Prioritizing Longitudinal Gene–Environment Interactions Using an FDR-Assisted Robust Bayesian Linear Mixed Model

BackgroundGlaucoma is a neurodegenerative condition that affects the eye and is associated with increased intraocular pressure. Intraocular pressure is the fluid pressure inside the eye and its disturbance often is implicated in the development of pathologies such as glaucoma, uveitis and retinal detachment. The aim of the present study was to identify factors that affect the longitudinal intraocular pressure of glaucoma patients attending the ophthalmology clinic at Felege Hiwot Comprehensive Specialized Hospital, Bahir Dar, Ethiopia, using a Bayesian linear mixed model analysis. MethodsIn a longitudinal study with data obtained from glaucoma patients admitted to Felege Hiwot Hospital, the measurement of intraocular pressure change was applied. The study subjects were enrolled in the period between 1st January 2016 and 1st January 2020 and a total of 328 patients were selected for the study. Data were explored using descriptive statistics and individual and mean profile plots throughout study time. A Bayesian linear mixed model for the longitudinal data was used along with their model comparison, model estimation, model diagnosis and missing data analysis. ResultsThe analysis included 328 individuals with 9 for maximum and 2 for minimum repeated measurements of intraocular pressure change, including the baseline. From the Bayesian linear mixed model variables, observation time, age, place of residence, gender, the cup-disk ratio of patients, type of medication (like Pilocarpin, Timolol with Pilocarpin, Timolol with Diamox with Pilocarpin), and blood pressure of the glaucoma patients significantly affected the intraocular pressure changes over time. However, the type of medication (Diamox and Timolol with Diamox) did not affect the intraocular pressure changes over time. ConclusionBased on the Bayesian linear mixed model analysis, we found that the predictor variables of age, blood pressure, family history, residence, gender, diabetic disease, treatment duration, stages of glaucoma, type of medication and cup-disk ratio significantly affected the average intraocular pressure and had a positive association with the responses of intraocular pressure of glaucoma patients. Furthermore, the type of medication was statistically significant and negatively associated with the responses to intraocular pressure. RecommendationWe recommend the health professionals to give more attention to the type of medication especially Timolol with Pilocarpin, Timolol with Diamox and Timolol with Diamox with Pilocarpine. And taking the combination with the other type of medication minimizes the risk of blindness and intraocular pressure.

The multiplicity problem has become increasingly important in genetic studies as the capacity for high-throughput genotyping has increased. The control of False Discovery Rate (FDR) (Benjamini and Hochberg. [1995] J. R. Stat. Soc. Ser. B 57:289-300) has been adopted to address the problems of false positive control and low power inherent in high-volume genome-wide linkage and association studies. In many genetic studies, there is often a natural stratification of the m hypotheses to be tested. Given the FDR framework and the presence of such stratification, we investigate the performance of a stratified false discovery control approach (i.e. control or estimate FDR separately for each stratum) and compare it to the aggregated method (i.e. consider all hypotheses in a single stratum). Under the fixed rejection region framework (i.e. reject all hypotheses with unadjusted p-values less than a pre-specified level and then estimate FDR), we demonstrate that the aggregated FDR is a weighted average of the stratum-specific FDRs. Under the fixed FDR framework (i.e. reject as many hypotheses as possible and meanwhile control FDR at a pre-specified level), we specify a condition necessary for the expected total number of true positives under the stratified FDR method to be equal to or greater than that obtained from the aggregated FDR method. Application to a recent Genome-Wide Association (GWA) study by Maraganore et al. ([2005] Am. J. Hum. Genet. 77:685-693) illustrates the potential advantages of control or estimation of FDR by stratum. Our analyses also show that controlling FDR at a low rate, e.g. 5% or 10%, may not be feasible for some GWA studies.

