Sensommatic: an efficient pipeline to mine and predict sensory receptor genes in the era of reference-quality genomes.

SUMMARY: Sensory receptor gene families have undergone extensive expansion and loss across vertebrate evolution, leading to significant variation in receptor counts between species. However, due to their species-specific nature, conventional reference-based annotation tools often underestimate the true number of sensory receptors in a given species. While there has been an exponential increase in the taxonomic diversity of publicly available genome assemblies in recent years, only ∼30% of vertebrate species on the NCBI database are currently annotated. To overcome these limitations, we developed 'Sensommatic', an automated and accessible sensory receptor annotation pipeline. Sensommatic implements BLAST and AUGUSTUS to mine and predict sensory receptor genes from whole genome assemblies, adopting a one-to-many gene mapping approach. While designed for vertebrates, Sensommatic can be extended to run on non-vertebrate species by generating customized reference files, making it a scalable and generalizable tool. AVAILABILITY AND IMPLEMENTATION: Source code and associated files are available at: https://github.com/GMHughes/Sensommatic.

Semi-parametric benchmark dose analysis with monotone additive models.

Benchmark dose analysis aims to estimate the level of exposure to a toxin associated with a clinically significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel framework for benchmark dose analysis based on monotone additive dose-response models. We first introduce a flexible approach for fitting monotone additive models via penalized B-splines and Laplace-approximate marginal likelihood. A reflective Newton method is then developed that employs de Boor's algorithm for computing splines and their derivatives for efficient estimation of the benchmark dose. Finally, we develop a novel approach for calculating benchmark dose lower limits based on an approximate pivot for the nonlinear equation solved by the estimated benchmark dose. The favorable properties of this approach compared to the Delta method and a parameteric bootstrap are discussed. We apply the new methods to make inferences about the level of prenatal alcohol exposure associated with clinically significant cognitive defects in children using data from six NIH-funded longitudinal cohort studies. Software to reproduce the results in this paper is available online and makes use of the novel semibmd  R package, which implements the methods in this paper.

Benchmark dose profiles for bivariate exposures.

While benchmark dose (BMD) methodology is well-established for settings with a single exposure, these methods cannot easily handle multidimensional exposures with nonlinear effects. We propose a framework for BMD analysis to characterize the joint effect of a two-dimensional exposure on a continuous outcome using a generalized additive model while adjusting for potential confounders via propensity scores. This leads to a dose-response surface which can be summarized in two dimensions by a contour plot in which combinations of exposures leading to the same expected effect are identified. In our motivating study of prenatal alcohol exposure, cognitive deficits in children are found to be associated with both the frequency of drinking as well as the amount of alcohol consumed on each drinking day during pregnancy. The general methodological framework is useful for a broad range of settings, including combinations of environmental stressors, such as chemical mixtures, and in explorations of the impact of dose rate rather than simply cumulative exposure on adverse outcomes.

Effects of prenatal alcohol exposure on cognitive and behavioral development: Findings from a hierarchical meta-analysis of data from six prospective longitudinal U.S. cohorts.

BACKGROUND: Cognitive and behavioral sequelae of prenatal alcohol exposure (PAE) continue to be prevalent in the United States and worldwide. Because these sequelae are also common in other neurodevelopmental disorders, researchers have attempted to identify a distinct neurobehavioral profile to facilitate the differential diagnosis of fetal alcohol spectrum disorders (FASD). We used an innovative, individual participant meta-analytic technique to combine data from six large U.S. longitudinal cohorts to provide a more comprehensive and reliable characterization of the neurobehavioral deficits seen in FASD than can be obtained from smaller samples. METHODS: Meta-analyses were performed on data from 2236 participants to examine effects of PAE (measured as oz absolute alcohol/day (AA/day)) on IQ, four domains of cognition function (learning and memory, executive function, reading achievement, and math achievement), sustained attention, and behavior problems, after adjusting for potential confounders using propensity scores. RESULTS: The effect sizes for IQ and the four domains of cognitive function were strikingly similar to one another and did not differ at school age, adolescence, or young adulthood. Effect sizes were smaller in the more middle-class Seattle cohort and larger in the three cohorts that obtained more detailed and comprehensive assessments of AA/day. PAE effect sizes were somewhat weaker for parent- and teacher-reported behavior problems and not significant for sustained attention. In a meta-analysis of five aspects of executive function, the strongest effect was on set-shifting. CONCLUSIONS: The similarity in the effect sizes for the four domains of cognitive function suggests that PAE affects an underlying component or components of cognition involving learning and memory and executive function that are reflected in IQ and academic achievement scores. The weaker effects in the more middle-class cohort may reflect a more cognitively stimulating environment, a different maternal drinking pattern (lower alcohol dose/occasion), and/or better maternal prenatal nutrition. These findings identify two domains of cognition-learning/memory and set-shifting-that are particularly affected by PAE, and one, sustained attention, which is apparently spared.

