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Cheng Y, Xu SM, Santucci K, Lindner G, Janitz M. Machine learning and related approaches in transcriptomics. Biochem Biophys Res Commun 2024; 724:150225. [PMID: 38852503 DOI: 10.1016/j.bbrc.2024.150225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 05/18/2024] [Accepted: 06/03/2024] [Indexed: 06/11/2024]
Abstract
Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.
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Affiliation(s)
- Yuning Cheng
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Si-Mei Xu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Kristina Santucci
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Grace Lindner
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Michael Janitz
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
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2
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Yang Y, Chen Y, Xu S, Guo X, Jia G, Ping J, Shu X, Zhao T, Yuan F, Wang G, Xie Y, Ci H, Liu H, Qi Y, Liu Y, Liu D, Li W, Ye F, Shu XO, Zheng W, Li L, Cai Q, Long J. Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk. Nat Commun 2024; 15:6071. [PMID: 39025880 PMCID: PMC11258330 DOI: 10.1038/s41467-024-50404-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 07/10/2024] [Indexed: 07/20/2024] Open
Abstract
The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.
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Affiliation(s)
- Yaohua Yang
- Center for Public Health Genomics, Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA.
| | - Yaxin Chen
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shuai Xu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiang Shu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tianying Zhao
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Fangcheng Yuan
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gang Wang
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yufang Xie
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hang Ci
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hongmo Liu
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yawen Qi
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yongjun Liu
- Department of Laboratory Medicine and Pathology, University of Washington Medical Center, Seattle, WA, USA
| | - Dan Liu
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Weimin Li
- Institute of Respiratory Health, Frontiers Science Center for Disease‑Related Molecular Network, State Key Laboratory of Respiratory Health and Multimorbidity, Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Fei Ye
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Li Li
- Department of Family Medicine, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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Zhao C, Su KJ, Wu C, Cao X, Sha Q, Li W, Luo Z, Qing T, Qiu C, Zhao LJ, Liu A, Jiang L, Zhang X, Shen H, Zhou W, Deng HW. Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data. Comput Biol Med 2024; 179:108813. [PMID: 38955127 DOI: 10.1016/j.compbiomed.2024.108813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.
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Affiliation(s)
- Chen Zhao
- Department of Computer Science, Kennesaw State University, 680 Arntson Dr, Marietta, GA, 30060, USA
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Chong Wu
- Department of Biostatistics, University of Texas MD Anderson, Pickens Academic Tower, 1400 Pressler St., Houston, TX, 77030, USA
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Wu Li
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Zhe Luo
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Tian Qing
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Chuan Qiu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Lan Juan Zhao
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Anqi Liu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Lindong Jiang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Xiao Zhang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, 49931, USA.
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
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Zhang J, Zhang Q, Hu W, Liang Y, Jiang D, Chen H. A transcriptome-wide association study identified susceptibility genes for hepatocellular carcinoma in East Asia. Gastroenterol Rep (Oxf) 2024; 12:goae057. [PMID: 38846986 PMCID: PMC11153834 DOI: 10.1093/gastro/goae057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 03/07/2024] [Accepted: 04/30/2024] [Indexed: 06/09/2024] Open
Abstract
Background Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and is prevalent in East Asia. Although genome-wide association studies (GWASs) of HCC have identified 23 risk regions, the susceptibility genes underlying these associations largely remain unclear. To identify novel candidate genes for HCC, we conducted liver single-tissue and cross-tissue transcriptome-wide association studies (TWASs) in two populations of East Asia. Methods GWAS summary statistics of 2,514 subjects (1,161 HCC cases and 1,353 controls) from the Chinese Qidong cohort and 161,323 subjects (2,122 HCC cases and 159,201 controls) from the BioBank Japan project were used to conduct TWAS analysis. The single-tissue and cross-tissue TWAS approaches were both used to detect the association between susceptible genes and the risk of HCC. TWAS identified genes were further annotated by Metascape, UALCAN, GEPIA2, and DepMap. Results We identified 22 novel genes at 16 independent loci significantly associated with HCC risk after Bonferroni correction. Of these, 13 genes were located in novel regions. Besides, we found 83 genes overlapped in the Chinese and Japanese cohorts with P < 0.05, of which, three genes (NUAK2, HLA-DQA1, and ATP6V1G2) were discerned by both single-tissue and cross-tissue TWAS approaches. Among the genes identified through TWAS, a significant proportion of them exhibit a credible role in HCC biology, such as FAM96B, HSPA5, POLRMT, MPHOSPH10, and RABL2A. HLA-DQA1, NUAK2, and HSPA5 associated with the process of carcinogenesis in HCC as previously reported. Conclusions Our findings highlight the value of leveraging the gene expression data to identify new candidate genes beyond the GWAS associations and could further provide a genetic insight for the biology of HCC.
