Fisher's Combined Probability Test Research Articles

Exercising prediction validation methods for analysis of quiet supersonic overflight

En route noise certification procedures for quiet supersonic aircraft are in development under the International Civil Aviation Organization for a proposed standard that could enable civil supersonic overland flight. While these quiet supersonic aircraft do not exist yet, methods are being developed and evaluated using existing N-wave empirical datasets and low-boom simulation datasets. One potential aspect of future certification methods is the ability to use a validated prediction model to predict the certification noise level under reference day weather conditions. Methods to validate predictions, such as the two-sample t-test, Fisher's combined probability test, two-sample Kolmogorov–Smirnov test, and equivalence testing, have been exercised and modified for this application. While some methods compare mean values, others compare values across the distribution, and a combination of methods can be used. The methods were applied to a subset of an N-wave dataset to demonstrate the applicability and identify potential areas for improvement. Atmospheric turbulence variability was included as a factor in these analyses through KZKFourier propagation simulations with parameters matching actual flights and weather conditions. Results are presented for six noise metrics (PL, ASEL, BSEL, DSEL, ESEL, and ISBAP), and plans to expand the analyses are discussed.

Sleep disturbance is associated with perturbations in immune-inflammatory pathways in oncology outpatients undergoing chemotherapy

Objective/backgroundSleep disturbance is a common problem in patients receiving chemotherapy. Purpose was to evaluate for perturbations in immune-inflammatory pathways between oncology patients with low versus very high levels of sleep disturbance. Patients/methodsSleep disturbance was evaluated using the General Sleep Disturbance Scale six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct sleep disturbance profiles. Pathway impact analyses were performed in two independent samples using gene expression data obtained from RNA sequencing (n = 198) and microarray (n = 162) technologies. Fisher's combined probability test was used to identify significantly perturbed pathways between Low versus Very High sleep disturbance classes. ResultsIn the RNA sequencing and microarray samples, 59.1% and 51.9% of patients were in the Very High sleep disturbance class, respectively. Thirteen perturbed pathways were related to immune-inflammatory mechanisms (i.e., endocytosis, phagosome, antigen processing and presentation, natural killer cell mediated cytotoxicity, cytokine-cytokine receptor interaction, apoptosis, neutrophil extracellular trap formation, nucleotide-binding and oligomerization domain-like receptor signaling, Th17 cell differentiation, intestinal immune network for immunoglobulin A production, T-cell receptor signaling, complement and coagulation cascades, and tumor necrosis factor signaling). ConclusionsFirst study to identify perturbations in immune-inflammatory pathways associated with very high levels of sleep disturbance in oncology outpatients. Findings suggest that complex immune-inflammatory interactions underlie sleep disturbance.

Perturbations in Neuroinflammatory Pathways Are Associated With a Worst Pain Profile in Oncology Patients Receiving Chemotherapy

Unrelieved pain occurs in 55% of cancer patients. Identification of molecular mechanisms for pain may provide insights into therapeutic targets. Purpose was to evaluate for perturbations in neuroinflammatory pathways between oncology patients with and without severe pain. Worst pain severity was rated using a 0 to 10 numeric rating scale six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct pain profiles. Pathway impact analyses were performed in two independent samples using gene expression data obtained from RNA sequencing (n = 192) and microarray (n = 197) technologies. Fisher's combined probability test was used to identify significantly perturbed pathways between None versus the Severe pain classes. In the RNA sequencing and microarray samples, 62.5% and 56.3% of patients were in the Severe pain class, respectively. Nine perturbed pathways were related to neuroinflammatory mechanisms (i.e., retrograde endocannabinoid signaling, gamma-aminobutyric acid synapse, glutamatergic synapse, Janus kinase-signal transducer and activator of transcription signaling, phagosome, complement and coagulation cascades, cytokine-cytokine receptor interaction, chemokine signaling, calcium signaling). First study to identify perturbations in neuroinflammatory pathways associated with severe pain in oncology outpatients. Findings suggest that complex neuroimmune interactions are involved in the maintenance of chronic pain conditions. PerspectiveIn this study that compared oncology patients with none versus severe pain, nine perturbed neuroinflammatory pathways were identified. Findings suggest that complex neuroimmune interactions are involved in the maintenance of persistent pain conditions.

