1
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Nishino J, Miya F, Kato M. Gene-based Hardy-Weinberg equilibrium test using genotype count data: application to six types of cancers. BMC Genomics 2025; 26:124. [PMID: 39930364 PMCID: PMC11809088 DOI: 10.1186/s12864-025-11321-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 02/04/2025] [Indexed: 02/14/2025] Open
Abstract
BACKGROUND An alternative approach to investigate associations between genetic variants and disease is to examine deviations from the Hardy-Weinberg equilibrium (HWE) in genotype frequencies within a case population, instead of case-control association analysis. The HWE analysis requires disease cases and demonstrates a notable ability in mapping recessive variants. Allelic heterogeneity is a common phenomenon in diseases. While gene-based case-control association analysis successfully incorporates this heterogeneity, there are no such approaches for HWE analysis. Therefore, we proposed a gene-based HWE test (gene-HWT) by aggregating single-nucleotide polymorphism (SNP)-level HWE test statistics in a gene to address allelic heterogeneity. RESULTS This method used only genotype count data and publicly available linkage disequilibrium information and has a very low computational cost. Extensive simulations demonstrated that gene-HWT effectively controls the type I error at a low significance level and outperforms SNP-level HWE test in power when there are multiple causal variants within a gene. Using gene-HWT, we analyzed genotype count data from a genome-wide association study of six cancer types in Japanese individuals and suggest DGKE and ANO3 as potential germline factors in colorectal cancer. Furthermore, FSTL4 was suggested through a combined analysis across the six cancer types, with particularly notable associations observed in colorectal and prostate cancers. CONCLUSIONS These findings indicate the potential of gene-HWT to elucidate the genetic basis of complex diseases, including cancer.
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Affiliation(s)
- Jo Nishino
- Division of Bioinformatics, National Cancer Center Research Institute, Tokyo, Japan.
| | - Fuyuki Miya
- Center for Medical Genetics, Keio University School of Medicine, Tokyo, Japan
| | - Mamoru Kato
- Division of Bioinformatics, National Cancer Center Research Institute, Tokyo, Japan
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2
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Ahmed S, Vaden KI, Leitao D, Dubno JR, Drögemöller BI. Large-scale audiometric phenotyping identifies distinct genes and pathways involved in hearing loss subtypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.01.14.24318673. [PMID: 39867375 PMCID: PMC11759831 DOI: 10.1101/2025.01.14.24318673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/28/2025]
Abstract
Age-related hearing loss affects one-third of the population over 65 years. However, the diverse pathologies underlying these heterogenous phenotypes complicate genetic studies. To overcome challenges associated with accurate phenotyping for older adults with hearing loss, we applied computational phenotyping approaches based on audiometrically measured hearing loss. This novel phenotyping strategy uncovered distinct genetic variants associated with sensory and metabolic hearing loss. Sex-stratified analyses of these sexually dimorphic hearing loss phenotypes revealed a novel locus of relevance to sensory hearing loss in males, but not females. Enrichment analyses revealed that genes involved in frontotemporal dementia were implicated in metabolic hearing loss, while genes relating to sensory processing of sound by hair cells were implicated in sensory hearing loss. Our study has enhanced our understanding of these two distinct hearing loss phenotypes, representing the first step in the development of more precise treatments for these pathologically distinct hearing loss phenotypes.
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Affiliation(s)
- Samah Ahmed
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Kenneth I Vaden
- Hearing Research Program, Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, South Carolina, Charleston, USA
| | - Darren Leitao
- Department of Otolaryngology-Head and Neck Surgery, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Judy R Dubno
- Hearing Research Program, Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, South Carolina, Charleston, USA
| | - Britt I Drögemöller
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
- The Children's Hospital Foundation of Manitoba, Winnipeg, Manitoba, Canada
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
- Centre on Aging, Winnipeg, Manitoba, Canada
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3
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Bai Z, Gholipourshahraki T, Shrestha M, Hjelholt A, Hu S, Kjolby M, Rohde PD, Sørensen P. Evaluation of Bayesian Linear Regression derived gene set test methods. BMC Genomics 2024; 25:1236. [PMID: 39716056 DOI: 10.1186/s12864-024-11026-2] [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: 05/14/2024] [Accepted: 11/08/2024] [Indexed: 12/25/2024] Open
Abstract
BACKGROUND Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations. RESULTS Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the percentage of genetic variance explained by the gene set, and the genetic architecture of the traits. By using KEGG pathways, eQTLs, and regulatory elements as different kinds of gene sets with T2D results, we also assessed the performance of gene set tests in explaining more about real phenotypes. CONCLUSIONS Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. Combining performance of our method in simulated and real phenotypes, this suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.
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Affiliation(s)
- Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
| | | | - Merina Shrestha
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Astrid Hjelholt
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Pharmacology, Aarhus University Hospital, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Mads Kjolby
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Pharmacology, Aarhus University Hospital, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Palle Duun Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
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4
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Gholipourshahraki T, Bai Z, Shrestha M, Hjelholt A, Hu S, Kjolby M, Rohde PD, Sørensen P. Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases. PLoS Genet 2024; 20:e1011463. [PMID: 39495786 PMCID: PMC11563439 DOI: 10.1371/journal.pgen.1011463] [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: 05/21/2024] [Revised: 11/14/2024] [Accepted: 10/17/2024] [Indexed: 11/06/2024] Open
Abstract
Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.
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Affiliation(s)
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Merina Shrestha
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Astrid Hjelholt
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Pharmacology, Aarhus University Hospital, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
| | - Mads Kjolby
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Pharmacology, Aarhus University Hospital, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Palle Duun Rohde
- Genomic Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
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5
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Deng Z, Dong Z, Wang Y, Dai Y, Liu J, Deng F. Identification of TACSTD2 as novel therapeutic targets for cisplatin-induced acute kidney injury by multi-omics data integration. Hum Genet 2024; 143:1061-1080. [PMID: 38369676 DOI: 10.1007/s00439-024-02641-w] [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: 11/26/2023] [Accepted: 01/11/2024] [Indexed: 02/20/2024]
Abstract
Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI.
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Affiliation(s)
- Zebin Deng
- Department of Urology, The Second Xiangya Hospital at Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China
| | - Zheng Dong
- Department of Cellular Biology and Anatomy, Medical College of Georgia at Augusta University, Augusta, GA, USA
- Department of Nephrology, The Second Xiangya Hospital at Central South University, Changsha, Hunan, China
| | - Yinhuai Wang
- Department of Urology, The Second Xiangya Hospital at Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China
| | - Yingbo Dai
- Department of Urology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong, China
| | - Jiachen Liu
- Xiangya Hospital, Central South University, Changsha, Hunan, China.
- The Center of Systems Biology and Data Science, Xiangya School of Medicine, Central South University, Changsha, Hunan, People's Republic of China.
| | - Fei Deng
- Department of Urology, The Second Xiangya Hospital at Central South University, 139 Middle Renmin Road, Changsha, 410011, Hunan, China.
- Department of Nephrology, The Second Xiangya Hospital at Central South University, Changsha, Hunan, China.
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6
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Zhu L, Zhang S, Sha Q. Meta-analysis of set-based multiple phenotype association test based on GWAS summary statistics from different cohorts. Front Genet 2024; 15:1359591. [PMID: 39301532 PMCID: PMC11410627 DOI: 10.3389/fgene.2024.1359591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 08/23/2024] [Indexed: 09/22/2024] Open
Abstract
Genome-wide association studies (GWAS) have emerged as popular tools for identifying genetic variants that are associated with complex diseases. Standard analysis of a GWAS involves assessing the association between each variant and a disease. However, this approach suffers from limited reproducibility and difficulties in detecting multi-variant and pleiotropic effects. Although joint analysis of multiple phenotypes for GWAS can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits, most of the multiple phenotype association tests are designed for a single variant, resulting in much lower power, especially when their effect sizes are small and only their cumulative effect is associated with multiple phenotypes. To overcome these limitations, set-based multiple phenotype association tests have been developed to enhance statistical power and facilitate the identification and interpretation of pleiotropic regions. In this research, we propose a new method, named Meta-TOW-S, which conducts joint association tests between multiple phenotypes and a set of variants (such as variants in a gene) utilizing GWAS summary statistics from different cohorts. Our approach applies the set-based method that Tests for the effect of an Optimal Weighted combination of variants in a gene (TOW) and accounts for sample size differences across GWAS cohorts by employing the Cauchy combination method. Meta-TOW-S combines the advantages of set-based tests and multi-phenotype association tests, exhibiting computational efficiency and enabling analysis across multiple phenotypes while accommodating overlapping samples from different GWAS cohorts. To assess the performance of Meta-TOW-S, we develop a phenotype simulator package that encompasses a comprehensive simulation scheme capable of modeling multiple phenotypes and multiple variants, including noise structures and diverse correlation patterns among phenotypes. Simulation studies validate that Meta-TOW-S maintains a desirable Type I error rate. Further simulation under different scenarios shows that Meta-TOW-S can improve power compared with other existing meta-analysis methods. When applied to four psychiatric disorders summary data, Meta-TOW-S detects a greater number of significant genes.
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Affiliation(s)
- Lirong Zhu
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, United States
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7
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He D, Li L, Zhang H, Liu F, Li S, Xiu X, Fan C, Qi M, Meng M, Ye J, Mort M, Stenson PD, Cooper DN, Zhao H. Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework. EBioMedicine 2024; 107:105286. [PMID: 39168091 PMCID: PMC11382033 DOI: 10.1016/j.ebiom.2024.105286] [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: 04/30/2023] [Revised: 07/29/2024] [Accepted: 08/01/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. METHODS Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages. FINDINGS The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. INTERPRETATION iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. FUNDING The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).
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Affiliation(s)
- Dan He
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Ling Li
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Huasong Zhang
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Feiyi Liu
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Shaoying Li
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Xuehao Xiu
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Cong Fan
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Mengling Qi
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Meng Meng
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Junping Ye
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China
| | - Matthew Mort
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Peter D Stenson
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, 510006, China.
