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Babik W, Dudek K, Marszałek M, Palomar G, Antunes B, Sniegula S. The genomic response to urbanization in the damselfly Ischnura elegans. Evol Appl 2023; 16:1805-1818. [PMID: 38029064 PMCID: PMC10681423 DOI: 10.1111/eva.13603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/19/2023] [Indexed: 12/01/2023] Open
Abstract
The complex and rapid environmental changes brought about by urbanization pose significant challenges to organisms. The multifaceted effects of urbanization often make it difficult to define and pinpoint the very nature of adaptive urban phenotypes. In such situations, scanning genomes for regions differentiated between urban and non-urban populations may be an attractive approach. Here, we investigated the genomic signatures of adaptation to urbanization in the damselfly Ischnura elegans sampled from 31 rural and urban localities in three geographic regions: southern and northern Poland, and southern Sweden. Genome-wide variation was assessed using more than 370,000 single nucleotide polymorphisms (SNPs) genotyped by ddRADseq. Associations between SNPs and the level of urbanization were tested using two genetic environment association methods: Latent Factors Mixed Models and BayPass. While we found numerous candidate SNPs and a highly significant overlap between candidates identified by the two methods within the geographic regions, there was a distinctive lack of repeatability between the geographic regions both at the level of individual SNPs and of genomic regions. However, we found "synapse organization" at the top of the functional categories enriched among the genes located in the proximity of the candidate urbanization SNPs. Interestingly, the overall significance of "synapse organization" was built up by the accretion of different genes associated with candidate SNPs in different geographic regions. This finding is consistent with the highly polygenic nature of adaptation, where the response may be achieved through a subtle adjustment of allele frequencies in different genes that contribute to adaptive phenotypes. Taken together, our results point to a polygenic adaptive response in the nervous system, specifically implicating genes involved in synapse organization, which mirrors the findings from several genomic and behavioral studies of adaptation to urbanization in other taxa.
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Affiliation(s)
- W. Babik
- Faculty of Biology, Institute of Environmental SciencesJagiellonian UniversityKrakówPoland
| | - K. Dudek
- Faculty of Biology, Institute of Environmental SciencesJagiellonian UniversityKrakówPoland
| | - M. Marszałek
- Faculty of Biology, Institute of Environmental SciencesJagiellonian UniversityKrakówPoland
| | - G. Palomar
- Faculty of Biology, Institute of Environmental SciencesJagiellonian UniversityKrakówPoland
- Department of Genetics, Physiology and Microbiology, Faculty of Biological SciencesComplutense University of MadridMadridSpain
| | - B. Antunes
- Faculty of Biology, Institute of Environmental SciencesJagiellonian UniversityKrakówPoland
| | - S. Sniegula
- Department of Ecosystem Conservation, Institute of Nature ConservationPolish Academy of SciencesKrakówPoland
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2
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Amezcua-Guerra LM, Guzmán-Martín CA, Montúfar-Robles I, Springall R, Hernández-Díazcouder A, Barbosa-Cobos RE, Sánchez-Muñoz F, Ramírez-Bello J. CD147 rs8259T>A Variant Confers Susceptibility to COVID-19 Infection within the Mexican Population. Microorganisms 2023; 11:1919. [PMID: 37630479 PMCID: PMC10458029 DOI: 10.3390/microorganisms11081919] [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: 06/05/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clinical manifestations of COVID-19 range from mild flu-like symptoms to severe respiratory failure. Nowadays, extracellular matrix metalloproteinase inducer (EMMPRIN), also known as cluster of differentiation 147 (CD147) or BASIGIN, has been studied as enabling viral entry and replication within host cells. However, the impact of the CD147 rs8259T>A single nucleotide variant (SNV) on SARS-CoV-2 susceptibility remains poorly investigated. OBJECTIVE To investigate the impact of rs8259T>A on the CD147 gene in individuals from Mexico with COVID-19 disease. METHODS We genotyped the CD147 rs8359T>A SNV in 195 patients with COVID-19 and 185 healthy controls from Mexico. In addition, we also measured the expression levels of CD147 and TNF mRNA and miR-492 from whole blood of patients with COVID-19 through RT-q-PCR. RESULTS We observed a significant association between the CD147 rs8259T>A SNV and susceptibility to COVID-19: T vs. A; OR 1.36, 95% CI 1.02-1.81; p = 0.037; and TT vs. AA; OR 1.77, 95% CI 1.01-3.09; p = 0.046. On the other hand, we did not find differences in CD147, TNF or miR-492 expression levels when considering the genotypes of the CD147 rs8259T>A SNV. CONCLUSIONS Our results suggest that the CD147 rs8259T>A variant is a risk factor for COVID-19.
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Affiliation(s)
- Luis M. Amezcua-Guerra
- Immunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City 14080, Mexico; (L.M.A.-G.); (R.S.)
| | - Carlos A. Guzmán-Martín
- Postgraduate Doctoral Program in Biological and Health Sciences, Universidad Autónoma Metropolitana, Mexico City 14387, Mexico;
| | | | - Rashidi Springall
- Immunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City 14080, Mexico; (L.M.A.-G.); (R.S.)
| | - Adrián Hernández-Díazcouder
- Obesity and Asthma Research Laboratory, Hospital Infantil de México Federico Gómez, Mexico City 06720, Mexico;
| | - Rosa Elda Barbosa-Cobos
- Rheumatology Department, Hospital Juárez de México, Mexico City 07760, Mexico;
- The American British Cowdray Medical Center, Mexico City 05348, Mexico
| | - Fausto Sánchez-Muñoz
- Immunology Department, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City 14080, Mexico; (L.M.A.-G.); (R.S.)
| | - Julián Ramírez-Bello
- Endocrinology Department, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City 14080, Mexico
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3
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Defo J, Awany D, Ramesar R. From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies? Brief Bioinform 2023; 24:6972298. [PMID: 36611240 DOI: 10.1093/bib/bbac600] [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: 09/08/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.
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Affiliation(s)
- Joel Defo
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
| | - Denis Awany
- South African Tuberculosis Vaccine Initiative (SATVI), University of Cape Town, 7925, South Africa
| | - Raj Ramesar
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
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4
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Ferrera D, Gómez-Esquer F, Peláez I, Barjola P, Fernandes-Magalhaes R, Carpio A, De Lahoz ME, Martín-Buro MC, Mercado F. Working memory dysfunction in fibromyalgia is associated with genotypes of the catechol- O-methyltransferase gene: an event-related potential study. Eur Arch Psychiatry Clin Neurosci 2023; 273:25-40. [PMID: 36100778 PMCID: PMC9958168 DOI: 10.1007/s00406-022-01488-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 08/29/2022] [Indexed: 11/03/2022]
Abstract
Recent findings have associated different COMT genotypes with working memory capacity in patients with fibromyalgia. Although it is thought that the COMT gene may influence neural correlates (P2 and P3 ERP components) underlying working memory impairment in this chronic-pain syndrome, it has not yet been explored. Therefore, the aim of the present research was to investigate the potential effect of the COMT gene in fibromyalgia patients on ERP working memory indices (P2 and P3 components). For this purpose, 102 participants (51 patients and 51 healthy control participants) took part in the experiment. Event-related potentials and behavioral responses were recorded while participants performed a spatial n-back task. Participants had to decide if the stimulus coincided or not in the same location as the one presented one (1-back condition) or two (2-back condition) trials before. Genotypes of the COMT gene were determined through a saliva sample from all participants. Present results significantly showed lower working memory performance (p < 0.05) in patients with fibromyalgia as compared to control participants (higher rate of errors and slower reaction times). At neural level, we found that patients exhibited enhanced frontocentral and parieto-occipital P2 amplitudes compared to control participants (p < 0.05). Interestingly, we also observed that only fibromyalgia patients carrying the Val/Val genotype of the COMT gene showed higher frontocentral P2 amplitudes than control participants (p < 0.05). Current results (behavioral outcomes and P2 amplitudes) confirmed the presence of an alteration in working memory functioning in fibromyalgia. The enhancement of frontocentral P2 could be reflecting that these patients would manifest an inefficient way of activating executive attention processes, in carriers of the Val/Val genotype of COMT. To our knowledge, the present findings are the first linking neural indices of working memory dysfunctions and COMT genotypes in fibromyalgia. Applying a subgroup of patient's strategy based on this genetic marker could be useful to establish more tailored therapeutical approaches.
