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Liu R, Shang X, Fu Y, Wang Y, Wang P, Yan S. Shared genetic architecture between hypothyroidism and rheumatoid arthritis: A large-scale cross-trait analysis. Mol Immunol 2024; 168:17-24. [PMID: 38368726 DOI: 10.1016/j.molimm.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/10/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND In recent years, mounting evidence has indicated a co-morbid relationship between hypothyroidism and rheumatoid arthritis (RA), however, the shared genetic factors underlying this association remain unclear. This study aims to investigate the common genetic architecture between hypothyroidism and RA. METHODS Genome-wide association study (GWAS) summary statistics from recently published studies were utilized to examine the genetic correlation, shared genetic loci, and potential causal relationship between hypothyroidism and RA. Statistical methods included linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), cross-trait meta-analyses, colocalization analysis, multi-marker analysis of genomic annotation (MAGMA), tissue-specific enrichment analysis (TSEA), functional enrichment analysis, and latent causal variable method (LCV). RESULTS Our study demonstrated a significant genetic correlation between hypothyroidism and RA(LDSC:rg=0.3803,p=7.23e-11;HDL:rg=0.3849,p=1.02e-21). Through cross-trait meta-analysis, we identified 1035 loci, including 43 novel genetic loci. By integrating colocalization analysis and the MAGMA algorithm, we found a substantial number of genes, such as PTPN22, TYK2, and CTLA-4, shared between the two diseases, which showed significant enrichment across 14 tissues. These genes were primarily associated with the regulation of alpha-beta T cell proliferation, positive regulation of T cell activation, positive regulation of leukocyte cell-cell adhesion, T cell receptor signaling pathway, and JAK-STAT signaling pathway. However, our study did not reveal a significant causal association between the two diseases using the LCV approach. CONCLUSION Based on these findings, there is a significant genetic correlation between hypothyroidism and RA, suggesting a shared genetic basis for these conditions.
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Affiliation(s)
- Ruiyan Liu
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Xin Shang
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Yu Fu
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China
| | - Ying Wang
- Department of Geriatrics, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Ping Wang
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
| | - Shuxun Yan
- Endocrine Ward II, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
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Wu Y, Li Y, Zhu J, Long J. Shared genetics and causality underlying epilepsy and attention-deficit hyperactivity disorder. Psychiatry Res 2022; 316:114794. [PMID: 35994864 DOI: 10.1016/j.psychres.2022.114794] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/06/2022] [Accepted: 08/12/2022] [Indexed: 11/18/2022]
Abstract
The prevalence of attention deficit hyperactivity disorder (ADHD) in patients with epilepsy was much higher than prevalence in general population, and vice versa. The mechanisms underlying comorbid ADHD and epilepsy remained largely unknown. Here, we systematically analyzed the genetic correlation, causality, shared genetics and specific trait related tissues by using linkage disequilibrium score regression (LDSC), two sample Mendelian randomization (TwoSampleMR), bivariate causal mixture model (MiXeR), conjunctional false discovery rate (conjFDR) and LDSC applied to specifically expressed genes based on genome wide association studies (GWASs) data of ADHD and epilepsy. We found that ADHD had significant positive genetic association with epilepsy. Two-sample Mendelian randomization analysis with genome wide significant single nucleotide polymorphisms (SNPs) as instrument variables suggested a positively causal effect of ADHD on epilepsy. Using MiXeR, which estimates the total amount of shared variants, we observed 1 K causal variants overlapped between ADHD and epilepsy. At conjFDR <0.05, ADHD shared 2 distinct genomic loci with Epilepsy. Further disease-relevant tissues analysis showed that cortex, substantia nigra, amygdala and hippocampus were both associated with ADHD and epilepsy. Our results suggested that ADHD was genetically correlated with epilepsy, which might be due to the fact that they shared common pathogenic sites and tissues origin.
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Affiliation(s)
- Yong Wu
- Research Center for Mental Health and Neuroscience, Wuhan Mental Health Center, Wuhan, Hubei 430012, China
| | - Yichen Li
- Radiology Department, Wuhan Mental Health Center, Wuhan, Hubei 430012, China
| | - Junhong Zhu
- Department of Mental Rehabilitation, Wuhan Mental Health Center, Wuhan, Hubei 430012, China.
| | - Jingyi Long
- Department of Child & Adolescent Psychiatry, Wuhan Mental Health Center, Wuhan, Hubei 430012, China.