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Affiliation(s)
- Jingjing Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- School of Public Health (Shenzhen), Shenzhen campus of Sun Yat-sen University, Shenzhen, Guangdong, P. R. China
| | - Qingrong Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- School of Public Health (Shenzhen), Shenzhen campus of Sun Yat-sen University, Shenzhen, Guangdong, P. R. China
| | - Wenyan Hu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- School of Public Health (Shenzhen), Shenzhen campus of Sun Yat-sen University, Shenzhen, Guangdong, P. R. China
| | - Yuxuan Liang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- School of Public Health (Shenzhen), Shenzhen campus of Sun Yat-sen University, Shenzhen, Guangdong, P. R. China
| | - Deke Jiang
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, P. R. China
| | - Haitao Chen
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- School of Public Health (Shenzhen), Shenzhen campus of Sun Yat-sen University, Shenzhen, Guangdong, P. R. China
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Wang JT, Chang XY, Zhao Q, Zhang YM. FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study. Brief Bioinform 2024; 25:bbae290. [PMID: 38888457 PMCID: PMC11184901 DOI: 10.1093/bib/bbae290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 05/19/2024] [Accepted: 06/09/2024] [Indexed: 06/20/2024] Open
Abstract
Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.
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Affiliation(s)
| | | | | | - Yuan-Ming Zhang
- Corresponding author. College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China. Tel.: +086-13505161564; E-mail:
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Luo L, Pang T, Zheng H, Liufu C, Chang S. xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data. J Transl Med 2024; 22:387. [PMID: 38664746 PMCID: PMC11044291 DOI: 10.1186/s12967-024-05065-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/05/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes. METHODS In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored. RESULTS Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes. CONCLUSIONS The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.
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Affiliation(s)
- Lingxue Luo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Tao Pang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Haohao Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Chao Liufu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuan Bei Road, Beijing, 100191, China.
- Research Units of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, Beijing, 100191, China.
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Lei H, Xing Z, Chen X, Dai Y, Cheng B, Wang S, Kang T, Wang Q, Zhang J, Jia J, Zheng Y. Exploration of the causality of frailty index on psoriasis: A Mendelian randomization study. Skin Res Technol 2024; 30:e13641. [PMID: 38426414 PMCID: PMC10905529 DOI: 10.1111/srt.13641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/19/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Frailty is associated with a variety of diseases, but the relationship between frailty and psoriasis remains unclear. METHODS First, we conducted a two-sample Mendelian randomization based on genome-wide association studies (GWAS) to investigate genetic causality between frailty index and common diseases in dermatology. Inverse variance weighted was used to estimate causality. Second, expression quantitative trait locus (eQTLs) analysis was conducted to identify the genes affected by Single nucleotide polymorphisms (SNPs). Third, we performed function and pathway enrichment, transcriptome-wide association studies (TWAS) analysis based on eQTLs. RESULTS It was shown that the rise of frailty index could increase the risk of psoriasis (IVW, beta = 0.916, OR = 2.500, 95%CI:1.418-4.408, p = 0.002) through Mendelian randomization (MR), and there was no heterogeneity and pleiotropy. There was no causality between the frailty index and other common diseases in dermatology. We found 31 eQTLs based on strongly correlated SNPs in the causality. TWAS analysis found that the expressions of four genes were closely related to psoriasis, including HLA-DQA1, HLA-DQA2, HLA-DRB1 and HLA-DQB1. CONCLUSION It suggested that the frailty index had a significant positive causality on the risk of psoriasis, which was well documented by combined genomic, transcriptome, and proteome analyses.
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Affiliation(s)
- Hao Lei
- Department of DermatologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Zixuan Xing
- Department of Infectious DiseasesThe Second Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Xin Chen
- State Key Laboratory of Oral & Maxillofacial Reconstruction and RegenerationNational Clinical Research Center for Oral DiseasesShaanxi Clinical Research Center for Oral DiseasesDepartment of Orthodontics, School of StomatologyThe Fourth Military Medical UniversityXi′anChina
| | - Yilin Dai
- Department of DermatologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Baochen Cheng
- Department of DermatologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Shengbang Wang
- Department of DermatologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Tong Kang
- Department of DermatologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Qian Wang
- Department of DermatologyTangdu HospitalAir Force Military Medical UniversityXi'anChina
| | - Jing Zhang
- Department of DermatologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Jinjing Jia
- State Key Laboratory of Dampness Syndrome of Chinese MedicineThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouChina
- Department of DermatologyThe Second Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouChina
- Department of Dermatology,Guangdong Provincial Key Laboratory of Chinese Medicine for Prevention and Treatment of Refractory Chronic DiseaseGuangzhouChina
| | - Yan Zheng
- Department of DermatologyThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
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Cao C, Shao M, Zuo C, Kwok D, Liu L, Ge Y, Zhang Z, Cui F, Chen M, Fan R, Ding Y, Jiang H, Wang G, Zou Q. RAVAR: a curated repository for rare variant-trait associations. Nucleic Acids Res 2024; 52:D990-D997. [PMID: 37831073 PMCID: PMC10767942 DOI: 10.1093/nar/gkad876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.