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Affiliation(s)
- David Ferrera
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Francisco Gómez-Esquer
- grid.28479.300000 0001 2206 5938Emerging Research Group of Anatomical, Molecular and Human Development Bases, Department of Basic Health Sciences, School of Health Sciences, Rey Juan Carlos University, Madrid, Spain
| | - Irene Peláez
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Paloma Barjola
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Roberto Fernandes-Magalhaes
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Alberto Carpio
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - María Eugenia De Lahoz
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - María Carmen Martín-Buro
- grid.28479.300000 0001 2206 5938Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain
| | - Francisco Mercado
- Department of Psychology, School of Health Sciences, Rey Juan Carlos University, Av. Atenas s/n. 28922, Alcorcón, Madrid, Spain.
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5
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Nabirotchkin S, Bouaziz J, Glibert F, Mandel J, Foucquier J, Hajj R, Callizot N, Cholet N, Guedj M, Cohen D. Combinational Drug Repurposing from Genetic Networks Applied to Alzheimer’s Disease. J Alzheimers Dis 2022; 88:1585-1603. [DOI: 10.3233/jad-220120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Human diseases are multi-factorial biological phenomena resulting from perturbations of numerous functional networks. The complex nature of human diseases explains frequently observed marginal or transitory efficacy of mono-therapeutic interventions. For this reason, combination therapy is being increasingly evaluated as a biologically plausible strategy for reversing disease state, fostering the development of dedicated methodological and experimental approaches. In parallel, genome-wide association studies (GWAS) provide a prominent opportunity for disclosing human-specific therapeutic targets and rational drug repurposing. Objective: In this context, our objective was to elaborate an integrated computational platform to accelerate discovery and experimental validation of synergistic combinations of repurposed drugs for treatment of common human diseases. Methods: The proposed approach combines adapted statistical analysis of GWAS data, pathway-based functional annotation of genetic findings using gene set enrichment technique, computational reconstruction of signaling networks enriched in disease-associated genes, selection of candidate repurposed drugs and proof-of-concept combinational experimental screening. Results: It enables robust identification of signaling pathways enriched in disease susceptibility loci. Therapeutic targeting of the disease-associated signaling networks provides a reliable way for rational drug repurposing and rapid development of synergistic drug combinations for common human diseases. Conclusion: Here we demonstrate the feasibility and efficacy of the proposed approach with an experiment application to Alzheimer’s disease.
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6
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Tang Y, You D, Yi H, Yang S, Zhao Y. IPRS: Leveraging Gene-Environment Interaction to Reconstruct Polygenic Risk Score. Front Genet 2022; 13:801397. [PMID: 35401709 PMCID: PMC8989431 DOI: 10.3389/fgene.2022.801397] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/08/2022] [Indexed: 12/30/2022] Open
Abstract
Background: Polygenic risk score (PRS) is widely regarded as a predictor of genetic susceptibility to disease, applied to individuals to predict the risk of disease occurrence. When the gene-environment (G×E) interaction is considered, the traditional PRS prediction model directly uses PRS to interact with the environment without considering the interactions between each variant and environment, which may lead to prediction performance and risk stratification of complex diseases are not promising. Methods: We developed a method called interaction PRS (iPRS), reconstructing PRS by leveraging G×E interactions. Two extensive simulations evaluated prediction performance, risk stratification, and calibration performance of the iPRS prediction model, and compared it with the traditional PRS prediction model. Real data analysis was performed using existing data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial study to predict genetic susceptibility, pack-years of smoking history, and G×E interactions in patients with lung cancer. Results: Two extensive simulations indicated iPRS prediction model could improve the prediction performance of disease risk, the accuracy of risk stratification, and clinical calibration performance compared with the traditional PRS prediction model, especially when antagonism accounted for the majority of the interaction. PLCO real data analysis also suggested that the iPRS prediction model was superior to the PRS prediction model in predictive effect (p = 0.0205). Conclusion: IPRS prediction model could have a good application prospect in predicting disease risk, optimizing the screening of high-risk populations, and improving the clinical benefits of preventive interventions among populations.
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Affiliation(s)
- Yingdan Tang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongfang You
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Honggang Yi
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Sheng Yang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
- *Correspondence: Sheng Yang, ; Yang Zhao,
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
- Center of Biomedical Big Data and the Laboratory of Biomedical Big Data, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Sheng Yang, ; Yang Zhao,
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7
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Anderson SJ, Côté SD, Richard JH, Shafer ABA. Genomic architecture of phenotypic extremes in a wild cervid. BMC Genomics 2022; 23:126. [PMID: 35151275 PMCID: PMC8841092 DOI: 10.1186/s12864-022-08333-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/24/2022] [Indexed: 12/30/2022] Open
Abstract
Identifying the genes underlying fitness-related traits such as body size and male ornamentation can provide tools for conservation and management and are often subject to various selective pressures. Here we performed high-depth whole genome re-sequencing of pools of individuals representing the phenotypic extremes for antler and body size in white-tailed deer (Odocoileus virginianus). Samples were selected from a tissue repository containing phenotypic data for 4,466 male white-tailed deer from Anticosti Island, Quebec, with four pools representing the extreme phenotypes for antler and body size after controlling for age. Our results revealed a largely homogenous population but detected highly divergent windows between pools for both traits, with the mean allele frequency difference of 14% for and 13% for antler and body SNPs in outlier windows, respectively. Genes in outlier antler windows were enriched for pathways associated with cell death and protein metabolism and some of the most differentiated windows included genes associated with oncogenic pathways and reproduction, processes consistent with antler evolution and growth. Genes associated with body size were more nuanced, suggestive of a highly complex trait. Overall, this study revealed the complex genomic make-up of both antler morphology and body size in free-ranging white-tailed deer and identified target loci for additional analyses.
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8
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Kuo CY, Chen TY, Kao PH, Huang W, Cho CR, Lai YS, Yiang GT, Kao CF. Genetic Pathways and Functional Subnetworks for the Complex Nature of Bipolar Disorder in Genome-Wide Association Study. Front Mol Neurosci 2021; 14:772584. [PMID: 34880727 PMCID: PMC8645771 DOI: 10.3389/fnmol.2021.772584] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/08/2021] [Indexed: 11/19/2022] Open
Abstract
Bipolar disorder is a complex psychiatric trait that is also recognized as a high substantial heritability from a worldwide distribution. The success in identifying susceptibility loci for bipolar disorder (BPD) has been limited due to its complex genetic architecture. Growing evidence from association studies including genome-wide association (GWA) studies points to the need of improved analytic strategies to pinpoint the missing heritability for BPD. More importantly, many studies indicate that BPD has a strong association with dementia. We conducted advanced pathway analytics strategies to investigate synergistic effects of multilocus within biologically functional pathways, and further demonstrated functional effects among proteins in subnetworks to examine mechanisms underlying the complex nature of bipolarity using a GWA dataset for BPD. We allowed bipolar susceptible loci to play a role that takes larger weights in pathway-based analytic approaches. Having significantly informative genes identified from enriched pathways, we further built function-specific subnetworks of protein interactions using MetaCore. The gene-wise scores (i.e., minimum p-value) were corrected for the gene-length, and the results were corrected for multiple tests using Benjamini and Hochberg’s method. We found 87 enriched pathways that are significant for BPD; of which 36 pathways were reported. Most of them are involved with several metabolic processes, neural systems, immune system, molecular transport, cellular communication, and signal transduction. Three significant and function-related subnetworks with multiple hotspots were reported to link with several Gene Ontology processes for BPD. Our comprehensive pathway-network frameworks demonstrated that the use of prior knowledge is promising to facilitate our understanding between complex psychiatric disorders (e.g., BPD) and dementia for the access to the connection and clinical implications, along with the development and progression of dementia.