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Xiu X, Zhang H, Xue A, Cooper DN, Yan L, Yang Y, Yang Y, Zhao H. Genetic evidence for a causal relationship between type 2 diabetes and peripheral artery disease in both Europeans and East Asians. BMC Med 2022; 20:300. [PMID: 36042491 PMCID: PMC9429730 DOI: 10.1186/s12916-022-02476-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 07/12/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Observational studies have revealed that type 2 diabetes (T2D) is associated with an increased risk of peripheral artery disease (PAD). However, whether the two diseases share a genetic basis and whether the relationship is causal remain unclear. It is also unclear as to whether these relationships differ between ethnic groups. METHODS By leveraging large-scale genome-wide association study (GWAS) summary statistics of T2D (European-based: Ncase = 21,926, Ncontrol = 342,747; East Asian-based: Ncase = 36,614, Ncontrol = 155,150) and PAD (European-based: Ncase = 5673, Ncontrol = 359,551; East Asian-based: Ncase = 3593, Ncontrol = 208,860), we explored the genetic correlation and putative causal relationship between T2D and PAD in both Europeans and East Asians using linkage disequilibrium score regression and seven Mendelian randomization (MR) models. We also performed multi-trait analysis of GWAS and two gene-based analyses to reveal candidate variants and risk genes involved in the shared genetic basis between T2D and PAD. RESULTS We observed a strong genetic correlation (rg) between T2D and PAD in both Europeans (rg = 0.51; p-value = 9.34 × 10-15) and East Asians (rg = 0.46; p-value = 1.67 × 10-12). The MR analyses provided consistent evidence for a causal effect of T2D on PAD in both ethnicities (odds ratio [OR] = 1.05 to 1.28 for Europeans and 1.15 to 1.27 for East Asians) but not PAD on T2D. This putative causal effect was not influenced by total cholesterol, body mass index, systolic blood pressure, or smoking initiation according to multivariable MR analysis, and the genetic overlap between T2D and PAD was further explored employing an independent European sample through polygenic risk score regression. Multi-trait analysis of GWAS revealed two novel European-specific single nucleotide polymorphisms (rs927742 and rs1734409) associated with the shared genetic basis of T2D and PAD. Gene-based analyses consistently identified one gene ANKFY1 and gene-gene interactions (e.g., STARD10 [European-specific] to AP3S2 [East Asian-specific]; KCNJ11 [European-specific] to KCNQ1 [East Asian-specific]) associated with the trans-ethnic genetic overlap between T2D and PAD, reflecting a common genetic basis for the co-occurrence of T2D and PAD in both Europeans and East Asians. CONCLUSIONS Our study provides the first evidence for a genetically causal effect of T2D on PAD in both Europeans and East Asians. Several candidate variants and risk genes were identified as being associated with this genetic overlap. Our findings emphasize the importance of monitoring PAD status in T2D patients and suggest new genetic biomarkers for screening PAD risk among patients with T2D.
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Affiliation(s)
- Xuehao Xiu
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
| | - Haoyang Zhang
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China.,School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510000, China
| | - Angli Xue
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia.,Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Li Yan
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
| | - Yuedong Yang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510000, China.
| | - Yuanhao Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. .,Mater Research Institute, Translational Research Institute, Brisbane, QLD, Australia.
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China.
