Multiple Testing Approach Research Articles

A Deep Dive into Statistical Modeling of RNA Splicing QTLs Reveals New Variants that Explain Neurodegenerative Disease.

Genome-wide association studies (GWAS) have identified thousands of putative disease causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a new pipeline for sQTL discovery. MAJIQTL includes two new statistical methods: a weighted multiple testing approach for sGene discovery and a model for sQTL effect size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the novel variant rs582283 in Alzheimer's disease. Using antisense oligonucleotides, we validate this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in the gene MS4A3.

Comprehensive Approach for the Genetic Diagnosis of Patients with Waardenburg Syndrome.

Waardenburg syndrome (WS) is a common genetic cause of syndromic hearing loss, accounting for 2-5% of congenital cases. It is characterized by hearing impairment and pigmentation abnormalities in the skin, hair, and eyes. Seven genes are associated with WS: PAX3, MITF, EDNRB, EDN3, SOX10, KITLG, and SNAI2. This study investigates the genetic causes of WS in three familial cases. Whole-exome sequencing (WES) was performed to identify single nucleotide variants (SNVs). Copy number variants (CNVs) were analyzed from the WES raw data and through multiplex ligation-dependent probe amplification (MLPA). The study identified one pathogenic SNV and two novel CNVs, corresponding to type I and type II WS patterns in the three families. The SNV, a nonsense variant (c.1198C>T p.Arg400*), was found in MITF and segregated in the affected father. The two CNVs were a deletion of exon 5 in PAX3 in a family with two affected members and a large novel deletion comprising seven genes, including SOX10, in a family with three affected members. These findings confirmed a WS diagnosis through genetic testing. The study emphasizes the importance of integrating multiple genetic testing approaches for accurate and reliable diagnosis, highlighting their role in improving patient management and providing tailored genetic counseling.

Studying Factorial Invariance With Nominal Items: A Note on a Latent Variable Modeling Procedure

A latent variable modeling procedure for studying factorial invariance and differential item functioning for multi-component measuring instruments with nominal items is discussed. The method is based on a multiple testing approach utilizing the false discovery rate concept and likelihood ratio tests. The procedure complements the Revuelta, Franco-Martinez, and Ximenez approach to factorial invariance examination, and permits localization of individual invariance violations. The outlined method does not require the selection of a reference observed variable and is illustrated with empirical data.

A one-covariate-at-a-time multiple testing approach to variable selection in additive models

. This article proposes a One-Covariate-at-a-time Multiple Testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similarly to Chudik, Kapetanios, and Pesaran, we consider the statistical significance of individual nonparametric additive components one at a time and take into account the multiple testing nature of the problem. Both one-stage and multiple-stage procedures are considered. The former works well in terms of the true positive rate only if the net effects of all signals are strong enough; the latter helps to pick up hidden signals that have weak net effects. Simulations demonstrate the good finite-sample performance of the proposed procedures. As an empirical illustration, we apply the OCMT procedure to a dataset extracted from the Longitudinal Survey on Rural Urban Migration in China. We find that our procedure works well in terms of out-of-sample root mean square forecast errors, compared with competing methods such as adaptive group Lasso (AGLASSO).

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

BackgroundExpression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals.ResultsWe conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases.ConclusionOur co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.