A method to fuse multiphysics waveforms and improve predictive explosion detection: theory, experiment and performance

SUMMARYNatural and human-made sources of transient energy often emit multiple geophysical signatures that include mechanical and electromagnetic waveforms. We present a constructive method to fuse and evaluate statistics that we derive from such multiphysics waveforms that improves our capability to detect small, near-ground explosions over similar methods that consume single signature waveforms. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both hypotheses of a binary test on noisy data and provide researchers with the density functions required to forecast the ability of Fisher's Method to screen fused explosion signatures from noise. We apply this method against 12 d, multisignature explosion and noise records to show (1) that a fused multiphysics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions roughly 0.8 magnitude units lower than an acoustic emission, STA/LTA detector for the same detection probability and (2) that we can quantitatively predict how this fused, multiphysics statistic performs with Fisher's Method. Our work thereby offers a baseline method for predictive waveform fusion that supports multiphenomenological explosion monitoring (multiPEM) and is applicable to any binary testing problem in observational geophysics.

Cardiac MR elastography using reduced-FOV, single-shot, spin-echo EPI.

PurposeTo implement a reduced field of view (rFOV) technique for cardiac MR elastography (MRE) and to demonstrate the improvement in image quality of both magnitude images and post‐processed MRE stiffness maps compared to the conventional full field of view (full‐FOV) acquisition.MethodsWith Institutional Review Board approval, 17 healthy volunteers underwent both full‐FOV and rFOV cardiac MRE scans using 140‐Hz vibrations. Two cardiac radiologists blindly compared the magnitude images and stiffness maps and graded the images based on several image quality attributes using a 5‐point ordinal scale. Fisher's combined probability test was performed to assess the overall evaluation. The octahedral shear strain‐based signal‐to‐noise ratio (OSS‐SNR) and median stiffness over the left ventricular myocardium were also compared.ResultsOne volunteer was excluded because of an inconsistent imaging resolution during the exam. In the remaining 16 volunteers (9 males, 7 females), the rFOV scans outperformed the full‐FOV scans in terms of subjective image quality and ghosting artifacts in the magnitude images and stiffness maps, as well as the overall preference. The quantitative measurements showed that rFOV had significantly higher OSS‐SNR (median: 1.4 [95% confidence interval (CI): 1.2–1.5] vs. 2.1 [95% CI: 1.8–2.4]), P < 0.05) compared to full‐FOV. Although no significant change was found in the median myocardial stiffness between the 2 scans, we observed a decrease in the stiffness variation within the myocardium from 2.1 kPa (95% CI: [1.9, 2.3]) to 1.9 kPa (95% CI: [1.7, 2.0]) for full‐FOV and rFOV, respectively (P < 0.05) in a subgroup of 7 subjects with ghosting present in the myocardium.ConclusionThis pilot volunteer study demonstrated that rFOV cardiac MRE has the capability to reduce ghosting and to improve image quality in both MRE magnitude images and stiffness maps. Magn Reson Med 80:231–238, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

IFORM: Incorporating Find Occurrence of Regulatory Motifs.

Accurately identifying the binding sites of transcription factors (TFs) is crucial to understanding the mechanisms of transcriptional regulation and human disease. We present incorporating Find Occurrence of Regulatory Motifs (iFORM), an easy-to-use and efficient tool for scanning DNA sequences with TF motifs described as position weight matrices (PWMs). Both performance assessment with a receiver operating characteristic (ROC) curve and a correlation-based approach demonstrated that iFORM achieves higher accuracy and sensitivity by integrating five classical motif discovery programs using Fisher’s combined probability test. We have used iFORM to provide accurate results on a variety of data in the ENCODE Project and the NIH Roadmap Epigenomics Project, and the tool has demonstrated its utility in further elucidating individual roles of functional elements. Both the source and binary codes for iFORM can be freely accessed at https://github.com/wenjiegroup/iFORM. The identified TF binding sites across human cell and tissue types using iFORM have been deposited in the Gene Expression Omnibus under the accession ID GSE53962.

