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Ballester M, Ramayo-Caldas Y, Revilla M, Corominas J, Castelló A, Estellé J, Fernández AI, Folch JM. Integration of liver gene co-expression networks and eGWAs analyses highlighted candidate regulators implicated in lipid metabolism in pigs. Sci Rep 2017; 7:46539. [PMID: 28422154 PMCID: PMC5396199 DOI: 10.1038/srep46539] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 03/22/2017] [Indexed: 12/14/2022] Open
Abstract
In the present study, liver co-expression networks and expression Genome Wide Association Study (eGWAS) were performed to identify DNA variants and molecular pathways implicated in the functional regulatory mechanisms of meat quality traits in pigs. With this purpose, the liver mRNA expression of 44 candidates genes related with lipid metabolism was analysed in 111 Iberian x Landrace backcross animals. The eGWAS identified 92 eSNPs located in seven chromosomal regions and associated with eight genes: CROT, CYP2U1, DGAT1, EGF, FABP1, FABP5, PLA2G12A, and PPARA. Remarkably, cis-eSNPs associated with FABP1 gene expression which may be determining the C18:2(n-6)/C18:3(n-3) ratio in backfat through the multiple interaction of DNA variants and genes were identified. Furthermore, a hotspot on SSC8 associated with the gene expression of eight genes was identified and the TBCK gene was pointed out as candidate gene regulating it. Our results also suggested that the PI3K-Akt-mTOR pathway plays an important role in the control of the analysed genes highlighting nuclear receptors as the NR3C1 or PPARA. Finally, sex-dimorphism associated with hepatic lipid metabolism was identified with over-representation of female-biased genes. These results increase our knowledge of the genetic architecture underlying fat composition traits.
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Affiliation(s)
- Maria Ballester
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Campus UAB, Bellaterra, 08193, Barcelona, Spain
- Plant and Animal Genomics, Centre de Recerca en Agrigenòmica (Consorci CSIC-IRTA-UAB-UB), Edifici CRAG, Campus UAB, Bellaterra, 08193, Barcelona, Spain
- IRTA, Genètica i Millora Animal, Torre Marimon, 08140 Caldes de Montbui, Spain
| | - Yuliaxis Ramayo-Caldas
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Campus UAB, Bellaterra, 08193, Barcelona, Spain
- Plant and Animal Genomics, Centre de Recerca en Agrigenòmica (Consorci CSIC-IRTA-UAB-UB), Edifici CRAG, Campus UAB, Bellaterra, 08193, Barcelona, Spain
- Génétique Animale et Biologie Intégrative, Institut National de la Recherche Agronomique, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Manuel Revilla
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Campus UAB, Bellaterra, 08193, Barcelona, Spain
- Plant and Animal Genomics, Centre de Recerca en Agrigenòmica (Consorci CSIC-IRTA-UAB-UB), Edifici CRAG, Campus UAB, Bellaterra, 08193, Barcelona, Spain
| | - Jordi Corominas
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Campus UAB, Bellaterra, 08193, Barcelona, Spain
- Plant and Animal Genomics, Centre de Recerca en Agrigenòmica (Consorci CSIC-IRTA-UAB-UB), Edifici CRAG, Campus UAB, Bellaterra, 08193, Barcelona, Spain
| | - Anna Castelló
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Campus UAB, Bellaterra, 08193, Barcelona, Spain
- Plant and Animal Genomics, Centre de Recerca en Agrigenòmica (Consorci CSIC-IRTA-UAB-UB), Edifici CRAG, Campus UAB, Bellaterra, 08193, Barcelona, Spain
| | - Jordi Estellé
- Génétique Animale et Biologie Intégrative, Institut National de la Recherche Agronomique, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Ana I. Fernández
- Departamento de Mejora Genética Animal, INIA, Ctra. de la Coruña km. 7, 28040, Madrid, Spain
| | - Josep M. Folch
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Campus UAB, Bellaterra, 08193, Barcelona, Spain
- Plant and Animal Genomics, Centre de Recerca en Agrigenòmica (Consorci CSIC-IRTA-UAB-UB), Edifici CRAG, Campus UAB, Bellaterra, 08193, Barcelona, Spain