The methodological advancement in microarray data analysis on the basis of false discovery rate (FDR) control, such as the q-value plots, allows the investigator to examine the FDR from several perspectives. However, when FDR control at the "customary" levels 0.01, 0.05, or 0.1 does not provide fruitful findings, there is little guidance for making the trade off between the significance threshold and the FDR level by sound statistical or biological considerations. Thus, meaningful statistical significance criteria that complement the existing FDR methods for large-scale multiple tests are desirable. Three statistical significance criteria, the profile information criterion, the total error proportion, and the guide-gene driven selection, are developed in this research. The first two are general significance threshold criteria for large-scale multiple tests; the profile information criterion is related to the recent theoretical studies of the connection between FDR control and minimax estimation, and the total error proportion is closely related to the asymptotic properties of FDR control in terms of the total error risk. The guide-gene driven selection is an approach to combining statistical significance and the existing biological knowledge of the study at hand. Error properties of these criteria are investigated theoretically and by simulation. The proposed methods are illustrated and compared using an example of genomic screening for novel Arf gene targets. Operating characteristics of q-value and the proposed significance threshold criteria are investigated and compared in a simulation study that employs a model mimicking a gene regulatory pathway. A guideline for using these criteria is provided. Splus/R code is available from the corresponding author upon request.

ABSTRACTIn many scientific fields, it is interesting and important to determine whether an observed data stream comes from a prespecified model or not, particularly when the number of data streams is of large scale, where multiple hypotheses testing is necessary. In this article, we consider large-scale model checking under certain dependence among different data streams observed at the same time. We propose a false discovery rate (FDR) control procedure to check those unusual data streams. Specifically, we derive an approximation of false discovery and construct a point estimate of FDR. Theoretical results show that, under some mild assumptions, our proposed estimate of FDR is simultaneously conservatively consistent with the true FDR, and hence it is an asymptotically strong control procedure. Simulation comparisons with some competing procedures show that our proposed FDR procedure behaves better in general settings. Application of our proposed FDR procedure is illustrated by the StarPlus fMRI data.

Recent advances in long-read sequencing technologies have empowered the detection of structural variations (SVs) associated with genetic diseases. Despite the availability of numerous SV callers and efforts to merge SVs from multiple tools, there remains limited research on quantifying the confidence levels for the reported results. In this work, we propose a Bayesian integration model that combines SV calls from different tools. Notably, we introduce an approach for false discovery rate (FDR) control and provide a quantitative measure for the merged SVs. Our model can handle cases where certain tools lack quality scores, showcasing its flexibility in incorporating additional tools. Through extensive simulation studies, we evaluate the performance of our method under various conditions, demonstrating the FDR estimation accuracy and improved F1 score. Furthermore, we validate our model using simulated human genome sequencing data and the HG002dataset.

The recent RNA-seq technology is an attractive method to study gene expression. One of the most important goals in RNA-seq data analysis is to detect genes differentially expressed across treatments. Although several statistical methods have been published, there are no theoretical justifications for whether these methods are optimal or how to search for the optimal test. Furthermore, most proposed tests are designed for testing whether the mean expression levels are exactly the same or not across treatments, whereas sometimes, biologists are interested in detecting genes with expression changes larger than a certain threshold. Another issue with current methods is that the false discovery rate (FDR) control is not well studied. In this manuscript, we propose a test to address all the above issues. Under model assumptions, we derive an optimal test that achieves the maximum of average power among those that control FDR at the same level. We also provide an approximated version, the approximated most average powerful (AMAP) test, for practical implementation. The proposed method allows for testing null hypotheses that are much more general than the ones most previous studies have considered, and it leads to a natural way of controlling the FDR. Through simulation studies, we show that our test has a higher power than other methods, including the widely-used edgeR, DESeq, and baySeq methods, as well as better FDR control than two other FDR control procedures commonly used in practice. For demonstration, we also apply the proposed method to a real RNA-seq dataset obtained from maize.

