Resampling-based Approach Research Articles

A non-parametric approach to predict the recruitment for randomized clinical trials: an example in elderly inpatient settings

BackgroundAccurate prediction of subject recruitment, which is critical to the success of a study, remains an ongoing challenge. Previous prediction models often rely on parametric assumptions which are not always met or may be difficult to implement. We aim to develop a novel method that is less sensitive to model assumptions and relatively easy to implement.MethodsWe create a weighted resampling-based approach to predict enrollment in year two based on recruitment data from year one of the completed GRIPS and PACE clinical trials. Different weight functions accounted for a range of potential enrollment trajectory patterns. Prediction accuracy was measured by Euclidean distance for enrollment sequence in year two, total enrollment over time, and total weeks to enroll a fixed number of subjects, against the actual year two enrollment data. We compare the performance of the proposed method with an existing Bayesian method.ResultsWeighted resampling using GRIPS data resulted in closer prediction evidenced by better coverage of observed enrollment with the prediction intervals and smaller Euclidean distance from actual enrollment in year 2, especially when enrollment gaps were filled prior to the weighted resampling. These scenarios also produced more accurate predictions for total enrollment and number of weeks to enroll 50 participants. These same scenarios outperformed an existing Bayesian method for all 3 accuracy measures. In PACE data, using a reduced year 1 enrollment resulted in closer prediction evidenced by better coverage of observed enrollment with the prediction intervals and smaller Euclidean distance from actual enrollment in year 2, with the weighted resampling scenarios better reflecting the seasonal variation seen in year (1) The reduced enrollment scenarios resulted in closer prediction for total enrollment over 6 and 12 months into year (2) These same scenarios also outperformed an existing Bayesian method for relevant accuracy measures.ConclusionThe results demonstrate the feasibility and flexibility for a resampling-based, non-parametric approach for prediction of clinical trial recruitment with limited early enrollment data. Application to a wider setting and long-term prediction accuracy require further investigation.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National ToxicologyProgram.

EVALUATION OF CLASSIFICATION ALGORITHMS WITH SOLUTION TO CLASS IMBALANCE PROBLEM ON ZAKAT DISTRIBUTION DATASET

Class imbalance is one of the most serious and significant issues in data mining classification studies. In recent years, there has been a lot of interest in the problem of imbalance in various real-world applications, such as fraud detection, medical diagnosis, and bank marketing datasets. Similarly, the Zakat distribution UiTM Perlis dataset was utilized to assess the imbalance in the dataset classes. The study aims to evaluate the performance of the resampling technique, Synthetic Minority Over-sampling Technique (SMOTE), and to identify the best classifiers for class-imbalanced Zakat datasets by comparing the classifier's performance. A resampling-based approach is proposed in this study to solve the imbalance dataset. This approach aims to enhance the true positive or the detection of the minority class, which in this case is the not eligible class. The evaluation is based on various metrics, which include accuracy, precision, recall, F-measure, and the ROC area. The algorithms considered in the study include Logistic Regression, Decision Tree (C.4.5), and Random Forest. The results indicate that Random Forest consistently performs well across all evaluation metrics after implementing SMOTE. It attains the highest accuracy, precision, recall, F-measure, and ROC area levels. In conclusion, this research highlights the effectiveness of SMOTE in resolving class imbalance in the Zakat distributions dataset. The evaluation of various classification algorithms will be conducted using WEKA.

Distribution-Free Prediction Intervals Under Covariate Shift, With an Application to Causal Inference

Owing to its appealing distribution-free feature, conformal inference has become a popular tool for constructing prediction intervals with a desired coverage rate. In scenarios involving covariate shift, where the shift function needs to be estimated from data, many existing methods resort to data-splitting techniques. However, these approaches often lead to wider intervals and less reliable coverage rates, especially when dealing with finite sample sizes. To address these challenges, we propose methods based on a pivotal quantity derived under a parametric working model and employ a resampling-based framework to approximate its distribution. The resampling-based approach can produce prediction intervals with a desired coverage rate without splitting the data and can be easily applied to causal inference settings where a shift in the covariate distribution can occur between treatment and control arms. Additionally, the proposed approaches enjoy a double robustness property and are adaptable to different prediction tasks. Our extensive numerical experiments demonstrate that, compared to existing methods, the proposed novel approaches can produce substantially shorter conformal prediction intervals with lower variability in the interval lengths while maintaining promising coverage rates and advantages in versatile usage. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

