COVID-19: test, trace and isolate-new epidemiological data.

In the absence of an efficient drug treatment or a vaccine, the control of the COVID-19 pandemic relies on classic infection control measures. Since these means are socially disruptive and come with substantial economic loss for societies, a better knowledge of the epidemiology of the new coronavirus epidemic is crucial to achieve control at a sustainable cost and within tolerable restrictions of civil rights.

Redundant meta-analyses are common in genetic epidemiology.

OBJECTIVES: The massive growth in the publication of meta-analyses may cause redundancy and wasted efforts. We performed a metaepidemiologic study to evaluate the extent of potential redundancy in published meta-analyses in genetic epidemiology. STUDY DESIGN AND SETTING: Using a sample of 38 index meta-analyses of genetic associations published in 2010, we retrieved additional meta-analyses that evaluated identical associations (same genetic variant and phenotype) using the Human Genome Epidemiology (HuGE) Navigator and PubMed databases. We analyzed the frequency of potential duplication and examined whether subsequent meta-analyses cited previous meta-analyses on the exact same association. RESULTS: Based on 38 index meta-analyses, we retrieved a total of 99 duplicate meta-analyses. Only 12 (32%) of the index meta-analyses were unambiguously unique. We found a mean of 2.6 duplicates and a median of 2 duplicates per meta-analysis. In case studies, only 29-54% of previously published meta-analyses were cited by subsequent ones. CONCLUSION: These results suggest that duplication is common in meta-analyses of genetic associations.

Extreme-value sampling design is cost-beneficial only with a valid statistical approach for exposure-secondary outcome association analyses.

In epidemiology cohort studies, exposure data are collected in sub-studies based on a primary outcome (PO) of interest, as with the extreme-value sampling design (EVSD), to investigate their correlation. Secondary outcomes (SOs) data are also readily available, enabling researchers to assess the correlations between the exposure and the SOs. However, when the EVSD is used, the data for SOs are not representative samples of a general population; thus, many commonly used statistical methods, such as the generalized linear model (GLM), are not valid. A prospective likelihood method has been developed to associate SOs with single-nucleotide polymorphisms under an extreme phenotype sequencing design. In this paper, we describe the application of the prospective likelihood method (STEVSD) to exposure-SO association analysis under an EVSD. We undertook extensive simulations to assess the performance of the STEVSD method in associating binary and continuous exposures with SOs, comparing it to the simple GLM method that ignores the EVSD. To demonstrate the cost-benefit of the STEVSD method, we also mimicked the design of two new retrospective studies, as would be done in actual practice, based on the PO of interest, which was the same as the SO in the EVSD study. We then analyzed these data by using the GLM method and compared its power to that of the STEVSD method. We demonstrated the usefulness of the STEVSD method by applying it to a benign ethnic neutropenia dataset. Our results indicate that the STEVSD method can control type I error well, whereas the GLM method cannot do so owing to its ignorance of EVSD, and that the STEVSD method is cost-effective because it has statistical power similar to that of two new retrospective studies that require collecting new exposure data for selected individuals.

Natural selection favoring more transmissible HIV detected in United States molecular transmission network.

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters.

Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters.

Next-generation sequencing based base-by-base distance measures have become an integral complement to epidemiological investigation of infectious disease outbreaks. This study introduces PANPASCO, a computational pan-genome mapping based, pairwise distance method that is highly sensitive to differences between cases, even when located in regions of lineage specific reference genomes. We show that our approach is superior to previously published methods in several datasets and across different Mycobacterium tuberculosis lineages, as its characteristics allow the comparison of a high number of diverse samples in one analysis-a scenario that becomes more and more likely with the increased usage of whole-genome sequencing in transmission surveillance.

Model diagnostics and refinement for phylodynamic models.