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8
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Aparo A, Bonnici V, Avesani S, Cascione L, Giugno R. DiGAS: Differential gene allele spectrum as a descriptor in genetic studies. Comput Biol Med 2024; 179:108924. [PMID: 39067286 DOI: 10.1016/j.compbiomed.2024.108924] [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: 02/01/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 07/30/2024]
Abstract
Diagnosing individuals with complex genetic diseases is a challenging task. Computational methodologies exploit information at the genotype level by taking into account single nucleotide polymorphisms (SNPs) leveraging the results of genome-wide association studies analysis to assign a statistical significance to each SNP. Recent methodologies extend such an approach by aggregating SNP significance at the genetic level to identify genes that are related to the condition under study. However, such methodologies still suffer from the initial SNP analysis limitations. Here, we present DiGAS, a tool for diagnosing genetic conditions by computing significance, by means of SNP information, directly at the complex level of genetic regions. Such an approach is based on a generalized notion of allele spectrum, which evaluates the complete genetic alterations of the SNP set belonging to a genetic region at the population level. The statistical significance of a region is then evaluated through a differential allele spectrum analysis between the conditions of individuals belonging to the population. Tests, performed on well-established datasets regarding Alzheimer's disease, show that DiGAS outperforms the state of the art in distinguishing between sick and healthy subjects.
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Affiliation(s)
- Antonino Aparo
- University of Verona, Strada le Grazie, 15, Verona, 37134, Italy; Research Center LURM (Interdepartmental Laboratory of Medical Research), University of Verona, Ple. L.A. Scuro 10, Verona, 37134, Italy
| | - Vincenzo Bonnici
- University of Parma, Parco Area delle Scienze, 53/A, Parma, 43124, Italy
| | - Simone Avesani
- University of Verona, Strada le Grazie, 15, Verona, 37134, Italy
| | - Luciano Cascione
- Institute of Oncology Research (IOR), Via Francesco Chiesa 5, Bellinzona, 6500, Switzerland
| | - Rosalba Giugno
- University of Verona, Strada le Grazie, 15, Verona, 37134, Italy.
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9
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024; 40:642-667. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [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: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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10
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Giel AS, Bigge J, Schumacher J, Maj C, Dasmeh P. Analysis of Evolutionary Conservation, Expression Level, and Genetic Association at a Genome-wide Scale Reveals Heterogeneity Across Polygenic Phenotypes. Mol Biol Evol 2024; 41:msae115. [PMID: 38865495 PMCID: PMC11247350 DOI: 10.1093/molbev/msae115] [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: 08/04/2023] [Revised: 03/22/2024] [Accepted: 05/03/2024] [Indexed: 06/14/2024] Open
Abstract
Understanding the expression level and evolutionary rate of associated genes with human polygenic diseases provides crucial insights into their disease-contributing roles. In this work, we leveraged genome-wide association studies (GWASs) to investigate the relationship between the genetic association and both the evolutionary rate (dN/dS) and expression level of human genes associated with the two polygenic diseases of schizophrenia and coronary artery disease. Our findings highlight a distinct variation in these relationships between the two diseases. Genes associated with both diseases exhibit a significantly greater variance in evolutionary rate compared to those implicated in monogenic diseases. Expanding our analyses to 4,756 complex traits in the GWAS atlas database, we unraveled distinct trait categories with a unique interplay among the evolutionary rate, expression level, and genetic association of human genes. In most polygenic traits, highly expressed genes were more associated with the polygenic phenotypes compared to lowly expressed genes. About 69% of polygenic traits displayed a negative correlation between genetic association and evolutionary rate, while approximately 30% of these traits showed a positive correlation between genetic association and evolutionary rate. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. We further established the polygenic evolution portal (evopolygen.de) as a resource for investigating relationships and generating hypotheses in the field of human polygenic trait evolution.
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Affiliation(s)
- Ann-Sophie Giel
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | - Jessica Bigge
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | | | - Carlo Maj
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | - Pouria Dasmeh
- Centre for Human Genetics, Marburg University, Marburg, Germany
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Institute for Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
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11
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Wang P, Xu X, Li M, Lou XY, Xu S, Wu B, Gao G, Yin P, Liu N. Gene-based association tests in family samples using GWAS summary statistics. Genet Epidemiol 2024; 48:103-113. [PMID: 38317324 DOI: 10.1002/gepi.22548] [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: 07/12/2023] [Revised: 11/18/2023] [Accepted: 01/08/2024] [Indexed: 02/07/2024]
Abstract
Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.
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Affiliation(s)
- Peng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hubei, People's Republic of China
| | - Xiao Xu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Ming Li
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Xiang-Yang Lou
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Siqi Xu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Baolin Wu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Ping Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hubei, People's Republic of China
| | - Nianjun Liu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
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12
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Perwad F, Akwo EA, Vartanian N, Suva LJ, Friedman PA, Robinson-Cohen C. Multi-trait Analysis of GWAS for circulating FGF23 Identifies Novel Network Interactions Between HRG-HMGB1 and Cardiac Disease in CKD. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.04.24303051. [PMID: 38496593 PMCID: PMC10942519 DOI: 10.1101/2024.03.04.24303051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Background Genome-wide association studies (GWAS) have identified numerous genetic loci associated with mineral metabolism (MM) markers but have exclusively focused on single-trait analysis. In this study, we performed a multi-trait analysis of GWAS (MTAG) of MM, exploring overlapping genetic architecture between traits, to identify novel genetic associations for fibroblast growth factor 23 (FGF23). Methods We applied MTAG to genetic variants common to GWAS of 5 genetically correlated MM markers (calcium, phosphorus, FGF23, 25-hydroxyvitamin D (25(OH)D) and parathyroid hormone (PTH)) in European-ancestry subjects. We integrated information from UKBioBank GWAS for blood levels for phosphate, 25(OH)D and calcium (n=366,484), and CHARGE GWAS for PTH (n=29,155) and FGF23 (n=16,624). We then used functional genomics to model interactive and dynamic networks to identify novel associations between genetic traits and circulating FGF23. Results MTAG increased the effective sample size for all MM markers to n=50,325 for FGF23. After clumping, MTAG identified independent genome-wide significant SNPs for all traits, including 62 loci for FGF23. Many of these loci have not been previously reported in single-trait analyses. Through functional genomics we identified Histidine-rich glycoprotein (HRG) and high mobility group box 1(HMGB1) genes as master regulators of downstream canonical pathways associated with FGF23. HRG-HMGB1 network interactions were also highly enriched in left ventricular heart tissue of a cohort of deceased hemodialysis patients. Conclusion Our findings highlight the importance of MTAG analysis of MM markers to boost the number of genome-wide significant loci for FGF23 to identify novel genetic traits. Functional genomics revealed novel networks that inform unique cellular functions and identified HRG-HMGB1 as key master regulators of FGF23 and cardiovascular disease in CKD. Future studies will provide a deeper understanding of genetic signatures associated with FGF23 and its role in health and disease.
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Affiliation(s)
- Farzana Perwad
- University of California San Francisco, San Francisco, CA
| | - Elvis A Akwo
- Vanderbilt University Medical Center, Nashville, TN
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13
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Visonà G, Bouzigon E, Demenais F, Schweikert G. Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery. Brief Bioinform 2024; 25:bbae014. [PMID: 38340090 PMCID: PMC10858647 DOI: 10.1093/bib/bbae014] [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: 10/16/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 02/12/2024] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes. RESULTS We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.
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Affiliation(s)
- Giovanni Visonà
- Empirical Inference, Max-Planck Institute for Intelligent Systems, Tübingen 72076, Germany
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14
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Moore A, Marks JA, Quach BC, Guo Y, Bierut LJ, Gaddis NC, Hancock DB, Page GP, Johnson EO. Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value. Commun Biol 2023; 6:1199. [PMID: 38001305 PMCID: PMC10673847 DOI: 10.1038/s42003-023-05413-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] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/03/2023] [Indexed: 11/26/2023] Open
Abstract
Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.
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Affiliation(s)
- Amy Moore
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA.
| | - Jesse A Marks
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Bryan C Quach
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Yuelong Guo
- GeneCentric Therapeutics, Inc., Cary, NC, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Nathan C Gaddis
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Dana B Hancock
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
| | - Grier P Page
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA
- Fellow Program, RTI International, Research Triangle Park, NC, 27709, USA
| | - Eric O Johnson
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, 27709, USA.
- Fellow Program, RTI International, Research Triangle Park, NC, 27709, USA.
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15
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Katsaouni N, Llavona P, Khodamoradi Y, Otto AK, Körber S, Geisen C, Seidl C, Vehreschild MJGT, Ciesek S, Ackermann J, Koch I, Schulz MH, Krause DS. Dataset of single nucleotide polymorphisms of immune-associated genes in patients with SARS-CoV-2 infection. PLoS One 2023; 18:e0287725. [PMID: 37971979 PMCID: PMC10653545 DOI: 10.1371/journal.pone.0287725] [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: 02/03/2023] [Accepted: 06/09/2023] [Indexed: 11/19/2023] Open
Abstract
The SARS-CoV-2 pandemic has affected nations globally leading to illness, death, and economic downturn. Why disease severity, ranging from no symptoms to the requirement for extracorporeal membrane oxygenation, varies between patients is still incompletely understood. Consequently, we aimed at understanding the impact of genetic factors on disease severity in infection with SARS-CoV-2. Here, we provide data on demographics, ABO blood group, human leukocyte antigen (HLA) type, as well as next-generation sequencing data of genes in the natural killer cell receptor family, the renin-angiotensin-aldosterone and kallikrein-kinin systems and others in 159 patients with SARS-CoV-2 infection, stratified into seven categories of disease severity. We provide single-nucleotide polymorphism (SNP) data on the patients and a protein structural analysis as a case study on a SNP in the SIGLEC7 gene, which was significantly associated with the clinical score. Our data represent a resource for correlation analyses involving genetic factors and disease severity and may help predict outcomes in infections with future SARS-CoV-2 variants and aid vaccine adaptation.