Netgwas: An R Package for Network-Based Genome Wide Association Studies

Graphical models are powerful tools for modeling and making statistical inferences regarding complex associations among variables in multivariate data. In this paper we introduce the R package netgwas, which is designed based on undirected graphical models to accomplish three important and interrelated goals in genetics: constructing linkage map, reconstructing linkage disequilibrium (LD) networks from multi-loci genotype data, and detecting high-dimensional genotype-phenotype networks. The netgwas package deals with species with any chromosome copy number in a unified way, unlike other software. It implements recent improvements in both linkage map construction (Behrouzi and Wit, 2018), and reconstructing conditional independence network for non-Gaussian continuous data, discrete data, and mixed discrete-and-continuous data (Behrouzi and Wit, 2017). Such datasets routinely occur in genetics and genomics such as genotype data, and genotype-phenotype data. We demonstrate the value of our package functionality by applying it to various multivariate example datasets taken from the literature. We show, in particular, that our package allows a more realistic analysis of data, as it adjusts for the effect of all other variables while performing pairwise associations. This feature controls for spurious associations between variables that can arise from classical multiple testing approach. This paper includes a brief overview of the statistical methods which have been implemented in the package. The main body of the paper explains how to use the package. The package uses a parallelization strategy on multi-core processors to speed-up computations for large datasets. In addition, it contains several functions for simulation and visualization. The netgwas package is freely available at this https URL

Shear thickening fabric composites for impact protection: a review

Fabrics with proper mechanical properties and high structural designability are frequently selected as the reinforcement of flexible composites. Shear thickening materials (STMs), including shear thickening fluid and shear thickening gel, are dense colloidal suspensions exhibiting an abrupt increase in viscosity with increasing shear rate. Shear thickening fabric composites (STFCs) which combined STM with fabrics, have been increasingly in demand for impact protective applications due to their tremendous potential. Herein, the characteristics of STM and the effects of fabric reinforcement on STFC impact behaviors are discussed for optimizing their properties and structures. Subsequently, the impact behaviors of STFCs obtained by multiple testing approaches with different impact threats are reviewed systematically. Furthermore, a comprehensive summary of the existing literature on stab, drop-weight, split Hopkinson pressure bar, and ballistic impact protective mechanisms of STFCs is presented. It is found that based on the proper selection of STM types, thorough mechanistic explanations of STFC impact behaviors are instructive and could facilitate the development of wearable protective equipment. The purpose of this critical review is to provide a framework for comparison of the effective impact resistances of STFCs across many applications and to discuss issues for future research.

IMI2-PainCare-BioPain-RCT2 protocol: a randomized, double-blind, placebo-controlled, crossover, multicenter trial in healthy subjects to investigate the effects of lacosamide, pregabalin, and tapentadol on biomarkers of pain processing observed by non-invasive neurophysiological measurements of human spinal cord and brainstem activity

BackgroundIMI2-PainCare-BioPain-RCT2 is one of four similarly designed clinical studies aiming at profiling a set of functional biomarkers of drug effects on specific compartments of the nociceptive system that could serve to accelerate the future development of analgesics. IMI2-PainCare-BioPain-RCT2 will focus on human spinal cord and brainstem activity using biomarkers derived from non-invasive neurophysiological measurements.MethodsThis is a multisite, single-dose, double-blind, randomized, placebo-controlled, 4-period, 4-way crossover, pharmacodynamic (PD) and pharmacokinetic (PK) study in healthy subjects. Neurophysiological biomarkers of spinal and brainstem activity (the RIII flexion reflex, the N13 component of somatosensory evoked potentials (SEP) and the R2 component of the blink reflex) will be recorded before and at three distinct time points after administration of three medications known to act on the nociceptive system (lacosamide, pregabalin, tapentadol), and placebo, given as a single oral dose in separate study periods. Medication effects on neurophysiological measures will be assessed in a clinically relevant hyperalgesic condition (high-frequency electrical stimulation of the skin), and in a non-sensitized normal condition. Patient-reported outcome measures (pain ratings and predictive psychological traits) will also be collected; and blood samples will be taken for pharmacokinetic modelling. A sequentially rejective multiple testing approach will be used with overall alpha error of the primary analysis split between the two primary endpoints, namely the percentage amplitude changes of the RIII area and N13 amplitude under tapentadol. Remaining treatment arm effects on RIII, N13 and R2 recovery cycle are key secondary confirmatory analyses. Complex statistical analyses and PK-PD modelling are exploratory.DiscussionThe RIII component of the flexion reflex is a pure nociceptive spinal reflex widely used for investigating pain processing at the spinal level. It is sensitive to different experimental pain models and to the antinociceptive activity of drugs. The N13 is mediated by large myelinated non-nociceptive fibers and reflects segmental postsynaptic response of wide dynamic range dorsal horn neurons at the level of cervical spinal cord, and it could be therefore sensitive to the action of drugs specifically targeting the dorsal horn. The R2 reflex is mediated by large myelinated non-nociceptive fibers, its circuit consists of a polysynaptic chain lying in the reticular formation of the pons and medulla. The recovery cycle of R2 is widely used for assessing brainstem excitability. For these reasons, IMI2-PainCare-BioPain-RCT2 hypothesizes that spinal and brainstem neurophysiological measures can serve as biomarkers of target engagement of analgesic drugs for future Phase 1 clinical trials. Phase 2 and 3 clinical trials could also benefit from these tools for patient stratification.Trial registrationThis trial was registered on 02 February 2019 in EudraCT (2019-000755-14).