A resampling-based approach to share reference panels

For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference panels have become larger and more diverse, leading to improvements in imputation accuracy. However, the latest generation of reference panels is subject to restrictions on data sharing due to concerns about privacy, limiting their usefulness for genotype imputation. In this context, here we propose RESHAPE, a method that employs a recombination Poisson process on a reference panel to simulate the genomes of hypothetical descendants after multiple generations. This data transformation helps to protect against re-identification threats and preserves data attributes, such as linkage disequilibrium patterns and, to some degree, identity-by-descent sharing, allowing for genotype imputation. Our experiments on gold-standard datasets show that simulated descendants up to eight generations can serve as reference panels without substantially reducing genotype imputation accuracy.

Variable selection and inference strategies for multiple compositional regression

An important problem in compositional data analysis is variable selection in linear regression models with compositional covariates. In the context of microbiome data analysis, there is a demand for considering grouping information such as structures among taxa and multiple sampling sites, resulting in multiple compositional covariates. We develop and compare two different methods of variable selection and inference strategies, based on the debiased lasso and a resampling-based approach. Confidence intervals for individual regression coefficients, obtained from each of the two methods, are shown to be asymptotically valid even in a high-dimension, low-sample-size regime. However, microbiome data often have extremely small sample sizes, rendering asymptotic results unreliable. Through extensive numerical comparisons of the finite-sample performances of the two methods, we find that resampling-based approaches outperform the debiased compositional lasso in cases of extremely small sample sizes, showing higher positive predictive values. Conversely, for larger sample sizes, debiasing yields better results. We apply the proposed multiple compositional regression to steer microbiome data, identifying key bacterial taxa associated with important cattle quality measures.

Sentiment analysis of imbalanced Arabic data using sampling techniques and classification algorithms

Sentiment analysis is a popular natural language processing task that recognizes the opinions or feelings of a piece of text. Microblogging platforms such as Twitter are a valuable resource for finding such people’s opinions. The majority of Arabic sentiment analysis studies indicated that the data utilized to train machine learning algorithms is balanced. In this paper, we investigated the impact of sampling techniques and classification algorithms on an imbalanced Arabic dataset about people’s perceptions of COVID-19, with the majority of opinions reflecting people’s fear and stress about the pandemic, and the minority reflecting the belief that the pandemic was a hoax. The experiments concentrated on analyzing the imbalanced learning of Arabic sentiments using over-sampling and under-sampling techniques on seven single machine learning algorithms and two common ensemble algorithms from the bagging and boosting families, respectively. Results show that resampling-based approaches can overcome the difficulty of an imbalanced dataset, and the use of over-sampled data leads to better performance than that of under-sampled data. The results also reveal that using oversampled data from synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, or adaptive synthetic sampling with random forest classifier is the most effective in addressing this classification problem, with F1-score value of 0.99.

SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

Classification of BATSE, Swift, and Fermi Gamma-Ray Bursts from Prompt Emission Alone

Although it is generally assumed that there are two dominant classes of gamma-ray bursts (GRBs) with different typical durations, it has been difficult to classify GRBs unambiguously as short or long from summary properties such as duration, spectral hardness, and spectral lag. Recent work used t-distributed stochastic neighborhood embedding (t-SNE), a machine-learning algorithm for dimensionality reduction, to classify all Swift GRBs as short or long. Here, the method is expanded, using two algorithms, t-SNE and UMAP, to produce embeddings that are used to provide a classification for 1911 BATSE bursts, 1321 Swift bursts, and 2294 Fermi bursts for which both spectra and metadata are available. Although the embeddings appear to produce a clear separation of each catalog into short and long bursts, a resampling-based approach is used to show that a small fraction of bursts cannot be robustly classified. Further, three of the 304 bursts observed by both Swift and Fermi have robust but conflicting classifications. A likely interpretation is that in addition to the two predominant classes of GRBs, there are additional, uncommon types of bursts which may require multiwavelength observations in order to separate them from more typical short and long GRBs.