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Stevens A, Meyer S, Hanson D, Clayton P, Donn RP. Network analysis identifies protein clusters of functional importance in juvenile idiopathic arthritis. Arthritis Res Ther 2014; 16:R109. [PMID: 24886659 PMCID: PMC4062926 DOI: 10.1186/ar4559] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 04/29/2014] [Indexed: 02/06/2023] Open
Abstract
Introduction Our objective was to utilise network analysis to identify protein clusters of greatest potential functional relevance in the pathogenesis of oligoarticular and rheumatoid factor negative (RF-ve) polyarticular juvenile idiopathic arthritis (JIA). Methods JIA genetic association data were used to build an interactome network model in BioGRID 3.2.99. The top 10% of this protein:protein JIA Interactome was used to generate a minimal essential network (MEN). Reactome FI Cytoscape 2.83 Plugin and the Disease Association Protein-Protein Link Evaluator (Dapple) algorithm were used to assess the functionality of the biological pathways within the MEN and to statistically rank the proteins. JIA gene expression data were integrated with the MEN and clusters of functionally important proteins derived using MCODE. Results A JIA interactome of 2,479 proteins was built from 348 JIA associated genes. The MEN, representing the most functionally related components of the network, comprised of seven clusters, with distinct functional characteristics. Four gene expression datasets from peripheral blood mononuclear cells (PBMC), neutrophils and synovial fluid monocytes, were mapped onto the MEN and a list of genes enriched for functional significance identified. This analysis revealed the genes of greatest potential functional importance to be PTPN2 and STAT1 for oligoarticular JIA and KSR1 for RF-ve polyarticular JIA. Clusters of 23 and 14 related proteins were derived for oligoarticular and RF-ve polyarticular JIA respectively. Conclusions This first report of the application of network biology to JIA, integrating genetic association findings and gene expression data, has prioritised protein clusters for functional validation and identified new pathways for targeted pharmacological intervention.
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Li WQ, Han J, Widlund HR, Correll M, Wang YE, Quackenbush J, Mihm MC, Canales AL, Wu S, Golub T, Hoshida Y, Hunter DJ, Murphy G, Kupper TS, Qureshi AA. CXCR4 pathway associated with family history of melanoma. Cancer Causes Control 2013; 25:125-32. [PMID: 24158781 DOI: 10.1007/s10552-013-0315-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Accepted: 10/15/2013] [Indexed: 01/24/2023]
Abstract
PURPOSE Genetic predisposition plays a major role in the etiology of melanoma, but known genetic markers only account for a limited fraction of family-history-associated melanoma cases. Expression microarrays have offered the opportunity to identify further genomic profiles correlated with family history of melanoma. We aimed to distinguish mRNA expression signatures between melanoma cases with and without a family history of melanoma. METHODS Based on the Nurses' Health Study, family history was defined as having one or more first-degree family members diagnosed with melanoma. Melanoma diagnosis was confirmed by reviewing pathology reports, and tumor blocks were collected by mail from across the USA. Genomic interrogation was accomplished through evaluating expression profiling of formalin-fixed paraffin-embedded tissues from 78 primary cutaneous invasive melanoma cases, on either a 6K or whole-genome (24K) Illumina gene chip. Gene set enrichment analysis was performed for each batch to determine the differentially enriched pathways and key contributing genes. RESULTS The CXC chemokine receptor 4 (CXCR4) pathway was consistently up-regulated within cases of familial melanoma in both platforms. Leading edge analysis showed four genes from the CXCR4 pathway, including MAPK1, PLCG1, CRK, and PTK2, were among the core members that contributed to the enrichment of this pathway. There was no association between the enrichment of CXCR4 pathway and NRAS, BRAF mutation, or Breslow thickness of the primary melanoma cases. CONCLUSIONS We found that the CXCR4 pathway might constitute a novel susceptibility pathway associated with family history of melanoma in first-degree relatives.