Localisation and substrate specificities of transglycanases in charophyte algae relate to development and morphology.

Cell wall-modifying enzymes have been previously investigated in charophyte green algae (CGA) in cultures of uniform age, giving limited insight into their roles. Therefore, we investigated the in situ localisation and specificity of enzymes acting on hemicelluloses in CGA genera of different morphologies and developmental stages. In vivo transglycosylation between xyloglucan and an endogenous donor in filamentous Klebsormidium and Zygnema was observed in longitudinal cell walls of young (1 month) but not old cells (1 year), suggesting that it has a role in cell growth. By contrast, in parenchymatous Chara, transglycanase action occurred in all cell planes. In Klebsormidium and Zygnema, the location of enzyme action mainly occurred in regions where xyloglucans and mannans, and to a lesser extent mixed-linkage ß-glucan (MLG), were present, indicating predominantly xyloglucan:xyloglucan endotransglucosylase (XET) activity. Novel transglycosylation activities between xyloglucan and xylan, and xyloglucan and galactomannan were identified in vitro in both genera. Our results show that several cell wall-modifying enzymes are present in CGA, and that differences in morphology and cell age are related to enzyme localisation and specificity. This indicates an evolutionary significance of cell wall modifications, as similar changes are known in their immediate descendants, the land plants. This article has an associated First Person interview with the first author of the paper.

Utility of Genomic Testing after Renal Biopsy.

BACKGROUND: Renal biopsy is the mainstay of renal pathological diagnosis. Despite sophisticated diagnostic techniques, it is not always possible to make a precise pathological diagnosis. Our aim was to identify a genetic cause of disease in patients who had undergone renal biopsy and determine if genetic testing altered diagnosis or treatment. METHODS: Patients with suspected familial kidney disease underwent a variety of next-generation sequencing (NGS) strategies. The subset of these patients who had also undergone native kidney biopsy was identified. Histological specimens were reviewed by a consultant pathologist, and genetic and pathological diagnoses were compared. RESULTS: Seventy-five patients in 47 families underwent genetic sequencing and renal biopsy. Patients were grouped into 5 diagnostic categories based on pathological diagnosis: tubulointerstitial kidney disease (TIKD; n = 18); glomerulonephritis (GN; n = 15); focal segmental glomerulosclerosis and Alport Syndrome (n = 11); thrombotic microangiopathy (TMA; n = 17); and nonspecific pathological changes (n = 14). Thirty-nine patients (52%) in 21 families (45%) received a genetic diagnosis; 13 cases (72%) with TIKD, 4 (27%) with GN, 6 (55%) with focal segmental glomerulosclerosis/Alport syndrome, and 10 (59%) with TMA and 6 cases (43%) with nonspecific features. Genetic testing resulted in changes in understanding of disease mechanism in 21 individuals (54%) in 12 families (57%). Treatment would have been altered in at least 26% of cases (10/39). CONCLUSIONS: An accurate genetic diagnosis can result in changes in clinical diagnosis, understanding of pathological mechanism, and treatment. NGS should be considered as a complementary diagnostic technique to kidney biopsy in the evaluation of patients with kidney disease.

Statistical strategies for the analysis of massive data sets.

The advent of the big data age has changed the landscape for statisticians. Public and private organizations alike these days are interested in capturing and analyzing complex customer data in order to improve their service and drive efficiency gains. However, the large volume of data involved often means that standard statistical methods fail and new ways of thinking are needed. Although great gains can be obtained through the use of more advanced computing environments or through developing sophisticated new statistical algorithms that handle data in a more efficient way, there are also many simpler things that can be done to handle large data sets in an efficient and intuitive manner. These include the use of distributed analysis methodologies, clever subsampling, data coarsening, and clever data reductions that exploit concepts such as sufficiency. These kinds of strategies represent exciting opportunities for statisticians to remain front and center in the data science world.

