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Yang Q, Liu H, Xi Y, Lu Y, Han X, He X, Qi J, Zhu Y, He H, Wang J, Hu J, Li L. Genome-wide association study for bone quality of ducks during the laying period. Poult Sci 2024; 103:103575. [PMID: 38447311 PMCID: PMC11067773 DOI: 10.1016/j.psj.2024.103575] [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: 12/13/2023] [Revised: 02/09/2024] [Accepted: 02/15/2024] [Indexed: 03/08/2024] Open
Abstract
The cage-rearing model of the modern poultry industry makes the bones of birds, especially egg-laying birds, more vulnerable to fracture, which poses serious damage to the health of birds. Research confirms that genetic material plays an important role in regulating bone growth, development, and remodeling. However, the genetic architecture underlying bone traits is not well understood. The objectives of this study are to identify valuable genes and genetic markers through a genome-wide association study (GWAS) for breeding to improve the duck bone quality. First, we quantified the tibia and femur quality traits of 260 laying ducks. Based on GWAS, a total of 75 SNP loci significantly associated with bone quality traits were identified, and 67 potential candidate genes were annotated. According to gene function analysis, genes P4HA2, WNT3A, and BST1 et al may influence bone quality by regulating bone cell activity, calcium and phosphate metabolism, or bone collagen maturation and cross-linking. Meanwhile, combined with the transcriptome results, we found that HOXB cluster genes are also important in bone growth and development. Therefore, our findings were helpful in further understanding the genetic architecture of the duck bone quality and provided a worthy theoretical basis and technological support to improve duck bone quality by breeding.
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Affiliation(s)
- Qinglan Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Hehe Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Yang Xi
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Yinjuan Lu
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Xu Han
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Xinxin He
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Jingjing Qi
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Yuanchun Zhu
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Hua He
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Jiwen Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Jiwei Hu
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China
| | - Liang Li
- State Key Laboratory of Swine and Poultry Breeding Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, and Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 613000, China.
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Razi F, Ostovar A, Fahimfar N, M. Amoli M, Fana SE, Dimai HP, Obermayer-Pietsch B, Luegger B, Rivadeneira F, Nabipour I, Larijani B, Khashayar P. Protocol for preliminary, multicenteric validation of "PoCOsteo device": A point of care tool for proteomic and genomic study of osteoporosis. Biol Methods Protoc 2024; 9:bpae006. [PMID: 38559752 PMCID: PMC10978377 DOI: 10.1093/biomethods/bpae006] [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: 12/30/2023] [Revised: 01/26/2024] [Accepted: 03/21/2024] [Indexed: 04/04/2024] Open
Abstract
One of the goals of the HORIZON 2020 project PoCOsteo was to develop a medical device, which would measure and/or quantify proteomic as well as genomic factors as present in whole blood samples collected through finger prick. After validating the tool in the clinical setting, the next step would be its clinical validation based on the existing guidelines. This article presents the protocol of a validation study to be carried out independently at two different centers (Division of Endocrinology and Diabetology at the Medical University of Graz as a clinic-based cohort, and the Endocrinology and Metabolism Research Institute at the Tehran University of Medical Sciences as a population-based cohort). It aims to assess the tool according to the Clinical & Laboratory Standards Institute guidelines, confirming if the proteomics and genomics measurements provided by the tool are accurate and reproducible compared with the existing state-of-the-art tests. This is the first time that such a detailed protocol for lab validation of a medical tool for proteomics and genomic measurement is designed based on the existing guidelines and thus could be used as a template for clinical validation of future point-of-care tools. Moreover, the multicentric cohort design will allow the study of a large number of diverse individuals, which will improve the validity and generalizability of the results for different settings.
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Affiliation(s)
- Farideh Razi
- Metabolomics and Genomics Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Afshin Ostovar
- Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Noushin Fahimfar
- Osteoporosis Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahsa M. Amoli
- Metabolic Disorders Research Center (MDRC), Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Saeed Ebrahimi Fana
- Department of Clinical Biochemistry, Tehran University of Medical Sciences, Tehran, Iran
| | - Hans Peter Dimai
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Styria, Austria
| | - Barbara Obermayer-Pietsch
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Styria, Austria
| | - Barbara Luegger
- Department of Internal Medicine, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Styria, Austria
| | | | - Iraj Nabipour
- The Persian Gulf Marine Biotechnology Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Patricia Khashayar
- Center for Microsystems Technology, Imec & Ghent University, Zwijnaarde, Gent, Belgium
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Haile M, Ro N, Ko HC, Oh H, Lee GA. A Comprehensive Genome-Wide Association Study of Carotenoid and Capsaicinoid Contents in Capsicum chinense Germplasm. Int J Mol Sci 2023; 24:13885. [PMID: 37762188 PMCID: PMC10531199 DOI: 10.3390/ijms241813885] [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: 07/31/2023] [Revised: 09/04/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Pepper is a highly important vegetable globally, both economically and nutritionally. However, to efficiently select and identify genetic resources for pepper breeding programs, it is crucial to understand the association between important traits and genetic factors. In this study, we investigated the genetic basis of carotenoid and capsaicinoid content in 160 Capsicum chinense germplasms. The study observed significant variability in carotenoid and capsaicinoid content among the germplasms. Correlation analysis revealed a strong positive correlation between violaxanthin and antheraxanthin. In contrast, capsaicin and dihydrocapsaicin displayed negative correlations with individual carotenoids but exhibited a strong positive correlation between the two compounds (r = 0.90 ***). Genotyping-by-sequencing (GBS) was performed on 160 genotypes of pepper germplasm, which identified 47,810 high-quality SNPs. A comprehensive genome-wide association analysis was performed using these SNPs to identify SNPs associated with carotenoids and capsaicinoids, revealing 193 SNPs that exhibited significant associations. Specifically, 4 SNPs were associated with violaxanthin, 2 with antheraxanthin, 86 with capsorubin, 5 with capsanthin, 63 with zeaxanthin, 3 with β-cryptoxanthin, and 2 with α-carotene. With further studies, the significantly associated SNPs identified in this study have the potential to be utilized for selecting pepper accessions with high carotenoid and capsaicinoid contents. Additionally, the genes associated with these significant SNPs will be used to understand their roles and involvement in the biosynthesis pathway of carotenoids and capsaicinoids. Understanding the function of these genes can provide insights into the molecular mechanisms underlying the production of these bioactive compounds in pepper. The findings of this study hold valuable implications for selecting pepper varieties with desirable traits and developing breeding programs aimed at enhancing the nutritional and medicinal properties of pepper.