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Affiliation(s)
- Wen-Qing Li
- Department of Dermatology, Brigham and Women's Hospital, Harvard Medical School, 45 Francis St, 221L, Boston, MA, 02115, USA
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Li J, Liu J, Zhao L, Ma Y, Jia M, Lu T, Ruan Y, Li Q, Yue W, Zhang D, Wang L. Association study between genes in Reelin signaling pathway and autism identifies DAB1 as a susceptibility gene in a Chinese Han population. Prog Neuropsychopharmacol Biol Psychiatry 2013; 44:226-32. [PMID: 23333377 DOI: 10.1016/j.pnpbp.2013.01.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Revised: 12/26/2012] [Accepted: 01/05/2013] [Indexed: 01/07/2023]
Abstract
Autism is a pervasive neurodevelopmental disorder diagnosed in early childhood. The genetic factors might play an important role in its pathogenesis. Previous studies revealed that Reelin (RELN) polymorphisms were associated with autism. However, the roles of genes in Reelin signaling pathway for autism are largely unknown. As several knockout mice models in which the Reelin pathway genes (i.e. DAB1, VLDLR/APOER2, FYN/SRC and CRK/CRKL) are deficient have the similar phenotype as the reeler mice (Reelin(-/-)), we hypothesized that the Reelin signaling pathway genes might play roles in the etiology of autism. Therefore, we conducted a family-based association study. Sixty-two tagged single nucleotide polymorphisms (SNPs) covering 15 genes in Reelin pathway were genotyped in 239 trios, and 14 significant SNPs were further investigated in the additional 188 trios. In the total 427 trios, we found significant genetic association between autism and four SNPs in DAB1 (rs12035887 G: p=0.0006; rs3738556 G: p=0.0044; rs1202773 A: p=0.0048; rs12740765 T: p=0.0196). After the Bonferroni correction, SNP rs12035887 remained significant. Furthermore, the haplotype constructed with rs1202773 and rs12023109 in DAB1 showed significant excess transmission in both individual and global haplotype analyses (p=0.0052 and 0.0279, respectively). Our findings suggested that variations in DAB1 involved in the Reelin signaling pathway might contribute to genetic susceptibility to autism with Chinese Han decent, supporting the defect in the Reelin signaling pathway as a predisposition factor for autism.
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Affiliation(s)
- Jun Li
- Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, PR China
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Layered signaling regulatory networks analysis of gene expression involved in malignant tumorigenesis of non-resolving ulcerative colitis via integration of cross-study microarray profiles. PLoS One 2013; 8:e67142. [PMID: 23825635 PMCID: PMC3692446 DOI: 10.1371/journal.pone.0067142] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 05/15/2013] [Indexed: 01/08/2023] Open
Abstract
Background Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups. Methodology/Principal Findings In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks. Results Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one. Conclusions The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.
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Zhang R, Zhao Y, Chu M, Wu C, Jin G, Dai J, Wang C, Hu L, Gou J, Qian C, Bai J, Wu T, Hu Z, Lin D, Shen H, Chen F. Pathway analysis for genome-wide association study of lung cancer in Han Chinese population. PLoS One 2013; 8:e57763. [PMID: 23469231 PMCID: PMC3585721 DOI: 10.1371/journal.pone.0057763] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Accepted: 01/24/2013] [Indexed: 11/30/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified a number of genetic variants associated with lung cancer risk. However, these loci explain only a small fraction of lung cancer hereditability and other variants with weak effect may be lost in the GWAS approach due to the stringent significance level after multiple comparison correction. In this study, in order to identify important pathways involving the lung carcinogenesis, we performed a two-stage pathway analysis in GWAS of lung cancer in Han Chinese using gene set enrichment analysis (GSEA) method. Predefined pathways by BioCarta and KEGG databases were systematically evaluated on Nanjing study (Discovery stage: 1,473 cases and 1,962 controls) and the suggestive pathways were further to be validated in Beijing study (Replication stage: 858 cases and 1,115 controls). We found that four pathways (achPathway, metPathway, At1rPathway and rac1Pathway) were consistently significant in both studies and the P values for combined dataset were 0.012, 0.010, 0.022 and 0.005 respectively. These results were stable after sensitivity analysis based on gene definition and gene overlaps between pathways. These findings may provide new insights into the etiology of lung cancer.
