Predict Gene Expression Levels Research Articles

Association of Gene Expression and Tremor Network Structure.

Transcriptomic changes in the essential tremor (ET)-associated cerebello-thalamo-cortical "tremor network" and their association to brain structure have not been investigated. The aim was to characterize molecular changes associated with network-level imaging-derived phenotypes (IDP) found in ET. We performed an imaging-transcriptomic study in British adults using imaging-genome-wide association study summary statistics (UK Biobank "BIG40" cohort; n = 33,224, aged 40-69 years). We imputed imaging-transcriptomic associations for 184 IDPs and analyzed functional enrichment of gene modules and aggregate network-level phenotypes. Validation was performed in cerebellar-tissue RNA-sequencing data from ET patients and controls (n = 55). Among 237,896 individual predicted gene expression levels for 6063 unique genes/transcripts, we detected 2269 genome-wide significant associations (Bonferroni P < 2.102e-7, 0.95%). These were concentrated in intracellular volume fraction measures of white matter pathways and in genes with putative links to tremor (MAPT, ARL17A, KANSL1, SPPL2C, LRRC37A4P, PLEKHM1, and FMNL1). Whole-tremor-network cortical thickness was associated with a gene module linked to mitochondrial organization and protein quality control (r = 0.91, P = 2e-70), whereas white-gray T1-weighted magnetic resonance imaging (MRI) contrast in the tremor network was associated with a gene module linked to sphingolipid synthesis and ethanolamine metabolism (r = -0.90, P = 2e-68). Imputed association effect sizes and RNA-sequencing log-fold change in the validation dataset were significantly correlated for cerebellar peduncular diffusion MRI phenotypes, and there was a close overlap of significant associations between both datasets for gray matter phenotypes (χ2 = 6.40, P = 0.006). The identified genes and processes are potential treatment targets for ET, and our results help characterize molecular changes that could in future be used for patient treatment selection or prognosis prediction. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Unraveling the dynamic transcriptomic changes during the dimorphic transition of Talaromyces marneffei through time-course analysis.

Systemic dimorphic fungi pose a significant public health challenge, causing over one million new infections annually. The dimorphic transition between saprophytic mycelia and pathogenic yeasts is strongly associated with the pathogenesis of dimorphic fungi. However, despite the dynamic nature of dimorphic transition, the current omics studies focused on dimorphic transition primarily employ static strategies, partly due to the lack of suitable dynamic analytical methods. We conducted time-course transcriptional profiling during the dimorphic transition of Talaromyces marneffei, a model organism for thermally dimorphic fungi. To capture non-uniform and nonlinear transcriptional changes, we developed DyGAM-NS (dynamic optimized generalized additive model with natural cubic smoothing). The performance of DyGAM-NS was evaluated by comparison with seven other commonly used time-course analysis methods. Based on dimorphic transition induced genes (DTIGs) identified by DyGAM-NS, cluster analysis was utilized to discern distinct gene expression patterns throughout dimorphic transitions of T. marneffei. Simultaneously, a gene expression regulatory network was constructed to probe pivotal regulatory elements governing the dimorphic transitions. By using DyGAM-NS, model, we identified 5,223 DTIGs of T. marneffei. Notably, the DyGAM-NS model showcases performance on par with or superior to other commonly used models, achieving the highest F1 score in our assessment. Moreover, the DyGAM-NS model also demonstrates potential in predicting gene expression levels throughout temporal processes. The cluster analysis of DTIGs suggests divergent gene expression patterns between mycelium-to-yeast and yeast-to-mycelium transitions, indicating the asymmetrical nature of two transition directions. Additionally, leveraging the identified DTIGs, we constructed a regulatory network for the dimorphic transition and identified two zinc finger-containing transcription factors that potentially regulate dimorphic transition in T. marneffei. Our study elucidates the dynamic transcriptional profile changes during the dimorphic transition of T. marneffei. Furthermore, it offers a novel perspective for unraveling the underlying mechanisms of fungal dimorphism, emphasizing the importance of dynamic analytical methods in understanding complex biological processes.