Phylodynamic modelling, which studies the joint dynamics of epidemiological and evolutionary processes, has made significant progress in recent years due to increasingly available genomic data and advances in statistical modelling. These advances have greatly improved our understanding of transmission dynamics of many important pathogens. Nevertheless, there remains a lack of effective, targetted diagnostic tools for systematically detecting model mis-specification. Development of such tools is essential for model criticism, refinement, and calibration. The idea of utilising latent residuals for model assessment has already been exploited in general spatio-temporal epidemiological settings. Specifically, by proposing appropriately designed non-centered, re-parameterizations of a given epidemiological process, one can construct latent residuals with known sampling distributions which can be used to quantify evidence of model mis-specification. In this paper, we extend this idea to formulate a novel model-diagnostic framework for phylodynamic models. Using simulated examples, we show that our framework may effectively detect a particular form of mis-specification in a phylodynamic model, particularly in the event of superspreading. We also exemplify our approach by applying the framework to a dataset describing a local foot-and-mouth (FMD) outbreak in the UK, eliciting strong evidence against the assumption of no within-host-diversity in the outbreak. We further demonstrate that our framework can facilitate model calibration in real-life scenarios, by proposing a within-host-diversity model which appears to offer a better fit to data than one that assumes no within-host-diversity of FMD virus.

Validación de un método seguro y sencillo para la elaboración de secuencias consenso del virus de la inmunodeficiencia humana a partir de los datos de secuenciación masiva 454 / A safe an easy method for building consensus HIV sequences from 454 massively parallel sequencing data

Objetivo: Generar una secuencia consenso a partir de los datos de secuenciación masiva obtenidos en estudios de resistencias a antiretrovirales, que sea representativa de la secuencia Sanger y que sirva para estudios de epidemiología molecular. Material y métodos: En 62 pacientes se obtuvo la secuencia de transcriptasa reversa-proteasa, mediante Sanger (Trugene-Siemens), y NGS (454GSJunior-Roche). Las secuencias consenso NGS se generaron con Mesquite, seleccionando umbrales 10%, 15% y 20%. Para el estudio filogenético se empleó MEGA. Resultados: Utilizando el umbral 10%, 17/62 pacientes presentaron secuencias pareadas NGS-Sanger, con una mediana de bootstrap del 88% (IQR83,5-95,5). La asociación aumenta a 36/62 pacientes y el bootstrap, a 94% (IQR85,5-98), y alcanza el máximo al 20% en 61/62 pacientes, bootstrap 99% (IQR98-100). Conclusión: Mostramos un método seguro para generar secuencias consenso NGS para su uso en estudios de epidemiología molecular procesadas con umbral 20%, de fácil uso y aplicación en los servicios de microbiología clínica (AU)

Objective: To show how to generate a consensus sequence from the information of massive parallel sequences data obtained from routine HIV anti-retroviral resistance studies, and that may be suitable for molecular epidemiology studies. Material and methods: Paired Sanger (Trugene-Siemens) and next-generation sequencing (NGS) (454 GSJunior-Roche) HIV RT and protease sequences from 62 patients were studied. NGS consensus sequences were generated using Mesquite, using 10%, 15%, and 20% thresholds. Molecular evolutionary genetics analysis (MEGA) was used for phylogenetic studies. Results: At a 10% threshold, NGS-Sanger sequences from 17/62 patients were phylogenetically related, with a median bootstrap-value of 88% (IQR83.5-95.5). Association increased to 36/62 sequences, median bootstrap 94% (IQR85.5-98)], using a 15% threshold. Maximum association was at the 20% threshold, with 61/62 sequences associated, and a median bootstrap value of 99% (IQR98-100). Conclusion: A safe method is presented to generate consensus sequences from HIV-NGS data at 20% threshold, which will prove useful for molecular epidemiological studies (AU)

Power evaluation of asymptotic tests for comparing two binomial proportions to detect direct and indirect association in large-scale studies.

Asymptotic tests are commonly used for comparing two binomial proportions when the sample size is sufficiently large. However, there is no consensus on the most powerful test. In this paper, we clarify this issue by comparing the power functions of three popular asymptotic tests: the Pearson's χ2 test, the likelihood-ratio test and the odds-ratio based test. Considering Taylor decompositions under local alternatives, the comparisons lead to recommendations on which test to use in view of both the experimental design and the nature of the investigated signal. We show that when the design is balanced between the two binomials, the three tests are equivalent in terms of power. However, when the design is unbalanced, differences in power can be substantial and the choice of the most powerful test also depends on the value of the parameters of the two compared binomials. We further investigated situations where the two binomials are not compared directly but through tag binomials. In these cases of indirect association, we show that the differences in power between the three tests are enhanced with decreasing values of the parameters of the tag binomials. Our results are illustrated in the context of genetic epidemiology where the analysis of genome-wide association studies provides insights regarding the low power for detecting rare variants.