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Ro N, Haile M, Ko HC, Cho GT, Lee J, Kim B, Lee S, Kim SH. Genome-Wide Association Study of Phenolic Content and Antioxidant Properties in Eggplant Germplasm. Genes (Basel) 2023; 14:1315. [PMID: 37510220 PMCID: PMC10379237 DOI: 10.3390/genes14071315] [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: 05/27/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/30/2023] Open
Abstract
The phenolic compounds in eggplant offer potential natural antioxidants for improved health. A large number of samples were examined in order to find eggplant germplasm with a high potential for health promotion. A genome-wide association study (GWAS) was conducted to identify single nucleotide polymorphisms (SNPs) associated with variations in total phenolic content (TPC) and antioxidant activity in eggplants, including ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) scavenging activity and ferric reducing antioxidant power (FRAP). TPC values varied from 14.19 to 842.90 mg gallic acid equivalent (GAE)/100 g of dry weight of eggplant fruit powder. TPC showed a strong positive correlation with both FRAP and ABTS (r = 0.89 *** and 0.77 ***, respectively). The GWAS identified 20 SNPs that were significantly associated out of 29,183 SNPs. Out of the 20 significant SNPs, 11 showed associations with TPC, 4 with ABTS activity, and 5 with FRAP. Among the SNPs associated with TPC, one SNP was found on each of Chromosomes 3, 4, 7, and 12. In contrast, Chromosome 5 comprised two SNPs associated to TPC. Furthermore, the gene encoding IRX12 laccase-4 on Chromosome 10 was found to contain five SNPs associated with TPC. Four significantly linked SNPs on Chromosomes 1 (1 SNP), 4 (2 SNPs), and 10 (1 SNP) were found to be related to ABTS activity. The identified SNPs will be further examined as markers for selecting desirable eggplant varieties and exploring the links between candidate genes, phenolic content, and antioxidant activity. The findings of this study could assist in further study and the development of eggplants with improved health advantages through targeted breeding.
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Affiliation(s)
- Nayoung Ro
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Republic of Korea
| | - Mesfin Haile
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Republic of Korea
| | - Ho-Cheol Ko
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Republic of Korea
| | - Gyu-Taek Cho
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Republic of Korea
| | - Jungro Lee
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Republic of Korea
| | - Bichsaem Kim
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Republic of Korea
| | - Sookyeong Lee
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Republic of Korea
| | - Seong-Hoon Kim
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Republic of Korea
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Sullivan M, Deng HW, Greenbaum J. Identification of genetic loci shared between Alzheimer's disease and hypertension. Mol Genet Genomics 2022; 297:1661-1670. [PMID: 36069947 DOI: 10.1007/s00438-022-01949-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/27/2022] [Indexed: 10/14/2022]
Abstract
Alzheimer's disease (AD) and high blood pressure (BP) are prevalent age-related diseases with significant unexplained heritability. A thorough analysis of genetic pleiotropy between AD and BP will lay a foundation for the study of the associated molecular mechanisms, leading to a better understanding of the development of each phenotype. We used the conditional false discovery rate (cFDR) method to identify novel genetic loci associated with both AD and BP. The cFDR approach improves the effective sample size for association testing by combining GWAS summary statistics for correlated phenotypes. We identified 50 pleiotropic SNPs for AD and BP, 7 of which are novel and have not previously been reported to be associated with either AD or BP. The novel SNPs located at STK3 are particularly noteworthy, as this gene may influence AD risk via the Hippo signaling network, which regulates cell death. Bayesian colocalization analysis demonstrated that although AD and BP are associated, they do not appear to share the same causal variants. We further performed two sample Mendelian randomization analysis, but could not detect a causal effect of BP on AD. Despite the inability to establish a causal link between AD and BP, our findings report some potential novel pleiotropic loci that may influence disease susceptibility. In summary, we identified 7 SNPs that annotate to 4 novel genes which have not previously been reported to be associated with AD nor with BP and discuss the possible role of one of these genes, STK3 in the Hippo signaling network.
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Affiliation(s)
- Megan Sullivan
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Jonathan Greenbaum
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, 70112, USA.
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Identification of PDXDC1 as a novel pleiotropic susceptibility locus shared between lumbar spine bone mineral density and birth weight. J Mol Med (Berl) 2022; 100:723-734. [PMID: 35314877 PMCID: PMC9110509 DOI: 10.1007/s00109-021-02165-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 10/13/2021] [Accepted: 11/04/2021] [Indexed: 02/04/2023]
Abstract
An increasing number of epidemiological studies have suggested that birth weight (BW) may be a determinant of bone health later in life, although the underlying genetic mechanism remains unclear. Here, we applied a pleiotropic conditional false discovery rate (cFDR) approach to the genome-wide association study (GWAS) summary statistics for lumbar spine bone mineral density (LS BMD) and BW, aiming to identify novel susceptibility variants shared between these two traits. We detected 5 novel potential pleiotropic loci which are located at or near 7 different genes (NTAN1, PDXDC1, CACNA1G, JAG1, FAT1P1, CCDC170, ESR1), among which PDXDC1 and FAT1P1 have not previously been linked to these phenotypes. To partially validate the findings, we demonstrated that the expression of PDXDC1 was dramatically reduced in ovariectomized (OVX) mice in comparison with sham-operated (SHAM) mice in both the growth plate and trabecula bone. Furthermore, immunohistochemistry assay with serial sections showed that both osteoclasts and osteoblasts express PDXDC1, supporting its potential role in bone metabolism. In conclusion, our study provides insights into some shared genetic mechanisms for BMD and BW as well as a novel potential therapeutic target for the prevention of OP in the early stages of the disease development. KEY MESSAGES : We investigated pleiotropy-informed enrichment between LS BMD and BW. We identified genetic variants related to both LS BMD and BW by utilizing a cFDR approach. PDXDC1 is a novel pleiotropic gene which may be related to both LS BMD and BW. Elevated expression of PDXDC1 is related to higher BMD and lower ratio n-6/n-3 PUFA indicating a bone protective effect of PDXDC1.
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Peng C, Liu F, Su KJ, Lin X, Song YQ, Shen J, Hu SD, Chen QC, Yuan HH, Li WX, Zeng CP, Deng HW, Lou HL. Enhanced Identification of Novel Potential Variants for Appendicular Lean Mass by Leveraging Pleiotropy With Bone Mineral Density. Front Immunol 2021; 12:643894. [PMID: 33889153 PMCID: PMC8056257 DOI: 10.3389/fimmu.2021.643894] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 03/09/2021] [Indexed: 11/22/2022] Open
Abstract
Strong relationships have been found between appendicular lean mass (ALM) and bone mineral density (BMD). It may be due to a shared genetic basis, termed pleiotropy. By leveraging the pleiotropy with BMD, the aim of this study was to detect more potential genetic variants for ALM. Using the conditional false discovery rate (cFDR) methodology, a combined analysis of the summary statistics of two large independent genome wide association studies (GWAS) of ALM (n = 73,420) and BMD (n = 10,414) was conducted. Strong pleiotropic enrichment and 26 novel potential pleiotropic SNPs were found for ALM and BMD. We identified 156 SNPs for ALM (cFDR <0.05), of which 74 were replicates of previous GWASs and 82 were novel SNPs potentially-associated with ALM. Eleven genes annotated by 31 novel SNPs (13 pleiotropic and 18 ALM specific) were partially validated in a gene expression assay. Functional enrichment analysis indicated that genes corresponding to the novel potential SNPs were enriched in GO terms and/or KEGG pathways that played important roles in muscle development and/or BMD metabolism (adjP <0.05). In protein–protein interaction analysis, rich interactions were demonstrated among the proteins produced by the corresponding genes. In conclusion, the present study, as in other recent studies we have conducted, demonstrated superior efficiency and reliability of the cFDR methodology for enhanced detection of trait-associated genetic variants. Our findings shed novel insight into the genetic variability of ALM in addition to the shared genetic basis underlying ALM and BMD.