Data adaptive functional outlier detection: Analysis of the Paris bike sharing system data

Bike sharing systems (BSSs) have become an increasingly popular means of sustainable transportation, and have been implemented in many cities worldwide. Our approach contributes to the identification of abnormal patterns by applying real-time occupancy data from Paris. In particular, we propose a novel functional outlier detection algorithm based on a two-step approach: In the first stage, a clean dataset is obtained based on the combined effect of two extreme statistics calculated from random sampling; in the second stage, a multiple testing approach based on the clean dataset is proposed, in which the false discovery rate (FDR) control procedure is used to adaptively choose the thresholds for the hypothesis tests. Extensive numerical simulations were conducted to compare the outlier detection performance with those of other state-of-art methods. The proposed approach is then applied to the Paris Vélib’ bike sharing system dataset to identify abnormal patterns that are of particular interest to BSS operators for identifying system inefficiencies and update policies.

Testing independence between exogenous variables and unobserved errors

Although the exogeneity condition is usually used in many econometric models to identify parameters, the stronger restriction that the error term is independent of a vector of exogenous variables might lead to theoretical benefits. In this paper, we develop a unified methodology for testing the independence assumption. Our methodology can deal with a wide class of parametric models and allows for endogeneity and instrumental variables. In the first-step development, we construct tests that are continuous functionals of the estimated difference of the joint distribution and the product marginal distributions. Next, to remedy the dimensionality issue that arises when the dimension of the exogenous random vector is large, we propose a multiple testing approach which combines marginal p-values obtained by employing the original tests to test independence between the error term and each exogenous variable, while taking full account of the multiplicity nature of the testing problem. We obtain null limiting distributions of our tests, establish the testing consistency, and justify the sensitivity to -local alternatives, with n the sample size. The multiplier bootstrap is employed to estimate the critical values. Our methodology is illustrated in the linear regression, the instrumental variables regression, and the nonlinear quantile regression. Our tests are found to perform well in simulations and are demonstrated via an empirical example.

IMI2-PainCare-BioPain-RCT1: study protocol for a randomized, double-blind, placebo-controlled, crossover, multi-center trial in healthy subjects to investigate the effects of lacosamide, pregabalin, and tapentadol on biomarkers of pain processing observed by peripheral nerve excitability testing (NET)