Fusing geophysical signatures of locally recorded surface explosions to improve blast detection

AbstractWe recorded acoustic, seismic and radio-frequency signatures of 70 solid charge (∼2–12 kg) surface explosions (shots) at local distances (0.1–1.5 km) to determine if such signals could be fused for blast monitoring. We observed that each geophysical signature was sufficiently repeatable between similar shots to be identifiable with multichannel correlation detectors. Using template signals from a large explosion, we then processed heavily contaminated data recording a smaller shot with these detectors, and missed or marginally detected the resultant target signals. By then fusing the p-values of these statistics through Fisher's combined probability test, we clearly identified the same explosion signals at thresholds consistent with the false alarm on noise rates of the correlation detectors. This resulting Fisher test thereby provided high-probability detections, zero false alarms and higher theoretical detection capability.

Statistical Method for Integrative Platform Analysis: Application to Integration of Proteomic and Microarray Data.

To perform integrative analysis on multiple genomic data sources, we propose to use Fisher's combined probability test for consolidated inference. The method combines the individual p-values from different data sources and constructs a chi-square test statistics for the overall significance. This method is valid to combine results across independent data sources. We further improve the method to accommodate the scenario that the data sources are dependent or the data samples are too small to obtain valid p-values through exact distributions. The proposed method is convenient to use in practice and is robust to distributional assumptions and small sample sizes.

TAFFY: incorporating tag information into a diffusion process for personalized recommendations

The last few years have witnessed an explosion of information caused by the exponential growth of the Internet and World Wide Web, which confronted us with information overload and brought about an era of big data, appealing for efficient personalized recommender systems to assist the screening of useful information from various sources. As for a recommender system with more than the fundamental object-user rating information, such accessorial information as tags can be exploited and integrated into final ranking lists to improve recommendation performance. However, although existing studies have demonstrated that tags, as the additional yet useful resource, can be designed to improve recommendation performance, most network-based approaches take users, objects and tags as two bipartite graphs, or a tripartite graph, and therefore overlook either the important information among homogeneous nodes in each sub-graph, or the bipartite relations between users, objects or tags. Moreover, recent studies have suggested that the filtration of weak relationships in networks may reasonably enhance recommendation performance of collaborative filtering methods, and it has also been demonstrated that approaches based on the diffusion processes could more effectively capture relationships between objects and users, hence exhibiting higher performance than a typical collaborative filtering method. Based on these understandings, we propose a data fusion approach that integrates historical and tag data towards personalized recommendations. Our method coverts historical and tag data into complex networks, resorts to a diffusion kernel to measure the strength of associations between users and objects, and adopts Fisher's combined probability test to obtain the statistical significance of such associations for personalized recommendations. We validate our approach via 10-fold cross-validation experiments. Results show that our method outperforms existing methods in not only the recommendation accuracy and diversity, but also retrieval performance. We further show the robustness of our method to related parameters.

Meta-analysis based on weighted ordered P-values for genomic data with heterogeneity.

BackgroundMeta-analysis has become increasingly popular in recent years, especially in genomic data analysis, due to the fast growth of available data and studies that target the same questions. Many methods have been developed, including classical ones such as Fisher’s combined probability test and Stouffer’s Z-test. However, not all meta-analyses have the same goal in mind. Some aim at combining information to find signals in at least one of the studies, while others hope to find more consistent signals across the studies. While many classical meta-analysis methods are developed with the former goal in mind, the latter goal has much more practicality for genomic data analysis.ResultsIn this paper, we propose a class of meta-analysis methods based on summaries of weighted ordered p-values (WOP) that aim at detecting significance in a majority of studies. We consider weighted versions of classical procedures such as Fisher’s method and Stouffer’s method where the weight for each p-value is based on its order among the studies. In particular, we consider weights based on the binomial distribution, where the median of the p-values are weighted highest and the outlying p-values are down-weighted. We investigate the properties of our methods and demonstrate their strengths through simulations studies, comparing to existing procedures. In addition, we illustrate application of the proposed methodology by several meta-analysis of gene expression data.ConclusionsOur proposed weighted ordered p-value (WOP) methods displayed better performance compared to existing methods for testing the hypothesis that there is signal in the majority of studies. They also appeared to be much more robust in applications compared to the rth ordered p-value (rOP) method (Song and Tseng, Ann. Appl. Stat. 2014, 8(2):777–800). With the flexibility of incorporating different p-value combination methods and different weighting schemes, the weighted ordered p-values (WOP) methods have great potential in detecting consistent signal in meta-analysis with heterogeneity.