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Affiliation(s)
- Cheng Peng
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Feng Liu
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Kuan-Jui Su
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, United States
| | - Xu Lin
- Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde), Foshan City, China
| | - Yu-Qian Song
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Jie Shen
- Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde), Foshan City, China
| | - Shi-Di Hu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Qiao-Cong Chen
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Hui-Hui Yuan
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wen-Xi Li
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Chun-Ping Zeng
- Department of Endocrinology and Metabolism, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, United States
| | - Hui-Ling Lou
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Liu RK, Lin X, Wang Z, Greenbaum J, Qiu C, Zeng CP, Zhu YY, Shen J, Deng HW. Identification of novel functional CpG-SNPs associated with Type 2 diabetes and birth weight. Aging (Albany NY) 2021; 13:10619-10658. [PMID: 33835050 PMCID: PMC8064204 DOI: 10.18632/aging.202828] [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: 11/06/2020] [Accepted: 03/04/2021] [Indexed: 12/18/2022]
Abstract
Genome-wide association studies (GWASs) have identified hundreds of genetic loci for type 2 diabetes (T2D) and birth weight (BW); however, a large proportion of the total trait heritability remains unexplained. The previous studies were generally focused on individual traits and largely failed to identify the majority of the variants that play key functional roles in the etiology of the disease. Here, we aim to identify novel functional loci for T2D, BW and the pleiotropic variants shared between them by performing a targeted conditional false discovery rate (cFDR) analysis that integrates two independent GWASs with summary statistics for T2D (n = 26,676 cases and 132,532 controls) and BW (n = 153,781) which entails greater statistical power than individual trait analyses. In this analysis, we considered CpG-SNPs, which are SNPs that may influence DNA methylation status, and are therefore considered to be functionally important. We identified 103 novel CpG-SNPs for T2D, 182 novel CpG-SNPs for BW (cFDR < 0.05), and 52 novel pleiotropic loci for both (conjunction cFDR [ccFDR] < 0.05). Among the identified novel CpG-SNPs, 33 were annotated as methylation quantitative trait loci (meQTLs) in whole blood, and 145 displayed at least some effects on meQTL, metabolic QTL (metaQTL), and/or expression QTL (eQTL). These findings may provide further insights into the shared biological mechanisms and functional genetic determinants that overlap between T2D and BW, thereby providing novel potential targets for treatment/intervention development.
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Affiliation(s)
- Rui-Ke Liu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
- Department of Endocrinology and Metabolism, SSL Central Hospital of Dongguan City, Dongguan 523326, China
| | - Xu Lin
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
| | - Zun Wang
- Xiangya Nursing School, Central South University, Changsha 410013, China
| | - Jonathan Greenbaum
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Chuan Qiu
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Chun-Ping Zeng
- Department of Endocrinology and metabolism, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510330, China
| | - Yong-Yao Zhu
- Department of Endocrinology and Metabolism, SSL Central Hospital of Dongguan City, Dongguan 523326, China
| | - Jie Shen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
| | - Hong-Wen Deng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
- School of Basic Medical Sciences, Central South University, Changsha 410000, China
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Fisher L, Fisher A, Smith PN. Helicobacter pylori Related Diseases and Osteoporotic Fractures (Narrative Review). J Clin Med 2020; 9:E3253. [PMID: 33053671 PMCID: PMC7600664 DOI: 10.3390/jcm9103253] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/28/2020] [Accepted: 10/07/2020] [Indexed: 02/06/2023] Open
Abstract
Osteoporosis (OP) and osteoporotic fractures (OFs) are common multifactorial and heterogenic disorders of increasing incidence. Helicobacter pylori (H.p.) colonizes the stomach approximately in half of the world's population, causes gastroduodenal diseases and is prevalent in numerous extra-digestive diseases known to be associated with OP/OF. The studies regarding relationship between H.p. infection (HPI) and OP/OFs are inconsistent. The current review summarizes the relevant literature on the potential role of HPI in OP, falls and OFs and highlights the reasons for controversies in the publications. In the first section, after a brief overview of HPI biological features, we analyze the studies evaluating the association of HPI and bone status. The second part includes data on the prevalence of OP/OFs in HPI-induced gastroduodenal diseases (peptic ulcer, chronic/atrophic gastritis and cancer) and the effects of acid-suppressive drugs. In the next section, we discuss the possible contribution of HPI-associated extra-digestive diseases and medications to OP/OF, focusing on conditions affecting both bone homeostasis and predisposing to falls. In the last section, we describe clinical implications of accumulated data on HPI as a co-factor of OP/OF and present a feasible five-step algorithm for OP/OF risk assessment and management in regard to HPI, emphasizing the importance of an integrative (but differentiated) holistic approach. Increased awareness about the consequences of HPI linked to OP/OF can aid early detection and management. Further research on the HPI-OP/OF relationship is needed to close current knowledge gaps and improve clinical management of both OP/OF and HPI-related disorders.
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Affiliation(s)
- Leon Fisher
- Department of Gastroenterology, Frankston Hospital, Peninsula Health, Melbourne 3199, Australia
| | - Alexander Fisher
- Department of Geriatric Medicine, The Canberra Hospital, ACT Health, Canberra 2605, Australia;
- Department of Orthopedic Surgery, The Canberra Hospital, ACT Health, Canberra 2605, Australia;
- Australian National University Medical School, Canberra 2605, Australia
| | - Paul N Smith
- Department of Orthopedic Surgery, The Canberra Hospital, ACT Health, Canberra 2605, Australia;
- Australian National University Medical School, Canberra 2605, Australia
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Abstract
PURPOSE OF REVIEW We summarize recent evidence on the shared genetics within and outside the musculoskeletal system (mostly related to bone density and osteoporosis). RECENT FINDINGS Osteoporosis is determined by an interplay between multiple genetic and environmental factors. Significant progress has been made regarding its genetic background revealing a number of robustly validated loci and respective pathways. However, pleiotropic factors affecting bone and other tissues are not well understood. The analytical methods proposed to test for potential associations between genetic variants and multiple phenotypes can be applied to bone-related data. A number of recent genetic studies have shown evidence of pleiotropy between bone density and other different phenotypes (traits, conditions, or diseases), within and outside the musculoskeletal system. Power benefits of combining correlated phenotypes, as well as unbiased discovery, make these studies promising. Studies in humans are supported by evidence from animal models. Drug development and repurposing should benefit from the pleiotropic approach. We believe that future studies should take into account shared genetics between the bone and related traits.