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Affiliation(s)
- Nikoletta Katsaouni
- Computational Epigenomics & Systems Cardiology, Institute of Cardiovascular Regeneration, Goethe University and University Clinic, Frankfurt am Main, Germany
- German Center for Cardiovascular Research (DZHK), Partner site Rhein Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Pablo Llavona
- Georg-Speyer-Haus, Institute for Tumor Biology and Experimental Therapy, Frankfurt am Main, Germany
| | - Yascha Khodamoradi
- Department of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany
| | - Ann-Kathrin Otto
- Molecular Bioinformatics, Institute of Computer Science, Goethe University Frankfurt am Main, Germany
| | - Stephanie Körber
- German Red Cross Blood Donor Service Baden-Württemberg Hessen, Frankfurt am Main, Germany
| | - Christof Geisen
- German Red Cross Blood Donor Service Baden-Württemberg Hessen, Frankfurt am Main, Germany
| | - Christian Seidl
- German Red Cross Blood Donor Service Baden-Württemberg Hessen, Frankfurt am Main, Germany
| | - Maria J. G. T. Vehreschild
- Department of Internal Medicine, Infectious Diseases, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany
| | - Sandra Ciesek
- Institute for Medical Virology, University Hospital, Goethe University, Frankfurt, Germany
- German Centre for Infection Research, External Partner Site, Frankfurt, Germany
- Fraunhofer Institute for Molecular Biology and Applied Ecology (IME), Branch Translational Medicine and Pharmacology, Frankfurt, Germany
| | - Jörg Ackermann
- Molecular Bioinformatics, Institute of Computer Science, Goethe University Frankfurt am Main, Germany
| | - Ina Koch
- Molecular Bioinformatics, Institute of Computer Science, Goethe University Frankfurt am Main, Germany
| | - Marcel H. Schulz
- Computational Epigenomics & Systems Cardiology, Institute of Cardiovascular Regeneration, Goethe University and University Clinic, Frankfurt am Main, Germany
- German Center for Cardiovascular Research (DZHK), Partner site Rhein Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Daniela S. Krause
- Georg-Speyer-Haus, Institute for Tumor Biology and Experimental Therapy, Frankfurt am Main, Germany
- German Red Cross Blood Donor Service Baden-Württemberg Hessen, Frankfurt am Main, Germany
- Institute of Biochemistry II and Institute of General Pharmacology and Toxicology, Goethe-University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Frankfurt, Germany
- Frankfurt Cancer Institute, Frankfurt am Main, Germany
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16
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Boothman I, Clayton LM, McCormack M, Driscoll AM, Stevelink R, Moloney P, Krause R, Kunz WS, Diehl S, O’Brien TJ, Sills GJ, de Haan GJ, Zara F, Koeleman BP, Depondt C, Marson AG, Stefansson H, Stefansson K, Craig J, Johnson MR, Striano P, Lerche H, Furney SJ, Delanty N, Sisodiya SM, Cavalleri GL. Testing for pharmacogenomic predictors of ppRNFL thinning in individuals exposed to vigabatrin. Front Neurosci 2023; 17:1156362. [PMID: 37790589 PMCID: PMC10542409 DOI: 10.3389/fnins.2023.1156362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/25/2023] [Indexed: 10/05/2023] Open
Abstract
Background The anti-seizure medication vigabatrin (VGB) is effective for controlling seizures, especially infantile spasms. However, use is limited by VGB-associated visual field loss (VAVFL). The mechanisms by which VGB causes VAVFL remains unknown. Average peripapillary retinal nerve fibre layer (ppRNFL) thickness correlates with the degree of visual field loss (measured by mean radial degrees). Duration of VGB exposure, maximum daily VGB dose, and male sex are associated with ppRNFL thinning. Here we test the hypothesis that common genetic variation is a predictor of ppRNFL thinning in VGB exposed individuals. Identifying pharmacogenomic predictors of ppRNFL thinning in VGB exposed individuals could potentially enable safe prescribing of VGB and broader use of a highly effective drug. Methods Optical coherence topography (OCT) and GWAS data were processed from VGB-exposed individuals (n = 71) recruited through the EpiPGX Consortium. We conducted quantitative GWAS analyses for the following OCT measurements: (1) average ppRNFL, (2) inferior quadrant, (3) nasal quadrant, (4) superior quadrant, (5) temporal quadrant, (6) inferior nasal sector, (7) nasal inferior sector, (8) superior nasal sector, and (9) nasal superior sector. Using the summary statistics from the GWAS analyses we conducted gene-based testing using VEGAS2. We conducted nine different PRS analyses using the OCT measurements. To determine if VGB-exposed individuals were predisposed to having a thinner RNFL, we calculated their polygenic burden for retinal thickness. PRS alleles for retinal thickness were calculated using published summary statistics from a large-scale GWAS of inner retinal morphology using the OCT images of UK Biobank participants. Results The GWAS analyses did not identify a significant association after correction for multiple testing. Similarly, the gene-based and PRS analyses did not reveal a significant association that survived multiple testing. Conclusion We set out to identify common genetic predictors for VGB induced ppRNFL thinning. Results suggest that large-effect common genetic predictors are unlikely to exist for ppRNFL thinning (as a marker of VAVFL). Sample size was a limitation of this study. However, further recruitment is a challenge as VGB is rarely used today because of this adverse reaction. Rare variants may be predictors of this adverse drug reaction and were not studied here.
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Affiliation(s)
- Isabelle Boothman
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
- The SFI Futureneuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
- The SFI Centre for Research Training in Genomics Data Science, Galway, Ireland
| | - Lisa M. Clayton
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
- Chalfont Centre for Epilepsy, Bucks, United Kingdom
| | - Mark McCormack
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | | | - Remi Stevelink
- Department of Genetics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Patrick Moloney
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Wolfram S. Kunz
- Division of Neurochemistry, Department of Epileptology, University Bonn Medical Center, Bonn, Germany
| | - Sarah Diehl
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Terence J. O’Brien
- Departments of Neuroscience and Neurology, Central Clinical School, The Alfred Hospital, Monash University, Melbourne, VIC, Australia
| | - Graeme J. Sills
- School of Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Gerrit-Jan de Haan
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, Netherlands
| | - Federico Zara
- "IRCCS”G. Gaslini" Institute, Genova, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genova, Italy
| | - Bobby P. Koeleman
- Department of Genetics, University Medical Center Utrecht, Utrecht, Netherlands
| | - Chantal Depondt
- Department of Neurology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Anthony G. Marson
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, United Kingdom
| | | | | | - John Craig
- Department of Neurology, Royal Victoria Hospital, Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Michael R. Johnson
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, United Kingdom
| | - Pasquale Striano
- "IRCCS”G. Gaslini" Institute, Genova, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genova, Italy
| | - Holger Lerche
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Simon J. Furney
- Genomic Oncology Research Group, Deptartment of Physiology and Medical Physics, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland
| | - Norman Delanty
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
- Chalfont Centre for Epilepsy, Bucks, United Kingdom
| | - Gianpiero L. Cavalleri
- School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland
- The SFI Futureneuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
- The SFI Centre for Research Training in Genomics Data Science, Galway, Ireland
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17
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Sadler MC, Auwerx C, Deelen P, Kutalik Z. Multi-layered genetic approaches to identify approved drug targets. CELL GENOMICS 2023; 3:100341. [PMID: 37492104 PMCID: PMC10363916 DOI: 10.1016/j.xgen.2023.100341] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 07/27/2023]
Abstract
Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR = 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.
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Affiliation(s)
- Marie C. Sadler
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Chiara Auwerx
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Patrick Deelen
- Department of Genetics, University of Groningen, 9700 Groningen, the Netherlands
- Oncode Institute, 3521 Utrecht, the Netherlands
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
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18
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Lee S, Yang HK, Lee HJ, Park DJ, Kong SH, Park SK. Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network. Gastric Cancer 2023; 26:517-527. [PMID: 36995485 DOI: 10.1007/s10120-023-01380-7] [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: 11/10/2022] [Accepted: 03/02/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach. METHODS Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes. RESULTS Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively. CONCLUSIONS These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.
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Affiliation(s)
- Sangjun Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongro-Gu, Seoul, 03080, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
| | - Han-Kwang Yang
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Hyuk-Joon Lee
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Do Joong Park
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Seong-Ho Kong
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongro-Gu, Seoul, 03080, Korea.
- Cancer Research Institute, Seoul National University, Seoul, Korea.
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, Korea.
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19
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Sahana G, Cai Z, Sanchez MP, Bouwman AC, Boichard D. Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle. J Dairy Sci 2023:S0022-0302(23)00357-0. [PMID: 37349208 DOI: 10.3168/jds.2022-22694] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 02/01/2023] [Indexed: 06/24/2023]
Abstract
Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.
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Affiliation(s)
- G Sahana
- Aarhus University, Center for Quantitative Genetic and Genomics, 8830 Tjele, Denmark.
| | - Z Cai
- Aarhus University, Center for Quantitative Genetic and Genomics, 8830 Tjele, Denmark
| | - M P Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - A C Bouwman
- Wageningen University & Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - D Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
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20
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Lu H, Zhang S, Jiang Z, Zeng P. Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations. Brief Bioinform 2023:bbad232. [PMID: 37332016 DOI: 10.1093/bib/bbad232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 05/06/2023] [Accepted: 06/04/2023] [Indexed: 06/20/2023] Open
Abstract
Trans-ethnic genome-wide association studies have revealed that many loci identified in European populations can be reproducible in non-European populations, indicating widespread trans-ethnic genetic similarity. However, how to leverage such shared information more efficiently in association analysis is less investigated for traits in underrepresented populations. We here propose a statistical framework, trans-ethnic genetic risk score informed gene-based association mixed model (GAMM), by hierarchically modeling single-nucleotide polymorphism effects in the target population as a function of effects of the same trait in well-studied populations. GAMM powerfully integrates genetic similarity across distinct ancestral groups to enhance power in understudied populations, as confirmed by extensive simulations. We illustrate the usefulness of GAMM via the application to 13 blood cell traits (i.e. basophil count, eosinophil count, hematocrit, hemoglobin concentration, lymphocyte count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean corpuscular volume, monocyte count, neutrophil count, platelet count, red blood cell count and total white blood cell count) in Africans of the UK Biobank (n = 3204) while utilizing genetic overlap shared in Europeans (n = 746 667) and East Asians (n = 162 255). We discovered multiple new associated genes, which had otherwise been missed by existing methods, and revealed that the trans-ethnic information indirectly contributed much to the phenotypic variance. Overall, GAMM represents a flexible and powerful statistical framework of association analysis for complex traits in underrepresented populations by integrating trans-ethnic genetic similarity across well-studied populations, and helps attenuate health inequities in current genetics research for people of minority populations.