The incidence of esophageal adenocarcinoma (EA) has increased approximately 500% in Western countries over the last four decades. The rapidly increasing incidence and sporadically development feature of EA suggest that gene-environment (G-E) interactions dominate the etiology. However, how G-E contributes to EA carcinogenesis is still poorly understood. We used a case-only approach to test the effect of G-E interactions in the occurrence of EA. 1330 functional and/or tagging SNPs selected from 354 cancer-associated genes were genotyped in 335 Caucasian EA cases. G-E (gastroesophageal reflux symptoms, GERD; BMI; and smoking) interactions were assessed by a two-step approach. First, we applied random forest (RF) to screen for important SNPs that had main effects as well as interaction effects. Second, we used case-only logistic regression (LR) models to estimate the G-E interaction effect, adjusting for covariates and false-discovery rate (FDR). RF analyses identified three sets of SNPs (94 SNPs-GERD, 38 SNPs-smoking, and 44 SNPs-BMI, respectively) that had the highest importance scores and lowest classification error rates. Further case-only LR analysis revealed that multiple interaction markers were significantly associated with EA risk: GERD*rs2237051 of EGF(OR = 1.39, P = 3.50E-07), GERD*rs2440 of XRCC5 (OR = 1,80, P = 0.0008), GERD*rs2237051*rs2440 (OR = 1.85, P = 0.004), smoking*rs10842514 of KRAS (OR = 0.85, P = 0.012), BMI*rs2305742 of IL12RB1 (OR = 0.84, P = 0.003), BMI*rs11568820 of VDR (OR = 0.97, P = 0.0005), BMI*rs11244142 of ABL1 (OR = 1.47, P = 2.09E-05), BMI*rs2800975 of XPA*rs743572 of CYP17A (OR = 3.62, P = 0.0009), and BMI*rs11244142*rs20547 of IL13 (OR = 1.89, P = 0.023). These results indicate that genetic variability may contribute to EA development through interactions with environmental factors. Our data also suggest that G-E interactions may be G- and E-specific in modulating the risk of EA. Supported by grants: NIH grants CA92824, CA74386, CA90578, and CA119650); Flight Attendant Medical Research Institute (FAMRI) grant 062459_YCSA; the Kevin Jackson Memorial Fund and Alan Brown Chair of Molecular Genomics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3742. doi:10.1158/1538-7445.AM2011-3742

False discovery rate (FDR) control serves as a critical safeguard in high-dimensional statistical analysis, increasing reliability and ensuring reproducibility of discoveries. Although extensively studied for real-valued data, FDR methodologies remain underdeveloped for the complex-valued data domain despite their importance in signal processing, physics, and engineering applications. This work confronts a central challenge in FDR-controlled analysis: settings in which the data contain groups of highly correlated variables. To achieve this, we advance our Complex-Valued Terminating-Random Experiments (CT-Rex) framework by developing the CT-Rex+GVS, a grouped variable selector. The method comes in two variants: the elastic net <tex>$(E N)$</tex> and the informed elastic net (IEN), both with isotropic and phase agnostic regularization. We validate the framework by benchmarking it in the selection of complex-valued grouped variables for linear regression and in the estimation of the singlesnapshot direction-of-arrival (DOA) based on compressed sensing using uniform linear arrays (ULA). The conducted numerical experiments confirm the FDR control property and demonstrate favorable performance compared to existing approaches.

The introduction of laboratory methods to animal dietary studies has allowed researchers to obtain results with accuracy and precision, not possible with observational techniques. For example, DNA barcoding, or the identification of prey with taxon-specific DNA sequences, allows researchers to classify digested prey tissues to the species-level, while stable isotope analysis paired with Bayesian mixing models can quantify dietary contributions by comparing a consumer's isotopic values to those derived from their prey. However, DNA-based methods are currently only able to classify, but not quantify, the taxa present in a diet sample, while stable isotope analysis can only quantify dietary taxa that are identified a priori as prey isotopic values are a result of life history traits, not phylogenetic relatedness. Recently, researchers have begun to couple these techniques in dietary studies to capitalize on the reciprocal benefits and drawbacks offered by each approach, with some even integrating DNA-based results directly into Bayesian mixing models as informative priors. As the informative priors used in these models must represent known dietary compositions (e.g., percentages of prey biomasses), researchers have scaled the DNA-based frequency of occurrence of major prey groups so that their normalized frequency of occurrence sums to 100%. Unfortunately, such an approach is problematic as priors stemming from binomial, DNA-based data do not truly reflect quantitative information about the consumer's diet and may skew the posterior distribution of prey quantities as a result. Therefore, we present a novel approach to incorporate DNA-based dietary information into Bayesian stable isotope mixing models that preserves the binomial nature of DNA-based results. This approach uses community-wide frequency of occurrence or logistic regression-based estimates of prey occurrence to dictate the probability that each prey group is included in each mixing model iteration, and, in turn, the probability that each iteration's results are included in the posterior distribution of prey composition possibilities. Here, we demonstrate the utility of this method by using it to quantify the prey composition of nestling Louisiana waterthrush (Parkesia motacilla).