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Affiliation(s)
- Chen Cao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Mengting Shao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Chunman Zuo
- Institute of Artificial Intelligence, Donghua University, Shanghai, China
| | - Devin Kwok
- School of Computer Science, McGill University, Montreal, Canada
| | - Lin Liu
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Yuli Ge
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Zilong Zhang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Feifei Cui
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Mingshuai Chen
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Rui Fan
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Hangjin Jiang
- Center for Data Science, Zhejiang University, Hangzhou, China
| | - Guishen Wang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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9
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Zhang X, Gomez L, Below JE, Naj AC, Martin ER, Kunkle BW, Bush WS. An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease. J Alzheimers Dis 2024; 98:1053-1067. [PMID: 38489177 DOI: 10.3233/jad-231075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer's disease (AD). Objective To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner. Methods Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. Results Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002). Conclusions We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
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Affiliation(s)
- Xueyi Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Lissette Gomez
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam C Naj
- Department of Biostatistics, Epidemiology, and Informatics, Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Eden R Martin
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Brian W Kunkle
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - William S Bush
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
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10
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Detera-Wadleigh SD, Kassem L, Besancon E, Lopes F, Akula N, Sung H, Blattner M, Sheridan L, Lacbawan LN, Garcia J, Gordovez F, Hosey K, Donner C, Salvini C, Schulze T, Chen DTW, England B, Cross J, Jiang X, Corona W, Russ J, Mallon B, Dutra A, Pak E, Steiner J, Malik N, de Guzman T, Horato N, Mallmann MB, Mendes V, Dűck AL, Nardi AE, McMahon FJ. A resource of induced pluripotent stem cell (iPSC) lines including clinical, genomic, and cellular data from genetically isolated families with mood and psychotic disorders. Transl Psychiatry 2023; 13:397. [PMID: 38104115 PMCID: PMC10725500 DOI: 10.1038/s41398-023-02641-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 10/05/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023] Open
Abstract
Genome-wide (GWAS) and copy number variant (CNV) association studies have reproducibly identified numerous risk alleles associated with bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), but biological characterization of these alleles lags gene discovery, owing to the inaccessibility of live human brain cells and inadequate animal models for human psychiatric conditions. Human-derived induced pluripotent stem cells (iPSCs) provide a renewable cellular reagent that can be differentiated into living, disease-relevant cells and 3D brain organoids carrying the full complement of genetic variants present in the donor germline. Experimental studies of iPSC-derived cells allow functional characterization of risk alleles, establishment of causal relationships between genes and neurobiology, and screening for novel therapeutics. Here we report the creation and availability of an iPSC resource comprising clinical, genomic, and cellular data obtained from genetically isolated families with BD and related conditions. Results from the first 324 study participants, 61 of whom have validated pluripotent clones, show enrichment of rare single nucleotide variants and CNVs overlapping many known risk genes and pathogenic CNVs. This growing iPSC resource is available to scientists pursuing functional genomic studies of BD and related conditions.
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Affiliation(s)
- Sevilla D Detera-Wadleigh
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA.
| | - Layla Kassem
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA.
| | - Emily Besancon
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Fabiana Lopes
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Nirmala Akula
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Heejong Sung
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Meghan Blattner
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Laura Sheridan
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Ley Nadine Lacbawan
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Joshua Garcia
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Francis Gordovez
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Katherine Hosey
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Cassandra Donner
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Claudio Salvini
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Thomas Schulze
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
- Institute of Psychiatric Phenomics and Genomics, LMU Munich, 80336, München, Germany
- Department of Psychiatry and Behavioral Sciences, Upstate University Hospital, Syracuse, NY, 13210, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - David T W Chen
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Bryce England
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Joanna Cross
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Xueying Jiang
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Winston Corona
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Jill Russ
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Barbara Mallon
- Center for Scientific Review, Neurotechnology and Vision Branch, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Amalia Dutra
- Cytogenetics and Microscopy Core, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Evgenia Pak
- Cytogenetics and Microscopy Core, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Joe Steiner
- Neurotherapeutics Development Unit, NINDS, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nasir Malik
- Neurotherapeutics Development Unit, NINDS, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Theresa de Guzman
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Natia Horato
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Mariana B Mallmann
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Victoria Mendes
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Amanda L Dűck
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Antonio E Nardi
- Laboratorio de Panico e Respiracao, Instituto de Psiquiatria, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 22410-003, Brazil
| | - Francis J McMahon
- Genetic Basis of Mood & Anxiety Disorders Section, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, 35 Convent Drive, Bethesda, MD, 20892, USA
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11
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Mai J, Lu M, Gao Q, Zeng J, Xiao J. Transcriptome-wide association studies: recent advances in methods, applications and available databases. Commun Biol 2023; 6:899. [PMID: 37658226 PMCID: PMC10474133 DOI: 10.1038/s42003-023-05279-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.
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Affiliation(s)
- Jialin Mai
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mingming Lu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qianwen Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyao Zeng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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