Risk of Cardiovascular Disease from Antiretroviral Therapy for HIV: A Systematic Review

BackgroundRecent studies suggest certain antiretroviral therapy (ART) drugs are associated with increases in cardiovascular disease.PurposeWe performed a systematic review and meta-analysis to summarize the available evidence, with the goal of elucidating whether specific ART drugs are associated with an increased risk of myocardial infarction (MI).Data SourcesWe searched Medline, Web of Science, the Cochrane Library, and abstract archives from the Conference on Retroviruses and Opportunistic Infections and International AIDS Society up to June 2011 to identify published articles and abstracts.Study SelectionEligible studies were comparative and included MI, strokes, or other cardiovascular events as outcomes.Data ExtractionEligibility screening, data extraction, and quality assessment were performed independently by two investigators.Data SynthesisRandom effects methods and Fisher’s combined probability test were used to summarize evidence.FindingsTwenty-seven studies met inclusion criteria, with 8 contributing to a formal meta-analysis. Findings based on two observational studies indicated an increase in risk of MI for patients recently exposed (usually defined as within last 6 months) to abacavir (RR 1.92, 95% CI 1.51–2.42) and protease inhibitors (PI) (RR 2.13, 95% CI 1.06–4.28). Our analysis also suggested an increased risk associated with each additional year of exposure to indinavir (RR 1.11, 95% CI 1.05–1.17) and lopinavir (RR 1.22, 95% CI 1.01–1.47). Our findings of increased cardiovascular risk from abacavir and PIs were in contrast to four published meta-analyses based on secondary analyses of randomized controlled trials, which found no increased risk from cardiovascular disease.ConclusionAlthough observational studies implicated specific drugs, the evidence is mixed. Further, meta-analyses of randomized trials did not find increased risk from abacavir and PIs. Our findings that implicate specific ARTs in the observational setting provide sufficient evidence to warrant further investigation of this relationship in studies designed for that purpose.

Multivariate acoustic detection of small explosions using Fisher's combined probability test

A methodology for the combined acoustic detection and discrimination of explosions, which uses three discriminants, is developed for the purpose of identifying weak explosion signals embedded in complex background noise. By utilizing physical models for simple explosions that are formulated as statistical hypothesis tests, the detection/discrimination approach does not require a model for the background noise, which can be highly complex and variable in practice. Fisher's Combined Probability Test is used to combine the p-values from all multivariate discriminants. This framework is applied to acoustic data from a 400 g explosion conducted at Los Alamos National Laboratory.

Searching for signatures of cosmic string wakes in 21cm redshift surveys using Minkowski Functionals

Minkowski Functionals are a powerful tool for analyzing large scale structure, in particular if the distribution of matter is highly non-Gaussian, as it is in models in whichcosmic strings contribute to structure formation. Here we applyMinkowski functionals to 21cm maps which arise if structure is seeded by a scaling distribution of cosmic strings embeddded in background fluctuations, and then test for the statistical significance of the cosmic string signals using the Fisher combined probability test. We find that this method allows for detection of cosmic strings with Gμ > 5 × 10−8, which would be improvement over current limits by a factorof about 3.

A study of the effects of saliva stimulation by nizatidine on dry mouth symptoms of primary biliary cirrhosis