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Affiliation(s)
- Haojie Lu
- 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
| | - Zhou Jiang
- 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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21
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Dai J, Xu Y, Wang T, Zeng P. Exploring the relationship between socioeconomic deprivation index and Alzheimer's disease using summary-level data: From genetic correlation to causality. Prog Neuropsychopharmacol Biol Psychiatry 2023; 123:110700. [PMID: 36566903 DOI: 10.1016/j.pnpbp.2022.110700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Patients with Alzheimer's disease (AD) are markedly increasing as population aging and no disease-modifying therapies are currently available for AD. Previous studies suggested a broad link between socioeconomic status and a variety of disorders, including mental illness and cognitive abilities. However, the association between socioeconomic deprivation and AD has been unknown. We here employed Townsend deprivation index (TDI) to explore such relation and found a positive genetic correlation (r̂g=0.211, P = 8.00 × 10-4) between the two traits with summary statistics data (N = 455,258 for TDI and N = 455,815 for AD). Then, we performed pleiotropy analysis at both variant and gene levels using a powerful method called PLACO and detected 87 distinct pleiotropic genes. Functional analysis demonstrated these genes were significantly enriched in pancreas, liver, heart, blood, brain, and muscle tissues. Using Mendelian randomization methods, we further found that one genetically predicted standard deviation elevation in TDI could lead to approximately 18.5% (95% confidence intervals 1.6- 38.2%, P = 0.031) increase of AD risk, and that the identified causal association was robust against used MR approaches, horizontal pleiotropy, and instrumental selection. Overall, this study provides deep insight into common genetic components underlying TDI and AD, and further reveals causal connection between them. It is also helpful to develop a more suitable plan for ameliorating inequities, hardship, and disadvantage, with the hope of improving health outcomes among economically disadvantaged people.
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Affiliation(s)
- Jing Dai
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yue Xu
- 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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22
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Genetic correlation and gene-based pleiotropy analysis for four major neurodegenerative diseases with summary statistics. Neurobiol Aging 2023; 124:117-128. [PMID: 36740554 DOI: 10.1016/j.neurobiolaging.2022.12.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/25/2022] [Accepted: 12/27/2022] [Indexed: 01/02/2023]
Abstract
Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases.
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23
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Luo J, Zhang T, Wang W, Zhang D. Genome-wide association study of handgrip strength in the Northern Chinese adult twins. Connect Tissue Res 2023; 64:117-125. [PMID: 35876483 DOI: 10.1080/03008207.2022.2104160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
PURPOSE Currently, new loci related to handgrip strength have been identified in genome-wide association studies. However, this topic is an understudied area in the Chinese population. MATERIALS AND METHODS A total of 135 dizygotic twin pairs recruited from the Qingdao Twin Registry system were included in the present study. Using GEMMA, VEAGSE2, and PASCAL software for SNP-based analysis, gene-based analysis, and pathway-based analysis, respectively. The resulting SNPs were subjected to eQTL analysis. RESULTS Although none of the loci reach the statistically significant level (p < 5 × 10-8), we found 19 SNPs exceeding the suggestive significant level (p < 1 × 10-5). After imputation, 162 SNPs reached suggestive evidence level for handgrip strength. A total of 1,118 genes reached the nominal significance level (p < 0.05) in gene-based analysis. A total of 626 potential biological pathways were associated with handgrip strength (p < 0.05). The results of eQTL analysis were mainly enriched in tissues such as the muscle-skeletal, brain, visceral fat, and brain-cortical. CONCLUSIONS Genetic variants may involve in regulatory domains, functional genes, and biological pathways that mediate handgrip strength.
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Affiliation(s)
- Jia Luo
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong Province, China
| | - Tianhao Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong Province, China
| | - Weijing Wang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong Province, China
| | - Dongfeng Zhang
- Department of Epidemiology and Health Statistics, Public Health College, Qingdao University, Qingdao, Shandong Province, China
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24
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Tsai SJ, Kao CF, Su TP, Li CT, Lin WC, Hong CJ, Bai YM, Tu PC, Chen MH. Cytokine- and Vascular Endothelial Growth Factor-Related Gene-Based Genome-Wide Association Study of Low-Dose Ketamine Infusion in Patients with Treatment-Resistant Depression. CNS Drugs 2023; 37:243-253. [PMID: 36763263 DOI: 10.1007/s40263-023-00989-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/15/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Ketamine may work as an anti-inflammatory agent, and it increases the levels of vascular endothelial growth factor (VEGF) in patients with treatment-resistant depression. However, whether genes related to pro-inflammatory and anti-inflammatory cytokines and VEGF may predict the treatment response to ketamine remains unknown.Therefore the aim of this study was to analyze whether specific genes related to inflammatory processes and VEGF were associated with treatment response to low-dose ketamine in patients with treatment-resistant depression. METHODS Based on the genome data from our clinical trial, this study was a secondary analysis of candidate genes correlated with different timepoints of depressive symptoms. In total, 65 patients with treatment-resistant depression (n = 21 for ketamine 0.5 mg/kg, 20 for ketamine 0.2 mg/kg, and 24 for normal saline) were genotyped for 684,616 single nucleotide polymorphisms. Genes associated with 80 cytokines (i.e., interleukin [IL]-1, IL-6, tumor necrosis factor-α, and adiponectin) and VEGF (i.e., VEGF and VEGF receptors) were selected for the gene-based genome-wide association study on the antidepressant effect of a ketamine infusion. RESULTS Specific single nucleotide polymorphisms, including rs2540315 and rs75746675 in IL1R1 and rs79568085 in VEGFC, were related to the rapid (within 240 min) antidepressant effect of a ketamine infusion; specific single nucleotide polymorphisms, such as Affx-20131665 in PIGF and rs8179353, rs8179353, and rs8179353 in TNFRSF8, were associated with the sustained (up to 2 weeks) antidepressant effect of low-dose (combined 0.5 mg/kg and 0.2 mg/kg) ketamine. CONCLUSIONS Our findings further revealed that genes related to both anti-inflammatory and pro-inflammatory cytokines (i.e., IL-1, IL-2, IL-6, tumor necrosis factor-α, C-reactive protein, and adiponectin) and VEGF-FLK signaling predicted the treatment response to a ketamine infusion in patients with treatment-resistant depression. The synergic modulation of inflammatory and VEGF systems may contribute to the antidepressant effect of ketamine. CLINICAL TRIAL REGISTRATION University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) number: UMIN000016985.
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Affiliation(s)
- Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chung-Feng Kao
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
- Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
| | - Tung-Ping Su
- Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Chen Lin
- Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chen-Jee Hong
- Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Pei-Chi Tu
- Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan.
- Division of Psychiatry, Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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25
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Li A, Liu S, Bakshi A, Jiang L, Chen W, Zheng Z, Sullivan PF, Visscher PM, Wray NR, Yang J, Zeng J. mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data. Am J Hum Genet 2023; 110:30-43. [PMID: 36608683 PMCID: PMC9892780 DOI: 10.1016/j.ajhg.2022.12.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/08/2022] [Indexed: 01/07/2023] Open
Abstract
Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.
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Affiliation(s)
- Ang Li
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Shouye Liu
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Andrew Bakshi
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | | | - Wenhan Chen
- Epigenetics Research Laboratory, Genomics and Epigenetics Theme, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Zhili Zheng
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden; Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter M Visscher
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia; Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Jian Zeng
- Institute for Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia.
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26
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Tielke A, Martins H, Pelzl MA, Maaser-Hecker A, David FS, Reinbold CS, Streit F, Sirignano L, Schwarz M, Vedder H, Kammerer-Ciernioch J, Albus M, Borrmann-Hassenbach M, Hautzinger M, Hünten K, Degenhardt F, Fischer SB, Beins EC, Herms S, Hoffmann P, Schulze TG, Witt SH, Rietschel M, Cichon S, Nöthen MM, Schratt G, Forstner AJ. Genetic and functional analyses implicate microRNA 499A in bipolar disorder development. Transl Psychiatry 2022; 12:437. [PMID: 36207305 PMCID: PMC9547016 DOI: 10.1038/s41398-022-02176-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 08/10/2022] [Accepted: 09/12/2022] [Indexed: 11/09/2022] Open
Abstract
Bipolar disorder (BD) is a complex mood disorder with a strong genetic component. Recent studies suggest that microRNAs contribute to psychiatric disorder development. In BD, specific candidate microRNAs have been implicated, in particular miR-137, miR-499a, miR-708, miR-1908 and miR-2113. The aim of the present study was to determine the contribution of these five microRNAs to BD development. For this purpose, we performed: (i) gene-based tests of the five microRNA coding genes, using data from a large genome-wide association study of BD; (ii) gene-set analyses of predicted, brain-expressed target genes of the five microRNAs; (iii) resequencing of the five microRNA coding genes in 960 BD patients and 960 controls and (iv) in silico and functional studies for selected variants. Gene-based tests revealed a significant association with BD for MIR499A, MIR708, MIR1908 and MIR2113. Gene-set analyses revealed a significant enrichment of BD associations in the brain-expressed target genes of miR-137 and miR-499a-5p. Resequencing identified 32 distinct rare variants (minor allele frequency < 1%), all of which showed a non-significant numerical overrepresentation in BD patients compared to controls (p = 0.214). Seven rare variants were identified in the predicted stem-loop sequences of MIR499A and MIR2113. These included rs142927919 in MIR2113 (pnom = 0.331) and rs140486571 in MIR499A (pnom = 0.297). In silico analyses predicted that rs140486571 might alter the miR-499a secondary structure. Functional analyses showed that rs140486571 significantly affects miR-499a processing and expression. Our results suggest that MIR499A dysregulation might contribute to BD development. Further research is warranted to elucidate the contribution of the MIR499A regulated network to BD susceptibility.