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Guo P, Gong W, Li Y, Liu L, Yan R, Wang Y, Zhang Y, Yuan Z. Pinpointing novel risk loci for Lewy body dementia and the shared genetic etiology with Alzheimer's disease and Parkinson's disease: a large-scale multi-trait association analysis. BMC Med 2022; 20:214. [PMID: 35729600 PMCID: PMC9214990 DOI: 10.1186/s12916-022-02404-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 05/13/2022] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The current genome-wide association study (GWAS) of Lewy body dementia (LBD) suffers from low power due to a limited sample size. In addition, the genetic determinants underlying LBD and the shared genetic etiology with Alzheimer's disease (AD) and Parkinson's disease (PD) remain poorly understood. METHODS Using the largest GWAS summary statistics of LBD to date (2591 cases and 4027 controls), late-onset AD (86,531 cases and 676,386 controls), and PD (33,674 cases and 449,056 controls), we comprehensively investigated the genetic basis of LBD and shared genetic etiology among LBD, AD, and PD. We first conducted genetic correlation analysis using linkage disequilibrium score regression (LDSC), followed by multi-trait analysis of GWAS (MTAG) and association analysis based on SubSETs (ASSET) to identify the trait-specific SNPs. We then performed SNP-level functional annotation to identify significant genomic risk loci paired with Bayesian fine-mapping and colocalization analysis to identify potential causal variants. Parallel gene-level analysis including GCTA-fastBAT and transcriptome-wide association analysis (TWAS) was implemented to explore novel LBD-associated genes, followed by pathway enrichment analysis to understand underlying biological mechanisms. RESULTS Pairwise LDSC analysis found positive genome-wide genetic correlations between LBD and AD (rg = 0.6603, se = 0.2001; P = 0.0010), between LBD and PD (rg = 0.6352, se = 0.1880; P = 0.0007), and between AD and PD (rg = 0.2136, se = 0.0860; P = 0.0130). We identified 13 significant loci for LBD, including 5 previously reported loci (1q22, 2q14.3, 4p16.3, 4q22.1, and 19q13.32) and 8 novel biologically plausible genetic associations (5q12.1, 5q33.3, 6p21.1, 8p23.1, 8p21.1, 16p11.2, 17p12, and 17q21.31), among which APOC1 (19q13.32), SNCA (4q22.1), TMEM175 (4p16.3), CLU (8p21.1), MAPT (17q21.31), and FBXL19 (16p11.2) were also validated by gene-level analysis. Pathway enrichment analysis of 40 common genes identified by GCTA-fastBAT and TWAS implicated significant role of neurofibrillary tangle assembly (GO:1902988, adjusted P = 1.55 × 10-2). CONCLUSIONS Our findings provide novel insights into the genetic determinants of LBD and the shared genetic etiology and biological mechanisms of LBD, AD, and PD, which could benefit the understanding of the co-pathology as well as the potential treatment of these diseases simultaneously.
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Affiliation(s)
- Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yuanming Li
- School of Medicine, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yanjun Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yanan Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. .,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
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Zhuang Z, Yao M, Wong JYY, Liu Z, Huang T. Shared genetic etiology and causality between body fat percentage and cardiovascular diseases: a large-scale genome-wide cross-trait analysis. BMC Med 2021; 19:100. [PMID: 33910581 PMCID: PMC8082910 DOI: 10.1186/s12916-021-01972-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 03/23/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Accumulating evidences have suggested that high body fat percentage (BF%) often occurs in parallel with cardiovascular diseases (CVDs), implying a common etiology between them. However, the shared genetic etiology underlying BF% and CVDs remains unclear. METHODS Using large-scale genome-wide association study (GWAS) data, we investigated shared genetics between BF% (N = 100,716) and 10 CVD-related traits (n = 6968-977,323) with linkage disequilibrium score regression, multi-trait analysis of GWAS, and transcriptome-wide association analysis, and evaluated causal associations using Mendelian randomization. RESULTS We found strong positive genetic correlations between BF% and heart failure (HF) (Rg = 0.47, P = 1.27 × 10- 22) and coronary artery disease (CAD) (Rg = 0.22, P = 3.26 × 10- 07). We identified 5 loci and 32 gene-tissue pairs shared between BF% and HF, as well as 16 loci and 28 gene-tissue pairs shared between BF% and CAD. The loci were enriched in blood vessels and brain tissues, while the gene-tissue pairs were enriched in the nervous, cardiovascular, and exo-/endocrine system. In addition, we observed that BF% was causally related with a higher risk of HF (odds ratio 1.63 per 1-SD increase in BF%, P = 4.16 × 10-04) using a MR approach. CONCLUSIONS Our findings suggest that BF% and CVDs have shared genetic etiology and targeted reduction of BF% may improve cardiovascular outcomes. This work advances our understanding of the genetic basis underlying co-morbid obesity and CVDs and opens up a new way for early prevention of CVDs.
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Affiliation(s)
- Zhenhuang Zhuang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, China. 38 Xueyuan Road, Beijing, 100191, China
| | - Minhao Yao
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China
| | - Jason Y Y Wong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China.
| | - Tao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, China. 38 Xueyuan Road, Beijing, 100191, China. .,Center for Intelligent Public Health, Academy for Artificial Intelligence, Peking University, Beijing, 100191, China. .,Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, 100191, China.