Personal transcriptome variation is poorly explained by current genomic deep learning models

Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the reference genome, their ability to explain expression variation between individuals due to cis-regulatory genetic variants remains largely unexplored. Here, we evaluate four state-of-the-art models on paired personal genome and transcriptome data and find limited performance when explaining variation in expression across individuals. In addition, models often fail to predict the correct direction of effect of cis-regulatory genetic variation on expression.

Abstract LB045: Transcriptome-wide association study identifies susceptibility loci and genes for lung cancer risk

Abstract Objective: Genome-wide association studies (GWAS) have identified more than 50 loci for lung cancer risk. However, susceptibility genes and the underlying mechanisms for these risk loci remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify susceptibility genes for lung cancer. Methods: Transcriptome data from normal lung tissue and whole-genome sequencing data from 444 participants of European ancestry in the Genotype-Tissue Expression (GTEx, version 8) were used to build lung-tissue models to predict gene expression levels. Lung-tissue prediction models were successfully established for 10,802 genes with a model performance r &amp;gt; 0.1 and P &amp;lt; 0.05. We also built joint-tissue models using the joint-tissue imputation (JTI) framework, which leverages transcriptome data from lung tissue and 48 other tissue types from the 444 participants in GTEx. Joint-tissue models were successfully established for 12,629 genes with a model performance r &amp;gt; 0.1 and P &amp;lt; 0.05. These prediction models were applied to the GWAS data comprised of 29,266 lung cancer cases and 56,450 controls of European ancestry, in order to evaluate genetically predicted gene expression levels in association with lung cancer risk. Results: We found 44 genes whose genetically predicted expression levels were significantly associated with overall lung cancer risk at the Bonferroni correction significance threshold. Among the 44 genes, four were located at least 500kb away from any of the leading variants identified previously in GWAS. Of these four genes, only the CCHCR1 gene has been reported in previous TWAS, and the other three genes, LY6G5B, PRSS16, and C19orf54, have never been reported in previous GWAS or TWAS. For each of these three novel genes, consistent associations were observed in both lung-tissue and joint-tissue models. For the 40 genes located in previously identified GWAS loci, after adjusting for GWAS-identified variants, the associations for the majority of them became non-significant. However, the associations did not change materially for UCKL1 or PRPF6. These results suggest that the associations for these two genes were independent from previous GWSA-identified signals. We also identified 10 genes located at least 500kb away from GWAS-identified loci that were associated with lung cancer histological subtypes, e.g., adenocarcinoma (DCBLD1 and AQP3), squamous cell carcinoma (ZSCAN26, BLOC1S2, ABCF1, and ZSCAN9), and small cell lung cancer (BTN2A2, TMA16, RP11-218F10.3, and FRS3). An additional 7 genes located in GWAS loci were associated with risk of lung cancer histological subtypes but not with overall lung cancer risk, including 4 genes for adenocarcinoma (TP63, STN1, FAM227B, and TPRG1) and 3 genes for squamous cell carcinoma (DDAH2, OR2H2, and NELFE). Conclusion: Our TWAS identified lung cancer susceptibility genes, providing new insight into the genetics of lung cancer etiology. Citation Format: Tianying Zhao, Jiajun Shi, Yaohua Yang, Jie Ping, Xiao Ou Shu, Wei Zheng, Jirong Long, Qiuyin Cai. Transcriptome-wide association study identifies susceptibility loci and genes for lung cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB045.

