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Cao X, Liang X, Zhang S, Sha Q. Gene selection by incorporating genetic networks into case-control association studies. Eur J Hum Genet 2024; 32:270-277. [PMID: 36529820 PMCID: PMC10923938 DOI: 10.1038/s41431-022-01264-x] [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: 04/08/2022] [Revised: 11/27/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Large-scale genome-wide association studies (GWAS) have been successfully applied to a wide range of genetic variants underlying complex diseases. The network-based regression approach has been developed to incorporate a biological genetic network and to overcome the challenges caused by the computational efficiency for analyzing high-dimensional genomic data. In this paper, we propose a gene selection approach by incorporating genetic networks into case-control association studies for DNA sequence data or DNA methylation data. Instead of using traditional dimension reduction techniques such as principal component analyses and supervised principal component analyses, we use a linear combination of genotypes at SNPs or methylation values at CpG sites in a gene to capture gene-level signals. We employ three linear combination approaches: optimally weighted sum (OWS), beta-based weighted sum (BWS), and LD-adjusted polygenic risk score (LD-PRS). OWS and LD-PRS are supervised approaches that depend on the effect of each SNP or CpG site on the case-control status, while BWS can be extracted without using the case-control status. After using one of the linear combinations of genotypes or methylation values in each gene to capture gene-level signals, we regularize them to perform gene selection based on the biological network. Simulation studies show that the proposed approaches have higher true positive rates than using traditional dimension reduction techniques. We also apply our approaches to DNA methylation data and UK Biobank DNA sequence data for analyzing rheumatoid arthritis. The results show that the proposed methods can select potentially rheumatoid arthritis related genes that are missed by existing methods.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Xiaoyu Liang
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
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Kim K, Jun TH, Ha BK, Wang S, Sun H. New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits. BMC Bioinformatics 2023; 24:381. [PMID: 37817069 PMCID: PMC10563219 DOI: 10.1186/s12859-023-05505-8] [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: 02/10/2023] [Accepted: 09/28/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND Identification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Overlapping genetic associations from multiple traits help to detect weak genetic associations missed by single-trait analyses. Many statistical methods were developed to identify pleiotropic variants with most of them being limited to quantitative traits when pleiotropic effects on both quantitative and qualitative traits have been observed. This is a statistically challenging problem because there does not exist an appropriate multivariate distribution to model both quantitative and qualitative data together. Alternatively, meta-analysis methods can be applied, which basically integrate summary statistics of individual variants associated with either a quantitative or a qualitative trait without accounting for correlations among genetic variants. RESULTS We propose a new statistical selection method based on a unified selection score quantifying how a genetic variant, i.e., a pleiotropic variant associates with both quantitative and qualitative traits. In our extensive simulation studies where various types of pleiotropic effects on both quantitative and qualitative traits were considered, we demonstrated that the proposed method outperforms the existing meta-analysis methods in terms of true positive selection. We also applied the proposed method to a peanut dataset with 6 quantitative and 2 qualitative traits, and a cowpea dataset with 2 quantitative and 6 qualitative traits. We were able to detect some potentially pleiotropic variants missed by the existing methods in both analyses. CONCLUSIONS The proposed method is able to locate pleiotropic variants associated with both quantitative and qualitative traits. It has been implemented into an R package 'UNISS', which can be downloaded from http://github.com/statpng/uniss.
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Affiliation(s)
- Kipoong Kim
- Department of Statistic, Pusan National University, 46241, Busan, Korea
| | - Tae-Hwan Jun
- Department of Plant Bioscience, Pusan National University, 50463, Miryang, Korea
| | - Bo-Keun Ha
- Department of Applied Plant Science, Chonnam National University, 61186, Gwangju, Korea
| | - Shuang Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, 10032, USA
| | - Hokeun Sun
- Department of Statistic, Pusan National University, 46241, Busan, Korea.
