Meta‐analysis approaches to combine multiple gene set enrichment studies

In the field of gene set enrichment analysis (GSEA), meta‐analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta‐Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta‐analysis approaches recently developed for genome‐wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size‐adjusted enrichment score based on a one‐sided Kolmogorov‐Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.     

PLNseq: a multivariate Poisson lognormal distribution for high‐throughput matched RNA‐sequencing read count data

High‐throughput RNA‐sequencing (RNA‐seq) technology provides an attractive platform for gene expression analysis. In many experimental settings, RNA‐seq read counts are measured from matched samples or taken from the same subject under multiple treatment conditions. The induced correlation therefore should be evaluated and taken into account in deriving tests of differential expression. We proposed a novel method ‘PLNseq’, which uses a multivariate Poisson lognormal distribution to model matched read count data. The correlation is directly modeled through Gaussian random effects, and inferences are made by likelihood methods. A three‐stage numerical algorithm is developed to estimate unknown parameters and conduct differential expression analysis. Results using simulated data demonstrate that our method performs reasonably well in terms of parameter estimation, DE analysis power, and robustness. PLNseq also has better control of FDRs than the benchmarks edgeR and DESeq2 in the situations where the correlation is different across the genes but can still be accurately estimated. Furthermore, direct evaluation of correlation through PLNseq enables us to develop a new and more powerful test for DE analysis. Application to a lung cancer study is provided to illustrate the practical utilities of our method. An R package implementing the method is also publicly available. Copyright © 2015 John Wiley & Sons, Ltd.     

Promoting similarity of model sparsity structures in integrative analysis of cancer genetic data

In profiling studies, the analysis of a single dataset often leads to unsatisfactory results because of the small sample size. Multi‐dataset analysis utilizes information of multiple independent datasets and outperforms single‐dataset analysis. Among the available multi‐dataset analysis methods, integrative analysis methods aggregate and analyze raw data and outperform meta‐analysis methods, which analyze multiple datasets separately and then pool summary statistics. In this study, we conduct integrative analysis and marker selection under the heterogeneity structure, which allows different datasets to have overlapping but not necessarily identical sets of markers. Under certain scenarios, it is reasonable to expect some similarity of identified marker sets – or equivalently, similarity of model sparsity structures – across multiple datasets. However, the existing methods do not have a mechanism to explicitly promote such similarity. To tackle this problem, we develop a sparse boosting method. This method uses a BIC/HDBIC criterion to select weak learners in boosting and encourages sparsity. A new penalty is introduced to promote the similarity of model sparsity structures across datasets. The proposed method has a intuitive formulation and is broadly applicable and computationally affordable. In numerical studies, we analyze right censored survival data under the accelerated failure time model. Simulation shows that the proposed method outperforms alternative boosting and penalization methods with more accurate marker identification. The analysis of three breast cancer prognosis datasets shows that the proposed method can identify marker sets with increased similarity across datasets and improved prediction performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Gene expression profiling in bone tissue of osteoporotic mice

Ovaricetomized (OVX) animals represent an optimal model to investigate bone loss in osteoporosis. To further elucidate the underlying mechanisms of decreased bone formation and increased bone resorption following OVX, we conducted gene expression profiling experiments using bone samples of ovariectomized C57BL/6J mice. Following OVX, genes involved in immune response, cell cycle regulation, growth, apoptosis and bone resorption were upregulated, while genes that are important for regular cell processes, mitosis, metabolism of carbohydrates, extracellular matrix structure, angiogenesis, skeletal development and morphogenesis were downregulated. Among bone specific genes we observed upregulation of interleukin 7 (IL-7), IL-7 receptor and matrix metallopeptidase 8, while genes such as transforming growth factor-beta 3, procollagen type I and procollagen type VI exhibited marked decrease in expression. We also observed downregulation of two genes, parathyroid hormone receptor 1 and WD repeat domain 5, that are involved in skeletal development but were not previously reported to be altered in osteoporosis. We further performed gene set enrichment analysis (GSEA) in order to calculate enrichment of pathways specifically altered in murine bones following ovariectomy. In conclusion, OVX greatly influences expression of various genes involved in diverse biological processes confirming the notion that numerous pathways play an important role in pathophysiology of osteoporosis.     