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Affiliation(s)
- Aileen Tielke
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,Salus Clinic Hürth, Hürth, Germany
| | - Helena Martins
- grid.5801.c0000 0001 2156 2780Lab of Systems Neuroscience, Department of Health Science and Technology, Institute for Neuroscience, Swiss Federal Institute of Technology ETH & Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Michael A. Pelzl
- grid.10253.350000 0004 1936 9756Institute for Physiological Chemistry, Philipps-University Marburg, Marburg, Germany ,grid.10392.390000 0001 2190 1447Present Address: Clinic for Psychiatry and Psychotherapy, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Anna Maaser-Hecker
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Friederike S. David
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Céline S. Reinbold
- grid.5510.10000 0004 1936 8921Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway ,grid.6612.30000 0004 1937 0642Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Fabian Streit
- grid.7700.00000 0001 2190 4373Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Lea Sirignano
- grid.7700.00000 0001 2190 4373Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | | | | | | | - Margot Albus
- grid.419834.30000 0001 0690 3065Isar Amper Klinikum München Ost, kbo, Haar, Germany
| | | | - Martin Hautzinger
- grid.10392.390000 0001 2190 1447Department of Psychology, Clinical Psychology and Psychotherapy, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Karola Hünten
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Franziska Degenhardt
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,grid.410718.b0000 0001 0262 7331Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Duisburg, Germany
| | - Sascha B. Fischer
- grid.6612.30000 0004 1937 0642Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Eva C. Beins
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Stefan Herms
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,grid.6612.30000 0004 1937 0642Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Per Hoffmann
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,grid.6612.30000 0004 1937 0642Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Thomas G. Schulze
- grid.7700.00000 0001 2190 4373Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany ,grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany ,grid.411984.10000 0001 0482 5331Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Stephanie H. Witt
- grid.7700.00000 0001 2190 4373Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany ,grid.7700.00000 0001 2190 4373Center for Innovative Psychiatry and Psychotherapy Research, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Marcella Rietschel
- grid.7700.00000 0001 2190 4373Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Sven Cichon
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,grid.6612.30000 0004 1937 0642Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - Markus M. Nöthen
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Gerhard Schratt
- grid.5801.c0000 0001 2156 2780Lab of Systems Neuroscience, Department of Health Science and Technology, Institute for Neuroscience, Swiss Federal Institute of Technology ETH & Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Andreas J. Forstner
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany ,grid.10253.350000 0004 1936 9756Centre for Human Genetics, University of Marburg, Marburg, Germany
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27
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Song K, Zheng X, Liu X, Sheng Y, Liu L, Wen L, Shang S, Deng Y, Ouyang Q, Sun X, Li Q, Chen P, Cai G, Chen M, Zhang Y, Liang B, Zhang J, Zhang X, Chen X. Genome-wide association study of SNP- and gene-based approaches to identify susceptibility candidates for lupus nephritis in the Han Chinese population. Front Immunol 2022; 13:908851. [PMID: 36275661 PMCID: PMC9580327 DOI: 10.3389/fimmu.2022.908851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundLupus nephritis (LN) is one of the most common and serious complications of systemic lupus erythaematosus (SLE). Genetic factors play important roles in the pathogenesis of LN and could be used to predict who might develop LN. The purpose of this study was to screen for susceptible candidates of LN across the whole genome in the Han Chinese population.Methods592 LN patients and 453 SLE patients without renal damage were genotyped at 492,970 single nucleotide polymorphisms (SNPs) in the genome-wide association study (GWAS). Fifty-six SNPs were selected for replication in an independent cohort of 188 LN and 171 SLE without LN patients. Further quantitative real-time (qRT) PCR was carried out in 6 LN patients and 6 healthy controls. Gene-based analysis was conducted using the versatile gene-based test for GWAS. Subsequently, enrichment and pathway analyses were performed in the DAVID database.ResultsThe GWAS analysis and the following replication research identified 9 SNPs showing suggestive correlation with LN (P<10-4). The most significant SNP was rs12606116 (18p11.32), at P=8.72×10−6. The qRT-PCR results verified the mRNA levels of LINC00470 and ADCYAP1, the closest genes to rs12606116, were significantly lower in LN patients. From the gene-based analysis, 690 genes had suggestive evidence of association (P<0.05), including LINC00470. The enrichment analysis identified the involvement of transforming growth factor beta (TGF-β) signalings in the development of LN. Lower plasma level of TGF-β1 (P<0.05) in LN patients and lower expression of transforming growth factor beta receptor 2 in lupus mice kidney (P<0.05) futher indicate the involvement of TGF-β in LN.ConclusionsOur analyses identified several promising susceptibility candidates involved in LN, and further verification of these candidates was necessary.
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Affiliation(s)
- Kangkang Song
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Xiaodong Zheng
- Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, China
| | - Xiaomin Liu
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Yujun Sheng
- Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, China
| | - Lu Liu
- Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, China
| | - Leilei Wen
- Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, China
| | - Shunlai Shang
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Yiyao Deng
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Qing Ouyang
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Xuefeng Sun
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Qinggang Li
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Pu Chen
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Guangyan Cai
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
| | - Mengyun Chen
- Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, China
| | - Yuanjing Zhang
- Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, China
| | - Bo Liang
- Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, China
| | - Jianglin Zhang
- Department of Rheumatology, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Xuejun Zhang
- Institute of Dermatology and Department of Dermatology at No.1 Hospital, Anhui Medical University, Hefei, China
- Institute of Dermatology and Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Xiangmei Chen, ; Xuejun Zhang,
| | - Xiangmei Chen
- Department of Nephrology, The First Medical Centre, Chinese PLA General Hospital, Medical School of Chinese PLA, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing, China
- Haihe Laboratory of Cell Ecosystem, Tianjin, China
- *Correspondence: Xiangmei Chen, ; Xuejun Zhang,
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Katsonis P, Wilhelm K, Williams A, Lichtarge O. Genome interpretation using in silico predictors of variant impact. Hum Genet 2022; 141:1549-1577. [PMID: 35488922 PMCID: PMC9055222 DOI: 10.1007/s00439-022-02457-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 04/17/2022] [Indexed: 02/06/2023]
Abstract
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
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Affiliation(s)
- Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Kevin Wilhelm
- Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Amanda Williams
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
- Department of Biochemistry, Human Genetics and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
- Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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Greco LA, Reay WR, Dayas CV, Cairns MJ. Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance. Transl Psychiatry 2022; 12:403. [PMID: 36151087 PMCID: PMC9508072 DOI: 10.1038/s41398-022-02186-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/25/2022] [Accepted: 09/13/2022] [Indexed: 12/04/2022] Open
Abstract
Almost half of individuals diagnosed with schizophrenia also present with a substance use disorder, however, little is known about potential molecular mechanisms underlying this comorbidity. We used genetic analyses to enhance our understanding of the molecular overlap between these conditions. Our analyses revealed a positive genetic correlation between schizophrenia and the following dependence phenotypes: alcohol (rg = 0.368, SE = 0.076, P = 1.61 × 10-6), cannabis use disorder (rg = 0.309, SE = 0.033, P = 1.97 × 10-20) and nicotine (rg = 0.117, SE = 0.043, P = 7.0 × 10-3), as well as drinks per week (rg = 0.087, SE = 0.021, P = 6.36 × 10-5), cigarettes per day (rg = 0.11, SE = 0.024, P = 4.93 × 10-6) and life-time cannabis use (rg = 0.234, SE = 0.029, P = 3.74 × 10-15). We further constructed latent causal variable (LCV) models to test for partial genetic causality and found evidence for a potential causal relationship between alcohol dependence and schizophrenia (GCP = 0.6, SE = 0.22, P = 1.6 × 10-3). This putative causal effect with schizophrenia was not seen using a continuous phenotype of drinks consumed per week, suggesting that distinct molecular mechanisms underlying dependence are involved in the relationship between alcohol and schizophrenia. To localise the specific genetic overlap between schizophrenia and substance use disorders (SUDs), we conducted a gene-based and gene-set pairwise meta-analysis between schizophrenia and each of the four individual substance dependence phenotypes in up to 790,806 individuals. These bivariate meta-analyses identified 44 associations not observed in the individual GWAS, including five shared genes that play a key role in early central nervous system development. The results from this study further supports the existence of underlying shared biology that drives the overlap in substance dependence in schizophrenia, including specific biological systems related to metabolism and neuronal function.
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Affiliation(s)
- Laura A Greco
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Christopher V Dayas
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.
- Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton, NSW, Australia.
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Qiao J, Shao Z, Wu Y, Zeng P, Wang T. Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing. Lab Invest 2022; 20:424. [PMID: 36138484 PMCID: PMC9503281 DOI: 10.1186/s12967-022-03637-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 09/12/2022] [Indexed: 11/21/2022]
Abstract
Background Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking. Methods By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement. Results Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones. Conclusion Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03637-8.
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Affiliation(s)
- 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
| | - Yuxuan Wu
- 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. .,Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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31
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Shao Z, Wang T, Qiao J, Zhang Y, Huang S, Zeng P. A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies. BMC Bioinformatics 2022; 23:359. [PMID: 36042399 PMCID: PMC9429742 DOI: 10.1186/s12859-022-04897-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/22/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods. RESULTS We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow. CONCLUSION In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .
