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Zhang S, Jiang Z, Zeng P. Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework. J Transl Med 2024; 22:258. [PMID: 38461317 PMCID: PMC10924384 DOI: 10.1186/s12967-024-05053-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/01/2024] [Indexed: 03/11/2024] Open
Abstract
BACKGROUND The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. METHODS We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. RESULTS We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. CONCLUSION Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.
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Affiliation(s)
- Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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Zhang J, Zhang S, Qiao J, Wang T, Zeng P. Similarity and diversity of genetic architecture for complex traits between East Asian and European populations. BMC Genomics 2023; 24:314. [PMID: 37308816 DOI: 10.1186/s12864-023-09434-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/07/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently. RESULTS By leveraging summary statistics of 37 traits from East Asian (Nmax=254,373) or European (Nmax=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρg) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with [Formula: see text] ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs. CONCLUSIONS Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.
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Affiliation(s)
- Jinhui Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
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Lu H, Qiao J, Shao Z, Wang T, Huang S, Zeng P. A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics. BMC Med 2021; 19:314. [PMID: 34895209 PMCID: PMC8667366 DOI: 10.1186/s12916-021-02186-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/10/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. METHODS We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. RESULTS We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. CONCLUSIONS Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.
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Affiliation(s)
- Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Zhonghe Shao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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Lu H, Wei Y, Jiang Z, Zhang J, Wang T, Huang S, Zeng P. Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies. J Transl Med 2021; 19:418. [PMID: 34627275 PMCID: PMC8502405 DOI: 10.1186/s12967-021-03090-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 09/26/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Integrating functional annotations into SNP-set association studies has been proven a powerful analysis strategy. Statistical methods for such integration have been developed for continuous and binary phenotypes; however, the SNP-set integrative approaches for time-to-event or survival outcomes are lacking. METHODS We here propose IEHC, an integrative eQTL (expression quantitative trait loci) hierarchical Cox regression, for SNP-set based survival association analysis by modeling effect sizes of genetic variants as a function of eQTL via a hierarchical manner. Three p-values combination tests are developed to examine the joint effects of eQTL and genetic variants after a novel decorrelated modification of statistics for the two components. An omnibus test (IEHC-ACAT) is further adapted to aggregate the strengths of all available tests. RESULTS Simulations demonstrated that the IEHC joint tests were more powerful if both eQTL and genetic variants contributed to association signal, while IEHC-ACAT was robust and often outperformed other approaches across various simulation scenarios. When applying IEHC to ten TCGA cancers by incorporating eQTL from relevant tissues of GTEx, we revealed that substantial correlations existed between the two types of effect sizes of genetic variants from TCGA and GTEx, and identified 21 (9 unique) cancer-associated genes which would otherwise be missed by approaches not incorporating eQTL. CONCLUSION IEHC represents a flexible, robust, and powerful approach to integrate functional omics information to enhance the power of identifying association signals for the survival risk of complex human cancers.
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Affiliation(s)
- Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yongyue Wei
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jinhui Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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