Molecular genetic contributions to self-rated health.

Background: Poorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition. Methods: We carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal single nucleotide polymorphisms (SNPs) for SRH. Linkage disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in the UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia. Results: The GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675, P = 1.77 x 10 -10 ) close to KLF7 . A second strong peak was identified on chromosome 6 in the major histocompatibility region (rs76380179, P = 6.15 x 10 -10 ). The proportion of variance in SRH that was explained by all common genetic variants was 13%. Polygenic scores for the following traits and disorders were associated with SRH: cognitive ability, education, neuroticism, body mass index (BMI), longevity, attention-deficit hyperactivity disorder (ADHD), major depressive disorder, schizophrenia, lung function, blood pressure, coronary artery disease, large vessel disease stroke and type 2 diabetes. Conclusions: Individual differences in how people respond to a single item on SRH are partly explained by their genetic propensity to many common psychiatric and physical disorders and psychological traits.

Likelihood-based inference for discretely observed birth-death-shift processes, with applications to evolution of mobile genetic elements.

Continuous-time birth-death-shift (BDS) processes are frequently used in stochastic modeling, with many applications in ecology and epidemiology. In particular, such processes can model evolutionary dynamics of transposable elements-important genetic markers in molecular epidemiology. Estimation of the effects of individual covariates on the birth, death, and shift rates of the process can be accomplished by analyzing patient data, but inferring these rates in a discretely and unevenly observed setting presents computational challenges. We propose a multi-type branching process approximation to BDS processes and develop a corresponding expectation maximization algorithm, where we use spectral techniques to reduce calculation of expected sufficient statistics to low-dimensional integration. These techniques yield an efficient and robust optimization routine for inferring the rates of the BDS process, and apply broadly to multi-type branching processes whose rates can depend on many covariates. After rigorously testing our methodology in simulation studies, we apply our method to study intrapatient time evolution of IS6110 transposable element, a genetic marker frequently used during estimation of epidemiological clusters of Mycobacterium tuberculosis infections.

A systematic appraisal of field synopses in genetic epidemiology: a HuGE review.

Evidence from genetic association studies is accumulating rapidly. Field synopses have recently arisen as an unbiased way of systematically synthesizing this evidence. We performed a systematic review and appraisal of published field synopses in genetic epidemiology and assessed their main findings and methodological characteristics. We identified 61 eligible field synopses, published between January 1, 2007, and October 31, 2013, on 52 outcomes reporting 734 significant associations at the P < 0.05 level. The median odds ratio for these associations was 1.25 (interquartile range, 1.15-1.43). Egger's test was the most common method (n = 30 synopses) of assessing publication bias. Only 12 synopses (20%) used the Venice criteria to evaluate the epidemiologic credibility of their findings (n = 449 variants). Eleven synopses (18%) were accompanied by an online database that has been regularly updated. These synopses received more citations (P = 0.01) and needed a larger research team (P = 0.02) than synopses without an online database. Overall, field synopses are becoming a valuable tool for the identification of common genetic variants, especially when researchers follow relevant methodological guidelines. Our work provides a summary of the current status of the field synopses published to date and may help interested readers efficiently identify the online resources containing the relevant genetic evidence.

Diagnóstico da infecção pelo HIV-1: proposta de um algoritmo para identificação de infecção aguda / Diagnosis of HIV-1 infection: proposal of an algorithm for the identification of acute infection

A epidemia de AIDS em nosso País apresenta relativa estabilidade, concentrada em populações de maior vulnerabilidade, porém informações são em geral relacionadas à fase avançada da doença. Nosso estudo procura contribuir com informações sobre casos incidentes de infeção pelo HIV-1, através de um algoritmo que favorece a identificação de casos em fase aguda da doença. Esse algoritmo, com critérios clínicos e laboratoriais, permitiu o recrutamento consecutivo de 99 pacientes em um universo de cerca de 300 casos novos incorporados ao SAE do Município de Santo André, no período de outubro de 2011 a novembro de 2014. Nesses pacientes recém diagnosticados foram analisadas características clínicas, epidemiológicas e aspectos moleculares do HIV-1. Alguns aspectos clínicos e laboratoriais foram avaliados em adicionais 154 casos incorporados nesta época, assim como algumas características do HIV-1 identificadas em teste de genotipagem no período entre 2001 e 2014, em pacientes do mesmo SAE, que permitiram contextualizar a coorte. Entre os 99 pacientes... O algoritmo proposto identificou 5 casos em 8 suspeitos de infecção aguda. A genotipagem pré-tratamento avaliou...Na coorte foi identificado ainda, por estudo clínico e epidemiológico, confirmado por associação filogenética, uma possível transmissão do HIV-1 por procedimento de manicure (Matsuda et al., 2014b).