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Affiliation(s)
- 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
| | - Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yuchen Zhang
- 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
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, 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.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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32
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Lee S, Yang HK, Lee HJ, Park DJ, Kong SH, Park SK. Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development. Front Genet 2022; 13:928783. [PMID: 36081994 PMCID: PMC9446437 DOI: 10.3389/fgene.2022.928783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/25/2022] [Indexed: 12/05/2022] Open
Abstract
Objective: Despite being a powerful tool to identify novel variants, genome-wide association studies (GWAS) are not sufficient to explain the biological function of variants. In this study, we aimed to elucidate at the gene level the biological mechanisms involved in gastric cancer (GC) development and to identify candidate drug target genes. Materials and methods: We conducted a systematic review for GWAS on GC following the PRISMA guidelines. Single nucleotide polymorphism (SNP)-level meta-analysis and gene-based analysis (GBA) were performed to identify SNPs and genes significantly associated with GC. Expression quantitative trait loci (eQTL), disease network, pathway enrichment, gene ontology, gene-drug, and chemical interaction analyses were conducted to elucidate the function of the genes identified by GBA. Results: A review of GWAS on GC identified 226 SNPs located in 91 genes. In the comprehensive GBA, 44 genes associated with GC were identified, among which 12 genes (THBS3, GBAP1, KRTCAP2, TRIM46, HCN3, MUC1, DAP3, EFNA1, MTX1, PRKAA1, PSCA, and ABO) were eQTL. Using disease network and pathway analyses, we identified that PRKAA, THBS3, and EFNA1 were significantly associated with the PI3K-Alt-mTOR-signaling pathway, which is involved in various oncogenic processes, and that MUC1 acts as a regulator in both the PI3K-Alt-mTOR and P53 signaling pathways. Furthermore, RPKAA1 had the highest number of interactions with drugs and chemicals. Conclusion: Our study suggests that PRKAA1, a gene in the PI3K-Alt-mTOR-signaling pathway, could be a potential target gene for drug development associated with GC in the future. Systematic Review Registration: website, identifier registration number.
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Affiliation(s)
- Sangjun Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea
| | - Han-Kwang Yang
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyuk-Joon Lee
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Do Joong Park
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Seong-Ho Kong
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Sue K. Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, South Korea
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Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph. PLoS One 2022; 17:e0271395. [PMID: 35830458 PMCID: PMC9278741 DOI: 10.1371/journal.pone.0271395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 06/29/2022] [Indexed: 12/24/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.
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Cinar O, Viechtbauer W. A Comparison of Methods for Gene-Based Testing That Account for Linkage Disequilibrium. Front Genet 2022; 13:867724. [PMID: 35601489 PMCID: PMC9117705 DOI: 10.3389/fgene.2022.867724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/07/2022] [Indexed: 11/16/2022] Open
Abstract
Controlling the type I error rate while retaining sufficient power is a major concern in genome-wide association studies, which nowadays often examine more than a million single-nucleotide polymorphisms (SNPs) simultaneously. Methods such as the Bonferroni correction can lead to a considerable decrease in power due to the large number of tests conducted. Shifting the focus to higher functional structures (e.g., genes) can reduce the loss of power. This can be accomplished via the combination of p-values of SNPs that belong to the same structural unit to test their joint null hypothesis. However, standard methods for this purpose (e.g., Fisher’s method) do not account for the dependence among the tests due to linkage disequilibrium (LD). In this paper, we review various adjustments to methods for combining p-values that take LD information explicitly into consideration and evaluate their performance in a simulation study based on data from the HapMap project. The results illustrate the importance of incorporating LD information into the methods for controlling the type I error rate at the desired level. Furthermore, some methods are more successful in controlling the type I error rate than others. Among them, Brown’s method was the most robust technique with respect to the characteristics of the genes and outperformed the Bonferroni method in terms of power in many scenarios. Examining the genetic factors of a phenotype of interest at the gene-rather than SNP-level can provide researchers benefits in terms of the power of the study. While doing so, one should be careful to account for LD in SNPs belonging to the same gene, for which Brown’s method seems the most robust technique.
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35
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Smith SP, Shahamatdar S, Cheng W, Zhang S, Paik J, Graff M, Haiman C, Matise TC, North KE, Peters U, Kenny E, Gignoux C, Wojcik G, Crawford L, Ramachandran S. Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries. Am J Hum Genet 2022; 109:871-884. [PMID: 35349783 PMCID: PMC9118115 DOI: 10.1016/j.ajhg.2022.03.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/02/2022] [Indexed: 12/12/2022] Open
Abstract
Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.
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Affiliation(s)
- Samuel Pattillo Smith
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Sahar Shahamatdar
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Wei Cheng
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Selena Zhang
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
| | - Joseph Paik
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
| | - Misa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, NC 27599, USA
| | - Christopher Haiman
- Department of Preventative Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - T C Matise
- Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Eimear Kenny
- The Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO 80204, USA
| | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Biostatistics, Brown University, Providence, RI 02906, USA; Microsoft Research New England, Cambridge, MA 02142, USA
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI 02912, USA; Data Science Initiative, Brown University, Providence, RI 02912, USA.
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Legault MA, Perreault LPL, Tardif JC, Dubé MP. ExPheWas: a platform for cis-Mendelian randomization and gene-based association scans. Nucleic Acids Res 2022; 50:W305-W311. [PMID: 35474380 PMCID: PMC9252780 DOI: 10.1093/nar/gkac289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/31/2022] [Accepted: 04/13/2022] [Indexed: 11/17/2022] Open
Abstract
Establishing the relationship between protein-coding genes and phenotypes has the potential to inform on the molecular etiology of diseases. Here, we describe ExPheWas (exphewas.ca), a gene-based phenome-wide association study browser and platform that enables the conduct of gene-based Mendelian randomization. The ExPheWas data repository includes sex-stratified and sex-combined gene-based association results from 26 616 genes with 1746 phenotypes measured in up to 413 133 individuals from the UK Biobank. Interactive visualizations are provided through a browser to facilitate data exploration supported by false discovery rate control, and it includes tools for enrichment analysis. The interactive Mendelian randomization module in ExPheWas allows the estimation of causal effects of a genetically predicted exposure on an outcome by using genetic variation in a single gene as the instrumental variable.
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Affiliation(s)
- Marc-André Legault
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, QC H1T 1C8, Canada.,Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Louis-Philippe Lemieux Perreault
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, QC H1T 1C8, Canada
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada.,Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Marie-Pierre Dubé
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, QC H1T 1C8, Canada.,Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
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Lin H, Lin WH, Lin F, Liu CY, Che CH, Huang HP. Potential Pleiotropic Genes and Shared Biological Pathways in Epilepsy and Depression Based on GWAS Summary Statistics. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6799285. [PMID: 35463244 PMCID: PMC9019309 DOI: 10.1155/2022/6799285] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/01/2022] [Accepted: 03/08/2022] [Indexed: 11/17/2022]
Abstract
Current epidemiological and experimental studies have indicated the overlapping genetic foundation of epilepsy and depression. However, the detailed pleiotropic genetic etiology and neurobiological pathways have not been well understood, and there are many variants with underestimated effect on the comorbidity of the two diseases. Utilizing genome-wide association study (GWAS) summary statistics of epilepsy (15,212 cases and 29,677 controls) and depression (170,756 cases and 329,443 controls) from large consortia, we assessed the integrated gene-based association with both diseases by Multimarker Analysis of Genomic Annotation (MAGMA) and Fisher's meta-analysis. On the one hand, shared genes with significantly altered transcripts in Gene Expression Omnibus (GEO) data sets were considered as possible pleiotropic genes. On the other hand, the pathway enrichment analysis was conducted based on the gene lists with nominal significance in the gene-based association test of each disease. We identified a total of two pleiotropic genes (CD3G and SLCO3A1) with gene expression analysis validated and interpreted twenty-five common biological process supported with literature mining. This study indicates the potentially shared genes associated with both epilepsy and depression based on gene expression, meta-data analysis, and pathway enrichment strategy along with traditional GWAS and provides insights into the possible intersecting pathways that were not previously reported.
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Affiliation(s)
- Han Lin
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Wan-Hui Lin
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Intensive Care Unit, Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fuzhou 350001, China
| | - Feng Lin
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Chang-Yun Liu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Chun-Hui Che
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Hua-Pin Huang
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Intensive Care Unit, Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fuzhou 350001, China
- Department of Geriatrics, Fujian Medical University Union Hospital, Fuzhou 350001, China
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38
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Li X, Jiang L, Xue C, Li MJ, Li M. A conditional gene-based association framework integrating isoform-level eQTL data reveals new susceptibility genes for schizophrenia. eLife 2022; 11:e70779. [PMID: 35412455 PMCID: PMC9005191 DOI: 10.7554/elife.70779] [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: 05/28/2021] [Accepted: 11/11/2021] [Indexed: 11/13/2022] Open
Abstract
Linkage disequilibrium and disease-associated variants in the non-coding regions make it difficult to distinguish the truly associated genes from the redundantly associated genes for complex diseases. In this study, we proposed a new conditional gene-based framework called eDESE that leveraged an improved effective chi-squared statistic to control the type I error rates and remove the redundant associations. eDESE initially performed the association analysis by mapping variants to genes according to their physical distance. We further demonstrated that the isoform-level eQTLs could be more powerful than the gene-level eQTLs in the association analysis using a simulation study. Then the eQTL-guided strategies, that is, mapping variants to genes according to their gene/isoform-level variant-gene cis-eQTLs associations, were also integrated with eDESE. We then applied eDESE to predict the potential susceptibility genes of schizophrenia and found that the potential susceptibility genes were enriched with many neuronal or synaptic signaling-related terms in the Gene Ontology knowledgebase and antipsychotics-gene interaction terms in the drug-gene interaction database (DGIdb). More importantly, seven potential susceptibility genes identified by eDESE were the target genes of multiple antipsychotics in DrugBank. Comparing the potential susceptibility genes identified by eDESE and other benchmark approaches (i.e., MAGMA and S-PrediXcan) implied that strategy based on the isoform-level eQTLs could be an important supplement for the other two strategies (physical distance and gene-level eQTLs). We have implemented eDESE in our integrative platform KGGSEE (http://pmglab.top/kggsee/#/) and hope that eDESE can facilitate the prediction of candidate susceptibility genes and isoforms for complex diseases in a multi-tissue context.