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Affiliation(s)
- Ruyang Zhang
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yang Zhao
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Minjie Chu
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Wu
- State Key Laboratory of Molecular Oncology and Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
- Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Cheng Wang
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Lingmin Hu
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jianwei Gou
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Qian
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jianling Bai
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Tangchun Wu
- Institute of Occupational Medicine and Ministry of Education, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
- Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Dongxin Lin
- State Key Laboratory of Molecular Oncology and Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
- Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Feng Chen
- Department of Epidemiology and Biostatistics and Ministry of Education (MOE) Key Lab for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China
- * E-mail:
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Holzinger ER, Ritchie MD. Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies. Pharmacogenomics 2012; 13:213-22. [PMID: 22256870 DOI: 10.2217/pgs.11.145] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data - transcriptomic, proteomic and methylomic to name a few. Analyzing this 'meta-dimensional' data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.
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Affiliation(s)
- Emily R Holzinger
- Center for Human Genetics Research, Vanderbilt University, Department of Molecular Physiology & Biophysics, Nashville, TN, USA
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Zhang M, Liang L, Morar N, Dixon AL, Lathrop GM, Ding J, Moffatt MF, Cookson WOC, Kraft P, Qureshi AA, Han J. Integrating pathway analysis and genetics of gene expression for genome-wide association study of basal cell carcinoma. Hum Genet 2012; 131:615-23. [PMID: 22006220 PMCID: PMC3303995 DOI: 10.1007/s00439-011-1107-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2011] [Accepted: 10/08/2011] [Indexed: 10/16/2022]
Abstract
Genome-wide association studies (GWASs) have primarily focused on marginal effects for individual markers and have incorporated external functional information only after identifying robust statistical associations. We applied a new approach combining the genetics of gene expression and functional classification of genes to the GWAS of basal cell carcinoma (BCC) to identify potential biological pathways associated with BCC. We first identified 322,324 expression-associated single-nucleotide polymorphisms (eSNPs) from two existing GWASs of global gene expression in lymphoblastoid cell lines (n = 955), and evaluated the association of these functionally annotated SNPs with BCC among 2,045 BCC cases and 6,013 controls in Caucasians. We then grouped them into 99 KEGG pathways for pathway analysis and identified two pathways associated with BCC with p value <0.05 and false discovery rate (FDR) <0.5: the autoimmune thyroid disease pathway (mainly HLA class I and II antigens, p < 0.001, FDR = 0.24) and Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway (p = 0.02, FDR = 0.49). Seventy-nine (25.7%) out of 307 significant eSNPs in the JAK-STAT pathway were associated with BCC risk (p < 0.05) in an independent replication set of 278 BCC cases and 1,262 controls. In addition, the association of JAK-STAT signaling pathway was marginally validated using 16,691 eSNPs identified from 110 normal skin samples (p = 0.08). Based on the evidence of biological functions of the JAK-STAT pathway on oncogenesis, it is plausible that this pathway is involved in BCC pathogenesis.
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Affiliation(s)
- Mingfeng Zhang
- Clinical Research Program, Department of Dermatology, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, China
| | - Liming Liang
- Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Nilesh Morar
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Anna L Dixon
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Jun Ding
- Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miriam F Moffatt
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Peter Kraft
- Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Abrar A. Qureshi
- Clinical Research Program, Department of Dermatology, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
- Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jiali Han
- Clinical Research Program, Department of Dermatology, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
- Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, USA
- Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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Zhang M, Liang L, Xu M, Qureshi AA, Han J. Pathway analysis for genome-wide association study of basal cell carcinoma of the skin. PLoS One 2011; 6:e22760. [PMID: 21829505 PMCID: PMC3145747 DOI: 10.1371/journal.pone.0022760] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2011] [Accepted: 06/28/2011] [Indexed: 01/03/2023] Open
Abstract
Background Recently, a pathway-based approach has been developed to evaluate the cumulative contribution of the functionally related genes for genome-wide association studies (GWASs), which may help utilize GWAS data to a greater extent. Methods In this study, we applied this approach for the GWAS of basal cell carcinoma (BCC) of the skin. We first conducted the BCC GWAS among 1,797 BCC cases and 5,197 controls in Caucasians with 740,760 genotyped SNPs. 115,688 SNPs were grouped into gene transcripts within 20 kb in distance and then into 174 Kyoto Encyclopedia of Genes and Genomes pathways, 205 BioCarta pathways, as well as two positive control gene sets (pigmentation gene set and BCC risk gene set). The association of each pathway with BCC risk was evaluated using the weighted Kolmogorov-Smirnov test. One thousand permutations were conducted to assess the significance. Results Both of the positive control gene sets reached pathway p-values<0.05. Four other pathways were also significantly associated with BCC risk: the heparan sulfate biosynthesis pathway (p = 0.007, false discovery rate, FDR = 0.35), the mCalpain pathway (p = 0.002, FDR = 0.12), the Rho cell motility signaling pathway (p = 0.011, FDR = 0.30), and the nitric oxide pathway (p = 0.022, FDR = 0.42). Conclusion We identified four pathways associated with BCC risk, which may offer new insights into the etiology of BCC upon further validation, and this approach may help identify potential biological pathways that might be missed by the standard GWAS approach.
