analyses revealed evidence for morphological and genetic distinction among localities comprising putative parental species, but a high degree of morpho-genetic intermediacy among localities with putative hybrids. Comparison of morphological and genetic population structure among localities revealed evidence for putative local adaptation or widespread phenotypic plasticity. Multiple regression models identified a weak but statistically significant negative association between leaf area and precipitation. Finally, species distribution modeling inferred northward range shifts over time, with both species predicted to occupy more coastal regions in the future, possibly increasing the frequency of hybridization among them. These findings underscore the importance of integrative assessment of multiple data sources in the study of hybridizing species and highlight the Rhus integrifolia-ovata complex as a powerful model for investigating the adaptive implications of hybridization.

evolutionary biology↗

bioRxiv2020

Trajectory analysis of cardiovascular phenotypes from biobank data uncovers novel genetic associations

Approximately 6 million adults in the US have heart failure (HF). HF progression is variable due in part to differences in sex, age, and genetic ancestry. Previous population-based genetic studies have largely focused on cross-sectional data related to HF, a disease known to change over time. Utilizing longitudinal data trajectory probabilities as a continuous trait may increase the likelihood of finding significant, biologically relevant associations in a genome-wide association (GWA) analysis. We analyzed data from the electronic health record in a medical biobank from a single, metropolitan US center to gather clinically pertinent data for analyses. We evaluated whole genome sequencing of 896 unrelated biobank participants, including 494 with at least 1 electrocardiogram and 324 who had more than 1 echocardiogram ([~]3 observations per person). A censored normal distribution multivariable mixture model was used to cluster phenotype measures for genome-wide analyses. GWA analysis on the trajectory probability of the corrected QT measurement (QTc) taken from electrocardiograms identified significant associations with variants in regulatory regions proximal to the WLS gene, which encodes the Wnt ligand secretion mediator, Wntless. WLS was previously associated with QT length using of approximately 16,000 participants supporting the utility of this method to uncover significant genetic associations in small datasets. GWA analysis on the trajectory probability of left ventricular diameter as taken from echocardiograms identified novel significant associations with variants in regulatory regions near MYO10, which encodes the unconventional Myosin-10. We found that trajectory probabilities improved the ability to discover significant and relevant genetic associations. This novel approach increased yield from smaller, well-phenotyped cohorts with longitudinal data from a medical biobank. AUTHOR SUMMARYApproximately 6 million adults in the US have heart failure, a disease known to change over time. In a hospital based electronic health record, electrocardiograms and echocardiograms, used to evaluate heart failure, can be tracked over time. We utilized these data to create a novel trait that can be applied to genetic analyses. We analyzed genome sequence of 896 biobank participants from diverse racial/ethnic backgrounds. Genome-wide association (GWA) analyses were performed on a subset of these individuals for heart failure outcomes. A statistical model that incorporates cardiac data that are tracked over time was used to cluster these data using a probabilistic approach. These probabilities were used for a GWA analysis for corrected QT measurement (QTc) and left ventricular diameter (LVID). The QTc interval analysis identified significant correlations with variants in regulatory regions near the WLS gene which encodes the Wnt ligand secretion mediator, Wntless. Analysis of LVID identified significant associations with variants in regulatory regions near the MYO10 gene which encodes the unconventional Myosin-10. Through these analyses, we found that using the trajectory probabilities can facilitate the discovery of novel significant, biologically relevant associations. This method reduces the need for larger cohorts, and increases yield from smaller, well-phenotyped cohorts.

genomics↗