The AIDS epidemic in our country shows a relative stability, concentrated in the most vulnerable populations, but the limited information is generally related to the advanced stage of the disease. Our study aims to contribute with information on incident cases of HIV-1 infection. In a universe of about 300 new cases incorporated into the Santo André AIDS Program from Oct/2011 to Nov/2014, 99 patients were recruited consecutively. The study evaluated patients admitted after a recent diagnostic through the usual demand for follow-up and from an active case finding, using clinical laboratory algorithm that aimed to identify patients with acute infection. Available clinical and laboratory data from 154 additional cases incorporated in the service during this period and molecular data from HIV-1 genotypic tests from patients of this service in the period of 2001-2012, allowed to contextualize the cohort findings. Among the 99 patients studied ...The proposed algorithm has identified 5/8 cases of suspected acute infection. Pretreatment genotyping, evaluated...In the cohort, we identified, based in epidemiological clinical and subsequent phylogeny a possible transmission of HIV-1 for manicure procedure (Matsuda et al., 2014b).

Estudios de epidemiología molecular en población inmigrante en España / Molecular epidemiology studies on the immigrant population in Spain

Fundamentos: La epidemiología molecular es una nueva disciplina que permite la integración de la información sobre la variabilidad genética de patógenos infecciosos con su difusión en la población y subgrupos de la misma incluyendo, por ejemplo, las mutaciones de resistencia a antibióticos y antivirales. El objetivo es conocer qué posibles diferencias existe en las características genéticas de los agentes infecciosos que afectan a las poblaciones inmigrante y autóctoctona en España.. Métodos: Se revisaron artículos originales publicados entre 1998- 2013, con las palabras clave "epidemiología molecular", "tipado molecular", "secuenciación", "inmigrante", "España". Resultados: De un total de 267 artículos identificados inicialmente, 50 pasaron los diferentes filtros establecidos. De ellos, 36 analizan las infecciones por Mycobacterium tuberculosis y VIH, seguidos de los que analizan infecciones por Staphylococcus aureus (3) y el Virus de la Hepatitis B (3). Conclusiones: Los objetivos principales de estos trabajos fueron el tipado del patógeno y la determinación de la frecuencia de mutaciones de resistencia. Los estudios más frecuentes correspondieron a cohortes retrospectivas, seguidos por los estudios ecológicos y los ensayos clínicos. En general los estudios son descriptivos y su ámbito por el tipo y tamaño de muestra es bastante restringido. En varios se determina que las cepas o variantes del patógeno encontradas en inmigrantes tienen su origen más probable en sus países de origen, si bien otros también ponen de manifiesto la transmisión desde la población autóctona a la inmigrante (AU)

Background: Molecular epidemiology is a new scientific discipline which allows to integrate information on the genetic variation of infectious pathogens with their diffusion in a population and its subgroups including, for instance, resistance mutations to antibiotics and antiretrovirals. We present the results of an analysis of scientific publications that analyze the health status of the immigrant population in Spain from a molecular epidemiology perspective. Methods:We reviewed original articles published in 1998-2014 with he keywords "molecular epidemiology", "molecular typing", "sequencing", "immigrant", and "Spain". Results: Froma total of 267 articles identified initially, only 50 passed through the established filters. Most of them (36) analyzed infections by Mycobacterium tuberculosis (3) and HIV (3), followed at a large distance by Staphylococcus aureus and hepatitis B virus. The main goal of these works was the typing of the pathogen and to determine the frequency of resistance mutations. Conclusion: Is difficult to generalize the conclusions from the analyzed articles because most of them have a purely descriptive and quite restricted scope, considering the type and size of the samples studied. Several studies are focused on the most likely origin for the strains or variants of the pathogen but others also reveal transmissions from the local to the immigrant populations (AU)

Statistical genetics with application to population-based study design: a primer for clinicians.

