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Feng X, Zan Y, Li T, Yao Y, Ning Z, Li J, Charati H, Xu W, Wan Q, Zeng D, Zeng Z, Liu Y, Shen X. Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics. Heredity (Edinb) 2024; 133:11-20. [PMID: 38822132 PMCID: PMC11222461 DOI: 10.1038/s41437-024-00688-z] [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: 04/25/2023] [Accepted: 05/07/2024] [Indexed: 06/02/2024] Open
Abstract
Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.
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Affiliation(s)
- Xiao Feng
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Yanjun Zan
- Key Laboratory of Tobacco Improvement and Biotechnology, Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Ting Li
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
| | - Yue Yao
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
| | - Zheng Ning
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jiabei Li
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
| | - Hadi Charati
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China
| | - Weilin Xu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Qianhui Wan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Mathematics, University of California, Davis, CA, USA
| | - Dongyu Zeng
- State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, Shenzhen, China
| | - Ziyi Zeng
- School of Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yang Liu
- State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, Shenzhen, China.
| | - Xia Shen
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- Center for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, UK.
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Svishcheva GR, Tiys ES, Elgaeva EE, Feoktistova SG, Timmers PRHJ, Sharapov SZ, Axenovich TI, Tsepilov YA. A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits. Genes (Basel) 2022; 13:1694. [PMID: 36292579 PMCID: PMC9602050 DOI: 10.3390/genes13101694] [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: 08/11/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.
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Affiliation(s)
- Gulnara R. Svishcheva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences, 117971 Moscow, Russia
| | - Evgeny S. Tiys
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Elizaveta E. Elgaeva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Sofia G. Feoktistova
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Paul R. H. J. Timmers
- MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH8 9YL, UK
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Sodbo Zh. Sharapov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Tatiana I. Axenovich
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Yakov A. Tsepilov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Novosibirsk State University, 630090 Novosibirsk, Russia
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3
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Zaytseva OO, Sharapov SZ, Perola M, Esko T, Landini A, Hayward C, Wilson JF, Lauc G, Aulchenko YS, Klarić L, Tsepilov YA. Investigation of the causal relationships between human IgG N-glycosylation and twelve common diseases associated with changes in the IgG N-glycome. Hum Mol Genet 2021; 31:1545-1559. [PMID: 34791244 DOI: 10.1093/hmg/ddab335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 11/03/2021] [Accepted: 11/04/2021] [Indexed: 11/12/2022] Open
Abstract
Changes in the N-glycosylation of immunoglobulin G (IgG) are often observed in pathological states, such as autoimmune, inflammatory, neurodegenerative, cardiovascular diseases and some types of cancer. However, in most cases it is not clear if the disease onset causes these changes, or if the changes in IgG N-glycosylation are among the risk factors for the diseases. The aim of this study was to investigate the casual relationships between IgG N-glycosylation traits and 12 diseases, in which the alterations of IgG N-glycome were previously reported, using Two Sample Mendelian Randomization (MR) approach. We have performed Two Sample MR using publicly available summary statistics of genome-wide association studies of IgG N-glycosylation and disease risks. Our results indicate positive causal effect of systemic lupus erythematosus (SLE) on the abundance of N-glycans with bisecting N-acetylglucosamine in the total IgG N-glycome. Therefore, we suggest regarding this IgG glycosylation trait as a biomarker of SLE. We also emphasize the need for more powerful GWAS studies of IgG N-glycosylation to further elucidate the causal effect of IgG N-glycome on the diseases.
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Affiliation(s)
- Olga O Zaytseva
- Genos Glycoscience Research Laboratory, Zagreb, 10000, Croatia
| | - Sodbo Zh Sharapov
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, 630090, Russia
| | - Marcus Perola
- Genomics and Biomarkers Unit, Department of Health, National Institute for Health and Welfare (THL), Helsinki, FI-00271, Finland
| | - Tonu Esko
- Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia
| | - Arianna Landini
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland.,MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, 10000, Croatia.,Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, 10000, Croatia
| | - Yurii S Aulchenko
- Laboratory of Glycogenomics, Institute of Cytology and Genetics, Novosibirsk, 630090, Russia
| | - Lucija Klarić
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - Yakov A Tsepilov
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, 630090, Russia.,Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, Novosibirsk, 630090, Russia
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