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Affiliation(s)
- Chan-Yen Kuo
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan.,Department of Nursing, Cardinal Tien College of Healthcare and Management, New Taipei, Taiwan
| | - Tsu-Yi Chen
- Department of Emergency Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan.,Department of Emergency Medicine, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Pei-Hsiu Kao
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Winifred Huang
- School of Management, University of Bath, Bath, United Kingdom
| | - Chun-Ruei Cho
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Ya-Syuan Lai
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan
| | - Giou-Teng Yiang
- Department of Emergency Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei, Taiwan.,Department of Emergency Medicine, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Chung-Feng Kao
- Department of Agronomy, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan.,Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
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9
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He Y, Liu H, Luo S, Amos CI, Lee JE, Li X, Nan H, Wei Q. Genetic variants of SDCCAG8 and MAGI2 in mitosis-related pathway genes are independent predictors of cutaneous melanoma-specific survival. Cancer Sci 2021; 112:4355-4364. [PMID: 34375487 PMCID: PMC8486203 DOI: 10.1111/cas.15102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/06/2021] [Accepted: 08/07/2021] [Indexed: 12/01/2022] Open
Abstract
Mitosis is a prognostic factor for cutaneous melanoma (CM), but accurate mitosis detection in CM tissues is difficult. Therefore, the 8th Edition of the American Joint Committee on Cancer staging system has removed the mitotic rate as a category criterion of the tumor T-category, based on the evidence that the mitotic rate was not an independent prognostic factor for melanoma survival. As single-nucleotide polymorphisms (SNPs) have been shown to be potential predictors for cutaneous melanoma-specific survival (CMSS), we investigated the potential prognostic value of SNPs in mitosis-related pathway genes in CMSS by analyzing their associations with outcomes of 850 CM patients from The University of Texas MD Anderson Cancer Center in a discovery dataset and validated the findings in another dataset of 409 CM patients from the Harvard University Nurses' Health Study and Health Professionals Follow-up Study. In both datasets, we identified two SNPs (SDCCAG8 rs10803138 G>A and MAGI2 rs3807694 C>T) as independent prognostic factors for CMSS, with adjusted allelic hazards ratios of 1.49 (95% confidence interval = 1.17-1.90, P = .001) and 1.45 (1.13-1.86, P = .003), respectively. Furthermore, their combined unfavorable alleles also predicted a poor survival in both discovery and validation datasets in a dose-response manner (Ptrend = .0006 and .0001, respectively). Additional functional analysis revealed that both SDCCAG8 rs10803138 A and MAGI2 rs3807694 T alleles were associated with elevated mRNA expression levels in normal tissues. Therefore, these findings suggest that SDCCAG8 rs10803138 G>A and MAGI2 rs3807694 C>T are independent prognostic biomarkers for CMSS, possibly by regulating the mRNA expression of the corresponding genes involved in mitosis.
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Affiliation(s)
- Yuanmin He
- Department of DermatologyThe Affiliated Hospital of Southwest Medical UniversityLuzhouChina
- Duke Cancer InstituteDuke University Medical CenterDurhamNCUSA
- Department of Population Health SciencesDuke University School of MedicineDurhamNCUSA
| | - Hongliang Liu
- Duke Cancer InstituteDuke University Medical CenterDurhamNCUSA
- Department of Population Health SciencesDuke University School of MedicineDurhamNCUSA
| | - Sheng Luo
- Department of Biostatistics and BioinformaticsDuke University School of MedicineDurhamNCUSA
| | - Christopher I. Amos
- Institute for Clinical and Translational ResearchBaylor College of MedicineHoustonTXUSA
| | - Jeffrey E. Lee
- Department of Surgical OncologyThe University of Texas M. D. Anderson Cancer CenterHoustonTXUSA
| | - Xin Li
- Department of EpidemiologyRichard M. Fairbanks School of Public HealthIndiana UniversityIndianapolisINUSA
| | - Hongmei Nan
- Department of EpidemiologyRichard M. Fairbanks School of Public HealthIndiana UniversityIndianapolisINUSA
| | - Qingyi Wei
- Duke Cancer InstituteDuke University Medical CenterDurhamNCUSA
- Department of Population Health SciencesDuke University School of MedicineDurhamNCUSA
- Department of MedicineDuke University School of MedicineDurhamNCUSA
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10
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Du H, Liu L, Liu H, Luo S, Patz EF, Glass C, Su L, Du M, Christiani DC, Wei Q. Genetic variants of DOCK2, EPHB1 and VAV2 in the natural killer cell-related pathway are associated with non-small cell lung cancer survival. Am J Cancer Res 2021; 11:2264-2277. [PMID: 34094683 PMCID: PMC8167686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 03/14/2021] [Indexed: 06/12/2023] Open
Abstract
Although natural killer (NK) cells are a known major player in anti-tumor immunity, the effect of genetic variation in NK-associated genes on survival in patients with non-small cell lung cancer (NSCLC) remains unknown. Here, in 1,185 with NSCLC cases of a discovery dataset, we evaluated associations of 28,219 single nucleotide polymorphisms (SNPs) in 276 NK-associated genes with their survival. These patients were from the reported genome-wide association study (GWAS) from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. We further validated the findings in an additional 984 cases from the Harvard Lung Cancer Susceptibility (HLCS) Study. We identified three SNPs (i.e., DOCK2 rs261083 G>C, VAV2 rs2519996 C>T and EPHB1 rs36215 A>G) to be independently associated with overall survival (OS) in NSCLC cases with adjusted hazards ratios (HRs) of 1.16 (95% confidence interval [CI] = 1.07-1.26, P = 3.34×10-4), 1.28 (1.12-1.47, P = 4.57×10-4) and 0.75 (0.67-0.83, P = 1.50×10-7), respectively. Additional joint assessment of the unfavorable genotypes of the three SNPs showed significant associations with OS and disease-specific survival of NSCLC cases in the PLCO dataset (P trend<0.0001 and <0.0001, respectively). Moreover, the survival-associated DOCK2 rs261083 C allele had a significant correlation with reduced DOCK2 transcript levels in lung adenocarcinoma (LUAD), while the rs36215 G allele was significantly correlated with reduced EPHB1 transcript levels in lymphoblastoid cell lines in the 1000 Genomes Project. These results revealed that DOCK2 and EPHB1 genetic variants may be prognostic biomarkers of NSCLC survival, likely via transcription regulation of respective genes.