To elucidate the effect of saliva stimulation by nizatidine on oral symptoms of primary biliary cirrhosis (PBC) by administering it to PBC cases. From among 73 cases that had been definitively diagnosed as PBC at our hospital by February 2010, we selected 27 cases of PBC, 4 males and 23 females, as subjects. We obtained subjects' consent after giving them a full explanation of the administration of nizatidine. Nizatidine 150 mg was administered internally twice daily, after morning and evening meals. To observe changes in the quantity of saliva secreted, chewing gum tests were carried out four times: before the initial dose, and after 6 mo, 12 mo and 24 mo of administration. For subjective dry mouth symptoms, a visual analog scale (VAS) method was used to assess their feelings of oral dryness and eating difficulty, five times: before the initial dose, and after 1, 6, 12 and 24 mo of administration in 8 cases. The nutritional condition and the hepatic functional reserve were compared between before and after the nizatidine treatment. The result of a chewing gum test on the subjects before the administration of nizatidine showed that 50% produced less than 10 mL of saliva, i.e., the standard under which cases are considered to have hyposalivation. The results of these tests showed that the quantity of saliva secreted was 10.5 ± 6.8 mL before administration of nizatidine, 10.9 ± 6.0 mL after 6 mo, 10.6 ± 4.9 mL after 12 mo, and 11.8 ± 6.8 mL after 24 mo administration. Thus, there was a slowly increasing trend in the quantity of saliva in the whole group. The percentage of subjects with saliva production above 10 mL was 45.8% after 6 mo administration of nizatidine, that is, only a slight change from before its administration, but it was 64.3% after 12 mo, that is, a significant increase. The saliva secretion by subject patients was examined before the beginning of administration of nizatidine, 12 mo later, and 24 mo later, and Fisher's combined probability test was used to examine the results for increases in saliva secretion. The analysis yielded P values of 0.51 and 0.53 for 12 mo later and 24 mo later, respectively. Thus, although there was no statistically significant increase, it was confirmed that saliva secretion tended to increase. A VAS method was employed to study the intensities of subjective symptoms of oral dryness and eating difficulty. Almost every case indicated some improvement of subjective oral dryness on the VAS early in the administration, i.e., one month after. We also studied the effects of the administration of nizatidine on nutritional condition, hepatic functional reserve, and long-term prognosis of PBC. No significant improvements in cholinesterase (ChE) level, albumin (Alb) level, or Child-Pugh score were found during the period of observation from the beginning to the end of administration of nizatidine, nor in comparison with the non-administration group. A comparative analysis between before administration and 24 mo later yielded P values of 0.41 for Alb, 0.56 for ChE, and 0.59 for the Child-Pugh scores. It was confirmed that administering nizatidine to cases of PBC with dry mouth increased the secretion of saliva and improved the symptoms.

Brief Report: Large‐scale analysis of tumor necrosis factor α levels in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) disease manifestations are highly variable among patients, and the prevalence of individual clinical features differs significantly by ancestry. Serum tumor necrosis factor α (TNFα) levels are elevated in some SLE patients and may play a role in disease pathogenesis. The aim of this study was to look for associations between serum TNFα levels, clinical manifestations of SLE, autoantibodies, and serum interferon-α (IFNα) levels in a large multiancestral SLE cohort. We studied serum TNFα levels in 653 SLE patients (214 African Americans, 298 European Americans, and 141 Hispanic Americans). TNFα was measured using an enzyme-linked immunosorbent assay, and IFNα was measured with a functional reporter cell assay. Stratified and multivariate analyses were used to detect associations in each ancestral background separately, with meta-analysis when appropriate. Serum TNFα levels were significantly higher in SLE patients than in non-autoimmune disease controls (P < 5.0 × 10(-3) for each ancestral background). High serum TNFα levels were positively correlated with high serum IFNα levels when tested in the same sample across all ancestral backgrounds (odds ratio range 1.76-1.86, P = 4.8 × 10(-3) by Fisher's combined probability test). While serum TNFα levels alone did not differ significantly among SLE patients of different ancestral backgrounds, the proportion of patients with concurrently high levels of TNFα and IFNα was highest in African Americans and lowest in European Americans (P = 5.0 × 10(-3) ). Serum TNFα levels were not associated with autoantibodies, clinical criteria for the diagnosis of SLE, or age at the time of sampling. Serum TNFα levels are high in many SLE patients, and we observed a positive correlation between serum TNFα and IFNα levels. These data support a role for TNFα in the pathogenesis of SLE across all ancestral backgrounds and suggest important cytokine subgroups within the disease.

Investigating Meta-Approaches for Reconstructing Gene Networks in a Mammalian Cellular Context

The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT) and Fisher's inverse combined probability test (FICPT); and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR), Maximum Relevance Minimum Redundancy (MRNET), Relevance Network (RN) and Bayesian Network (BN). We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI) methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.

Identifying Consensus Disease Pathways in Parkinson's Disease Using an Integrative Systems Biology Approach

Parkinson's disease (PD) has had six genome-wide association studies (GWAS) conducted as well as several gene expression studies. However, only variants in MAPT and SNCA have been consistently replicated. To improve the utility of these approaches, we applied pathway analyses integrating both GWAS and gene expression. The top 5000 SNPs (p<0.01) from a joint analysis of three existing PD GWAS were identified and each assigned to a gene. For gene expression, rather than the traditional comparison of one anatomical region between sets of patients and controls, we identified differentially expressed genes between adjacent Braak regions in each individual and adjusted using average control expression profiles. Over-represented pathways were calculated using a hyper-geometric statistical comparison. An integrated, systems meta-analysis of the over-represented pathways combined the expression and GWAS results using a Fisher's combined probability test. Four of the top seven pathways from each approach were identical. The top three pathways in the meta-analysis, with their corrected p-values, were axonal guidance (p = 2.8E-07), focal adhesion (p = 7.7E-06) and calcium signaling (p = 2.9E-05). These results support that a systems biology (pathway) approach will provide additional insight into the genetic etiology of PD and that these pathways have both biological and statistical support to be important in PD.