BackgroundFew new drugs have been developed for chronic pain. Drug development is challenged by uncertainty about whether the drug engages the human target sufficiently to have a meaningful pharmacodynamic effect. IMI2-PainCare-BioPain-RCT1 is one of four similarly designed studies that aim to link different functional biomarkers of drug effects on the nociceptive system that could serve to accelerate the future development of analgesics. This study focusses on biomarkers derived from nerve excitability testing (NET) using threshold tracking of the peripheral nervous system.MethodsThis is a multisite single-dose, subject and assessor-blind, randomized, placebo-controlled, 4-period, 4-way crossover, pharmacodynamic (PD), and pharmacokinetic (PK) study in healthy subjects. Biomarkers derived from NET of large sensory and motor fibers and small sensory fibers using perception threshold tracking will be obtained before and three times after administration of three medications known to act on the nociceptive system (lacosamide, pregabalin, tapentadol) and placebo, given as a single oral dose with at least 1 week apart. Motor and sensory NET will be assessed on the right wrist in a non-sensitized normal condition while perception threshold tracking will be performed bilaterally on both non-sensitized and sensitized forearm skin. Cutaneous high-frequency electrical stimulation is used to induce hyperalgesia. Blood samples will be taken for pharmacokinetic purposes and pain ratings as well as predictive psychological traits will be collected. A sequentially rejective multiple testing approach will be used with overall alpha error of the primary analysis split across the two primary outcomes: strength-duration time constant (SDTC; a measure of passive membrane properties and nodal persistent Na+ conductance) of large sensory fibers and SDTC of large motor fibers comparing lacosamide and placebo. The key secondary endpoint is the SDTC measured in small sensory fibers. Remaining treatment arm effects on key NET outcomes and PK modelling are other prespecified secondary or exploratory analyses.DiscussionMeasurements of NET using threshold tracking protocols are sensitive to membrane potential at the site of stimulation. Sets of useful indices of axonal excitability collectively may provide insights into the mechanisms responsible for membrane polarization, ion channel function, and activity of ionic pumps during the process of impulse conduction. IMI2-PainCare-BioPain-RCT1 hypothesizes that NET can serve as biomarkers of target engagement of analgesic drugs in this compartment of the nociceptive system for future Phase 1 clinical trials. Phase 2 and 3 clinical trials could also benefit from these tools for patient stratification.Trial registrationThis trial was registered 25/06/2019 in EudraCT (2019-000942-36).

A one covariate at a time, multiple testing approach to variable selection in high‐dimensional linear regression models: A replication in a narrow sense

SummaryChudik, Kapetanios, & Pesaran (Econometrica 2018, 86, 1479‐1512) propose a one covariate at a time, multiple testing (OCMT) approach to variable selection in high‐dimensional linear regression models as an alternative approach to penalised regression. We offer a narrow replication of their key OCMT results based on the Stata software instead of the original MATLAB routines. Using the new user‐written Stata commands baing and ocmt, we find results that match closely those reported by these authors in their Monte Carlo simulations. In addition, we replicate exactly their findings in the empirical illustration, which relate to top five variables with highest inclusion frequencies based on the OCMT selection method.

IMI2-PainCare-BioPain-RCT3: a randomized, double-blind, placebo-controlled, crossover, multi-center trial in healthy subjects to investigate the effects of lacosamide, pregabalin, and tapentadol on biomarkers of pain processing observed by electroencephalography (EEG)

BackgroundIMI2-PainCare-BioPain-RCT3 is one of four similarly designed clinical studies aiming at profiling a set of functional biomarkers of drug effects on the nociceptive system that could serve to accelerate the future development of analgesics, by providing a quantitative understanding between drug exposure and effects of the drug on nociceptive signal processing in human volunteers. IMI2-PainCare-BioPain-RCT3 will focus on biomarkers derived from non-invasive electroencephalographic (EEG) measures of brain activity.MethodsThis is a multisite single-dose, double-blind, randomized, placebo-controlled, 4-period, 4-way crossover, pharmacodynamic (PD) and pharmacokinetic (PK) study in healthy subjects. Biomarkers derived from scalp EEG measurements (laser-evoked brain potentials [LEPs], pinprick-evoked brain potentials [PEPs], resting EEG) will be obtained before and three times after administration of three medications known to act on the nociceptive system (lacosamide, pregabalin, tapentadol) and placebo, given as a single oral dose in separate study periods. Medication effects will be assessed concurrently in a non-sensitized normal condition and a clinically relevant hyperalgesic condition (high-frequency electrical stimulation of the skin). Patient-reported outcomes will also be collected. A sequentially rejective multiple testing approach will be used with overall alpha error of the primary analysis split between LEP and PEP under tapentadol. Remaining treatment arm effects on LEP or PEP or effects on EEG are key secondary confirmatory analyses. Complex statistical analyses and PK-PD modeling are exploratory.DiscussionLEPs and PEPs are brain responses related to the selective activation of thermonociceptors and mechanonociceptors. Their amplitudes are dependent on the responsiveness of these nociceptors and the state of the pathways relaying nociceptive input at the level of the spinal cord and brain. The magnitude of resting EEG oscillations is sensitive to changes in brain network function, and some modulations of oscillation magnitude can relate to perceived pain intensity, variations in vigilance, and attentional states. These oscillations can also be affected by analgesic drugs acting on the central nervous system. For these reasons, IMI2-PainCare-BioPain-RCT3 hypothesizes that EEG-derived measures can serve as biomarkers of target engagement of analgesic drugs for future Phase 1 clinical trials. Phase 2 and 3 clinical trials could also benefit from these tools for patient stratification.Trial registrationThis trial was registered 25/06/2019 in EudraCT (2019%2D%2D001204-37).