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood–brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947–11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

Predicting gene expression levels from DNA sequences and post-transcriptional information with transformers

Background and objectives:In the latest years, the prediction of gene expression levels has been crucial due to its potential applications in the clinics. In this context, Xpresso and others methods based on Convolutional Neural Networks and Transformers were firstly proposed to this aim. However, all these methods embed data with a standard one-hot encoding algorithm, resulting in impressively sparse matrices. In addition, post-transcriptional regulation processes, which are of uttermost importance in the gene expression process, are not considered in the model.Methods:This paper presents Transformer DeepLncLoc, a novel method to predict the abundance of the mRNA (i.e., gene expression levels) by processing gene promoter sequences, managing the problem as a regression task. The model exploits a transformer-based architecture, introducing the DeepLncLoc method to perform the data embedding. Since DeepLncloc is based on word2vec algorithm, it avoids the sparse matrices problem.Results:Post-transcriptional information related to mRNA stability and transcription factors is included in the model, leading to significantly improved performances compared to the state-of-the-art works. Transformer DeepLncLoc reached 0.76 of R2 evaluation metric compared to 0.74 of Xpresso.Conclusion:The Multi-Headed Attention mechanisms which characterizes the transformer methodology is suitable for modeling the interactions between DNA’s locations, overcoming the recurrent models. Finally, the integration of the transcription factors data in the pipeline leads to impressive gains in predictive power.

Improved prediction of gene expression through integrating cell signalling models with machine learning

BackgroundA key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically predict expression levels from descriptors of the experiments. There are advantages and disadvantages to both approaches: mechanistic models more directly reflect the underlying biological causation, but do not directly utilize the available empirical data; while ML methods do not fully utilize existing biological knowledge.ResultsHere, we investigate overcoming these disadvantages by integrating mechanistic cell signalling models with ML. Our approach to integration is to augment ML with similarity features (attributes) computed from cell signalling models. Seven sets of different similarity feature were generated using graph theory. Each set of features was in turn used to learn multi-target regression models. All the features have significantly improved accuracy over the baseline model - without the similarity features. Finally, the seven multi-target regression models were stacked together to form an overall prediction model that was significantly better than the baseline on 95% of genes on an independent test set. The similarity features enable this stacking model to provide interpretable knowledge about cancer, e.g. the role of ERBB3 in the MCF7 breast cancer cell line.ConclusionIntegrating mechanistic models as graphs helps to both improve the predictive results of machine learning models, and to provide biological knowledge about genes that can help in building state-of-the-art mechanistic models.

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition.

RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.

A transcriptome-wide association study to detect novel genes for volumetric bone mineral density.

Transcriptome-wide association studies (TWAS) systematically investigate the association of genetically predicted gene expression with disease risk, providing an effective approach to identify novel susceptibility genes. Osteoporosis is the most common metabolic bone disease, associated with reduced bone mineral density (BMD) and increased risk of osteoporotic fractures, whereas genetic factors explain approximately 70% of the variance in phenotypes associated with bone. BMD is commonly assessed using dual-energy X-ray absorptiometry (DXA) to obtain measurements (g/cm2) of areal BMD. However, quantitative computed tomography (QCT) measured 3D volumetric BMD (vBMD) (g/cm3) has important advantages compared with DXA since it can evaluate cortical and trabecular microstructural features of bone quality, which can be used to directly predict fracture risk. Here, we performed the first TWAS for volumetric BMD (vBMD) by integrating genome-wide association studies (GWAS) data from two independent cohorts, namely the Framingham Heart Study (FHS, n=3298) and the Osteoporotic Fractures in Men (MrOS, n=4641), with tissue-specific gene expression data from the Genotype-Tissue Expression (GTEx) project. We first used stratified linkage disequilibrium (LD) score regression approach to identify 12 vBMD-relevant tissues, for which vBMD heritability is enriched in tissue-specific genes of the given tissue. Focusing on these tissues, we subsequently leveraged GTEx expression reference panels to predict tissue-specific gene expression levels based on the genotype data from FHS and MrOS. The associations between predicted gene expression levels and vBMD variation were then tested by MultiXcan, an innovative TWAS method that integrates information available across multiple tissues. We identified 70 significant genes associated with vBMD, including some previously identified osteoporosis-related genes such as LYRM2 and NME8, as well as some novel loci such as DNAAF2 and SPAG16. Our findings provide novel insights into the pathophysiological mechanisms of osteoporosis and highlight several novel vBMD-associated genes that warrant further investigation.