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Affiliation(s)
- Hailei Du
- Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineShanghai 200025, P. R. China
- Duke Cancer Institute, Duke University Medical CenterDurham, NC 27710, USA
- Department of Population Health Sciences, Duke University School of MedicineDurham, NC 27710, USA
| | - Lihua Liu
- Duke Cancer Institute, Duke University Medical CenterDurham, NC 27710, USA
- Department of Population Health Sciences, Duke University School of MedicineDurham, NC 27710, USA
| | - Hongliang Liu
- Duke Cancer Institute, Duke University Medical CenterDurham, NC 27710, USA
- Department of Population Health Sciences, Duke University School of MedicineDurham, NC 27710, USA
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University School of MedicineDurham, NC 27710, USA
| | - Edward F Patz
- Duke Cancer Institute, Duke University Medical CenterDurham, NC 27710, USA
- Departments of Radiology, Pharmacology and Cancer Biology, Duke University Medical CenterDurham, NC 27710, USA
| | - Carolyn Glass
- Duke Cancer Institute, Duke University Medical CenterDurham, NC 27710, USA
- Department of Pathology, Duke University School of MedicineDurham, NC 27710, USA
| | - Li Su
- Departments of Environmental Health and Epidemiology, Harvard TH Chan School of Public HealthBoston, MA 02115, USA
| | - Mulong Du
- Departments of Environmental Health and Epidemiology, Harvard TH Chan School of Public HealthBoston, MA 02115, USA
| | - David C Christiani
- Departments of Environmental Health and Epidemiology, Harvard TH Chan School of Public HealthBoston, MA 02115, USA
- Department of Medicine, Massachusetts General HospitalBoston, MA 02114, USA
| | - Qingyi Wei
- Duke Cancer Institute, Duke University Medical CenterDurham, NC 27710, USA
- Department of Population Health Sciences, Duke University School of MedicineDurham, NC 27710, USA
- Department of Medicine, Duke University School of MedicineDurham, NC 27710, USA
- Duke Global Health Institute, Duke UniversityDurham, Durham, NC 27710, USA
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11
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Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions. TOXICS 2021; 9:toxics9040077. [PMID: 33917455 PMCID: PMC8067468 DOI: 10.3390/toxics9040077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 03/27/2021] [Accepted: 04/02/2021] [Indexed: 12/17/2022]
Abstract
Exposure to endocrine-disrupting chemicals (EDCs) is linked to myriad disorders, characterized by the disruption of the complex endocrine signaling pathways that govern development, physiology, and even behavior across the entire body. The mechanisms of endocrine disruption involve a complex system of pathways that communicate across the body to stimulate specific receptors that bind DNA and regulate the expression of a suite of genes. These mechanisms, including gene regulation, DNA binding, and protein binding, can be tied to differences in individual susceptibility across a genetically diverse population. In this review, we posit that EDCs causing such differential responses may be identified by looking for a signal of population variability after exposure. We begin by summarizing how the biology of EDCs has implications for genetically diverse populations. We then describe how gene-environment interactions (GxE) across the complex pathways of endocrine signaling could lead to differences in susceptibility. We survey examples in the literature of individual susceptibility differences to EDCs, pointing to a need for research in this area, especially regarding the exceedingly complex thyroid pathway. Following a discussion of experimental designs to better identify and study GxE across EDCs, we present a case study of a high-throughput screening signal of putative GxE within known endocrine disruptors. We conclude with a call for further, deeper analysis of the EDCs, particularly the thyroid disruptors, to identify if these chemicals participate in GxE leading to differences in susceptibility.
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12
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Dutta D, VandeHaar P, Fritsche LG, Zöllner S, Boehnke M, Scott LJ, Lee S. A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank. Am J Hum Genet 2021; 108:669-681. [PMID: 33730541 DOI: 10.1016/j.ajhg.2021.02.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Accepted: 02/19/2021] [Indexed: 02/06/2023] Open
Abstract
Tests of association between a phenotype and a set of genes in a biological pathway can provide insights into the genetic architecture of complex phenotypes beyond those obtained from single-variant or single-gene association analysis. However, most existing gene set tests have limited power to detect gene set-phenotype association when a small fraction of the genes are associated with the phenotype and cannot identify the potentially "active" genes that might drive a gene set-based association. To address these issues, we have developed Gene set analysis Association Using Sparse Signals (GAUSS), a method for gene set association analysis that requires only GWAS summary statistics. For each significantly associated gene set, GAUSS identifies the subset of genes that have the maximal evidence of association and can best account for the gene set association. Using pre-computed correlation structure among test statistics from a reference panel, our p value calculation is substantially faster than other permutation- or simulation-based approaches. In simulations with varying proportions of causal genes, we find that GAUSS effectively controls type 1 error rate and has greater power than several existing methods, particularly when a small proportion of genes account for the gene set signal. Using GAUSS, we analyzed UK Biobank GWAS summary statistics for 10,679 gene sets and 1,403 binary phenotypes. We found that GAUSS is scalable and identified 13,466 phenotype and gene set association pairs. Within these gene sets, we identify an average of 17.2 (max = 405) genes that underlie these gene set associations.
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Affiliation(s)
- Diptavo Dutta
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Peter VandeHaar
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lars G Fritsche
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sebastian Zöllner
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael Boehnke
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Laura J Scott
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Seunggeun Lee
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea.
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13
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Zhang L, Papachristou C, Choudhary PK, Biswas S. A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies. Hum Hered 2020; 84:240-255. [PMID: 32966977 DOI: 10.1159/000508664] [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: 07/12/2019] [Accepted: 05/14/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway. METHODS We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. To deal with the high dimensionality, we regularize the regression coefficients through an appropriate choice of priors. The model is fit using a combination of iteratively weighted least squares and expectation-maximization algorithms to estimate the posterior modes and their standard errors. A normal approximation is used for inference. RESULTS We conduct simulations to study the proposed method and find that our method has higher power than some standard approaches in several settings for identifying pathways with multiple modest-sized variants. We illustrate the method by analyzing data from two genome-wide association studies on breast and renal cancers. CONCLUSION Our method can be helpful in detecting pathway association.
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Affiliation(s)
- Lei Zhang
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | | | - Pankaj K Choudhary
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA,
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14
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Ho AMC, Coombes BJ, Nguyen TTL, Liu D, McElroy SL, Singh B, Nassan M, Colby CL, Larrabee BR, Weinshilboum RM, Frye MA, Biernacka JM. Mood-Stabilizing Antiepileptic Treatment Response in Bipolar Disorder: A Genome-Wide Association Study. Clin Pharmacol Ther 2020; 108:1233-1242. [PMID: 32627186 PMCID: PMC7669647 DOI: 10.1002/cpt.1982] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 06/15/2020] [Indexed: 12/31/2022]
Abstract
Several antiepileptic drugs (AEDs) have US Food and Drug Administration (FDA) approval for use as mood stabilizers in bipolar disorder (BD), but not all BD patients respond to these AED mood stabilizers (AED‐MSs). To identify genetic polymorphisms that contribute to the variability in AED‐MS response, we performed a discovery genome‐wide association study (GWAS) of 199 BD patients from the Mayo Clinic Bipolar Disorder Biobank. Most of these patients had been treated with the AED‐MS valproate/divalproex and/or lamotrigine. AED‐MS response was assessed using the Alda scale, which quantifies clinical improvement while accounting for potential confounding factors. We identified two genome‐wide significant single‐nucleotide polymorphism (SNP) signals that mapped to the THSD7A (rs78835388, P = 7.1E‐09) and SLC35F3 (rs114872993, P = 3.2E‐08) genes. We also identified two genes with statistically significant gene‐level associations: ABCC1 (P = 6.7E‐07; top SNP rs875740, P = 2.0E‐6), and DISP1 (P = 8.9E‐07; top SNP rs34701716, P = 8.9E‐07). THSD7A SNPs were previously found to be associated with risk for several psychiatric disorders, including BD. Both THSD7A and SLC35F3 are expressed in excitatory/glutamatergic and inhibitory/γ‐aminobutyric acidergic (GABAergic) neurons, which are targets of AED‐MSs. ABCC1 is involved in the transport of valproate and lamotrigine metabolites, and the SNPs in ABCC1 and DISP1 with the strongest evidence of association in our GWAS are strong splicing quantitative trait loci in the human gut, suggesting a possible influence on drug absorption. In conclusion, our pharmacogenomic study identified novel genetic loci that appear to contribute to AED‐MS treatment response, and may facilitate precision medicine in BD.