How many days are needed? Measurement reliability of wearable device data to assess physical activity.

Physical activity studies often utilize wearable devices to measure participants' habitual activity levels by averaging values across several valid observation days. These studies face competing demands-available resources and the burden to study participants must be balanced with the goal to obtain reliable measurements of a person's longer-term average. Information about the number of valid observation days required to reliably measure targeted metrics of habitual activity is required to inform study design. To date, the number of days required to achieve a desired level of aggregate long-term reliability (typically 0.80) has often been estimated by applying the Spearman-Brown Prophecy formula to short-term test-retest reliability data from studies with single, relatively brief observation windows. Our work, in contrast, utilizes a resampling-based approach to quantify the long-term test-retest reliability of aggregate measures of activity in a cohort of 79 participants who were asked to wear a FitBit Flex every day for approximately one year. The conventional approach can produce reliability estimates that substantially overestimate the actual test-retest reliability. Six or more valid days of observation for each participant appear necessary to obtain 0.80 reliability for the average amount of time spent in light physical activity; 8 and 10 valid days are needed for sedentary time and moderate/vigorous activity respectively. Protocols that result in 7-10 valid observation days for each participant may be needed to obtain reliable measurements of key physical activity metrics.

Association of Glyphosate Exposure with Blood DNA Methylation in a Cross-Sectional Study of Postmenopausal Women.

Background:Glyphosate is the most commonly used herbicide in the world and is purported to have a variety of health effects, including endocrine disruption and an elevated risk of several types of cancer. Blood DNA methylation has been shown to be associated with many other environmental exposures, but to our knowledge, no studies to date have examined the association between blood DNA methylation and glyphosate exposure.Objective:We conducted an epigenome-wide association study to identify DNA methylation loci associated with urinary glyphosate and its metabolite aminomethylphosphonic acid (AMPA) levels. Secondary goals were to determine the association of epigenetic age acceleration with glyphosate and AMPA and develop blood DNA methylation indices to predict urinary glyphosate and AMPA levels.Methods:For 392 postmenopausal women, white blood cell DNA methylation was measured using the Illumina Infinium MethylationEPIC BeadChip array. Glyphosate and AMPA were measured in two urine samples per participant using liquid chromatography–tandem mass spectrometry. Methylation differences at the probe and regional level associated with glyphosate and AMPA levels were assessed using a resampling-based approach. Probes and regions that had an false discovery rate in of 1,000 subsamples of the study population were considered differentially methylated. Differentially methylated sites from the probe-specific analysis were combined into a methylation index. Epigenetic age acceleration from three epigenetic clocks and an epigenetic measure of pace of aging were examined for associations with glyphosate and AMPA.Results:We identified 24 CpG sites whose methylation level was associated with urinary glyphosate concentration and two associated with AMPA. Four regions, within the promoters of the MSH4, KCNA6, ABAT, and NDUFAF2/ERCC8 genes, were associated with glyphosate levels, along with an association between ESR1 promoter hypomethylation and AMPA. The methylation index accurately predicted glyphosate levels in an internal validation cohort. AMPA, but not glyphosate, was associated with greater epigenetic age acceleration.Discussion:Glyphosate and AMPA exposure were associated with DNA methylation differences that could promote the development of cancer and other diseases. Further studies are warranted to replicate our results, determine the functional impact of glyphosate- and AMPA-associated differential DNA methylation, and further explore whether DNA methylation could serve as a biomarker of glyphosate exposure. https://doi.org/10.1289/EHP10174