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Affiliation(s)
- Xiangyi Li
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen UniversityGuangzhouChina
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of EducationGuangzhouChina
- Center for Precision Medicine, Sun Yat-sen UniversityGuangzhouChina
| | - Lin Jiang
- Research Center of Medical Sciences, Guangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Chao Xue
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen UniversityGuangzhouChina
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of EducationGuangzhouChina
- Center for Precision Medicine, Sun Yat-sen UniversityGuangzhouChina
| | - Mulin Jun Li
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Medical UniversityTianjinChina
| | - Miaoxin Li
- Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen UniversityGuangzhouChina
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of EducationGuangzhouChina
- Center for Precision Medicine, Sun Yat-sen UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen UniversityZhuhaiChina
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Hébert F, Causeur D, Emily M. Omnibus testing approach for gene-based gene-gene interaction. Stat Med 2022; 41:2854-2878. [PMID: 35338506 DOI: 10.1002/sim.9389] [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: 07/12/2020] [Revised: 03/03/2022] [Accepted: 03/04/2022] [Indexed: 11/07/2022]
Abstract
Genetic interaction is considered as one of the main heritable component of complex traits. With the emergence of genome-wide association studies (GWAS), a collection of statistical methods dedicated to the identification of interaction at the SNP level have been proposed. More recently, gene-based gene-gene interaction testing has emerged as an attractive alternative as they confer advantage in both statistical power and biological interpretation. Most of the gene-based interaction methods rely on a multidimensional modeling of the interaction, thus facing a lack of robustness against the huge space of interaction patterns. In this paper, we study a global testing approaches to address the issue of gene-based gene-gene interaction. Based on a logistic regression modeling framework, all SNP-SNP interaction tests are combined to produce a gene-level test for interaction. We propose an omnibus test that takes advantage of (1) the heterogeneity between existing global tests and (2) the complementarity between allele-based and genotype-based coding of SNPs. Through an extensive simulation study, it is demonstrated that the proposed omnibus test has the ability to detect with high power the most common interaction genetic models with one causal pair as well as more complex genetic models where more than one causal pair is involved. On the other hand, the flexibility of the proposed approach is shown to be robust and improves power compared to single global tests in replication studies. Furthermore, the application of our procedure to real datasets confirms the adaptability of our approach to replicate various gene-gene interactions.
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Affiliation(s)
- Florian Hébert
- Department of Statistics and Computer Science, Institut Agro, CNRS, IRMAR, Univ Rennes, F-35000, Rennes, France
| | - David Causeur
- Department of Statistics and Computer Science, Institut Agro, CNRS, IRMAR, Univ Rennes, F-35000, Rennes, France
| | - Mathieu Emily
- Department of Statistics and Computer Science, Institut Agro, CNRS, IRMAR, Univ Rennes, F-35000, Rennes, France
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40
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Marley AR, Li M, Champion VL, Song Y, Han J, Li X. Citrus-Gene interaction and melanoma risk in the UK Biobank. Int J Cancer 2022; 150:976-983. [PMID: 34724200 PMCID: PMC10015424 DOI: 10.1002/ijc.33862] [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: 05/23/2021] [Revised: 09/29/2021] [Accepted: 10/15/2021] [Indexed: 11/10/2022]
Abstract
High citrus consumption may increase melanoma risk; however, little is known about the biological mechanisms of this association, or whether it is modified by genetic variants. We conducted a genome-wide analysis of gene-citrus consumption interactions on melanoma risk among 1563 melanoma cases and 193 296 controls from the UK Biobank. Both the 2-degrees-of-freedom (df) joint test of genetic main effect and gene-environment (G-E) interaction and the standard 1-df G-E interaction test were performed. Three index SNPs (lowest P-value SNP among highly correlated variants [r2 > .6]) were identified from among the 365 genome-wide significant 2-df test results (rs183783391 on chromosome 3 [MITF], rs869329 on chromosome 9 [MTAP] and rs11446223 on chromosome 16 [DEF8]). Although all three were statistically significant for the 2-df test (4.25e-08, 1.98e-10 and 4.93e-13, respectively), none showed evidence of interaction according to the 1-df test (P = .73, .24 and .12, respectively). Eight nonindex, 2-df test significant SNPs on chromosome 16 were significant (P < .05) according to the 1-df test, providing evidence of citrus-gene interaction. Seven of these SNPs were mapped to AFG3L1P (rs199600347, rs111822773, rs113178244, rs3803683, rs73283867, rs78800020, rs73283871), and one SNP was mapped to GAS8 (rs74583214). We identified several genetic loci that may elucidate the association between citrus consumption and melanoma risk. Further studies are needed to confirm these findings.
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Affiliation(s)
- Andrew R Marley
- Department of Epidemiology, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
| | - Ming Li
- Department of Epidemiology and Biostatistics, Indiana University School of Public health, Bloomington, Indiana, USA
| | - Victoria L Champion
- Department of Community Health Systems, Indiana University School of Nursing, Indianapolis, Indiana, USA.,Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, USA
| | - Yiqing Song
- Department of Epidemiology, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
| | - Jiali Han
- Department of Epidemiology, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA.,Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, USA
| | - Xin Li
- Department of Epidemiology, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA.,Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, Indiana, USA
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41
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Zhang L, Wang W, Xu C, Duan H, Tian X, Zhang D. Potential genetic biomarkers are found to be associated with both cognitive function and blood pressure: a bivariate genome-wide association analysis. Mech Ageing Dev 2022; 204:111671. [PMID: 35364053 DOI: 10.1016/j.mad.2022.111671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/17/2022] [Accepted: 03/25/2022] [Indexed: 11/15/2022]
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He R, Cai H, Jiang Y, Liu R, Zhou Y, Qin Y, Yao C, Wang S, Hu Z. Integrative analysis prioritizes the relevant genes and risk factors for chronic venous disease. J Vasc Surg Venous Lymphat Disord 2022; 10:738-748.e5. [PMID: 35218958 DOI: 10.1016/j.jvsv.2022.02.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 02/03/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVES Chronic venous disease (CVD) refers to a range of symptoms resulting from long-term morphological and functional abnormalities of the venous system. However, the mechanism of CVD development remains largely unknown. Here we aim to provide more information on CVD pathogenesis, prevention strategies and therapy development through the integrative analysis of large-scale genetic data. METHODS Genetic data were obtained from publicly accessible databases. We utilized different approaches, including FUMA, DEPICT, Sherlock, SMR/HEIDIS, DEPICT and NetWAS to identify possible causal genes for CVD. Candidate genes were prioritized to further literature-based review. The differential expression of prioritized genes was validated by microarray from the Gene Expression Omnibus (GEO), a public genomics data repository" and Real-time quantitative PCR (qPCR) of varicose veins (VVs) specimens. The causal relationships between risk factors and CVD were assessed using the Two-sample Mendelian randomization (MR) approach. RESULTS We identified 46 lead single-nucleotide polymorphisms (SNPs) and 26 plausible causal genes for CVD. Microarray data indicated differential expression of possible causal genes in CVD when compared to controls. The expression levels of WDR92, RSPO3, LIMA, ABCB10, DNAJC7, C1S, CXCL1 were significantly down-regulated (P<0.05). PHLDA1 and SERPINE1 were significantly upregulated (P<0.05). Dysregulated expression of WDR92, RSPO3 and CASZ1 was also found in varicose vein specimens by qPCR. Two-sample MR suggested causative effects of BMI (OR, 1.008, 95%CI, 1.005-1.010), standing height (OR, 1.009, 95%CI, 1.007-1.011), college degree (OR, 0.983, 95%CI, 0.991-0.976), insulin (OR, 0.858, 95%CI, 0.794-0.928) and metformin (OR, 0.944, 95%CI, 0.904-0.985) on CVD. CONCLUSIONS Our study integrates genetic and gene expression data to make an effective risk gene prediction and etiological inferences for CVD. Prioritized candidate genes provide more insights into CVD pathogenesis, and the causative effects of risk factors on CVD that deserve further investigation.
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Affiliation(s)
- Rongzhou He
- Division of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; National-Guangdong Joint Engineering Laboratory for Vascular Disease Treatment, Guangdong Engineering and Technology Center for Diagnosis and Treatment of Vascular Diseases, Guangzhou, China
| | - Huoying Cai
- Division of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; National-Guangdong Joint Engineering Laboratory for Vascular Disease Treatment, Guangdong Engineering and Technology Center for Diagnosis and Treatment of Vascular Diseases, Guangzhou, China
| | - Yu Jiang
- Department of Ophthalmology, the First People's Hospital of Guangzhou City, Guangzhou, China; Zhongshan ophthalmic center, Sun Yat-sen University, Guangzhou, China
| | - Ruiming Liu
- National-Guangdong Joint Engineering Laboratory for Vascular Disease Treatment, Guangdong Engineering and Technology Center for Diagnosis and Treatment of Vascular Diseases, Guangzhou, China; Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yu Zhou
- Division of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; National-Guangdong Joint Engineering Laboratory for Vascular Disease Treatment, Guangdong Engineering and Technology Center for Diagnosis and Treatment of Vascular Diseases, Guangzhou, China
| | - Yuansen Qin
- Division of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; National-Guangdong Joint Engineering Laboratory for Vascular Disease Treatment, Guangdong Engineering and Technology Center for Diagnosis and Treatment of Vascular Diseases, Guangzhou, China
| | - Chen Yao
- Division of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; National-Guangdong Joint Engineering Laboratory for Vascular Disease Treatment, Guangdong Engineering and Technology Center for Diagnosis and Treatment of Vascular Diseases, Guangzhou, China
| | - Shenming Wang
- Division of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; National-Guangdong Joint Engineering Laboratory for Vascular Disease Treatment, Guangdong Engineering and Technology Center for Diagnosis and Treatment of Vascular Diseases, Guangzhou, China
| | - Zuojun Hu
- Division of Vascular Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; National-Guangdong Joint Engineering Laboratory for Vascular Disease Treatment, Guangdong Engineering and Technology Center for Diagnosis and Treatment of Vascular Diseases, Guangzhou, China.
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Joo J, Himes B. Gene-Based Analysis Reveals Sex-Specific Genetic Risk Factors of COPD. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:601-610. [PMID: 35308900 PMCID: PMC8861659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Sex-specific differences have been noted among people with chronic obstructive pulmonary disease (COPD), but whether these differences are attributable to genetic variation is poorly understood. The availability of large biobanks with deeply phenotyped subjects such as the UK Biobank enables the investigation of sex-specific genetic associations that may provide new insights into COPD risk factors. We performed sex-stratified genome-wide association studies (GWAS) of COPD (male: 12,958 cases and 95,631 controls; female: 11,311 cases and 123,714 controls) and found that while most associations were shared between sexes, several regions had sex-specific contributions, including respiratory viral infection-related loci in/near C5orf56 and PELI1. Using the newly developed R package 'snpsettest', we performed gene-based association tests and identified gene-level sex-specific associations, including C5orf56 on 5q31.1, CFDP1/TMEM170A/CHST6 on 16q23.1 and ASTN2/TRIM32 on 9q33.1. Our results identified promising genes to pursue in functional studies to better understand sexual dimorphism in COPD.