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Hu Y, Tan A, Yu L, Hou C, Kuang H, Wu Q, Su J, Zhou Q, Zhu Y, Zhang C, Wei W, Li L, Li W, Huang Y, Huang H, Xie X, Lu T, Zhang H, Yang X, Gao Y, Li T, Jiang Y, Mo Z. A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in southern Chinese men. BMC Genomics 2019; 20:983. [PMID: 31842750 PMCID: PMC6916074 DOI: 10.1186/s12864-019-6363-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Accepted: 12/02/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Phenomics provides new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the systematic mining of shared genetics among clinical biochemical indices based on phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort. RESULT A total of 1999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms (SNPs) were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson's test, Jaccard's index, and linkage disequilibrium score regression, were used. The results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, and shared genetics analysis showed that 29 SNPs (P < 10- 4) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalogue, 31 SNPs were found to be associated with several diseases (P < 10- 8). Using ALDH2 as an example to preliminarily explore its biological function, we also confirmed that the rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3 T3-L1 preadipocytes. CONCLUSION All these findings indicated a network of shared genetics and 29 biochemical indices, which will help fully understand the genetics participating in biochemical metabolism.
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Affiliation(s)
- Yanling Hu
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.,Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Aihua Tan
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.,Department of chemotherapy, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Lei Yu
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Chenyang Hou
- Department of Information and Management, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Haofa Kuang
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Qunying Wu
- Department of Biochemistry and Molecular Biology, School of Pre-Clinical Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Jinghan Su
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Qingniao Zhou
- Department of Biochemistry and Molecular Biology, School of Pre-Clinical Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yuanyuan Zhu
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Chenqi Zhang
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Wei Wei
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Lianfeng Li
- Department of Information and Management, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Weidong Li
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yuanjie Huang
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Hongli Huang
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Xing Xie
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Tingxi Lu
- Department of Information and Management, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Haiying Zhang
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Xiaobo Yang
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yong Gao
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Tianyu Li
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yonghua Jiang
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.
| | - Zengnan Mo
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.
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Guimaraes JPOFT, Bralten J, Greven CU, Franke B, Sprooten E, Beckmann CF. Discovering the shared biology of cognitive traits determined by genetic overlap. Neuroimage 2020; 208:116409. [PMID: 31785419 DOI: 10.1016/j.neuroimage.2019.116409] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 10/30/2019] [Accepted: 11/26/2019] [Indexed: 11/20/2022] Open
Abstract
Investigating the contribution of biology to human cognition has assumed a bottom-up causal cascade where genes influence brain systems that activate, communicate, and ultimately drive behavior. Yet few studies have directly tested whether cognitive traits with overlapping genetic underpinnings also rely on overlapping brain systems. Here, we report a step-wise exploratory analysis of genetic and functional imaging overlaps among cognitive traits. We used twin-based genetic analyses in the human connectome project (HCP) dataset (N = 486), in which we quantified the heritability of measures of cognitive functions, and tested whether they were driven by common genetic factors using pairwise genetic correlations. Subsequently, we derived activation maps associated with cognitive tasks via functional imaging meta-analysis in BrainMap (N = 4484), and tested whether cognitive traits that shared genetic variation also exhibited overlapping brain activation. Our genetic analysis determined that six cognitive measures (cognitive flexibility, no-go continuous performance, fluid intelligence, processing speed, reading decoding and vocabulary comprehension) were heritable (0.3 < h2 < 0.5), and genetically correlated with at least one other heritable cognitive measure (0.2 < ρg < 0.35). The meta-analysis showed that two genetically-correlated traits, cognitive flexibility and fluid intelligence (ρg = 0.24), also had a significant brain activation overlap (ρperm = 0.29). These findings indicate that fluid intelligence and cognitive flexibility rely on overlapping biological features, both at the neural systems level and at the molecular level. The cross-disciplinary approach we introduce provides a concrete framework for data-driven quantification of biological convergence between genetics, brain function, and behavior in health and disease.