With the completion of the entire human genome sequence and remarkable advances in genotyping technologies, there has been an increased interest in the application of genetics and genomics in biomedical research over the last decade. Large-scale population-based genetic association studies have now become routine and their application to several multifactorial diseases such as cardiovascular disorders has led to the identification of a number of novel susceptibility genes. However, to be able to interpret results from such studies, clinicians need to have a basic understanding of unique concepts and issues related to this fast-moving area of research. In this primer, we provide a broad overview of design, analysis, and methodological issues with a focus on population-based study design.

Distinguishing true from false positives in genomic studies: p values.

Distinguishing true from false positive findings is a major challenge in human genetic epidemiology. Several strategies have been devised to facilitate this, including the positive predictive value (PPV) and a set of epidemiological criteria, known as the "Venice" criteria. The PPV measures the probability of a true association, given a statistically significant finding, while the Venice criteria grade the credibility based on the amount of evidence, consistency of replication and protection from bias. A vast majority of journals use significance thresholds to identify the true positive findings. We studied the effect of p value thresholds on the PPV and used the PPV and Venice criteria to define usable thresholds of statistical significance. Theoretical and empirical analyses of data published on AlzGene show that at a nominal p value threshold of 0.05 most "positive" findings will turn out to be false if the prior probability of association is below 0.10 even if the statistical power of the study is higher than 0.80. However, in underpowered studies (0.25) with a low prior probability of 1 × 10(-3), a p value of 1 × 10(-5) yields a high PPV (>96 %). Here we have shown that the p value threshold of 1 × 10(-5) gives a very strong evidence of association in almost all studies. However, in the case of a very high prior probability of association (0.50) a p value threshold of 0.05 may be sufficient, while for studies with very low prior probability of association (1 × 10(-4); genome-wide association studies for instance) 1 × 10(-7) may serve as a useful threshold to declare significance.

The false-positive to false-negative ratio in epidemiologic studies.

The ratio of false-positive to false-negative findings (FP:FN ratio) is an informative metric that warrants further evaluation. The FP:FN ratio varies greatly across different epidemiologic areas. In genetic epidemiology, it has varied from very high values (possibly even >100:1) for associations reported in candidate-gene studies to very low values (1:100 or lower) for associations with genome-wide significance. The substantial reduction over time in the FP:FN ratio in human genome epidemiology has corresponded to the routine adoption of stringent inferential criteria and comprehensive, agnostic reporting of all analyses. Most traditional fields of epidemiologic research more closely follow the practices of past candidate gene epidemiology, and thus have high FP:FN ratios. Further, FP and FN results do not necessarily entail the same consequences, and their relative importance may vary in different settings. This ultimately has implications for what is the acceptable FP:FN ratio and for how the results of published epidemiologic studies should be presented and interpreted.

Information metrics in genetic epidemiology.

Information-theoretic metrics have been proposed for studying gene-gene and gene-environment interactions in genetic epidemiology. Although these metrics have proven very promising, they are typically interpreted in the context of communications and information transmission, diminishing their tangibility for epidemiologists and statisticians. In this paper, we clarify the interpretation of information-theoretic metrics. In particular, we develop the methods so that their relation to the global properties of probability models is made clear and contrast them with log-linear models for multinomial data. Hopefully, a better understanding of their properties and probabilistic implications will promote their acceptance and correct usage in genetic epidemiology. Our novel development also suggests new approaches to model search and computation.

Quantifying and correcting for the winner's curse in quantitative-trait association studies.

Quantitative traits (QT) are an important focus of human genetic studies both because of interest in the traits themselves and because of their role as risk factors for many human diseases. For large-scale QT association studies including genome-wide association studies, investigators usually focus on genetic loci showing significant evidence for SNP-QT association, and genetic effect size tends to be overestimated as a consequence of the winner's curse. In this paper, we study the impact of the winner's curse on QT association studies in which the genetic effect size is parameterized as the slope in a linear regression model. We demonstrate by analytical calculation that the overestimation in the regression slope estimate decreases as power increases. To reduce the ascertainment bias, we propose a three-parameter maximum likelihood method and then simplify this to a one-parameter method by excluding nuisance parameters. We show that both methods reduce the bias when power to detect association is low or moderate, and that the one-parameter model generally results in smaller variance in the estimate.