The intersection of genetics and COVID-19 in 2021: preview of the 2021 Rodney Howell Symposium

The intersection of genetics and COVID-19 in 2021: preview of the 2021 Rodney Howell Symposium

Application of the Closure Principle Computational Approach Test to Assess Ecotoxicological Field Studies: Comparative Analysis Using Earthworm Field Test Abundance Data.

Field studies to determine the effects of chemicals on earthworm communities are generally conducted according to International Organization for Standardization standard 11268-3 (and later comments). However, statistical test procedures suggested in the guideline are frequently criticized, mainly for 2 reasons: 1) Earthworm abundances are count data and often do not fulfill requirements for multiple t tests (normal distribution and homogeneity of variance), and 2) the resulting toxicity metrics of multiple testing procedures (no/lowest-observed-effect concentrations [NOEC/LOEC]) fail to adequately detect the actual level of effects. Recently, a new method to overcome these shortcomings was presented by the introduction of the closure principle computational approach test (CPCAT). We applied this statistical method to assess chemical effects on abundance in a large dataset of 26 earthworm field studies (with up to 3 test chemical application rates) and an additional extended study with 6 application rates. A comparative analysis was provided considering results of well-established multiple testing approaches (Dunnett's test) with particular consideration of the degree of overdispersion found in these data. It was shown that the CPCAT detects substantially more effects in earthworm field tests as statistically significant than standard t test approaches. This lowered the LOEC/NOEC for many chemical treatments to control comparisons. As a consequence, the statistically detected NOECs/LOECs were often set at lower percentage deviations between control and chemical treatment. This is the first time the performance of the CPCAT has been assessed within a comprehensive analysis of earthworm field study data. Environ Toxicol Chem 2021;40:1750-1760. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Investment styles and the multiple testing of cross-sectional stock return predictability

The scheme of simultaneously testing many profitable strategies may conceal the hazard of data-snooping bias. However, certain portfolio returns are also more likely to exhibit codependency because of their same investment styles. Aiming at the phenomena of stock return anomalies, we consider two multiple testing approaches: one ignores the classification of portfolios and the other utilizes such information. The results based on grouped multiple testing suggest that the implied adjusted critical values for t-statistics may vary across investment styles, and several statistically significant portfolios may be unidentified under the pooled setup.

Detection of Local Differences in Spatial Characteristics Between Two Spatiotemporal Random Fields

Comparing the spatial characteristics of spatiotemporal random fields is often at demand. However, the comparison can be challenging due to the high-dimensional feature and dependency in the data. We develop a new multiple testing approach to detect local differences in the spatial characteristics of two spatiotemporal random fields by taking the spatial information into account. Our method adopts a two-component mixture model for location wise p-values and then derives a new false discovery rate (FDR) control, called mirror procedure, to determine the optimal rejection region. This procedure is robust to model misspecification and allows for weak dependency among hypotheses. To integrate the spatial heterogeneity, we model the mixture probability as well as study the benefit if any of allowing the alternative distribution to be spatially varying. An EM-algorithm is developed to estimate the mixture model and implement the FDR procedure. We study the FDR control and the power of our new approach both theoretically and numerically, and apply the approach to compare the mean and teleconnection pattern between two synthetic climate fields. Supplementary materials for this article are available online.