Predicting gene expression levels from histone modification profiles by a hybrid deep learning network

Post-translational modifications of histone play a key role in controlling gene expression, as they are involved in gene transcription regulation through controlling DNA accessibility or recruiting specific transcription factors. Developing computational methods to predict gene expression levels from histone modification data may facilitate understanding their function in gene regulation and contribute to develop ‘epigenetic drugs’ for disease treatment. Several studies have reported that transcription termination plays a crucial role in gene regulation as it is able to premature termination and determine the cellular fate of transcripts. However, previous work mainly focused on the histone characteristics flanking transcription start site (TSS) of genes, while ignoring the histone modification features around transcription termination site (TTS). Thus, we introduced a hybrid convolutional and bi-directional long short-term memory network with attention mechanism to predict gene expression from histone modification signals in both regions. Our model achieved higher results than the state-of-the-art model in both classification and regression tasks to infer gene expression level. The predictive results of model demonstrated that histone signals of TTS can provide additional information to improve the model performance. In addition, attention weights and transcription factor binding data indicated that histone modifications in TSS might regulate transcription by recruiting TFs, but the regulation mechanisms of it in TTS need to be further explored.

Deep Learning Methods in Predicting Gene Expression Levels for the Malaria Parasite.

Malaria is a mosquito-borne disease caused by single-celled blood parasites of the genus Plasmodium. The most severe cases of this disease are caused by the Plasmodium species, Falciparum. Once infected, a human host experiences symptoms of recurrent and intermittent fevers occurring over a time-frame of 48 hours, attributed to the synchronized developmental cycle of the parasite during the blood stage. To understand the regulated periodicity of Plasmodium falciparum transcription, this paper forecast and predict the P. falciparum gene transcription during its blood stage life cycle implementing a well-tuned recurrent neural network with gated recurrent units. Additionally, we also employ a spiking neural network to predict the expression levels of the P. falciparum gene. We provide results of this prediction on multiple genes including potential genes that express possible drug target enzymes. Our results show a high level of accuracy in being able to predict and forecast the expression levels of the different genes.

Abstract 827: Transcriptome-wide association study of endometrial cancer risk

Abstract Background Endometrial cancer is the most common malignancy of the female reproductive system, and the third leading cause of cancer death among women. In contrast to other common cancers, endometrial cancer is one of the few caners with a rapidly increasing incidence rate but a decreasing survival rate. To date, genome-wide association studies (GWAS) have identified 17 risk loci for endometrial cancer among European descendants, however, much of its heritability remains unexplained. For many of the GWAS identified loci, the target genes remain elusive. Methods We performed a large transcriptome-wide association study to discover novel endometrial cancer risk loci and possible target genes by studying 12,906 endometrial cases and 108,979 controls of European ancestry included in the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium, and UK Biobank. We applied gene expression predicting models established using data of mostly European descent in the Genotype-Tissue Expression Project (version 8) in uterus, vagina, and whole blood tissues with the newly developed joint-tissue imputation approach. We systematically assessed the associations between genetically predicted gene expression levels and endometrial cancer risk. For genes that were associated with endometrial cancer risk, we performed an enrichment analysis by using Ingenuity Pathway Analysis. Results Overall, we identified 26 genes whose genetically predicted expression was significantly associated with endometrial cancer risk (false discovery rate (FDR) &amp;lt; 0.05), including 15 genes at 9 novel loci (3q22.1: AC022296.4; 3q21.3: EEFSEC; 3q25.31: LEKR1; 6p21.32: HLA-DMA; 6p21.33: C4A, C4B, APOM, and PPP1R18; 16p11.2: AC109460.2, EIF3C, and NFATC2IP; 16p12.1: NPIPB6; 16p13.2: ATF7IP2; 20q11.22: GDF5 and UQCC1) and 11 genes at 5 known risk loci (14q32.33: SIVA1 and AL583810.1; 15q15.1: AC021755.2, AC021755.3 and EIF2AK4; 15q21.2: CYP19A1 and TNFAIP8L3; 17q11.2: EVI2A; 17q21.32: SKAP1, SNX11 and AC004477.3). Of them, AC021755.3 (P-value = 1.77 × 10-6), EIF2AK4 (P-value = 1.07 × 10-7), and CYP19A1 (P-value = 1.61 × 10-9) showed an association even at the stringent Bonferroni threshold (P-value &amp;lt; 2 × 10-6). Pathway enrichment analysis showed that the identified genes were significantly enriched in cancer-related pathways, such as FXR/RXR activation, EIF2 signaling and LXR/RXR activation. Conclusion By integrating gene expression and genotype data, our comprehensive transcriptome-wide association study identified novel endometrial cancer risk loci and candidate target genes that warrant further investigation. Citation Format: Jingjing Zhu, Tracy A. O'Mara, Veronica W. Setiawan, Mengmeng M. Du, the ECAC consortium, Christine Friedenreich, Linda S. Cook, James Lacey, Chu Chen, Lisa Johnson, Immaculata De Vivo, Nicolas Wentzensen, Xiao-Ou Shu, Jirong Long, Wei Zheng, Lang Wu, Herbert Yu. Transcriptome-wide association study of endometrial cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 827.