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Affiliation(s)
- M A Christou
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
| | - E E Ntzani
- Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
- Center for Research Synthesis in Health, Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - D Karasik
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA.
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel.
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11
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He P, Meng XH, Zhang X, Lin X, Zhang Q, Jiang RL, Schiller MR, Deng FY, Deng HW. Identifying Pleiotropic SNPs Associated With Femoral Neck and Heel Bone Mineral Density. Front Genet 2020; 11:772. [PMID: 32774344 PMCID: PMC7388689 DOI: 10.3389/fgene.2020.00772] [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: 02/12/2020] [Accepted: 06/29/2020] [Indexed: 01/06/2023] Open
Abstract
Background Genome-wide association studies (GWASs) routinely identify loci associated with risk factors for osteoporosis. However, GWASs with relatively small sample sizes still lack sufficient power to ascertain the majority of genetic variants with small to modest effect size, which may together truly influence the phenotype. The loci identified only account for a small percentage of the heritability of osteoporosis. This study aims to identify novel genetic loci associated with DXA-derived femoral neck (FNK) bone mineral density (BMD) and quantitative ultrasound of the heel calcaneus estimated BMD (eBMD), and to detect shared/causal variants for the two traits, to assess whether the SNPs or putative causal SNPs associated with eBMD were also associated with FNK-BMD. Methods Novel loci associated with eBMD and FNK-BMD were identified by the genetic pleiotropic conditional false discovery rate (cFDR) method. Shared putative causal variants between eBMD and FNK-BMD and putative causal SNPs for each trait were identified by the colocalization method. Mendelian randomization analysis addresses the causal relationship between eBMD/FNK-BMD and fracture. Results We identified 9,500 (cFDR < 9.8E-6), 137 (cFDR < 8.9E-4) and 124 SNPs associated with eBMD, FNK-BMD, and both eBMD and FNK-BMD, respectively, with 37 genomic regions where there was a SNP that influences both eBMD and FNK-BMD. Most genomic regions only contained putative causal SNPs associated with eBMD and 3 regions contained two distinct putative causal SNPs influenced both traits, respectively. We demonstrated a causal effect of FNK-BMD/eBMD on fracture. Conclusion Most of SNPs or putative causal SNPs associated with FNK-BMD were also associated with eBMD. However, most of SNPs or putative causal SNPs associated with eBMD were not associated with FNK-BMD. The novel variants we identified may help to account for the additional proportion of variance of each trait and advance our understanding of the genetic mechanisms underlying osteoporotic fracture.
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Affiliation(s)
- Pei He
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China.,Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Xiang-He Meng
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.,Center of Reproductive Health, System Biology and Data Information, School of Basic Medical Science, Central South University, Changsha, China.,Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, China
| | - Xiao Zhang
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Xu Lin
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.,Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Qiang Zhang
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.,College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Ri-Li Jiang
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Martin R Schiller
- Nevada Institute of Personalized Medicine, School of Life Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States
| | - Fei-Yan Deng
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States.,Center of Reproductive Health, System Biology and Data Information, School of Basic Medical Science, Central South University, Changsha, China.,Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, China
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12
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Wang Z, Greenbaum J, Qiu C, Li K, Wang Q, Tang SY, Deng HW. Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis. Mol Genet Genomics 2020; 295:1173-1185. [PMID: 32474671 DOI: 10.1007/s00438-020-01692-8] [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: 03/28/2020] [Accepted: 05/25/2020] [Indexed: 12/11/2022]
Abstract
Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial fibrillation, hypertension, coronary artery disease, heart failure, diabetes, body mass index, and total cholesterol level) by integrating seven corresponding GWAS data. We detected 20 potential pleiotropic genes that may be associated with multiple risk factors of stroke. Furthermore, using gene-to-trait pathway analysis, we suggested six potential risk genes (FUT8, GMIP, PLA2G6, PDE3A, SMARCA4, SKAPT) that may affect ischemic or hemorrhage stroke through multiple intermediate factors such as MAPK family. These findings provide novel insight into the genetic determinants contributing to the concurrent development of biological conditions that may influence stroke susceptibility, and also indicate some potential therapeutic targets that can be further studied for the prevention of cerebrovascular disease.
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Affiliation(s)
- Zun Wang
- Xiangya Nursing School, Central South University, Changsha, 410013, China.,Department of Global Biostatistics and Data Science, Tulane Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Jonathan Greenbaum
- Department of Global Biostatistics and Data Science, Tulane Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Chuan Qiu
- Department of Global Biostatistics and Data Science, Tulane Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Kelvin Li
- Department of Global Biostatistics and Data Science, Tulane Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Qian Wang
- Xiangya Nursing School, Central South University, Changsha, 410013, China
| | - Si-Yuan Tang
- Xiangya Nursing School, Central South University, Changsha, 410013, China.,Hunan Women's Research Association, Changsha, 410011, China
| | - Hong-Wen Deng
- Department of Global Biostatistics and Data Science, Tulane Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA. .,School of Basic Medical Science, Central South University, Changsha, 410013, China.
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13
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Yang TL, Shen H, Liu A, Dong SS, Zhang L, Deng FY, Zhao Q, Deng HW. A road map for understanding molecular and genetic determinants of osteoporosis. Nat Rev Endocrinol 2020; 16:91-103. [PMID: 31792439 PMCID: PMC6980376 DOI: 10.1038/s41574-019-0282-7] [Citation(s) in RCA: 188] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/18/2019] [Indexed: 12/16/2022]
Abstract
Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and genomics'. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.
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Affiliation(s)
- Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Hui Shen
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Lei Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, China
| | - Fei-Yan Deng
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, China
| | - Qi Zhao
- Department of Preventive Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Hong-Wen Deng
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.
- School of Basic Medical Science, Central South University, Changsha, China.
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14
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Improved detection of common variants in coronary artery disease and blood pressure using a pleiotropy cFDR method. Sci Rep 2019; 9:10340. [PMID: 31316127 PMCID: PMC6637206 DOI: 10.1038/s41598-019-46808-2] [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: 09/07/2018] [Accepted: 07/04/2019] [Indexed: 11/24/2022] Open
Abstract
Plenty of genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms (SNPs) for coronary artery disease (CAD) and blood pressure (BP). However, these SNPs only explain a small proportion of the heritability of two traits/diseases. Although high BP is a major risk factor for CAD, the genetic intercommunity between them remain largely unknown. To recognize novel loci associated with CAD and BP, a genetic-pleiotropy-informed conditional false discovery rate (cFDR) method was applied on two summary statistics of CAD and BP from existing GWASs. Stratified Q-Q and fold enrichment plots showed a high pleiotropic enrichment of SNPs associated with two traits. Adopting a cFDR of 0.05 as a threshold, 55 CAD-associated loci (25 variants being novel) and 47 BP loci (18 variants being novel) were identified, 25 of which were pleiotropic loci (13 variants being novel) for both traits. Among the 32 genes these 25 SNPs were annotated to, 20 genes were newly detected compared to previous GWASs. This study showed the cFDR approach could improve gene discovery by incorporating GWAS datasets of two related traits. These findings may provide novel understanding of etiology relationships between CAD and BP.