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Cao X, Zhang L, Islam MK, Zhao M, He C, Zhang K, Liu S, Sha Q, Wei H. TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization. NAR Genom Bioinform 2023; 5:lqad083. [PMID: 37711605 PMCID: PMC10498345 DOI: 10.1093/nargab/lqad083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/30/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023] Open
Abstract
Four statistical selection methods for inferring transcription factor (TF)-target gene (TG) pairs were developed by coupling mean squared error (MSE) or Huber loss function, with elastic net (ENET) or least absolute shrinkage and selection operator (Lasso) penalty. Two methods were also developed for inferring pathway gene regulatory networks (GRNs) by combining Huber or MSE loss function with a network (Net)-based penalty. To solve these regressions, we ameliorated an accelerated proximal gradient descent (APGD) algorithm to optimize parameter selection processes, resulting in an equally effective but much faster algorithm than the commonly used convex optimization solver. The synthetic data generated in a general setting was used to test four TF-TG identification methods, ENET-based methods performed better than Lasso-based methods. Synthetic data generated from two network settings was used to test Huber-Net and MSE-Net, which outperformed all other methods. The TF-TG identification methods were also tested with SND1 and gl3 overexpression transcriptomic data, Huber-ENET and MSE-ENET outperformed all other methods when genome-wide predictions were performed. The TF-TG identification methods fill the gap of lacking a method for genome-wide TG prediction of a TF, and potential for validating ChIP/DAP-seq results, while the two Net-based methods are instrumental for predicting pathway GRNs.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Ling Zhang
- Computational Science and Engineering Program, Michigan Technological University, Houghton, MI 49931, USA
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
| | - Md Khairul Islam
- Computational Science and Engineering Program, Michigan Technological University, Houghton, MI 49931, USA
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
| | - Mingxia Zhao
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Cheng He
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Kui Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Sanzhen Liu
- Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
| | - Hairong Wei
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
- Computational Science and Engineering Program, Michigan Technological University, Houghton, MI 49931, USA
- College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
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Tang X, Mo Z, Chang C, Qian X. Group-shrinkage feature selection with a spatial network for mining DNA methylation data. Comput Biol Med 2023; 154:106573. [PMID: 36706568 DOI: 10.1016/j.compbiomed.2023.106573] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 01/05/2023] [Accepted: 01/22/2023] [Indexed: 01/25/2023]
Abstract
Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network constraints. However, these methods still have some key limitations as they cannot exclude isolated differential sites and only consider adjacent site ordering. Therefore, we propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. Specifically, a network-guided group-shrinkage strategy is developed to penalize weakly-correlated isolated methylation sites through a network structure constraint. The spatial network is constructed based on spatial correlation information of DNA methylation sites, where this information accounts for the uneven site distribution. The experimental simulations and applications demonstrated that the proposed method outperforms the advanced regularization methods, especially in rejecting isolated methylation sites; hence this study provides an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data. Additionally, the proposed method exhibits enhanced reliability due to introducing biological prior knowledge into a regularization-based feature selection framework and could promote more research in the integration between biological prior knowledge and classical feature selection methods, thus facilitating their clinical application. Our source codes will be released at https://github.com/SJTUBME-QianLab/Group-shrinkage-Spatial-Network once this manuscript is accepted for publication.
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Affiliation(s)
- Xinlu Tang
- Medical Image and Health Informatics Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zhanfeng Mo
- School of Computer Science and Engineering, Nanyang Technological University, Singapore.
| | - Cheng Chang
- Department of Nuclear Medicine, Shanghai, Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Xiaohua Qian
- Medical Image and Health Informatics Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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Levy JJ, Chen Y, Azizgolshani N, Petersen CL, Titus AJ, Moen EL, Vaickus LJ, Salas LA, Christensen BC. MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Networks, Inspired by Capsule Networks. NPJ Syst Biol Appl 2021; 7:33. [PMID: 34417465 PMCID: PMC8379254 DOI: 10.1038/s41540-021-00193-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 07/01/2021] [Indexed: 02/07/2023] Open
Abstract
DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules-such as gene promoter context, CpG island relationship, or user-defined groupings-and relate them to diagnostic and prognostic outcomes. We demonstrate these models' utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep-learning analyses' interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.
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Affiliation(s)
- Joshua J Levy
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA.
| | - Youdinghuan Chen
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Curtis L Petersen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA
| | - Alexander J Titus
- Department of Life Sciences, University of New Hampshire, Manchester, NH, USA
| | - Erika L Moen
- The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Louis J Vaickus
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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Selection probability of multivariate regularization to identify pleiotropic variants in genetic association studies. COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS 2020. [DOI: 10.29220/csam.2020.27.5.535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Kim K, Koo J, Sun H. An empirical threshold of selection probability for analysis of high-dimensional correlated data. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1739286] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Kipoong Kim
- Department of Statistics, Pusan National University, Busan, South Korea
| | - Jajoon Koo
- Department of Statistics, Pusan National University, Busan, South Korea
| | - Hokeun Sun
- Department of Statistics, Pusan National University, Busan, South Korea
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