Incorporation of diet information derived from Bayesian stable isotope mixing models into mass-balanced marine ecosystem models: A case study from the Marennes-Oléron Estuary, France

설문조사를 실시할 때 응답자가 설문조사의 일부 문항에 대하여 응답하지 않는 경우 항목무응답이 발생한다. 무응답이 발생한 자료를 제외하고 완전하게 응답된 자료 만에 근거한 분석은 분석 결과에 편의가 발생할 수 있으므로, 이를 채워 넣어 완전한 형태의 자료로 분석하기 위해서 무응답 대체가 흔히 사용되고 있으며 여러 가지 무응답 대체 기법들을 비교하는 연구들도 많이 존재한다. 패널조사 연구는 연구 대상 패널에 대하여 정해진 시간에 따라 반복적으로 동일한 설문 문항에 대하여 응답을 조사하여 시간에 따른 변화를 살펴보는 조사 방법을 나타낸다. 패널조사 자료의 항목 무응답을 대체할 때 이전 시점의 응답 자료가 존재한다면 이를 포함하여 대체를 실시하는 것이 바람직한 것으로 여겨져 왔으나 이에 관한 직접적인 연구는 찾기 힘들다. 따라서 본 연구에서는 패널자료에서 이전 시점의 정보를 고려하지 않고 대체를 실시하는 방법과 이전 시점의 정보를 활용하여 대체하는 방법들 중에서 어느 대체 방법이 보다 적절한 대체를 제공하는지 살펴보았다. 특히 이전 시점의 응답 정보를 이용하는 방법인 비대체, 선형혼합모형을 이용한 대체와 선형혼합모형에 근거한 베이지안 대체 방법을 고려하였고, 이를 이전 시점의 정보를 고려하지 않는 대체 방법들 중 흔히 사용되는 평균대체, 핫덱대체 방법과 비교하였다. 모의실험 결과 선형혼합모형에 근거한 베이지 안 대체 방법이 다른 대체 방법에 비해 무응답 비율이 높아지더라도 편의도 작으며 평균에 관한 95% 신뢰구간의 포함률도 높게 나타나서 가장 좋은 대체 방법으로 확인되었다. When conducting a survey, item nonresponse occurs if the respondent does not respond to some items. Since analysis based only on completely observed data may cause biased results, imputation is often conducted to analyze data in its complete form. The panel study is a survey method that examines changes of responses over time. In panel studies, there has been a preference for using information from response values of previous waves when the imputation of item nonresponses is performed; however, limited research has been conducted to support this preference. Therefore, this study compares the performance of imputation methods according to whether or not information from previous waves is utilized in the panel study. Among imputation methods that utilize information from previous responses, we consider ratio imputation, imputation based on the linear mixed model, and imputation based on the Bayesian linear mixed model approach. We compare the results from these methods against the results of methods that do not use information from previous responses, such as mean imputation and hot deck imputation. Simulation results show that imputation based on the Bayesian linear mixed model performs best and yields small biases and high coverage rates of the 95% confidence interval even at higher nonresponse rates.