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15
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Hu Y, Tan LJ, Chen XD, Greenbaum J, Deng HW. Identification of novel variants associated with osteoporosis, type 2 diabetes and potentially pleiotropic loci using pleiotropic cFDR method. Bone 2018; 117:6-14. [PMID: 30172742 PMCID: PMC6364698 DOI: 10.1016/j.bone.2018.08.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 08/27/2018] [Accepted: 08/29/2018] [Indexed: 12/16/2022]
Abstract
AIMS Clinical and epidemiological findings point to an association between type 2 diabetes (T2D) and osteoporosis. Genome-wide association studies (GWASs) have been fruitful in identifying some loci potentially associated with osteoporosis and T2D respectively. However, the total genetic variance for each of these two diseases and the shared genetic determination between them are largely unknown. The aim of this study was to identify novel genetic variants for osteoporosis and/or T2D. METHODS First, using a pleiotropic conditional false discovery rate (cFDR) method, we analyzed two GWAS summary data of femoral neck bone mineral density (FN_BMD, n = 53,236) and T2D (n = 159,208) to identify novel shared genetic loci. FN_BMD is an important risk factor for osteoporosis. Next, to explore the potential functions of the identified potential pleiotropic genes, differential expression analysis was performed for them in monocytes and peripheral blood mononuclear cells (PBMCs) as these cells are relevant to the etiology of osteoporosis and/or T2D. Further, weighted gene co-expression analysis (WGCNA) was conducted to identify functional connections between novel pleiotropic genes and known osteoporosis/T2D susceptibility genes by using transcriptomic expression datasets in bone biopsies (E-MEXP-1618) and pancreatic islets (GSE50397). Finally, multi-trait fine mapping for the detected pleiotropic risk loci were conducted to identify the SNPs that have the highest probability of being causal for both FN_BMD and T2D. RESULTS We identified 27 significant SNPs with cFDR<0.05 for FN_BMD and 61 SNPs for T2D respectively. Four loci, rs7068487 (PLEKHA1), rs10885421 (TCF7L2), rs944082 (GNG12-AS1 (WLS)) and rs2065929 (PIFO||PGCP1), were found to be potentially pleiotropic and shared between FN_BMD and T2D (ccFDR<0.05). PLEKHA1 was found differentially expressed in circulating monocytes between high and low BMD subjects, and PBMCs between diabetic and non-diabetic conditions. WGCNA showed that PLEKHA1 and TCF7L2 were interconnected with multiple osteoporosis and T2D associated genes in bone biopsy and pancreatic islets, such as JAG, EN1 and CPE. Fine mapping showed that rs11200594 was a potentially causal variant in the locus of PLEKHA1. rs11200594 is also an eQTL of PLEKHA1 in multiple tissue (e.g. peripheral blood cells, adipose and ovary) and is in strong LD with a number of functional variants. CONCLUSIONS Four potential pleiotropic loci were identified for shared genetic determination of osteoporosis and T2D. Our study highlights PLEKHA1 as an important potentially pleiotropic gene. The findings may help us gain a better understanding of the shared genetic determination between these two important disorders.
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Affiliation(s)
- Yuan Hu
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, China
| | - Li-Jun Tan
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, China
| | - Xiang-Ding Chen
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, China
| | - Jonathan Greenbaum
- School of Basic Medical Sciences, Central South University, Changsha, Hunan 410013, China
| | - Hong-Wen Deng
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, China; School of Basic Medical Sciences, Central South University, Changsha, Hunan 410013, China; Center of Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.
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16
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Zhang Q, Liu HM, Lv WQ, He JY, Xia X, Zhang WD, Deng HW, Sun CQ. Additional common variants associated with type 2 diabetes and coronary artery disease detected using a pleiotropic cFDR method. J Diabetes Complications 2018; 32:1105-1112. [PMID: 30270018 PMCID: PMC6743331 DOI: 10.1016/j.jdiacomp.2018.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 04/11/2018] [Accepted: 09/02/2018] [Indexed: 12/27/2022]
Abstract
Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and coronary artery disease (CARD) are generally performed as single-trait studies, rather than analyzing the related traits simultaneously. Despite the extensive evidence suggesting that these two phenotypes share both genetic and environmental risk factors, the shared overlapping genetic biological mechanisms between these traits remain largely unexplored. Here, we adopted a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with T2D and CARD by incorporating the summary statistics from existing GWASs of these two traits. Applying the cFDR level of 0.05, 33 loci were identified for T2D and 34 loci for CARD, 9 of which for both. By incorporating pleiotropic effects into a conditional analysis framework, we observed that there is significant pleiotropic enrichment between T2D and CARD. These findings may provide novel insights into the etiology of T2D and CARD, as well as the processes that may influence disease development both individually and jointly.
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Affiliation(s)
- Qiang Zhang
- College of Public Health, Zhengzhou University, Zhengzhou, No. 100 Kexue Road, High-Tech Development Zone Of States, PR China
| | - Hui-Min Liu
- College of Public Health, Zhengzhou University, Zhengzhou, No. 100 Kexue Road, High-Tech Development Zone Of States, PR China
| | - Wan-Qiang Lv
- College of Public Health, Zhengzhou University, Zhengzhou, No. 100 Kexue Road, High-Tech Development Zone Of States, PR China
| | - Jing-Yang He
- College of Public Health, Zhengzhou University, Zhengzhou, No. 100 Kexue Road, High-Tech Development Zone Of States, PR China
| | - Xin Xia
- College of Public Health, Zhengzhou University, Zhengzhou, No. 100 Kexue Road, High-Tech Development Zone Of States, PR China
| | - Wei-Dong Zhang
- College of Public Health, Zhengzhou University, Zhengzhou, No. 100 Kexue Road, High-Tech Development Zone Of States, PR China
| | - Hong-Wen Deng
- College of Public Health, Zhengzhou University, Zhengzhou, No. 100 Kexue Road, High-Tech Development Zone Of States, PR China; Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Chang-Qing Sun
- College of Public Health, Zhengzhou University, Zhengzhou, No. 100 Kexue Road, High-Tech Development Zone Of States, PR China.