A Longitudinal Analysis of the Executive Functions in High-Level Soccer Players.

INTRODUCTION: Assessments of executive functions (EFs) with varying levels of perceptual information or action fidelity are common talent-diagnostic tools in soccer, yet their validity still has to be established. Therefore, a longitudinal development of EFs in high-level players to understand their relationship with increased exposure to training is required. METHODS: A total of 304 high-performing male youth soccer players (10-21 years old) in Germany were assessed across three seasons on various sport-specific and non-sport-specific cognitive functioning assessments. RESULTS: The posterior means (90% highest posterior density) of random slopes indicated that both abilities predominantly developed between 10 and 15 years of age. A plateau was apparent for domain-specific abilities during adolescence, whereas domain-generic abilities improved into young adulthood. CONCLUSION: The developmental trajectories of soccer players' EFs follow the general populations' despite long-term exposure to soccer-specific training and game play. This brings into question the relationship between high-level experience and EFs and renders including EFs in talent identification questionable.

Urinary and serum soluble CD25 complements urinary soluble CD163 to detect active renal anti-neutrophil cytoplasmic autoantibody-associated vasculitis: a cohort study.

Background: Early detection of renal involvement in anti-neutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis (AAV) is of major clinical importance to allow prompt initiation of treatment and limit renal damage. Urinary soluble cluster of differentiation 163 (usCD163) has recently been identified as a potential biomarker for active renal vasculitis. However, a significant number of patients with active renal vasculitis test negative using usCD163. We therefore studied whether soluble CD25 (sCD25), a T cell activation marker, could improve the detection of renal flares in AAV. Methods: sCD25 and sCD163 levels in serum and urine were measured by enzyme-linked immunosorbent assay in 72 patients with active renal AAV, 20 with active extrarenal disease, 62 patients in remission and 18 healthy controls. Urinary and blood CD4+ T and CD4+ T effector memory (TEM) cell counts were measured in 22 patients with active renal vasculitis. Receiver operating characteristics (ROC) curves were generated and recursive partitioning was used to calculate whether usCD25 and serum soluble CD25 (ssCD25) add utility to usCD163. Results: usCD25, ssCD25 and usCD163 levels were significantly higher during active renal disease and significantly decreased after induction of remission. A combination of usCD25, usCD163 and ssCD25 outperformed all individual markers (sensitivity 84.7%, specificity 95.1%). Patients positive for sCD25 but negative for usCD163 (n = 10) had significantly higher C-reactive protein levels and significantly lower serum creatinine and proteinuria levels compared with the usCD163-positive patients. usCD25 correlated positively with urinary CD4+ T and CD4+ TEM cell numbers, whereas ssCD25 correlated negatively with circulating CD4+ T and CD4+ TEM cells. Conclusion: Measurement of usCD25 and ssCD25 complements usCD163 in the detection of active renal vasculitis.

Comparing predictive abilities of longitudinal child growth models.

The Bill and Melinda Gates Foundation's Healthy Birth, Growth and Development knowledge integration project aims to improve the overall health and well-being of children across the world. The project aims to integrate information from multiple child growth studies to allow health professionals and policy makers to make informed decisions about interventions in lower and middle income countries. To achieve this goal, we must first understand the conditions that impact on the growth and development of children, and this requires sensible models for characterising different growth patterns. The contribution of this paper is to provide a quantitative comparison of the predictive abilities of various statistical growth modelling techniques based on a novel leave-one-out validation approach. The majority of existing studies have used raw growth data for modelling, but we show that fitting models to standardised data provide more accurate estimation and prediction. Our work is illustrated with an example from a study into child development in a middle income country in South America.

A novel case-control subsampling approach for rapid model exploration of large clustered binary data.

In many settings, an analysis goal is the identification of a factor, or set of factors associated with an event or outcome. Often, these associations are then used for inference and prediction. Unfortunately, in the big data era, the model building and exploration phases of analysis can be time-consuming, especially if constrained by computing power (ie, a typical corporate workstation). To speed up this model development, we propose a novel subsampling scheme to enable rapid model exploration of clustered binary data using flexible yet complex model set-ups (GLMMs with additive smoothing splines). By reframing the binary response prospective cohort study into a case-control-type design, and using our knowledge of sampling fractions, we show one can approximate the model estimates as would be calculated from a full cohort analysis. This idea is extended to derive cluster-specific sampling fractions and thereby incorporate cluster variation into an analysis. Importantly, we demonstrate that previously computationally prohibitive analyses can be conducted in a timely manner on a typical workstation. The approach is applied to analysing risk factors associated with adverse reactions relating to blood donation.