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Affiliation(s)
- Ada Man-Choi Ho
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.,Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Brandon J Coombes
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Thanh Thanh L Nguyen
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Duan Liu
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan L McElroy
- Lindner Center of HOPE/University of Cincinnati, Cincinnati, Ohio, USA
| | - Balwinder Singh
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Malik Nassan
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Colin L Colby
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Beth R Larrabee
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Richard M Weinshilboum
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark A Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joanna M Biernacka
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.,Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
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15
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Mueller JC, Carrete M, Boerno S, Kuhl H, Tella JL, Kempenaers B. Genes acting in synapses and neuron projections are early targets of selection during urban colonization. Mol Ecol 2020; 29:3403-3412. [DOI: 10.1111/mec.15451] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 04/08/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Jakob C. Mueller
- Department of Behavioural Ecology & Evolutionary Genetics Max Planck Institute for Ornithology Seewiesen Germany
| | - Martina Carrete
- Department of Conservation Biology Estación Biológica de Doñana – CSIC Sevilla Spain
- Department of Physical, Chemical and Natural Systems University Pablo de Olavide Sevilla Spain
| | - Stefan Boerno
- Sequencing Core Facility Max Planck Institute for Molecular Genetics Berlin Germany
| | - Heiner Kuhl
- Sequencing Core Facility Max Planck Institute for Molecular Genetics Berlin Germany
- Department of Ecophysiology and Aquaculture Leibniz‐Institute of Freshwater Ecology and Inland Fisheries Berlin Germany
| | - José L. Tella
- Department of Conservation Biology Estación Biológica de Doñana – CSIC Sevilla Spain
| | - Bart Kempenaers
- Department of Behavioural Ecology & Evolutionary Genetics Max Planck Institute for Ornithology Seewiesen Germany
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16
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Zhao YC, Tang D, Yang S, Liu H, Luo S, Stinchcombe TE, Glass C, Su L, Shen S, Christiani DC, Wei Q. Novel Variants of ELP2 and PIAS1 in the Interferon Gamma Signaling Pathway Are Associated with Non-Small Cell Lung Cancer Survival. Cancer Epidemiol Biomarkers Prev 2020; 29:1679-1688. [PMID: 32493705 DOI: 10.1158/1055-9965.epi-19-1450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 03/12/2020] [Accepted: 05/29/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND IFNγ is a pleiotropic cytokine that plays critical immunomodulatory roles in intercellular communication in innate and adaptive immune responses. Despite recognition of IFNγ signaling effects on host defense against viral infection and its utility in immunotherapy and tumor progression, the roles of genetic variants of the IFNγ signaling pathway genes in survival of patients with cancer remain unknown. METHODS We used a discovery genotyping dataset from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (n = 1,185) and a replication genotyping dataset from the Harvard Lung Cancer Susceptibility Study (n = 984) to evaluate associations between 14,553 genetic variants in 150 IFNγ pathway genes and survival of non-small cell lung cancer (NSCLC). RESULTS The combined analysis identified two independent potentially functional SNPs, ELP2 rs7242481G>A and PIAS1 rs1049493T>C, to be significantly associated with NSCLC survival, with a combined HR of 0.85 (95% confidence interval, 0.78-0.92; P < 0.0001) and 0.87 (0.81-0.93; P < 0.0001), respectively. Expression quantitative trait loci analyses showed that the survival-associated ELP2 rs7242481A allele was significantly associated with increased mRNA expression levels of elongator acetyltransferase complex subunit 2 (ELP2) in 373 lymphoblastoid cell lines and 369 whole-blood samples. The PIAS1 rs1049493C allele was significantly associated with decreased mRNA expression levels of PIAS1 in 383 normal lung tissues and 369 whole-blood samples. CONCLUSIONS Genetic variants of IFNγ signaling genes are potential prognostic markers for NSCLC survival, likely through modulating the expression of key genes involved in host immune response. IMPACT Once validated, these variants could be useful predictors of NSCLC survival.
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Affiliation(s)
- Yu Chen Zhao
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Dongfang Tang
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Sen Yang
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Hongliang Liu
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Thomas E Stinchcombe
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina.,Department of Medicine, Duke University Medical Center, Durham, North Carolina
| | - Carolyn Glass
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina.,Department of Pathology, Duke University School of Medicine, Durham, North Carolina
| | - Li Su
- Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Sipeng Shen
- Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - David C Christiani
- Department of Environmental Health and Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts.,Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Qingyi Wei
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina. .,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina.,Department of Medicine, Duke University Medical Center, Durham, North Carolina
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17
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Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges. ENTROPY 2020; 22:e22040427. [PMID: 33286201 PMCID: PMC7516904 DOI: 10.3390/e22040427] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/18/2020] [Accepted: 04/03/2020] [Indexed: 12/22/2022]
Abstract
Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.
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18
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Chimusa ER, Dalvie S, Dandara C, Wonkam A, Mazandu GK. Post genome-wide association analysis: dissecting computational pathway/network-based approaches. Brief Bioinform 2020; 20:690-700. [PMID: 29701762 DOI: 10.1093/bib/bby035] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 04/04/2018] [Indexed: 02/02/2023] Open
Abstract
Over thousands of genetic associations to diseases have been identified by genome-wide association studies (GWASs), which conceptually is a single-marker-based approach. There are potentially many uses of these identified variants, including a better understanding of the pathogenesis of diseases, new leads for studying underlying risk prediction and clinical prediction of treatment. However, because of inadequate power, GWAS might miss disease genes and/or pathways with weak genetic or strong epistatic effects. Driven by the need to extract useful information from GWAS summary statistics, post-GWAS approaches (PGAs) were introduced. Here, we dissect and discuss advances made in pathway/network-based PGAs, with a particular focus on protein-protein interaction networks that leverage GWAS summary statistics by combining effects of multiple loci, subnetworks or pathways to detect genetic signals associated with complex diseases. We conclude with a discussion of research areas where further work on summary statistic-based methods is needed.
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Affiliation(s)
- Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Level 3, Wernher and Beit North, Private Bag, Rondebosch, 7700, Anzio road, Observatory Cape Town, South Africa
| | - Shareefa Dalvie
- Department of Psychiatry and Mental Health, University of Cape Town, Observatory, 7925, Cape Town, South Africa
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Private Bag, Rondebosch, 7700, Cape Town, South Africa; African Institute for Mathematical Sciences, 7945 Muizenberg, Cape Town, South Africa and Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Medical School, Anzio Road, Observatory, 7925, Cape Town, South Africa
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19
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Scott MA, Woolums AR, Swiderski CE, Perkins AD, Nanduri B, Smith DR, Karisch BB, Epperson WB, Blanton JR. Whole blood transcriptomic analysis of beef cattle at arrival identifies potential predictive molecules and mechanisms that indicate animals that naturally resist bovine respiratory disease. PLoS One 2020; 15:e0227507. [PMID: 31929561 PMCID: PMC6957175 DOI: 10.1371/journal.pone.0227507] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 12/19/2019] [Indexed: 12/12/2022] Open
Abstract
Bovine respiratory disease (BRD) is a multifactorial disease complex and the leading infectious disease in post-weaned beef cattle. Clinical manifestations of BRD are recognized in beef calves within a high-risk setting, commonly associated with weaning, shipping, and novel feeding and housing environments. However, the understanding of complex host immune interactions and genomic mechanisms involved in BRD susceptibility remain elusive. Utilizing high-throughput RNA-sequencing, we contrasted the at-arrival blood transcriptomes of 6 beef cattle that ultimately developed BRD against 5 beef cattle that remained healthy within the same herd, differentiating BRD diagnosis from production metadata and treatment records. We identified 135 differentially expressed genes (DEGs) using the differential gene expression tools edgeR and DESeq2. Thirty-six of the DEGs shared between these two analysis platforms were prioritized for investigation of their relevance to infectious disease resistance using WebGestalt, STRING, and Reactome. Biological processes related to inflammatory response, immunological defense, lipoxin metabolism, and macrophage function were identified. Production of specialized pro-resolvin mediators (SPMs) and endogenous metabolism of angiotensinogen were increased in animals that resisted BRD. Protein-protein interaction modeling of gene products with significantly higher expression in cattle that naturally acquire BRD identified molecular processes involving microbial killing. Accordingly, identification of DEGs in whole blood at arrival revealed a clear distinction between calves that went on to develop BRD and those that resisted BRD. These results provide novel insight into host immune factors that are present at the time of arrival that confer protection from BRD.