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Affiliation(s)
- Mingfeng Zhang
- Department of Dermatology, Clinical Research Program, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
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Ritchie MD. Using biological knowledge to uncover the mystery in the search for epistasis in genome-wide association studies. Ann Hum Genet 2011; 75:172-82. [PMID: 21158748 DOI: 10.1111/j.1469-1809.2010.00630.x] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The search for the missing heritability in genome-wide association studies (GWAS) has become an important focus for the human genetics community. One suspected location of these genetic effects is in gene-gene interactions, or epistasis. The computational burden of exploring gene-gene interactions in the wealth of data generated in GWAS, along with small to moderate sample sizes, have led to epistasis being an afterthought, rather than a primary focus of GWAS analyses. In this review, I discuss some potential approaches to filter a GWAS dataset to a smaller, more manageable dataset where searching for epistasis is considerably more feasible. I describe a number of alternative approaches, but primarily focus on the use of prior biological knowledge from databases in the public domain to guide the search for epistasis. The manner in which prior knowledge is incorporated into a GWA study can be many and these data can be extracted from a variety of database sources. I discuss a number of these approaches and propose that a comprehensive approach will likely be most fruitful for searching for epistasis in large-scale genomic studies of the current state-of-the-art and into the future.
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Affiliation(s)
- Marylyn D Ritchie
- Department of Molecular Physiology, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232-0700, USA.
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Li J, Humphreys K, Darabi H, Rosin G, Hannelius U, Heikkinen T, Aittomäki K, Blomqvist C, Pharoah PD, Dunning AM, Ahmed S, Hooning MJ, Hollestelle A, Oldenburg RA, Alfredsson L, Palotie A, Peltonen-Palotie L, Irwanto A, Low HQ, Teoh GH, Thalamuthu A, Kere J, D'Amato M, Easton DF, Nevanlinna H, Liu J, Czene K, Hall P. A genome-wide association scan on estrogen receptor-negative breast cancer. Breast Cancer Res 2010; 12:R93. [PMID: 21062454 PMCID: PMC3046434 DOI: 10.1186/bcr2772] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2010] [Revised: 10/06/2010] [Accepted: 11/09/2010] [Indexed: 12/20/2022] Open
Abstract
Introduction Breast cancer is a heterogeneous disease and may be characterized on the basis of whether estrogen receptors (ER) are expressed in the tumour cells. ER status of breast cancer is important clinically, and is used both as a prognostic indicator and treatment predictor. In this study, we focused on identifying genetic markers associated with ER-negative breast cancer risk. Methods We conducted a genome-wide association analysis of 285,984 single nucleotide polymorphisms (SNPs) genotyped in 617 ER-negative breast cancer cases and 4,583 controls. We also conducted a genome-wide pathway analysis on the discovery dataset using permutation-based tests on pre-defined pathways. The extent of shared polygenic variation between ER-negative and ER-positive breast cancers was assessed by relating risk scores, derived using ER-positive breast cancer samples, to disease state in independent, ER-negative breast cancer cases. Results Association with ER-negative breast cancer was not validated for any of the five most strongly associated SNPs followed up in independent studies (1,011 ER-negative breast cancer cases, 7,604 controls). However, an excess of small P-values for SNPs with known regulatory functions in cancer-related pathways was found (global P = 0.052). We found no evidence to suggest that ER-negative breast cancer shares a polygenic basis to disease with ER-positive breast cancer. Conclusions ER-negative breast cancer is a distinct breast cancer subtype that merits independent analyses. Given the clinical importance of this phenotype and the likelihood that genetic effect sizes are small, greater sample sizes and further studies are required to understand the etiology of ER-negative breast cancers.
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Affiliation(s)
- Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden.
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