Conservation of intron and intein insertion sites: implications for life histories of parasitic genetic elements

BackgroundInteins and introns are genetic elements that are removed from proteins and RNA after translation or transcription, respectively. Previous studies have suggested that these genetic elements are found in conserved parts of the host protein. To our knowledge this type of analysis has not been done for group II introns residing within a gene. Here we provide quantitative statistical support from an analyses of proteins that host inteins, group I introns, group II introns and spliceosomal introns across all three domains of life.ResultsTo determine whether or not inteins, group I, group II, and spliceosomal introns are found preferentially in conserved regions of their respective host protein, conservation profiles were generated and intein and intron positions were mapped to the profiles. Fisher's combined probability test was used to determine the significance of the distribution of insertion sites across the conservation profile for each protein. For a subset of studied proteins, the conservation profile and insertion positions were mapped to protein structures to determine if the insertion sites correlate to regions of functional activity. All inteins and most group I introns were found to be preferentially located within conserved regions; in contrast, a bacterial intein-like protein, group II and spliceosomal introns did not show a preference for conserved sites.ConclusionsThese findings demonstrate that inteins and group I introns are found preferentially in conserved regions of their respective host proteins. Homing endonucleases are often located within inteins and group I introns and these may facilitate mobility to conserved regions. Insertion at these conserved positions decreases the chance of elimination, and slows deletion of the elements, since removal of the elements has to be precise as not to disrupt the function of the protein. Furthermore, functional constrains on the targeted site make it more difficult for hosts to evolve immunity to the homing endonuclease. Therefore, these elements will better survive and propagate as molecular parasites in conserved sites. In contrast, spliceosomal introns and group II introns do not show significant preference for conserved sites and appear to have adopted a different strategy to evade loss.

Epigenetic silencers are enriched in dormant desert frog muscle

Green-striped burrowing frogs, Cyclorana alboguttata, survive droughts by entering a metabolic depression called aestivation, characterised by a reduction in resting oxygen consumption by 80%. Aestivation in C. alboguttata is manifest by transcriptional silencing of skeletal muscle bioenergetic genes, such as NADH ubiquinone oxidoreductase 1, ATP synthase and superoxide dismutase 2. In this study, we hypothesised that aestivation is associated with epigenetic change in frog muscle. We assessed mRNA transcript abundance of seven genes that code for proteins with established roles in epigenetically-mediated gene silencing [transcriptional co-repressor SIN3A, DNA (cytosine-5-) methyltransferase 1, methyl CpG binding protein 2, chromodomain helicase DNA binding protein 4, histone binding protein rbbp4, histone deacetylase 1 and nuclear receptor co-repressor 2] using qRT-PCR. These seven genes showed a modest (1.1-3.5-fold) but coordinated upregulation in 6-month aestivating muscle. This reached significance for SIN3A and DNA cytosine-5-methyltransferase 1 in standard pair-wise comparisons (p < 0.05), and the candidates as a whole when analysed by Fisher's combined probability test (p < 0.01). These data are consistent with the hypothesis that the transcriptional silencing and metabolic depression that occurs during seasonal dormancy are associated with chromatin remodelling, and present a novel example of an environmentally induced epigenetic modification in an adult vertebrate.

Combining probability from independent tests: the weighted Z‐method is superior to Fisher's approach

The most commonly used method in evolutionary biology for combining information across multiple tests of the same null hypothesis is Fisher's combined probability test. This note shows that an alternative method called the weighted Z-test has more power and more precision than does Fisher's test. Furthermore, in contrast to some statements in the literature, the weighted Z-method is superior to the unweighted Z-transform approach. The results in this note show that, when combining P-values from multiple tests of the same hypothesis, the weighted Z-method should be preferred.