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Affiliation(s)
- Jaehyun Joo
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Blanca Himes
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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44
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Zhang H, Wu Z. The generalized Fisher's combination and accurate p-value calculation under dependence. Biometrics 2022. [PMID: 35178716 DOI: 10.1111/biom.13634] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 02/03/2022] [Indexed: 11/28/2022]
Abstract
Combining dependent tests of significance has broad applications but the related p-value calculation is challenging. For Fisher's combination test, current p-value calculation methods (e.g., Brown's approximation) tend to inflate the type I error rate when the desired significance level is substantially less than 0.05. The problem could lead to significant false discoveries in big data analyses. This paper provides two main contributions. First, it presents a general family of Fisher type statistics, referred to as the GFisher, which covers many classic statistics, such as Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. The GFisher allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and the statistic-defining parameters to a given data. Second, the paper presents several new p-value calculation methods based on two novel ideas: moment-ratio matching and joint-distribution surrogating. Systematic simulations show that the new calculation methods are more accurate under multivariate Gaussian, and more robust under the generalized linear model and the multivariate t-distribution. The applications of the GFisher and the new p-value calculation methods are demonstrated by a gene-based SNP-set association study. Relevant computation has been implemented to an R package GFisher available on the Comprehensive R Archive Network. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hong Zhang
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Rahway, New Jersey, U.S.A
| | - Zheyang Wu
- Department of Mathematical Sciences, Worcester Polytechnic Institute, Worcester, Massachusetts, U.S.A
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Wang F, Panjwani N, Wang C, Sun L, Strug LJ. A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus. Am J Hum Genet 2022; 109:253-269. [PMID: 35065708 PMCID: PMC8874229 DOI: 10.1016/j.ajhg.2021.12.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 12/15/2021] [Indexed: 12/18/2022] Open
Abstract
Mucus obstruction is a central feature in the cystic fibrosis (CF) airways. A genome-wide association study (GWAS) of lung disease by the CF Gene Modifier Consortium (CFGMC) identified a significant locus containing two mucin genes, MUC20 and MUC4. Expression quantitative trait locus (eQTL) analysis using human nasal epithelia (HNE) from 94 CF-affected Canadians in the CFGMC demonstrated MUC4 eQTLs that mirrored the lung association pattern in the region, suggesting that MUC4 expression may mediate CF lung disease. Complications arose, however, with colocalization testing using existing methods: the locus is complex and the associated SNPs span a 0.2 Mb region with high linkage disequilibrium (LD) and evidence of allelic heterogeneity. We previously developed the Simple Sum (SS), a powerful colocalization test in regions with allelic heterogeneity, but SS assumed eQTLs to be present to achieve type I error control. Here we propose a two-stage SS (SS2) colocalization test that avoids a priori eQTL assumptions, accounts for multiple hypothesis testing and the composite null hypothesis, and enables meta-analysis. We compare SS2 to published approaches through simulation and demonstrate type I error control for all settings with the greatest power in the presence of high LD and allelic heterogeneity. Applying SS2 to the MUC20/MUC4 CF lung disease locus with eQTLs from CF HNE revealed significant colocalization with MUC4 (p = 1.31 × 10-5) rather than with MUC20. The SS2 is a powerful method to inform the responsible gene(s) at a locus and guide future functional studies. SS2 has been implemented in the application LocusFocus.
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Affiliation(s)
- Fan Wang
- Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada; Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Naim Panjwani
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Cheng Wang
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Lei Sun
- Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada; Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada.
| | - Lisa J Strug
- Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada; Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.
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Li M, Wang H. Pathway analysis identified a significant association between cell-cell adherens junctions-related genes and non-syndromic cleft lip/palate in 895 Asian case-parent trios. Arch Oral Biol 2022; 136:105384. [DOI: 10.1016/j.archoralbio.2022.105384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 01/14/2022] [Accepted: 02/15/2022] [Indexed: 12/31/2022]
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47
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Wang YC, Wu Y, Choi J, Allington G, Zhao S, Khanfar M, Yang K, Fu PY, Wrubel M, Yu X, Mekbib KY, Ocken J, Smith H, Shohfi J, Kahle KT, Lu Q, Jin SC. Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy. J Pers Med 2022; 12:175. [PMID: 35207663 PMCID: PMC8878256 DOI: 10.3390/jpm12020175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/18/2022] [Accepted: 01/24/2022] [Indexed: 02/04/2023] Open
Abstract
Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.
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Affiliation(s)
- Yung-Chun Wang
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Yuchang Wu
- Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Julie Choi
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Garrett Allington
- Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA;
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; (H.S.); (K.T.K.)
| | - Shujuan Zhao
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Mariam Khanfar
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Kuangying Yang
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Po-Ying Fu
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Max Wrubel
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Xiaobing Yu
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
- Department of Computer Science & Engineering, Washington University, St. Louis, MO 63130, USA
| | - Kedous Y. Mekbib
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA; (K.Y.M.); (J.O.); (J.S.)
| | - Jack Ocken
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA; (K.Y.M.); (J.O.); (J.S.)
| | - Hannah Smith
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; (H.S.); (K.T.K.)
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA; (K.Y.M.); (J.O.); (J.S.)
| | - John Shohfi
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA; (K.Y.M.); (J.O.); (J.S.)
| | - Kristopher T. Kahle
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; (H.S.); (K.T.K.)
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Qiongshi Lu
- Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Sheng Chih Jin
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
- Department of Pediatrics, School of Medicine, Washington University, St. Louis, MO 63110, USA
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Zhu Q, Meng Y, Li S, Xin J, Du M, Wang M, Cheng G. Association of genetic variants in autophagy-lysosome pathway genes with susceptibility and survival to prostate cancer. Gene 2022; 808:145953. [PMID: 34500048 DOI: 10.1016/j.gene.2021.145953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 08/20/2021] [Accepted: 09/03/2021] [Indexed: 12/31/2022]
Abstract
BACKGROUND Previous studies have indicated the connections between autophagy-lysosome pathway genes dysfunction and prostate cancer, but few studies have investigated whether single nucleotide polymorphisms (SNPs) in autophagy-lysosome pathway genes are implicated in prostate cancer risk and survival. MATERIALS AND METHODS Logistic regression analysis and stepwise Cox regression analysis were conducted in 4,662 cases and 3,114 controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. The false positive rate probability (FPRP) method was applied to correct for multiple comparisons. Gene-based analysis was calculated by versatile gene-based association study approach. RESULTS We found that SLC11A1 rs7573065 significantly increased the risk of prostate cancer [adjusted odds ratio (OR) = 1.24, 95% confidence interval (CI) = 1.06-1.46, P = 7.02 × 10-3, FPRP = 0.082]. Furthermore, rs7573065 was confirmed as the independent predicator of overall survival (OS) for prostate cancer patients [Hazard ratio (HR) = 1.30, 95% CI = 1.01-1.66, P = 0.041]. The significant association between SLC11A1 and prostate cancer risk was calculated by gene-based analysis (P = 0.030). We also observed that the mRNA of SLC11A1 in prostate tumor tissues was significantly over-expressed than that in normal tissues. CONCLUSION This study suggested that rs7573065 in SLC11A1 was associated with an increased risk and poor OS of prostate cancer. Our findings may provide evidence for genetic variants in autophagy-lysosome pathway as the risk and prognostic biomarkers for prostate cancer.
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Affiliation(s)
- Qiuyuan Zhu
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yixuan Meng
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuwei Li
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Junyi Xin
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Mulong Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meilin Wang
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Gong Cheng
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Zhang M, Cheng W, Yuan X, Wang J, Cheng T, Zhang Q. Integrated transcriptome and small RNA sequencing in revealing miRNA-mediated regulatory network of floral bud break in Prunus mume. FRONTIERS IN PLANT SCIENCE 2022; 13:931454. [PMID: 35937373 PMCID: PMC9355595 DOI: 10.3389/fpls.2022.931454] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/30/2022] [Indexed: 05/08/2023]
Abstract
MicroRNAs is one class of small non-coding RNAs that play important roles in plant growth and development. Though miRNAs and their target genes have been widely studied in many plant species, their functional roles in floral bud break and dormancy release in woody perennials is still unclear. In this study, we applied transcriptome and small RNA sequencing together to systematically explore the transcriptional and post-transcriptional regulation of floral bud break in P. mume. Through expression profiling, we identified a few candidate genes and miRNAs during different developmental stage transitions. In total, we characterized 1,553 DEGs associated with endodormancy release and 2,084 DEGs associated with bud flush. Additionally, we identified 48 known miRNAs and 53 novel miRNAs targeting genes enriched in biological processes such as floral organ morphogenesis and hormone signaling transudation. We further validated the regulatory relationship between differentially expressed miRNAs and their target genes combining computational prediction, degradome sequencing, and expression pattern analysis. Finally, we integrated weighted gene co-expression analysis and constructed miRNA-mRNA regulatory networks mediating floral bud flushing competency. In general, our study revealed the miRNA-mediated networks in modulating floral bud break in P. mume. The findings will contribute to the comprehensive understanding of miRNA-mediated regulatory mechanism governing floral bud break and dormancy cycling in wood perennials.
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Guo Y, Cheng H, Yuan Z, Liang Z, Wang Y, Du D. Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies. Front Genet 2021; 12:801261. [PMID: 34956337 PMCID: PMC8693929 DOI: 10.3389/fgene.2021.801261] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/15/2021] [Indexed: 12/21/2022] Open
Abstract
Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical p-value to represent the significance of the interaction. Experimental results using both simulation and real data showed that our approach outperformed earlier methods for detecting GGIs.
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Affiliation(s)
- Yingjie Guo
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Honghong Cheng
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Zhian Yuan
- Research Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China
| | - Zhen Liang
- School of Life Science, Shanxi University, Taiyuan, China
| | - Yang Wang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Debing Du
- Beidahuang Industry Group General Hospital, Harbin, China
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