BackgroundThe Significance Analysis of Microarrays (SAM) is a popular method for detecting significantly expressed genes and controlling the false discovery rate (FDR). Recently, it has been reported in the literature that the FDR is not well controlled by SAM. Due to the vast application of SAM in microarray data analysis, it is of great importance to have an extensive evaluation of SAM and its associated R-package (sam2.20).ResultsOur study has identified several discrepancies between SAM and sam2.20. One major difference is that SAM and sam2.20 use different methods for estimating FDR. Such discrepancies may cause confusion among the researchers who are using SAM or are developing the SAM-like methods. We have also shown that SAM provides no meaningful estimates of FDR and this problem has been corrected in sam2.20 by using a different formula for estimating FDR. However, we have found that, even with the improvement sam2.20 has made over SAM, sam2.20 may still produce erroneous and even conflicting results under certain situations. Using an example, we show that the problem of sam2.20 is caused by its use of asymmetric cutoffs which are due to the large variability of null scores at both ends of the order statistics. An obvious approach without the complication of the order statistics is the conventional symmetric cutoff method. For this reason, we have carried out extensive simulations to compare the performance of sam2.20 and the symmetric cutoff method. Finally, a simple modification is proposed to improve the FDR estimation of sam2.20 and the symmetric cutoff method.ConclusionOur study shows that the most serious drawback of SAM is its poor estimation of FDR. Although this drawback has been corrected in sam2.20, the control of FDR by sam2.20 is still not satisfactory. The comparison between sam2.20 and the symmetric cutoff method reveals that the relative performance of sam2.20 to the symmetric cutff method depends on the ratio of induced to repressed genes in a microarray data, and is also affected by the ratio of DE to EE genes and the distributions of induced and repressed genes. Numerical simulations show that the symmetric cutoff method has the biggest advantage over sam2.20 when there are equal number of induced and repressed genes (i.e., the ratio of induced to repressed genes is 1). As the ratio of induced to repressed genes moves away from 1, the advantage of the symmetric cutoff method to sam2.20 is gradually diminishing until eventually sam2.20 becomes significantly better than the symmetric cutoff method when the differentially expressed (DE) genes are either all induced or all repressed genes. Simulation results also show that our proposed simple modification provides improved control of FDR for both sam2.20 and the symmetric cutoff method.

Multivariate statistics are often available as well as necessary in hypothesis tests. We study how to use such statistics to control not only false discovery rate (FDR) but also positive FDR (pFDR) with good power. We show that FDR can be controlled through nested regions of multivari- ate p-values of test statistics. If the distributions of the test statistics are known, then the regions can be constructed explicitly to achieve FDR con- trol with maximum power among procedures satisfying certain conditions. On the other hand, our focus is where the distributions are only partially known. Under certain conditions, a type of nested regions are proposed and shown to attain (p)FDR control with asymptotically maximum power as the pFDR control level approaches its attainable limit. The procedure based on the nested regions is compared with those based on other nested regions that are easier to construct as well as those based on more straightforward combinations of the test statistics. AMS 2000 subject classifications: Primary 62G10, 62H15; secondary 62G20.

Evaluation of the effect of chance correlations on variable selection using Partial Least Squares-Discriminant Analysis

Gastroesophageal reflux disease (GERD), higher body mass index (BMI), smoking, and genetic variants in angiogenic pathway genes have been individually associated with increased risk of esophageal adenocarcinoma. However, how angiogenic gene polymorphisms and environmental factors jointly affect esophageal adenocarcinoma development remains unclear. By using a case-only design (n = 335), the authors examined interactions between 141 functional/tagging angiogenic single nucleotide polymorphisms (SNPs) and environmental factors (GERD, BMI, smoking) in modulating esophageal adenocarcinoma risk. Gene-environment interactions were assessed by a 2-step approach. First, the authors applied random forest to screen for important SNPs that had either main or interaction effects. Second, they used case-only logistic regression to assess the effects of gene-environment interactions on esophageal adenocarcinoma risk, adjusting for covariates and false-discovery rate. Random forest analyses identified 3 sets of SNPs (17 SNPs-GERD, 26 SNPs-smoking, and 34 SNPs-BMI) that had the highest importance scores. In subsequent logistic regression analyses, interactions between 2 SNPs (rs2295778 of HIF1AN, rs13337626 of TSC2) and GERD, 2 SNPs (rs2295778 of HIF1AN, rs2296188 of VEGFR1) and smoking, and 7 SNPs (rs2114039 of PDGRFA, rs2296188 of VEGFR1, rs11941492 of VEGFR1, rs17708574 of PDGFRB, rs7324547 of VEGFR1, rs17619601 of VEGFR1, and rs17625898 of VEGFR1) and BMI were significantly associated with esophageal adenocarcinoma development (all false-discovery rates ≤0.10). Moreover, these interactions tended to have SNP dose-response effects for increased esophageal adenocarcinoma risk with increasing number of combined risk genotypes. These findings suggest that genetic variations in angiogenic genes may modify esophageal adenocarcinoma susceptibility through interactions with environmental factors in an SNP dose-response manner.