Multitrait transcriptome-wide association study (TWAS) tests.

Multitrait tests can improve power to detect associations between individual single-nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi-SNP transcriptome-wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single-SNP setting. Thus, established methods for combining single-SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene-phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well-calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single-trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levelscompared with 87 identified through Bonferroni-corrected single-trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single-trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome-wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.

Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expression quantitative trait locus (eQTL) data used to train the genetic predictors have small sample sizes, or when data from causally relevant tissues are not available. Here, we propose to address these issues by integrating multiple tissues in the TWAS using sparse canonical correlation analysis (sCCA). We show that sCCA-TWAS combined with single-tissue TWAS using an aggregate Cauchy association test (ACAT) outperforms traditional single-tissue TWAS. In empirically motivated simulations, the sCCA+ACAT approach yielded the highest power to detect a gene associated with phenotype, even when expression in the causal tissue was not directly measured, while controlling the Type I error when there is no association between gene expression and phenotype. For example, when gene expression explains 2% of the variability in outcome, and the GWAS sample size is 20,000, the average power difference between the ACAT combined test of sCCA features and single-tissue, versus single-tissue combined with Generalized Berk-Jones (GBJ) method, single-tissue combined with S-MultiXcan, UTMOST, or summarizing cross-tissue expression patterns using Principal Component Analysis (PCA) approaches was 5%, 8%, 5% and 38%, respectively. The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. When applied to publicly available summary statistics from 10 complex traits, the sCCA+ACAT test was able to increase the number of testable genes and identify on average an additional 400 additional gene-trait associations that single-trait TWAS missed. Our results suggest that aggregating eQTL data across multiple tissues using sCCA can improve the sensitivity of TWAS while controlling for the false positive rate.

Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data.

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly different from colon cancer in terms of treatment, prognosis, and metastasis. Recently, the differential mRNA expression of colon cancer and rectal cancer has received a great deal of attention. The current study aimed to identify significant differences between colon cancer and rectal cancer based on RNA sequencing (RNA-seq) data via support vector machines (SVM). Here, 393 CRC samples from the The Cancer Genome Atlas (TCGA) database were investigated, including 298 patients with colon cancer and 95 with rectal cancer. Following the random forest (RF) analysis of the mRNA expression data, 96 genes such as HOXB13, PRAC, and BCLAF1 were identified and utilized to build the SVM classification model with the Leave-One-Out Cross-validation (LOOCV) algorithm. In the training (n=196) and the validation cohorts (n=197), the accuracy (82.1 % and 82.2 %, respectively) and the AUC (0.87 and 0.91, respectively) indicated that the established optimal SVM classification model distinguished colon cancer from rectal cancer reasonably. However, additional experiments are required to validate the predicted gene expression levels and functions.