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Affiliation(s)
- Matthew A. Scott
- Department of Pathobiology and Population Medicine, Mississippi State University, Mississippi State, MS, United States of America
- * E-mail:
| | - Amelia R. Woolums
- Department of Pathobiology and Population Medicine, Mississippi State University, Mississippi State, MS, United States of America
| | - Cyprianna E. Swiderski
- Department of Clinical Sciences, Mississippi State University, Mississippi State, MS, United States of America
| | - Andy D. Perkins
- Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, United States of America
| | - Bindu Nanduri
- Department of Basic Sciences, Mississippi State University College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, United States of America
| | - David R. Smith
- Department of Pathobiology and Population Medicine, Mississippi State University, Mississippi State, MS, United States of America
| | - Brandi B. Karisch
- Department of Animal and Dairy Sciences, Mississippi State University, Mississippi State, MS, United States of America
| | - William B. Epperson
- Department of Pathobiology and Population Medicine, Mississippi State University, Mississippi State, MS, United States of America
| | - John R. Blanton
- Department of Animal and Dairy Sciences, Mississippi State University, Mississippi State, MS, United States of America
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20
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He T, Hill CB, Angessa TT, Zhang XQ, Chen K, Moody D, Telfer P, Westcott S, Li C. Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:5603-5616. [PMID: 31504706 PMCID: PMC6812734 DOI: 10.1093/jxb/erz332] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 08/13/2019] [Indexed: 05/10/2023]
Abstract
Single-marker genome-wide association studies (GWAS) have successfully detected associations between single nucleotide polymorphisms (SNPs) and agronomic traits such as flowering time and grain yield in barley. However, the analysis of individual SNPs can only account for a small proportion of genetic variation, and can only provide limited knowledge on gene network interactions. Gene-based GWAS approaches provide enormous opportunity both to combine genetic information and to examine interactions among genetic variants. Here, we revisited a previously published phenotypic and genotypic data set of 895 barley varieties grown in two years at four different field locations in Australia. We employed statistical models to examine gene-phenotype associations, as well as two-way epistasis analyses to increase the capability to find novel genes that have significant roles in controlling flowering time in barley. Genetic associations were tested between flowering time and corresponding genotypes of 174 putative flowering time-related genes. Gene-phenotype association analysis detected 113 genes associated with flowering time in barley, demonstrating the unprecedented power of gene-based analysis. Subsequent two-way epistasis analysis revealed 19 pairs of gene×gene interactions involved in controlling flowering time. Our study demonstrates that gene-based association approaches can provide higher capacity for future crop improvement to increase crop performance and adaptation to different environments.
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Affiliation(s)
- Tianhua He
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Camilla Beate Hill
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Tefera Tolera Angessa
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Xiao-Qi Zhang
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | - Kefei Chen
- SAGI-WEST, Faculty of Science and Engineering, Curtin University, Bentley, WA, Australia
| | | | - Paul Telfer
- Australian Grain Technologies Pty Ltd (AGT), SA, Australia
| | - Sharon Westcott
- Agriculture and Food, Department of Primary Industries and Regional Development, South Perth, WA, Australia
| | - Chengdao Li
- Western Barley Genetics Alliance, Western Australian State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
- Agriculture and Food, Department of Primary Industries and Regional Development, South Perth, WA, Australia
- Hubei Collaborative Innovation Centre for Grain Industry, Yangtze University, Hubei Jingzhou, China
- Correspondence:
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21
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Mora A. Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data. Brief Bioinform 2019; 21:1495-1508. [DOI: 10.1093/bib/bbz090] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/30/2019] [Accepted: 06/28/2019] [Indexed: 12/31/2022] Open
Abstract
Abstract
Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.
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Affiliation(s)
- Antonio Mora
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health - Chinese Academy of Sciences
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22
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Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies. Sci Rep 2019; 9:13686. [PMID: 31548641 PMCID: PMC6757104 DOI: 10.1038/s41598-019-50229-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 09/09/2019] [Indexed: 12/18/2022] Open
Abstract
Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approach by showing better controls of false discovery rate (FDR) and higher sensitivity. We re-analyzed two real studies to identifying SNPs associated with severe bortezomib-induced peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The original analysis in the literature failed to identify SNPs after FDR adjustment. Our proposed method not only detected the reported SNPs after FDR adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has been reported to be related to MM in another study.
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Abbas-Aghababazadeh F, Mo Q, Fridley BL. Statistical genomics in rare cancer. Semin Cancer Biol 2019; 61:1-10. [PMID: 31437624 DOI: 10.1016/j.semcancer.2019.08.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/14/2019] [Accepted: 08/17/2019] [Indexed: 12/26/2022]
Abstract
Rare cancers make of more than 20% of cancer cases. Due to the rare nature, less research has been conducted on rare cancers resulting in worse outcomes for patients with rare cancers compared to common cancers. The ability to study rare cancers is impaired by the ability to collect a large enough set of patients to complete an adequately powered genomic study. In this manuscript we outline analytical approaches and public genomic datasets that have been used in genomic studies of rare cancers. These statistical analysis approaches and study designs include: gene set / pathway analyses, pedigree and consortium studies, meta-analysis or horizontal integration, and integration of multiple types of genomic information or vertical integration. We also discuss some of the publicly available resources that can be leveraged in rare cancer genomic studies.
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Affiliation(s)
| | - Qianxing Mo
- Department of Biostatistics & Bioinformatics, Moffitt Cancer Center, Tampa, FL, 33612, USA.
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, Moffitt Cancer Center, Tampa, FL, 33612, USA.
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Ali A, Al-Tobasei R, Lourenco D, Leeds T, Kenney B, Salem M. Genome-Wide Association Study Identifies Genomic Loci Affecting Filet Firmness and Protein Content in Rainbow Trout. Front Genet 2019; 10:386. [PMID: 31130980 PMCID: PMC6509548 DOI: 10.3389/fgene.2019.00386] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 04/10/2019] [Indexed: 01/10/2023] Open
Abstract
Filet quality traits determine consumer satisfaction and affect profitability of the aquaculture industry. Soft flesh is a criterion for fish filet downgrades, resulting in loss of value. Filet firmness is influenced by many factors, including rate of protein turnover. A 50K transcribed gene SNP chip was used to genotype 789 rainbow trout, from two consecutive generations, produced in the USDA/NCCCWA selective breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform genome-wide association (GWA) analyses to identify quantitative trait loci affecting filet firmness and protein content. Applying genomic sliding windows of 50 adjacent SNPs, 212 and 225 SNPs were associated with genetic variation in filet shear force and protein content, respectively. Four common SNPs in the ryanodine receptor 3 gene (RYR3) affected the aforementioned filet traits; this association suggests common mechanisms underlying filet shear force and protein content. Genes harboring SNPs were mostly involved in calcium homeostasis, proteolytic activities, transcriptional regulation, chromatin remodeling, and apoptotic processes. RYR3 harbored the highest number of SNPs (n = 32) affecting genetic variation in shear force (2.29%) and protein content (4.97%). Additionally, based on single-marker analysis, a SNP in RYR3 ranked at the top of all SNPs associated with variation in shear force. Our data suggest a role for RYR3 in muscle firmness that may be considered for genomic- and marker-assisted selection in breeding programs of rainbow trout.
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Affiliation(s)
- Ali Ali
- Department of Biology and Molecular Biosciences Program, Middle Tennessee State University, Murfreesboro, TN, United States
| | - Rafet Al-Tobasei
- Computational Science Program, Middle Tennessee State University, Murfreesboro, TN, United States.,Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| | - Tim Leeds
- National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, United States Department of Agriculture, Kearneysville, WV, United States
| | - Brett Kenney
- Division of Animal and Nutritional Sciences, West Virginia University, Morgantown, WV, United States
| | - Mohamed Salem
- Department of Biology and Molecular Biosciences Program, Middle Tennessee State University, Murfreesboro, TN, United States.,Computational Science Program, Middle Tennessee State University, Murfreesboro, TN, United States
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Sun R, Hui S, Bader GD, Lin X, Kraft P. Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic. PLoS Genet 2019; 15:e1007530. [PMID: 30875371 PMCID: PMC6436759 DOI: 10.1371/journal.pgen.1007530] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 03/27/2019] [Accepted: 02/28/2019] [Indexed: 11/19/2022] Open
Abstract
A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.