Association of mammographic density with blood DNA methylation

ABSTRACT Background: Altered DNA methylation may be an intermediate phenotype between breast cancer risk factors and disease. Mammographic density is a strong risk factor for breast cancer. However, no studies to date have identified an epigenetic signature of mammographic density. We performed an epigenome-wide association study of mammographic density. Methods: White blood cell DNA methylation was measured for 385 postmenopausal women using the Illumina Infinium MethylationEPIC BeadChip array. Differential methylation was assessed using genome-wide, probe-level, and regional analyses. We implemented a resampling-based approach to improve the stability of our findings. Results: On average, women with elevated mammographic density exhibited DNA hypermethylation within CpG islands and gene promoters compared to women with lower mammographic density. We identified 250 CpG sites for which DNA methylation was significantly associated with mammographic density. The top sites were located within genes associated with cancer, including HDLBP, TGFB2, CCT4, and PAX8, and were more likely to be located in regulatory regions of the genome. We also identified differential DNA methylation in 37 regions, including within the promoters of PAX8 and PF4, a gene involved in the regulation of angiogenesis. Overall, our results paint a picture of epigenetic dysregulation associated with mammographic density. Conclusion: Mammographic density is associated with differential DNA methylation throughout the genome, including within genes associated with cancer. Our results suggest the potential involvement of several genes in the biological mechanisms behind differences in breast density between women. Further studies are warranted to explore these potential mechanisms and potential links to breast cancer risk.

Unobtrusive Screening of Central Sleep Apnea From Pressure Sensors Measurements: A Patient-Specific Longitudinal Study

Historically, the lack of patients’ sleep histories has caused low identification of sleep apnea (SA) and referral rates. Moreover, the costly and time-consuming nature of polysomnography (PSG) as a standard clinical test for detecting SA and the lack of sleep clinics has created a demand for suitable home-based monitoring devices. Pressure measurement using a pressure sensitive mat (PSM) can address the challenges found in current sleep-monitoring solutions. The noncontact PSM has a potential to replace obtrusive breathing sensors in the sleep lab and to be used as a prescreening tool for patients suspected of having SA. Applying classical support vector machine (SVM), this article presents a personalized system based on the measurements of each patient to detect central SA (CSA) events and monitor sleep characteristics longitudinally. For this purpose, sensor set-ups were installed in nine seniors’ homes to collect unsupervised pressure data in approximately one year ranging from 8 to 12 months. Cost-based and resampling-based approaches were examined to combat imbalanced data. The results showed that the cost-based method outperformed other methods. Next, the patient-specific system was used to determine the total number of CSA events, as well as their starting time and duration in each day. The SA severity was measured by the central apnea index (CAI). In addition, other sleep characteristics such as bed occupancy (BO), day clock, and night clock were extracted from the PSM measurements. The impact of longitudinal sleep monitoring could be in tracking SA treatment progression, and possibly providing information on the interaction between SA and other disease progressions.

Measuring economic contributions of the marine recreational charter fishing sector using a resampling approach

AbstractPolicy makers and stakeholders often desire information on the economic impact of fishing, which is frequently measured through its contribution to the economy using regional economic impact models. The variance of fishery-related economic contribution estimates is seldom calculated but can improve the quality of policy information. In this study, we illustrate a resampling-based approach for calculating standard errors of contribution estimates within a social accounting matrix (SAM) model with inputs calculated from survey data with missing data. We estimate the contribution of the saltwater recreational charter fishing industry in Alaska to the economy for 2011–2013 and 2015. Statistical tests are then conducted to assess differences between estimates across the years. Of the years studied, the total output (sales) from the Alaska saltwater charter fishing industry in Alaska was found to be (statistically) largest in 2011 ($248 million in 2013 dollars) and lowest in the next year, 2012 (about $141 million in 2013 dollars). Subsequently, the total output increased in 2013 and then remained at a statistically similar level in 2015.

A Mean-Field Optimal Control Formulation for Global Optimization

This paper is concerned with variational optimal control constructions whose solution yields a sampling algorithm. The particular form of the sampling algorithm considered here is a particle filter, designed to numerically approximate the solution to the global optimization problem. The theoretical significance of this study comes from its variational aspects. Specifically, the control input represents the solution of a mean-field-type optimal control problem. Its parametric counterpart, obtained when a parametric form of density is known a priori , is shown to be equivalent to the natural gradient algorithm. Explicit formulae for the filter are derived when the objective function is quadratic and the density is Gaussian. The optimal control construction of the particle filter is a significant departure from the classical importance sampling–resampling-based approaches.