Smooth individual level covariates adjustment in disease mapping.

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia.

Exposure Enriched Case-Control (EECC) Design for the Assessment of Gene-Environment Interaction.

Genetic susceptibility and environmental exposure both play an important role in the aetiology of many diseases. Case-control studies are often the first choice to explore the joint influence of genetic and environmental factors on the risk of developing a rare disease. In practice, however, such studies may have limited power, especially when susceptibility genes are rare and exposure distributions are highly skewed. We propose a variant of the classical case-control study, the exposure enriched case-control (EECC) design, where not only cases, but also high (or low) exposed individuals are oversampled, depending on the skewness of the exposure distribution. Of course, a traditional logistic regression model is no longer valid and results in biased parameter estimation. We show that addition of a simple covariate to the regression model removes this bias and yields reliable estimates of main and interaction effects of interest. We also discuss optimal design, showing that judicious oversampling of high/low exposed individuals can boost study power considerably. We illustrate our results using data from a study involving arsenic exposure and detoxification genes in Bangladesh.

Non-ignorable missingness in logistic regression.

Nonresponses and missing data are common in observational studies. Ignoring or inadequately handling missing data may lead to biased parameter estimation, incorrect standard errors and, as a consequence, incorrect statistical inference and conclusions. We present a strategy for modelling non-ignorable missingness where the probability of nonresponse depends on the outcome. Using a simple case of logistic regression, we quantify the bias in regression estimates and show the observed likelihood is non-identifiable under non-ignorable missing data mechanism. We then adopt a selection model factorisation of the joint distribution as the basis for a sensitivity analysis to study changes in estimated parameters and the robustness of study conclusions against different assumptions. A Bayesian framework for model estimation is used as it provides a flexible approach for incorporating different missing data assumptions and conducting sensitivity analysis. Using simulated data, we explore the performance of the Bayesian selection model in correcting for bias in a logistic regression. We then implement our strategy using survey data from the 45 and Up Study to investigate factors associated with worsening health from the baseline to follow-up survey. Our findings have practical implications for the use of the 45 and Up Study data to answer important research questions relating to health and quality-of-life. Copyright © 2017 John Wiley & Sons, Ltd.

On the impact of nonresponse in logistic regression: application to the 45 and Up study.

BACKGROUND: In longitudinal studies, nonresponse to follow-up surveys poses a major threat to validity, interpretability and generalisation of results. The problem of nonresponse is further complicated by the possibility that nonresponse may depend on the outcome of interest. We identified sociodemographic, general health and wellbeing characteristics associated with nonresponse to the follow-up questionnaire and assessed the extent and effect of nonresponse on statistical inference in a large-scale population cohort study. METHODS: We obtained the data from the baseline and first wave of the follow-up survey of the 45 and Up Study. Of those who were invited to participate in the follow-up survey, 65.2% responded. Logistic regression model was used to identify baseline characteristics associated with follow-up response. A Bayesian selection model approach with sensitivity analysis was implemented to model nonignorable nonresponse. RESULTS: Characteristics associated with a higher likelihood of responding to the follow-up survey include female gender, age categories 55-74, high educational qualification, married/de facto, worked part or partially or fully retired and higher household income. Parameter estimates and conclusions are generally consistent across different assumptions on the missing data mechanism. However, we observed some sensitivity for variables that are strong predictors for both the outcome and nonresponse. CONCLUSIONS: Results indicated in the context of the binary outcome under study, nonresponse did not result in substantial bias and did not alter the interpretation of results in general. Conclusions were still largely robust under nonignorable missing data mechanism. Use of a Bayesian selection model is recommended as a useful strategy for assessing potential sensitivity of results to missing data.

Spatial regression with covariate measurement error: A semiparametric approach.

Spatial data have become increasingly common in epidemiology and public health research thanks to advances in GIS (Geographic Information Systems) technology. In health research, for example, it is common for epidemiologists to incorporate geographically indexed data into their studies. In practice, however, the spatially defined covariates are often measured with error. Naive estimators of regression coefficients are attenuated if measurement error is ignored. Moreover, the classical measurement error theory is inapplicable in the context of spatial modeling because of the presence of spatial correlation among the observations. We propose a semiparametric regression approach to obtain bias-corrected estimates of regression parameters and derive their large sample properties. We evaluate the performance of the proposed method through simulation studies and illustrate using data on Ischemic Heart Disease (IHD). Both simulation and practical application demonstrate that the proposed method can be effective in practice.