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Affiliation(s)
- Ryan Sun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Shirley Hui
- The Donnelly Center, University of Toronto, Toronto, Ontario, Canada
| | - Gary D. Bader
- The Donnelly Center, University of Toronto, Toronto, Ontario, Canada
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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Salvatore JE, Han S, Farris SP, Mignogna KM, Miles MF, Agrawal A. Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder. Addict Biol 2019; 24:275-289. [PMID: 29316088 PMCID: PMC6037617 DOI: 10.1111/adb.12591] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 11/20/2017] [Accepted: 11/26/2017] [Indexed: 12/16/2022]
Abstract
Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.
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Affiliation(s)
- Jessica E. Salvatore
- Department of Psychology; Virginia Commonwealth University; Richmond VA USA
- Virginia Institute for Psychiatric and Behavioral Genetics; Virginia Commonwealth University; Richmond VA USA
| | - Shizhong Han
- Department of Psychiatry; University of Iowa; Iowa City IA USA
- Department of Psychiatry and Behavioral Sciences; Johns Hopkins School of Medicine; Baltimore MD USA
| | - Sean P. Farris
- Waggoner Center for Alcohol and Addiction Research; The University of Texas at Austin; Austin TX USA
| | - Kristin M. Mignogna
- Virginia Institute for Psychiatric and Behavioral Genetics; Virginia Commonwealth University; Richmond VA USA
| | - Michael F. Miles
- Department of Pharmacology and Toxicology; Virginia Commonwealth University; Richmond VA USA
| | - Arpana Agrawal
- Department of Psychiatry; Washington University School of Medicine; Saint Louis MO USA
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Wang X, Boekstegers F, Brinster R. Methods and results from the genome-wide association group at GAW20. BMC Genet 2018; 19:79. [PMID: 30255814 PMCID: PMC6157187 DOI: 10.1186/s12863-018-0649-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. The GWAS group contributions focused on topics such as association tests, phenotype imputation, and application of empirical kinships. The goals of the GWAS group contributions were varied. A real or a simulated data set based on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was employed by different methods. Different outcomes and covariates were considered, and quality control procedures varied throughout the contributions. RESULTS The consideration of heritability and family structure played a major role in some contributions. The inclusion of family information and adaptive weights based on data were found to improve power in genome-wide association studies. It was proven that gene-level approaches are more powerful than single-marker analysis. Other contributions focused on the comparison between pedigree-based kinship and empirical kinship matrices, and investigated similar results in heritability estimation, association mapping, and genomic prediction. A new approach for linkage mapping of triglyceride levels was able to identify a novel linkage signal. CONCLUSIONS This summary paper reports on promising statistical approaches and findings of the members of the GWAS group applied on real and simulated data which encompass the current topics of epigenetic and pharmacogenomics.
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Affiliation(s)
- Xuexia Wang
- University of North Texas, GAB 459, 1155 Union Circle #311430, Denton, TX 76203 USA
| | - Felix Boekstegers
- Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany
| | - Regina Brinster
- Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany
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Application of the parametric bootstrap for gene-set analysis of gene-environment interactions. Eur J Hum Genet 2018; 26:1679-1686. [PMID: 30089830 DOI: 10.1038/s41431-018-0236-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 07/02/2018] [Accepted: 07/19/2018] [Indexed: 02/07/2023] Open
Abstract
Testing for gene-environment (GE) interactions in a gene-set defined by a biological pathway can help us understand the interplay between genes and environments and provide insight into disease etiology. A self-contained gene-set analysis can be performed by combining gene-level p-values using approaches such as the Gamma Method. In a gene-set analysis of genetic main effects, permutation approaches are commonly used to avoid inflated probability of a type 1 error caused by correlation of genes within the same pathway. However, when testing interaction effects, it is typically not possible to construct an exact permutation test. We therefore propose using a parametric bootstrap. For testing an interaction term, this approach requires fitting the null model, which only contains main effects; however, for a gene-set GE interaction model, the number of main effects can be large and therefore they may not be estimable. To estimate the main effects of SNPs in a gene-set, we propose modeling them as random effects. We then repetitively simulate null data from this model and analyze it to generate the null distribution of gene-set GE p-values, allowing for an empirical assessment of significance of the global GE effect in the gene-set of interest. Through simulation, we demonstrate that this approach maintains correct type I error, and is well powered to detect GE interactions. We apply our method to test whether the association of obesity with bipolar disorder (BD) is modified by genetic variation in the Wnt signaling pathway.
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MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle. Sci Rep 2018; 8:9345. [PMID: 29921979 PMCID: PMC6008395 DOI: 10.1038/s41598-018-27729-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 06/06/2018] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNA) are key modulators of gene expression and so act as putative fine-tuners of complex phenotypes. Here, we hypothesized that causal variants of complex traits are enriched in miRNAs and miRNA-target networks. First, we conducted a genome-wide association study (GWAS) for seven functional and milk production traits using imputed sequence variants (13~15 million) and >10,000 animals from three dairy cattle breeds, i.e., Holstein (HOL), Nordic red cattle (RDC) and Jersey (JER). Second, we analyzed for enrichments of association signals in miRNAs and their miRNA-target networks. Our results demonstrated that genomic regions harboring miRNA genes were significantly (P < 0.05) enriched with GWAS signals for milk production traits and mastitis, and that enrichments within miRNA-target gene networks were significantly higher than in random gene-sets for the majority of traits. Furthermore, most between-trait and across-breed correlations of enrichments with miRNA-target networks were significantly greater than with random gene-sets, suggesting pleiotropic effects of miRNAs. Intriguingly, genes that were differentially expressed in response to mammary gland infections were significantly enriched in the miRNA-target networks associated with mastitis. All these findings were consistent across three breeds. Collectively, our observations demonstrate the importance of miRNAs and their targets for the expression of complex traits.
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Koufariotis LT, Chen YPP, Stothard P, Hayes BJ. Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits. BMC Genomics 2018; 19:237. [PMID: 29618315 PMCID: PMC5885354 DOI: 10.1186/s12864-018-4617-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 03/22/2018] [Indexed: 02/03/2023] Open
Abstract
Background There are an exceedingly large number of sequence variants discovered through whole genome sequencing in most populations, including cattle. Deciphering which of these affect complex traits is a major challenge. In this study we hypothesize that variants in some functional classes, such as splice site regions, coding regions, DNA methylated regions and long noncoding RNA will explain more variance in complex traits than others. Two variance component approaches were used to test this hypothesis – the first determines if variants in a functional class capture a greater proportion of the variance, than expected by chance, the second uses the proportion of variance explained when variants in all annotations are fitted simultaneously. Results Our data set consisted of 28.3 million imputed whole genome sequence variants in 16,581 dairy cattle with records for 6 complex trait phenotypes, including production and fertility. We found that sequence variants in splice site regions and synonymous classes captured the greatest proportion of the variance, explaining up to 50% of the variance across all traits. We also found sequence variants in target sites for DNA methylation (genomic regions that are found be highly methylated in bovine placentas), captured a significant proportion of the variance. Per sequence variant, splice site variants explain the highest proportion of variance in this study. The proportion of variance captured by the missense predicted deleterious (from SIFT) and missense tolerated classes was relatively small. Conclusion The results demonstrate using functional annotations to filter whole genome sequence variants into more informative subsets could be useful for prioritization of the variants that are more likely to be associated with complex traits. In addition to variants found in splice sites and protein coding genes regulatory variants and those found in DNA methylated regions, explained considerable variation in milk production and fertility traits. In our analysis synonymous variants captured a significant proportion of the variance, which raises the possible explanation that synonymous mutations might have some effects, or more likely that these variants are miss-annotated, or alternatively the results reflect imperfect imputation of the actual causative variants. Electronic supplementary material The online version of this article (10.1186/s12864-018-4617-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lambros T Koufariotis
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, The University of Queensland, Building 80, 306 Carmody Road, Brisbane, St Lucia, QLD, 4072, Australia. .,Collage of Science, Health and Engineering, La Trobe University, Melbourne, VIC, 3086, Australia. .,Department of Economic Development, Jobs, Transport and Resources, AgriBio Building, 5 Ring Road, Bundoora, VIC, 3086, Australia. .,Dairy Bio, 5 Ring Road, Bundoora, VIC, 3086, Australia.