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17
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Lin X, Peng C, Greenbaum J, Li ZF, Wu KH, Ao ZX, Zhang T, Shen J, Deng HW. Identifying potentially common genes between dyslipidemia and osteoporosis using novel analytical approaches. Mol Genet Genomics 2018; 293:711-723. [PMID: 29327327 PMCID: PMC5949092 DOI: 10.1007/s00438-017-1414-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 12/29/2017] [Indexed: 02/07/2023]
Abstract
Dyslipidemia (DL) is closely related to osteoporosis (OP), while the exact common genetic mechanisms are still largely unknown. We proposed to use novel genetic analysis methods with pleiotropic information to identify potentially novel and/or common genes for the potential shared pathogenesis associated with OP and/or DL. We assessed the pleiotropy between plasma lipid (PL) and femoral neck bone mineral density (FNK BMD). We jointly applied the conditional false discovery rate (cFDR) method and the genetic analysis incorporating pleiotropy and annotation (GPA) method to the summary statistics provided by genome-wide association studies (GWASs) of FNK BMD (n = 49,988) and PL (n = 188,577) to identify potentially novel and/or common genes for BMD/PL. We found strong pleiotropic enrichment between PL and FNK BMD. Two hundred and forty-five PL SNPs were identified as potentially novel SNPs by cFDR and GPA. The corresponding genes were enriched in gene ontology (GO) terms "phospholipid homeostasis" and "chylomicron remnant clearance". Three SNPs (rs2178950, rs9939318, and rs9368716) might be the pleiotropic ones and the corresponding genes NLRC5 (rs2178950) and TRPS1 (rs9939318) were involved in NF-κB signaling pathway and Wnt signaling pathway as well as inflammation and innate immune processes. Our study validated the pleiotropy between PL and FNK BMD, and corroborated the reliability and high-efficiency of cFDR and GPA methods in further analyses of existing GWASs with summary statistics. We identified potentially common and/or novel genes for PL and/or FNK BMD, which may provide new insight and direction for further research.
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Affiliation(s)
- Xu Lin
- Southern Medical University, No.1023, South Shatai Road, Baiyun District, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Cheng Peng
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, The Second Affiliated Hospital of South China University of Technology, Guangzhou, 510180, People's Republic of China
| | - Jonathan Greenbaum
- Center for Bioinformatics and Genomics, Department of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Zhang-Fang Li
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China
| | - Ke-Hao Wu
- Center for Bioinformatics and Genomics, Department of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Zeng-Xin Ao
- Southern Medical University, No.1023, South Shatai Road, Baiyun District, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Tong Zhang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China
| | - Jie Shen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA.
- School of Basic Medical Sciences, Central South University, Changsha, 410000, People's Republic of China.
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18
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Liu HM, He JY, Zhang Q, Lv WQ, Xia X, Sun CQ, Zhang WD, Deng HW. Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method. Mol Genet Genomics 2018; 293:225-235. [PMID: 29038864 PMCID: PMC5819009 DOI: 10.1007/s00438-017-1381-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 10/06/2017] [Indexed: 01/19/2023]
Abstract
Genome-wide association studies (GWAS) have been shown to have the potential of explaining more of the "missing heritability" of complex human phenotypes by improving statistical approaches. Here, we applied a genetic-pleiotropy-informed conditional false discovery rate (cFDR) to capture additional polygenic effects associated with estimated glomerular filtration rate (creatinine) (eGFRcrea) and type 2 diabetes (T2D). The cFDR analysis improves the identification of pleiotropic variants by incorporating potentially shared genetic mechanisms between two related traits. The Q-Q and fold-enrichment plots were used to assess the enrichment of SNPs associated with eGFRcrea or T2D, and Manhattan plots were used for showing chromosomal locations of the significant loci detected. By applying the cFDR method, we newly identified 74 loci for eGFRcrea and 3 loci for T2D with the cFDR criterion of 0.05 compared with previous related GWAS studies. Four shared SNPs were detected to be associated with both eGFRcrea and T2D at the significant conjunction cFDR level of 0.05, and these shared SNPs had not been reported in previous studies. In addition, we used DAVID analysis to perform functional analysis of the shared SNPs' annotated genes and found their potential hidden associations with eGFRcrea and T2D. In this study, the cFDR method shows the feasibility to detect more genetic variants underlying the heritability of eGFRcrea and T2D, and the overlapping SNPs identified could be regarded as candidate loci that provide a thread of genetic mechanisms between eGFRcrea and T2D in future research.
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Affiliation(s)
- Hui-Min Liu
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Jing-Yang He
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Qiang Zhang
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Wan-Qiang Lv
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Xin Xia
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Chang-Qing Sun
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Wei-Dong Zhang
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China.
| | - Hong-Wen Deng
- College of Public Health Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China.
- Department of Biostatistics and Data Science, Tulane Center of Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA.
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19
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Hu Y, Tan LJ, Chen XD, Liu Z, Min SS, Zeng Q, Shen H, Deng HW. Identification of Novel Potentially Pleiotropic Variants Associated With Osteoporosis and Obesity Using the cFDR Method. J Clin Endocrinol Metab 2018; 103:125-138. [PMID: 29145611 PMCID: PMC6061219 DOI: 10.1210/jc.2017-01531] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 10/12/2017] [Indexed: 01/10/2023]
Abstract
CONTEXT Genome-wide association studies (GWASs) have been successful in identifying loci associated with osteoporosis and obesity. However, the findings explain only a small fraction of the total genetic variance. OBJECTIVE The aim of this study was to identify novel pleiotropic genes important in osteoporosis and obesity. DESIGN AND SETTING A pleiotropic conditional false discovery rate method was applied to three independent GWAS summary statistics of femoral neck bone mineral density, body mass index, and waist-to-hip ratio. Next, differential expression analysis was performed for the potentially pleiotropic genes, and weighted genes coexpression network analysis (WGCNA) was conducted to identify functional connections between the suggested pleiotropic genes and known osteoporosis/obesity genes using transcriptomic expression data sets in osteoporosis/obesity-related cells. RESULTS We identified seven potentially pleiotropic loci-rs3759579 (MARK3), rs2178950 (TRPS1), rs1473 (PUM1), rs9825174 (XXYLT1), rs2047937 (ZNF423), rs17277372 (DNM3), and rs335170 (PRDM6)-associated with osteoporosis and obesity. Of these loci, the PUM1 gene was differentially expressed in osteoporosis-related cells (B lymphocytes) and obesity-related cells (adipocytes). WGCNA showed that PUM1 positively interacted with several known osteoporosis genes (AKAP11, JAG1, and SPTBN1). ZNF423 was the highly connected intramodular hub gene and interconnected with 21 known osteoporosis-related genes, including JAG1, EN1, and FAM3C. CONCLUSIONS Our study identified seven potentially pleiotropic genes associated with osteoporosis and obesity. The findings may provide new insights into a potential genetic determination and codetermination mechanism of osteoporosis and obesity.
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Affiliation(s)
- Yuan Hu
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Li-Jun Tan
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Xiang-Ding Chen
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Zhen Liu
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Shi-Shi Min
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Qin Zeng
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Hui Shen
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
| | - Hong-Wen Deng
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
- Correspondence and Reprint Requests: Hong-Wen Deng, PhD, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 1610, New Orleans, Louisiana 70112. E-mail:
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Zhang Q, Wu KH, He JY, Zeng Y, Greenbaum J, Xia X, Liu HM, Lv WQ, Lin X, Zhang WD, Xi YL, Shi XZ, Sun CQ, Deng HW. Novel Common Variants Associated with Obesity and Type 2 Diabetes Detected Using a cFDR Method. Sci Rep 2017; 7:16397. [PMID: 29180724 PMCID: PMC5703959 DOI: 10.1038/s41598-017-16722-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 11/16/2017] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and obesity are generally focused on individual traits independently, and genetic intercommunity (common genetic contributions or the product of over correlated phenotypic world) between them are largely unknown, despite extensive data showing that these two phenotypes share both genetic and environmental risk factors. Here, we applied a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with BMI and T2D by incorporating the summary statistics from existing GWASs of these two traits. Conditional Q-Q and fold enrichment plots were used to visually demonstrate the strength of pleiotropic enrichment. Adopting a cFDR nominal significance level of 0.05, 287 loci were identified for BMI and 75 loci for T2D, 23 of which for both traits. By incorporating related traits into a conditional analysis framework, we observed significant pleiotropic enrichment between obesity and T2D. These findings may provide novel insights into the etiology of obesity and T2D, individually and jointly.