Genetic risk prediction of late‐onset Alzheimer’s disease based on tissue‐specific transcriptomic analysis and polygenic risk scores

AbstractBackgroundLarge‐scale genome‐wide association studies (GWAS) have identified more than 25 genomic regions related to Alzheimer’s disease (AD). Recent studies showed polygenic risk score (PRS) could be used to identify individuals at high risk of AD. Despite this success, prediction and early intervention of AD still remain challenging. In this study, we constructed prediction models for AD with clinical features, PRS, and tissue‐specifically predicted gene expression levels, and evaluated prediction performance.MethodWe used whole‐genome sequencing data of 446 European participants (207 AD cases and 239 cognitively normal controls) from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and performed quality control of genotyped data using PLINK (Table 1). PRS was derived based on a large‐scale GWAS summary statistics (Lambert et al. 2013) using PRS‐CS. We selected genes significantly associated with AD in brain using MetaXcan and the GWAS summary statistics. For these genes, we predicted gene expression levels in 13 brain regions for our study participants by using PrediXcan and GTEx V7. Then, we built a set of different prediction models that incorporated clinical features, PRS, and transcriptomic features using the Random Forest algorithm for the comparison. We evaluated prediction performance in 10‐fold cross‐validation.ResultAn AD prediction model using only demographic information and APOE ε4 status yielded an AUC of 0.726 (±0.055). As shown in Table 2, the inclusion of PRS significantly improved the prediction performance with an AUC of 0.758 (±0.0489). In addition, the inclusion of PRS and transcriptomic features produced much higher performance with an AUC of 0.794 (±0.061). Furthermore, the top 10 overlapping genes in 13 brain regions used in the prediction model were known AD susceptibility genes (Table 3).ConclusionOur study has improved the performance of existing AD genetic risk prediction models by incorporating tissue‐specific transcriptomic factors and PRS, suggesting that tissue‐specific transcriptomic factors and conventional PRS may be partly independent and complementary, so that both are important genetic risk predictors for the classification of AD. In particular, predicted brain expression factors could further provide an indication of tissue‐specific regulatory effects in AD.

FrDriver: A Functional Region Driver Identification for Protein Sequence.

Identifying cancer drivers is a crucial challenge to explain the underlying mechanisms of cancer development. There are many methods to identify cancer drivers based on the single mutation site or the entire gene. But they ignore a large number of functional elements with medium in size. It is hypothesized that mutations occurring in different regions of the protein sequence have different effects on the progression of cancer. Here, we develop a novel functional region driver(frDriver) identification method based on Bayesian probability and multiple linear regression models to identify protein regions that can regulate gene expression levels and have high functional impact potential. Combining gene expression data and somatic mutation data, with functional impact scores(SIFT, PROVEAN) as a priori knowledge, we identified cancer driver regions that are most accurate in predicting gene expression levels. We evaluated the performance of frDriver on the BRCA and GBM datasets from TCGA. The results showed that frDriver identified known cancer drivers and outperformed the other three state-of-the-art methods(eDriver, ActiveDriver and OncodriveCLUST). In addition, we performed KEGG pathway and GO term enrichment analysis, and the results indicated that the cancer drivers predicted by frDriver were related to processes such as cancer formation and gene regulation.

Exploratory Genome-Wide Interaction Analysis of Nonsteroidal Anti-inflammatory Drugs and Predicted Gene Expression on Colorectal Cancer Risk.