A scale-invariant change detection method for land use/cover change research

Land Use/Cover Change (LUCC) detection relies increasingly on comparing remote sensing images with different spatial and spectral scales. Based on scale-invariant image analysis algorithms in computer vision, we propose a scale-invariant LUCC detection method to identify changes from scale heterogeneous images. This method is composed of an entropy-based spatial decomposition, two scale-invariant feature extraction methods, Maximally Stable Extremal Region (MSER) and Scale-Invariant Feature Transformation (SIFT) algorithms, a spatial regression voting method to integrate MSER and SIFT results, a Markov Random Field-based smoothing method, and a support vector machine classification method to assign LUCC labels. We test the scale invariance of our new method with a LUCC case study in Montreal, Canada, 2005–2012. We found that the scale-invariant LUCC detection method provides similar accuracy compared with the resampling-based approach but this method avoids the LUCC distortion incurred by resampling.

One day ahead wind speed forecasting: A resampling-based approach

Wind speed forecasting plays a vital role in dispatch planning and operational security for wind farms, however, its difficulty is commonly accepted. This paper develops a nonlinear autoregressive (exogenous) model for one-day-ahead mean hourly wind speed forecasting, where general regression neural network is employed to model nonlinearities of the system. Specifically, this model is a two-stage method consisting of the model selection and training stage along with the iterative forecasting and correcting stage. In the former stage, the model is in the series-parallel configuration, and its test error is estimated by the cross-validation (CV) method. With the help of ARIMA identification results, CV errors are minimized by the Fibonacci search method to select the best lag structure and the only adjustable parameter. In the latter stage, the model is in the parallel configuration, and the so-called leave-one-day-out resampling method is proposed to iteratively estimate correction parameters for horizons up to 24h ahead, which holds out each full-day data segment from the sample of observations in turn to faithfully reproduce the entire process of training, iterative forecasting and correcting in the in-sample period. Finally, the out-of-sample corrected forecasts can be successively obtained by using the model selected and trained in the former stage and the correction parameters estimated in the latter stage. Furthermore, effectiveness of this model is verified with four real-world case studies of two wind farms in China.

Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS.

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic (“z-score”) of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a “relative enrichment score” for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3.

A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

BackgroundThere are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist.ResultsHere, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis.ConclusionsWe demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0194-7) contains supplementary material, which is available to authorized users.

SmileFinder: a resampling-based approach to evaluate signatures of selection from genome-wide sets of matching allele frequency data in two or more diploid populations.

BackgroundAdaptive alleles may rise in frequency as a consequence of positive selection, creating a pattern of decreased variation in the neighboring loci, known as a selective sweep. When the region containing this pattern is compared to another population with no history of selection, a rise in variance of allele frequencies between populations is observed. One challenge presented by large genome-wide datasets is the ability to differentiate between patterns that are remnants of natural selection from those expected to arise at random and/or as a consequence of selectively neutral demographic forces acting in the population.FindingsSmileFinder is a simple program that looks for diversity and divergence patterns consistent with selection sweeps by evaluating allele frequencies in windows, including neighboring loci from two or more populations of a diploid species against the genome-wide neutral expectation. The program calculates the mean of heterozygosity and FST in a set of sliding windows of incrementally increasing sizes, and then builds a resampled distribution (the baseline) of random multi-locus sets matched to the sizes of sliding windows, using an unrestricted sampling. Percentiles of the values in the sliding windows are derived from the superimposed resampled distribution. The resampling can easily be scaled from 1 K to 100 M; the higher the number, the more precise the percentiles ascribed to the extreme observed values.ConclusionsThe output from SmileFinder can be used to plot percentile values to look for population diversity and divergence patterns that may suggest past actions of positive selection along chromosome maps, and to compare lists of suspected candidate genes under random gene sets to test for the overrepresentation of these patterns among gene categories. Both applications of the algorithm have already been used in published studies. Here we present a publicly available, open source program that will serve as a useful tool for preliminary scans of selection using worldwide databases of human genetic variation, as well as population datasets for many non-human species, from which such data is rapidly emerging with the advent of new genotyping and sequencing technologies.