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Affiliation(s)
- Qiang Zhang
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China
| | - Ke-Hao Wu
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Jing-Yang He
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China
| | - Yong Zeng
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA.,College of Sciences, Beijing Jiao Tong University, Beijing, China
| | - Jonathan Greenbaum
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Xin Xia
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China
| | - Hui-Min Liu
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China
| | - Wan-Qiang Lv
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China
| | - Xu Lin
- Department of Endocrinology and Metabolism, the Third Affiliated Hospital of Southern Medical University, Guang Zhou, P.R. China
| | - Wei-Dong Zhang
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China
| | - Yuan-Lin Xi
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China
| | - Xue-Zhong Shi
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China
| | - Chang-Qing Sun
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China.
| | - Hong-Wen Deng
- College of Public Health, Zhengzhou University, Zhengzhou, NO.100 Kexue Road, High-Tech Development Zone Of States, Zhengzhou, P.R. China. .,Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA.
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21
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Peng C, Lou HL, Liu F, Shen J, Lin X, Zeng CP, Long JR, Su KJ, Zhang L, Greenbaum J, Deng WF, Li YM, Deng HW. Enhanced Identification of Potential Pleiotropic Genetic Variants for Bone Mineral Density and Breast Cancer. Calcif Tissue Int 2017; 101:489-500. [PMID: 28761973 PMCID: PMC5796546 DOI: 10.1007/s00223-017-0308-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 07/22/2017] [Indexed: 10/19/2022]
Abstract
Epidemiological and clinical evidences have shown that bone mineral density (BMD) has a close relationship with breast cancer (BC). They might potentially have a shared genetic basis. By incorporating information about these pleiotropic effects, we may be able to explore more of the traits' total heritability. We applied a recently developed conditional false discovery rate (cFDR) method to the summary statistics from two independent GWASs to identify the potential pleiotropic genetic variants for BMD and BC. By jointly analyzing two large independent GWASs of BMD and BC, we found strong pleiotropic enrichment between them and identified 102 single-nucleotide polymorphisms (SNPs) in BMD and 192 SNPs in BC with cFDR < 0.05, including 230 SNPs that might have been overlooked by the standard GWAS analysis. cFDR-significant genes were enriched in GO terms and KEGG pathways which were crucial to bone metabolism and/or BC pathology (adjP < 0.05). Some cFDR-significant genes were partially validated in the gene expressional validation assay. Strong interactions were found between proteins produced by cFDR-significant genes in the context of biological mechanism of bone metabolism and/or BC etiology. Totally, we identified 7 pleiotropic SNPs that were associated with both BMD and BC (conjunction cFDR < 0.05); CCDC170, ESR1, RANKL, CPED1, and MEOX1 might play important roles in the pleiotropy of BMD and BC. Our study highlighted the significant pleiotropy between BMD and BC and shed novel insight into trait-specific as well as the potentially shared genetic architecture for both BMD and BC.
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Affiliation(s)
- Cheng Peng
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, People's Republic of China
| | - Hui-Ling Lou
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, People's Republic of China
| | - Feng Liu
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, 510180, People's Republic of China
| | - Jie Shen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China
| | - Xu Lin
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China
| | - Chun-Ping Zeng
- Department of Endocrinology and Metabolism, Affiliated Nanhai Hospital of Southern Medical University, Guangzhou, People's Republic of China
| | - Ji-Rong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kuan-Jui Su
- Department of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA
| | - Lan Zhang
- Department of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA
| | - Jonathan Greenbaum
- Department of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA
| | - Wei-Feng Deng
- Hunan University of Medicine, Huaihua, 418000, People's Republic of China
| | - Yu-Mei Li
- School of Mathematics and Computational Science, Huaihua University, Huaihua, 418008, Hunan, People's Republic of China
| | - Hong-Wen Deng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, People's Republic of China.
- Department of Global Statistics and Data Science, School of Public Health and Tropical Medicine, Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, 70112, USA.
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22
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Lv WQ, Zhang X, Zhang Q, He JY, Liu HM, Xia X, Fan K, Zhao Q, Shi XZ, Zhang WD, Sun CQ, Deng HW. Novel common variants associated with body mass index and coronary artery disease detected using a pleiotropic cFDR method. J Mol Cell Cardiol 2017; 112:1-7. [PMID: 28843344 PMCID: PMC5812278 DOI: 10.1016/j.yjmcc.2017.08.011] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 08/18/2017] [Accepted: 08/22/2017] [Indexed: 12/20/2022]
Abstract
Genome-wide association studies (GWAS) have been successfully applied in identifying single nucleotide polymorphisms (SNPs) associated with body mass index (BMI) and coronary heart disease (CAD). However, the SNPs to date can only explain a small percentage of the genetic variances of traits. Here, we applied a genetic pleiotropic conditional false discovery rate (cFDR) method that combines summary statistic p values from different multi-center GWAS datasets, to detect common genetic variants associated with these two traits. The enrichment of SNPs associated with BMI and CAD was assessed by conditional Q-Q plots and the common variants were identified by the cFDR method. By applying the cFDR level of 0.05, 7 variants were identified to be associated with CAD (2 variants being novel), 34 variants associated with BMI (11 variants being novel), and 3 variants associated with both BMI and CAD (2 variants being novel). The SNP rs653178 (ATXN2) is noteworthy as this variant was replicated in an independent analysis. SNP rs12411886 (CNNM2) and rs794356 (HIP1) were of note as the annotated genes may be associated with processes that are functionally important in lipid metabolism. In conclusion, the cFDR method identified novel variants associated with BMI and/or CAD by effectively incorporating different GWAS datasets.
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Affiliation(s)
- Wan-Qiang Lv
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Xue Zhang
- Department of Geriatrics, Renmin Hospital of Wuhan University, Hubei Zhang Road (Formerly Ziyang Road), Wuchang District No. 99 Jiefang Road 238, Wuhan 430060, People's Republic of China
| | - Qiang Zhang
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Jing-Yang He
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Hui-Min Liu
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Xin Xia
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Kun Fan
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Qi Zhao
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA; Center for Genomics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Xue-Zhong Shi
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Wei-Dong Zhang
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Chang-Qing Sun
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China
| | - Hong-Wen Deng
- College of Public Health, Zhengzhou University, No.100 Kexue Road, High-Tech Development Zone of States, Zhengzhou, People's Republic of China; Center for Genomics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.