Integrating genome‐wide association study summaries and element‐gene interaction datasets identified multiple associations between elements and complex diseases

Chemical elements are closely related to human health. Extensive genomic profile data of complex diseases offer us a good opportunity to systemically investigate the relationships between elements and complex diseases/traits. In this study, we applied gene set enrichment analysis (GSEA) approach to detect the associations between elements and complex diseases/traits though integrating element‐gene interaction datasets and genome‐wide association study (GWAS) data of complex diseases/traits. To illustrate the performance of GSEA, the element‐gene interaction datasets of 24 elements were extracted from the comparative toxicogenomics database (CTD). GWAS summary datasets of 24 complex diseases or traits were downloaded from the dbGaP or GEFOS websites. We observed significant associations between 7 elements and 13 complex diseases or traits (all false discovery rate (FDR) < 0.05), including reported relationships such as aluminum vs. Alzheimer's disease (FDR = 0.042), calcium vs. bone mineral density (FDR = 0.031), magnesium vs. systemic lupus erythematosus (FDR = 0.012) as well as novel associations, such as nickel vs. hypertriglyceridemia (FDR = 0.002) and bipolar disorder (FDR = 0.027). Our study results are consistent with previous biological studies, supporting the good performance of GSEA. Our analyzing results based on GSEA framework provide novel clues for discovering causal relationships between elements and complex diseases.     

Pathway‐Based Approaches for Sequencing‐Based Genome‐Wide Association Studies

For analyzing complex trait association with sequencing data, most current studies test aggregated effects of variants in a gene or genomic region. Although gene‐based tests have insufficient power even for moderately sized samples, pathway‐based analyses combine information across multiple genes in biological pathways and may offer additional insight. However, most existing pathway association methods are originally designed for genome‐wide association studies, and are not comprehensively evaluated for sequencing data. Moreover, region‐based rare variant association methods, although potentially applicable to pathway‐based analysis by extending their region definition to gene sets, have never been rigorously tested. In the context of exome‐based studies, we use simulated and real datasets to evaluate pathway‐based association tests. Our simulation strategy adopts a genome‐wide genetic model that distributes total genetic effects hierarchically into pathways, genes, and individual variants, allowing the evaluation of pathway‐based methods with realistic quantifiable assumptions on the underlying genetic architectures. The results show that, although no single pathway‐based association method offers superior performance in all simulated scenarios, a modification of Gene Set Enrichment Analysis approach using statistics from single‐marker tests without gene‐level collapsing (weighted Kolmogrov‐Smirnov [WKS]‐Variant method) is consistently powerful. Interestingly, directly applying rare variant association tests (e.g., sequence kernel association test) to pathway analysis offers a similar power, but its results are sensitive to assumptions of genetic architecture. We applied pathway association analysis to an exome‐sequencing data of the chronic obstructive pulmonary disease, and found that the WKS‐Variant method confirms associated genes previously published.     

多组学数据整合分析的统计方法研究进展

复杂疾病往往是由环境因素、遗传因素（多个组学层面）共同作用所致。全面分析不同分子水平的信息对认识疾病的发生发展至关重要。多组学数据整合分析能够提高特征筛选检验效能、改善疾病预测精度。本文从统计学角度出发,对多组学数据整合分析的统计理论方法研究进展做一述评。     

Genistein reduced insulin resistance index through modulating lipid metabolism in ovariectomized rats

Postmenopausal women are at higher risk for obesity and insulin resistance due to the decline of estrogen, but genistein, a phytoestrogen, may reduce the risks of these diet-related diseases. In this study, we hypothesized that supplemental genistein has beneficial effects on insulin resistance in an ovariectomized rat model by modulating lipid metabolism. Three weeks after a sham surgery (sham) or an ovariectomy (OVX), ovariectomized Sprague-Dawley rats were placed on a diet containing 0 (OVX group) or 0.1% genistein for 4 weeks. The sham rats were fed a high-fat diet containing 0% genistein and served as the control group (sham group). The ovariectomized rats showed increases in body weight and insulin resistance index, but genistein reduced insulin resistance index and the activity of hepatic fatty acid synthetase. Genistein was also associated with increased activity of succinate dehydrogenase and carnitine palmitoyltransferase and the rate of β-oxidation in the fat tissue of rats. The ovariectomized rats given genistein had smaller-sized adipocytes. Using gene-set enrichment analysis (GSEA) of microarray data, we found that a number of gene sets of fatty acid metabolism, insulin resistance, and oxidative stress were differentially expressed by OVX and reversed by genistein. This systemic approach of GSEA enables the identification of such consensus between the gene expression changes and phenotypic changes caused by OVX and genistein supplementation. Genistein treatment could help reduce insulin resistance through the amelioration of OVX-induced metabolic dysfunction, and the GSEA approach may be useful in proposing putative targets related to insulin resistance.