A flexible parametric approach to examining spatial variation in relative survival.

Most of the few published models used to obtain small-area estimates of relative survival are based on a generalized linear model with piecewise constant hazards under a Bayesian formulation. Limitations of these models include the need to artificially split the time scale, restricted ability to include continuous covariates, and limited predictive capacity. Here, an alternative Bayesian approach is proposed: a spatial flexible parametric relative survival model. This overcomes previous limitations by combining the benefits of flexible parametric models: the smooth, well-fitting baseline hazard functions and predictive ability, with the Bayesian benefits of robust and reliable small-area estimates. Both spatially structured and unstructured frailty components are included. Spatial smoothing is conducted using the intrinsic conditional autoregressive prior. The model was applied to breast, colorectal, and lung cancer data from the Queensland Cancer Registry across 478 geographical areas. Advantages of this approach include the ease of including more realistic complexity, the feasibility of using individual-level input data, and the capacity to conduct overall, cause-specific, and relative survival analysis within the same framework. Spatial flexible parametric survival models have great potential for exploring small-area survival inequalities, and we hope to stimulate further use of these models within wider contexts. Copyright © 2016 John Wiley & Sons, Ltd.

Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping.

BACKGROUND: Mapping disease rates over a region provides a visual illustration of underlying geographical variation of the disease and can be useful to generate new hypotheses on the disease aetiology. However, methods to fit the popular and widely used conditional autoregressive (CAR) models for disease mapping are not feasible in many applications due to memory constraints, particularly when the sample size is large. We propose a new algorithm to fit a CAR model that can accommodate both individual and group level covariates while adjusting for spatial correlation in the disease rates, termed indiCAR. Our method scales well and works in very large datasets where other methods fail. RESULTS: We evaluate the performance of the indiCAR method through simulation studies. Our simulation results indicate that the indiCAR provides reliable estimates of all the regression and random effect parameters. We also apply indiCAR to the analysis of data on neutropenia admissions in New South Wales (NSW), Australia. Our analyses reveal that lower rates of neutropenia admissions are significantly associated with individual level predictors including higher age, male gender, residence in an outer regional area and a group level predictor of social disadvantage, the socio-economic index for areas. A large value for the spatial dependence parameter is estimated after adjusting for individual and area level covariates. This suggests the presence of important variation in the management of cancer patients across NSW. CONCLUSIONS: Incorporating individual covariate data in disease mapping studies improves the estimation of fixed and random effect parameters by utilizing information from multiple sources. Health registries routinely collect individual and area level information and thus could benefit by using indiCAR for mapping disease rates. Moreover, the natural applicability of indiCAR in a distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. CI NSW Study Reference Number: 2012/07/410. Dated: July 2012.

Log-Linear Modeling of Agreement among Expert Exposure Assessors.

BACKGROUND: Evaluation of expert assessment of exposure depends, in the absence of a validation measurement, upon measures of agreement among the expert raters. Agreement is typically measured using Cohen's Kappa statistic, however, there are some well-known limitations to this approach. We demonstrate an alternate method that uses log-linear models designed to model agreement. These models contain parameters that distinguish between exact agreement (diagonals of agreement matrix) and non-exact associations (off-diagonals). In addition, they can incorporate covariates to examine whether agreement differs across strata. METHODS: We applied these models to evaluate agreement among expert ratings of exposure to sensitizers (none, likely, high) in a study of occupational asthma. RESULTS: Traditional analyses using weighted kappa suggested potential differences in agreement by blue/white collar jobs and office/non-office jobs, but not case/control status. However, the evaluation of the covariates and their interaction terms in log-linear models found no differences in agreement with these covariates and provided evidence that the differences observed using kappa were the result of marginal differences in the distribution of ratings rather than differences in agreement. Differences in agreement were predicted across the exposure scale, with the likely moderately exposed category more difficult for the experts to differentiate from the highly exposed category than from the unexposed category. CONCLUSIONS: The log-linear models provided valuable information about patterns of agreement and the structure of the data that were not revealed in analyses using kappa. The models' lack of dependence on marginal distributions and the ease of evaluating covariates allow reliable detection of observational bias in exposure data.