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Song W, Yuan K, Liu Z, Cai W, Chen J, Yu S, Zhao M, Lin GN. Locus-level antagonistic selection shaped the polygenic architecture of human complex diseases. Hum Genet 2022; 141:1935-1947. [PMID: 35943608 DOI: 10.1007/s00439-022-02471-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/11/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND We aimed to evaluate the potential role of antagonistic selection in polygenic diseases: if one variant increases the risk of one disease and decreases the risk of another disease, the signals of genetic risk elimination by natural selection will be distorted, which leads to a higher frequency of risk alleles. METHODS We applied local genetic correlations and transcriptome-wide association studies to identify genomic loci and genes adversely associated with at least two diseases. Then, we used different population genetic metrics to measure the signals of natural selection for these loci and genes. RESULTS First, we identified 2120 cases of antagonistic pleiotropy (negative local genetic correlation) among 87 diseases in 716 genomic loci (antagonistic loci). Next, by comparing with non-antagonistic loci, we observed that antagonistic loci explained an excess proportion of disease heritability (median 6%), showed enhanced signals of balancing selection, and reduced signals of directional polygenic adaptation. Then, at the gene expression level, we identified 31,991 cases of antagonistic pleiotropy among 98 diseases at 4368 genes. However, evidence of altered signals of selection pressure and heritability distribution at the gene expression level is limited. CONCLUSION We conclude that antagonistic pleiotropy is widespread among human polygenic diseases, and it has distorted the evolutionary signal and genetic architecture of diseases at the locus level.
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Affiliation(s)
- Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Kai Yuan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhe Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenxiang Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jue Chen
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shunying Yu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
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mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights. Metabolites 2022; 12:metabo12060526. [PMID: 35736459 PMCID: PMC9230867 DOI: 10.3390/metabo12060526] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.
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Muntané G, Farré X, Bosch E, Martorell L, Navarro A, Vilella E. The shared genetic architecture of schizophrenia, bipolar disorder and lifespan. Hum Genet 2020; 140:441-455. [PMID: 32772156 DOI: 10.1007/s00439-020-02213-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 07/27/2020] [Indexed: 12/11/2022]
Abstract
Psychiatric disorders such as Schizophrenia (SCZ) and Bipolar Disorder (BD) represent an evolutionary paradox, as they exhibit strong negative effects on fitness, such as decreased fecundity and early mortality, yet they persist at a worldwide prevalence of approximately 1%. Molecular mechanisms affecting lifespan, which may be widely common among complex diseases with fitness effects, can be studied by the integrated analysis of data from genome-wide association studies (GWAS) of human longevity together with any disease of interest. Here, we report the first of such studies, focusing on the genetic overlap-pleiotropy-between two psychiatric disorders with shortened lifespan, SCZ and BD, and human parental lifespan (PLS) as a surrogate of life expectancy. Our results are twofold: first, we demonstrate extensive polygenic overlap between SCZ and PLS and to a lesser extent between BD and PLS. Second, we identified novel loci shared between PLS and SCZ (n = 39), and BD (n = 8). Whereas most of the identified SCZ (66%) and BD (62%) pleiotropic risk alleles were associated with reduced lifespan, we also detected some antagonistic protective alleles associated to shorter lifespans. In fact, top-associated SNPs with SCZ seems to explain longevity variance explained (LVE) better than many other life-threatening diseases, including Type 2 diabetes and most cancers, probably due to a high overlap with smoking-related pathways. Overall, our study provides evidence of a genetic burden driven through premature mortality among people with SCZ, which can have profound implications for understanding, and potentially treating, the mortality gap associated with this psychiatric disorder.
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Affiliation(s)
- Gerard Muntané
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Hospital Universitari Institut Pere Mata, IISPV Universitat Rovira i Virgili, Reus, Spain. .,Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.
| | - Xavier Farré
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Elena Bosch
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Lourdes Martorell
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Hospital Universitari Institut Pere Mata, IISPV Universitat Rovira i Virgili, Reus, Spain
| | - Arcadi Navarro
- Departament de Ciències Experimentals i de la Salut, Institut de Biologia Evolutiva (UPF-CSIC), Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.,Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats, ICREA, Barcelona, Spain.,Barcelonaβeta Brain Research Center, Fundació Pasqual Maragall, Barcelona, Spain
| | - Elisabet Vilella
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Hospital Universitari Institut Pere Mata, IISPV Universitat Rovira i Virgili, Reus, Spain
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