Regular use of nonsteroidal anti-inflammatory drugs (NSAID) is associated with lower risk of colorectal cancer. Genome-wide interaction analysis on single variants (G × E) has identified several SNPs that may interact with NSAIDs to confer colorectal cancer risk, but variations in gene expression levels may also modify the effect of NSAID use. Therefore, we tested interactions between NSAID use and predicted gene expression levels in relation to colorectal cancer risk. Genetically predicted gene expressions were tested for interaction with NSAID use on colorectal cancer risk among 19,258 colorectal cancer cases and 18,597 controls from 21 observational studies. A Mixed Score Test for Interactions (MiSTi) approach was used to jointly assess G × E effects which are modeled via fixed interaction effects of the weighted burden within each gene set (burden) and residual G × E effects (variance). A false discovery rate (FDR) at 0.2 was applied to correct for multiple testing. Among the 4,840 genes tested, genetically predicted expression levels of four genes modified the effect of any NSAID use on colorectal cancer risk, including DPP10 (PG×E = 1.96 × 10-4), KRT16 (PG×E = 2.3 × 10-4), CD14 (PG×E = 9.38 × 10-4), and CYP27A1 (PG×E = 1.44 × 10-3). There was a significant interaction between expression level of RP11-89N17 and regular use of aspirin only on colorectal cancer risk (PG×E = 3.23 × 10-5). No interactions were observed between predicted gene expression and nonaspirin NSAID use at FDR < 0.2. By incorporating functional information, we discovered several novel genes that interacted with NSAID use. These findings provide preliminary support that could help understand the chemopreventive mechanisms of NSAIDs on colorectal cancer.

Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk.

BackgroundBody mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology.MethodsTo improve statistical power and interpretation for gene‐environment interaction (G × E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2) and diabetes on CRC risk among 26 017 cases and 20 692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype‐Tissue Expression Project for all genes with heritability ≥1%. We used a mixed‐effects model to jointly measure the G × E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G × E effects (random effects). G × BMI analyses were stratified by sex as BMI‐CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR <0.2.ResultsAmong 4839 genes tested, genetically predicted expressions of FOXA1 (P = 3.15 × 10−5), PSMC5 (P = 4.51 × 10−4) and CD33 (P = 2.71 × 10−4) modified the association of BMI on CRC risk for men; KIAA0753 (P = 2.29 × 10−5) and SCN1B (P = 2.76 × 10−4) modified the association of BMI on CRC risk for women; and PTPN2 modified the association between diabetes and CRC risk in both sexes (P = 2.31 × 10−5).ConclusionsAggregating G × E interactions and incorporating functional information, we discovered novel genes that may interact with BMI and diabetes on CRC risk.

Therapeutic effects of Aconiti Lateralis Radix Praeparata combined with Zingiberis Rhizoma on doxorubicin-induced chronic heart failure in rats based on an integrated approach.

This study was aimed to explore the mechanism of Aconiti Lateralis Radix Praeparata (ALRP) and Zingiberis Rhizoma (ZR) on doxorubicin (DOX)-induced chronic heart failure (CHF) in rats by integrated approaches. Effects of ALRP and ZR on cardiac function, serum biochemical indicators and histopathology in rats were analysed. Moreover, UHPLC-Q-TOF/MS was performed to identify the potential metabolites affecting the pathological process of CHF. Metabolomics and network pharmacology analyses were conducted to illustrate the possible pathways and network in CHF treatment. The predicted gene expression levels in heart tissue were verified and assessed by RT-PCR. ALRP-ZR demonstrated remarkable promotion of hemodynamic indices and alleviated histological damage of heart tissue. Metabolomics analyses showed that the therapeutic effect of ALRP and ZR is mainly associated with the regulation of eight metabolites and ten pathways, which may be responsible for the therapeutic efficacy of ALRP-ZR. Moreover, the results of RT-PCR showed that ALRP-ZR could substantially increase the expression level of energy metabolism-related genes, including PPARδ, PPARγ, Lpl, Scd, Fasn and Pla2g2e. The results highlighted the role of ALRP-ZR in the treatment of CHF by influencing the metabolites related to energy metabolism pathway via metabolomics and network pharmacology analyses.