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23
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Peng C, Shen J, Lin X, Su KJ, Greenbaum J, Zhu W, Lou HL, Liu F, Zeng CP, Deng WF, Deng HW. Genetic sharing with coronary artery disease identifies potential novel loci for bone mineral density. Bone 2017; 103:70-77. [PMID: 28651948 PMCID: PMC5796548 DOI: 10.1016/j.bone.2017.06.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 06/21/2017] [Accepted: 06/22/2017] [Indexed: 12/30/2022]
Abstract
Bone mineral density (BMD) is a complex trait with high missing heritability. Numerous evidences have shown that BMD variation has a relationship with coronary artery disease (CAD). This relationship may come from a common genetic basis called pleiotropy. By leveraging the pleiotropy with CAD, we may be able to improve the detection power of genetic variants associated with BMD. Using a recently developed conditional false discovery rate (cFDR) method, we jointly analyzed summary statistics from two large independent genome wide association studies (GWAS) of lumbar spine (LS) BMD and CAD. Strong pleiotropic enrichment and 7 pleiotropic SNPs were found for the two traits. We identified 41 SNPs for LS BMD (cFDR<0.05), of which 20 were replications of previous GWASs and 21 were potential novel SNPs that were not reported before. Four genes encompassed by 9 cFDR-significant SNPs were partially validated in the gene expression assay. Further functional enrichment analysis showed that genes corresponding to the cFDR-significant LS BMD SNPs were enriched in GO terms and KEGG pathways that played crucial roles in bone metabolism (adjP<0.05). In protein-protein interaction analysis, strong interactions were found between the proteins produced by the corresponding genes. Our study demonstrated the reliability and high-efficiency of the cFDR method on the detection of trait-associated genetic variants, the present findings shed novel insights into the genetic variability of BMD as well as the shared genetic basis underlying osteoporosis and CAD.
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Affiliation(s)
- Cheng Peng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, 510180, China
| | - Jie Shen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
| | - Xu Lin
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
| | - Kuan-Jui Su
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Jonathan Greenbaum
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Wei Zhu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Hui-Ling Lou
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, 510180, China
| | - Feng Liu
- Department of Geriatrics, National Key Clinical Specialty, Guangzhou First People's Hospital, Guangzhou Medical University, 510180, China
| | - Chun-Ping Zeng
- Department of Endocrinology and Metabolism, Affiliated Nanhai Hospital of Southern Medical University, Guangzhou, China
| | | | - Hong-Wen Deng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.
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Identification of novel genetic loci for osteoporosis and/or rheumatoid arthritis using cFDR approach. PLoS One 2017; 12:e0183842. [PMID: 28854271 PMCID: PMC5576737 DOI: 10.1371/journal.pone.0183842] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 08/12/2017] [Indexed: 12/19/2022] Open
Abstract
There are co-morbidity between osteoporosis (OP) and rheumatoid arthritis (RA). Some genetic risk factors have been identified for these two phenotypes respectively in previous research; however, they accounted for only a small portion of the underlying total genetic variances. Here, we sought to identify additional common genetic loci associated with OP and/or RA. The conditional false discovery rate (cFDR) approach allows detection of additional genetic factors (those respective ones as well as common pleiotropic ones) for the two associated phenotypes. We collected and analyzed summary statistics provided by large, multi-center GWAS studies of FNK (femoral neck) BMD (a major risk factor for osteoporosis) (n = 53,236) and RA (n = 80,799). The conditional quantile-quantile (Q-Q) plots can assess the enrichment of SNPs related to FNK BMD and RA, respectively. Furthermore, we identified shared loci between FNK BMD and RA using conjunction cFDR (ccFDR). We found strong enrichment of p-values in FNK BMD when conditional Q-Q was done on RA and vice versa. We identified 30 novel OP-RA associated pleiotropic loci that have not been reported in previous OP or RA GWAS, 18 of which located in the MHC (major histocompatibility complex) region previously reported to play an important role in immune system and bone health. We identified some specific novel polygenic factors for OP and RA respectively, and identified 30 novel OP-RA associated pleiotropic loci. These discovery findings may offer novel pathobiological insights, and suggest new targets and pathways for drug development in OP and RA patients.
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Wang XF, Lin X, Li DY, Zhou R, Greenbaum J, Chen YC, Zeng CP, Peng LP, Wu KH, Ao ZX, Lu JM, Guo YF, Shen J, Deng HW. Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach. J Neurol Sci 2017; 380:262-272. [PMID: 28870582 DOI: 10.1016/j.jns.2017.07.044] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/29/2017] [Accepted: 07/28/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND Both type 2 diabetes (T2D) and Alzheimer's disease (AD) occur commonly in the aging populations and T2D has been considered as an important risk factor for AD. The heritability of both diseases is estimated to be over 50%. However, common pleiotropic single-nucleotide polymorphisms (SNPs)/loci have not been well-defined. The aim of this study is to analyze two large public accessible GWAS datasets to identify novel common genetic loci for T2D and/or AD. METHODS AND MATERIALS The recently developed novel conditional false discovery rate (cFDR) approach was used to analyze the summary GWAS datasets from International Genomics of Alzheimer's Project (IGAP) and Diabetes Genetics Replication And Meta-analysis (DIAGRAM) to identify novel susceptibility genes for AD and T2D. RESULTS We identified 78 SNPs (including 58 novel SNPs) that were associated with AD in Europeans conditional on T2D (cFDR<0.05). 66 T2D SNPs (including 40 novel SNPs) were identified by conditioning on SNPs association with AD (cFDR<0.05). A conjunction-cFDR (ccFDR) analysis detected 8 pleiotropic SNPs with a significance threshold of ccFDR<0.05 for both AD and T2D, of which 5 SNPs (rs6982393, rs4734295, rs7812465, rs10510109, rs2421016) were novel findings. Furthermore, among the 8 SNPs annotated at 6 different genes, 3 corresponding genes TP53INP1, TOMM40 and C8orf38 were related to mitochondrial dysfunction, critically involved in oxidative stress, which potentially contribute to the etiology of both AD and T2D. CONCLUSION Our study provided evidence for shared genetic loci between T2D and AD in European subjects by using cFDR and ccFDR analyses. These results may provide novel insight into the etiology and potential therapeutic targets of T2D and/or AD.
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Affiliation(s)
- Xia-Fang Wang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Xu Lin
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Ding-You Li
- Department of Gastroenterology, Children's Mercy Kansas City, University of Missouri Kansas City School of Medicine, Kansas City MO 64108, USA
| | - Rou Zhou
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Jonathan Greenbaum
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Yuan-Cheng Chen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Chun-Ping Zeng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Lin-Ping Peng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Ke-Hao Wu
- Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Zeng-Xin Ao
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Jun-Min Lu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Yan-Fang Guo
- Institute of Bioinformatics, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong 510515, PR China
| | - Jie Shen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China
| | - Hong-Wen Deng
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, PR China; Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA.
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