Sperm Quality Assessment in Honey Bee Drones.

The quality of honey bee drone semen is relevant in different contexts, ranging from colony productivity to pathology, toxicology and biodiversity preservation. Despite its importance, considerably less knowledge is available on this subject for the honey bee when compared to other domestic animal species. A proper assessment of sperm quality requires a multiple testing approach which discriminates between the different aspects of sperm integrity and functionality. Most studies on drone semen quality have only assessed a few parameters, such as sperm volume, sperm concentration and/or sperm plasma membrane integrity. Although more recent studies have focused on a broader variety of aspects of semen quality, some techniques currently used in vertebrates, such as computer-assisted sperm analysis (CASA) or multiparametric sperm quality testing, still remain to be developed in the honey bee. This may be attributed to the particular sperm morphology and physiology in this species, requiring the development of technologies specifically adapted to it. This article reviews the present knowledge of sperm quality in honey bee drones, highlighting its peculiarities and proposing future lines of research.

Natural variation of root lesion nematode antagonism in the biocontrol fungus Clonostachys rosea and identification of biocontrol factors through genome-wide association mapping.

Biological control is a promising approach to reduce plant diseases caused by nematodes to ensure high productivity in agricultural production. Large‐scale analyses of genetic variation in fungal species used for biocontrol can generate knowledge regarding interaction mechanisms that can improve efficacy of biocontrol applications. In this study, we performed a genome‐wide association study (GWAS) for in vitro antagonism against the root lesion nematode Pratylenchus penetrans in 53 previously genome re‐sequenced strains of the biocontrol fungus Clonostachys rosea. Nematode mortality in C. rosea potato dextrose broth (PDB) culture filtrates was highly variable and showed continuous variation (p < .001) between strains, indicating a polygenic inheritance. Twenty‐one strains produced culture filtrates with higher (p ≤ .05) nematode mortality compared with the PDB control treatment, while ten strains lowered (p ≤ .05) the mortality. The difference in in vitro antagonism against P. penetrans correlated with antagonism against the soybean cyst nematode Heterodera glycines, indicating lack of host specificity in C. rosea. An empirical Bayesian multiple hypothesis testing approach identified 279 single nucleotide polymorphism markers significantly (local false sign rate < 10–10) associated with the trait. Genes present in the genomic regions associated with nematicidal activity included several membrane transporters, a chitinase and genes encoding proteins predicted to biosynthesize secondary metabolites. Gene deletion strains of the predicted nonribosomal peptide synthetase genes nps4 and nps5 were generated and showed increased (p ≤ .001) fungal growth and conidiation rates compared to the wild type. Deletion strains also exhibited reduced (p < .001) nematicidal activity and reduced (p ≤ .05) biocontrol efficacy against nematode root disease and against fusarium foot rot on wheat. In summary, we show that the GWAS approach can be used to identify biocontrol factors in C. rosea, specifically the putative nonribosomal peptide synthetases NPS4 and NPS5.

Variational discriminant analysis with variable selection

A fast Bayesian method that seamlessly fuses classification and hypothesis testing via discriminant analysis is developed. Building upon the original discriminant analysis classifier, modelling components are added to identify discriminative variables. A combination of cake priors and a novel form of variational Bayes we call reverse collapsed variational Bayes gives rise to variable selection that can be directly posed as a multiple hypothesis testing approach using likelihood ratio statistics. Some theoretical arguments are presented showing that Chernoff-consistency (asymptotically zero type I and type II error) is maintained across all hypotheses. We apply our method on some publicly available genomics datasets and show that our method performs well in practice for its computational cost. An R package VaDA has also been made available on Github.