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Grassmann F, Kiel C, Zimmermann ME, Gorski M, Grassmann V, Stark K, Heid IM, Weber BHF. Genetic pleiotropy between age-related macular degeneration and 16 complex diseases and traits. Genome Med 2017; 9:29. [PMID: 28347358 PMCID: PMC5368911 DOI: 10.1186/s13073-017-0418-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 03/02/2017] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Age-related macular degeneration (AMD) is a common condition of vision loss with disease development strongly influenced by environmental and genetic factors. Recently, 34 loci were associated with AMD at genome-wide significance. So far, little is known about a genetic overlap between AMD and other complex diseases or disease-relevant traits. METHODS For each of 60 complex diseases/traits with publicly available genome-wide significant association data, the lead genetic variant per independent locus was extracted and a genetic score was calculated for each disease/trait as the weighted sum of risk alleles. The association with AMD was estimated based on 16,144 AMD cases and 17,832 controls using logistic regression. RESULTS Of the respective disease/trait variance, the 60 genetic scores explained on average 4.8% (0.27-20.69%) and 16 of them were found to be significantly associated with AMD (Q-values < 0.01, p values from < 1.0 × 10-16 to 1.9 × 10-3). Notably, an increased risk for AMD was associated with reduced risk for cardiovascular diseases, increased risk for autoimmune diseases, higher HDL and lower LDL levels in serum, lower bone-mineral density as well as an increased risk for skin cancer. By restricting the analysis to 1824 variants initially used to compute the 60 genetic scores, we identified 28 novel AMD risk variants (Q-values < 0.01, p values from 1.1 × 10-7 to 3.0 × 10-4), known to be involved in cardiovascular disorders, lipid metabolism, autoimmune diseases, anthropomorphic traits, ocular disorders, and neurological diseases. The latter variants represent 20 novel AMD-associated, pleiotropic loci. Genes in the novel loci reinforce previous findings strongly implicating the complement system in AMD pathogenesis. CONCLUSIONS We demonstrate a substantial overlap of the genetics of several complex diseases/traits with AMD and provide statistically significant evidence for an additional 20 loci associated with AMD. This highlights the possibility that so far unrelated pathologies may have disease pathways in common.
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Affiliation(s)
- Felix Grassmann
- Institute of Human Genetics, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany
| | - Christina Kiel
- Institute of Human Genetics, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany
| | - Martina E Zimmermann
- Department of Genetic Epidemiology, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany
| | - Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany
| | - Veronika Grassmann
- Institute of Medical Microbiology and Hygiene, University of Regensburg, Franz-Josef-Strauss-Allee 11, Regensburg, 93053, Germany
| | - Klaus Stark
- Department of Genetic Epidemiology, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany
| | | | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany
| | - Bernhard H F Weber
- Institute of Human Genetics, University of Regensburg, Franz-Josef-Strauss-Allee 11, 93053, Regensburg, Germany.
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Emilsson L, Abdul Sultan A, Ludvigsson JF. No increased mortality in 109,000 first-degree relatives of celiac individuals. Dig Liver Dis 2016; 48:376-80. [PMID: 26748422 DOI: 10.1016/j.dld.2015.11.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Revised: 11/09/2015] [Accepted: 11/20/2015] [Indexed: 12/11/2022]
Abstract
BACKGROUND Several studies have shown an excess mortality in individuals with celiac disease (CD). However, it is unknown if also first-degree relatives (FDRs) to celiac patients are at increased risk of death. AIM We aimed to assess mortality in FDRs to celiac patients. METHODS Individuals with CD were identified through biopsy reports (equal to Marsh grade III). Each celiac individual was matched on sex, age, county and calendar year with up to five control individuals. Through Swedish healthcare registries we identified all FDRs (father, mother, sibling, offspring) of CD individuals and controls. Through Cox regression we calculated hazard ratios (HRs) for mortality (all-cause death, circulatory, cancer and other). RESULTS We identified 109,309 FDRs of celiac individuals and 549,098 FDRs of controls. Overall mortality was increased in FDRs to celiac individuals (HR=1.02, 95%CI=1.00-1.04, p=0.03). This corresponded to an excess risk of 5.9 deaths per 100,000 person-years of follow-up. When limiting follow-up to time since celiac diagnosis in the index individual, we found no increased risk of death (HR=1.01; 95%CI=0.98-1.03). CONCLUSION FDRs to individuals with CD are at increased risk of death. This excess risk is however minimal and unlikely to be of any clinical importance to the individual.
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Affiliation(s)
- Louise Emilsson
- Primary Care Research Unit, Vårdcentralen Värmlands Nysäter, Värmland County, Sweden; Department of Health Management and Health Economy, Institute of Health and Society, University of Oslo, Norway.
| | - Alyshah Abdul Sultan
- Division of Epidemiology and Public Health, University of Nottingham, Clinical Sciences Building, City Hospital, Hucknall Road, Nottingham NG5 1PB, UK; National Institute of Health Research Nottingham Digestive Diseases Centre Biomedical Research Unit, Nottingham University Hospital NHS Trust and University of Nottingham, Nottingham, UK
| | - Jonas F Ludvigsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Pediatrics, Örebro University Hospital, Örebro, Sweden
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