| | - Yi-Ping Phoebe Chen
- Collage of Science, Health and Engineering, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Paul Stothard
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2C8, Canada
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, The University of Queensland, Building 80, 306 Carmody Road, Brisbane, St Lucia, QLD, 4072, Australia.,Department of Economic Development, Jobs, Transport and Resources, AgriBio Building, 5 Ring Road, Bundoora, VIC, 3086, Australia.,Dairy Bio, 5 Ring Road, Bundoora, VIC, 3086, Australia
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31
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Silva CT, Zorkoltseva IV, Niemeijer MN, van den Berg ME, Amin N, Demirkan A, van Leeuwen E, Iglesias AI, Piñeros-Hernández LB, Restrepo CM, Kors JA, Kirichenko AV, Willemsen R, Oostra BA, Stricker BH, Uitterlinden AG, Axenovich TI, van Duijn CM, Isaacs A. A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy. BMC Med Genomics 2018; 11:22. [PMID: 29506515 PMCID: PMC5838853 DOI: 10.1186/s12920-018-0339-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 02/21/2018] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Electrocardiographic measures of left ventricular hypertrophy (LVH) are used as predictors of cardiovascular risk. We combined linkage and association analyses to discover novel rare genetic variants involved in three such measures and two principal components derived from them. METHODS The study was conducted among participants from the Erasmus Rucphen Family Study (ERF), a Dutch family-based sample from the southwestern Netherlands. Variance components linkage analyses were performed using Merlin. Regions of interest (LOD > 1.9) were fine-mapped using microarray and exome sequence data. RESULTS We observed one significant LOD score for the second principal component on chromosome 15 (LOD score = 3.01) and 12 suggestive LOD scores. Several loci contained variants identified in GWAS for these traits; however, these did not explain the linkage peaks, nor did other common variants. Exome sequence data identified two associated variants after multiple testing corrections were applied. CONCLUSIONS We did not find common SNPs explaining these linkage signals. Exome sequencing uncovered a relatively rare variant in MAPK3K11 on chromosome 11 (MAF = 0.01) that helped account for the suggestive linkage peak observed for the first principal component. Conditional analysis revealed a drop in LOD from 2.01 to 0.88 for MAP3K11, suggesting that this variant may partially explain the linkage signal at this chromosomal location. MAP3K11 is related to the JNK pathway and is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies, including LVH.
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Affiliation(s)
- Claudia Tamar Silva
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), GENIUROS Research group, School of Medicine and Health Science, Universidad del Rosario, Bogotá, Colombia
- Doctoral Program in Biomedical Sciences, Universidad del Rosario, Bogotá, Colombia
| | | | - Maartje N. Niemeijer
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Marten E. van den Berg
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Najaf Amin
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ayşe Demirkan
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Elisa van Leeuwen
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Adriana I. Iglesias
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Laura B. Piñeros-Hernández
- Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), GENIUROS Research group, School of Medicine and Health Science, Universidad del Rosario, Bogotá, Colombia
| | - Carlos M. Restrepo
- Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), GENIUROS Research group, School of Medicine and Health Science, Universidad del Rosario, Bogotá, Colombia
| | - Jan A. Kors
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Rob Willemsen
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ben A. Oostra
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Center for Medical Systems Biology, Leiden, the Netherlands
| | - Bruno H. Stricker
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- Inspectorate of Health care, The Hague, the Netherlands
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Cornelia M. van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Center for Medical Systems Biology, Leiden, the Netherlands
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- CARIM School for Cardiovascular Diseases, Maastricht Centre for Systems Biology (MaCSBio), and Department of Biochemistry, Maastricht University, Maastricht, the Netherlands
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Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment. Mamm Genome 2018; 29:190-204. [DOI: 10.1007/s00335-018-9738-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 01/31/2018] [Indexed: 12/19/2022]
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Rohde PD, Gaertner B, Ward K, Sørensen P, Mackay TFC. Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior. Genetics 2017; 206:1969-1984. [PMID: 28550016 PMCID: PMC5560801 DOI: 10.1534/genetics.117.200642] [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] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 05/22/2017] [Indexed: 02/06/2023] Open
Abstract
Human psychiatric disorders such as schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder often include adverse behaviors including increased aggressiveness. Individuals with psychiatric disorders often exhibit social withdrawal, which can further increase the probability of conducting a violent act. Here, we used the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP) to investigate the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. We identified genetic variation for aggressive behavior, as well as significant genotype-by-social environmental interaction (GSEI); i.e., variation among DGRP genotypes in the degree to which social isolation affected aggression. We performed genome-wide association (GWA) analyses to identify genetic variants associated with aggression within each environment. We used genomic prediction to partition genetic variants into gene ontology (GO) terms and constituent genes, and identified GO terms and genes with high prediction accuracies in both social environments and for GSEI. The top predictive GO terms significantly increased the proportion of variance explained, compared to prediction models based on all segregating variants. We performed genomic prediction across environments, and identified genes in common between the social environments that turned out to be enriched for genome-wide associated variants. A large proportion of the associated genes have previously been associated with aggressive behavior in Drosophila and mice. Further, many of these genes have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders.
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Affiliation(s)
- Palle Duun Rohde
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, 8000 Aarhus, Denmark
- ISEQ, Center for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark
| | - Bryn Gaertner
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Kirsty Ward
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Trudy F C Mackay
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
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de Jong K, Vonk JM, Imboden M, Lahousse L, Hofman A, Brusselle GG, Probst-Hensch NM, Postma DS, Boezen HM. Genes and pathways underlying susceptibility to impaired lung function in the context of environmental tobacco smoke exposure. Respir Res 2017; 18:142. [PMID: 28738859 PMCID: PMC5525356 DOI: 10.1186/s12931-017-0625-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 07/18/2017] [Indexed: 12/19/2022] Open
Abstract
Background Studies aiming to assess genetic susceptibility for impaired lung function levels upon exposure to environmental tobacco smoke (ETS) have thus far focused on candidate-genes selected based on a-priori knowledge of potentially relevant biological pathways, such as glutathione S-transferases and ADAM33. By using a hypothesis-free approach, we aimed to identify novel susceptibility loci, and additionally explored biological pathways potentially underlying this susceptibility to impaired lung function in the context of ETS exposure. Methods Genome-wide interactions of single nucleotide polymorphism (SNP) by ETS exposure (0 versus ≥1 h/day) in relation to the level of forced expiratory volume in one second (FEV1) were investigated in 10,817 subjects from the Dutch LifeLines cohort study, and verified in subjects from the Swiss SAPALDIA study (n = 1276) and the Dutch Rotterdam Study (n = 1156). SNP-by-ETS exposure p-values obtained from the identification analysis were used to perform a pathway analysis. Results Fourty Five SNP-by-ETS exposure interactions with p-values <10−4 were identified in the LifeLines study, two being replicated with nominally significant p-values (<0.05) in at least one of the replication cohorts. Three pathways were enriched in the pathway-level analysis performed in the identification cohort LifeLines, i.E. the apoptosis, p38 MAPK and TNF pathways. Conclusion This unique, first genome-wide gene-by-ETS interaction study on the level of FEV1 showed that pathways previously implicated in chronic obstructive pulmonary disease (COPD), a disease characterized by airflow obstruction, may also underlie susceptibility to impaired lung function in the context of ETS exposure. Electronic supplementary material The online version of this article (doi:10.1186/s12931-017-0625-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- K de Jong
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB, Groningen, the Netherlands.,Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - J M Vonk
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB, Groningen, the Netherlands.,Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - M Imboden
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - L Lahousse
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - A Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - G G Brusselle
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium.,Department of Respiratory Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - N M Probst-Hensch
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - D S Postma
- Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.,Department of Pulmonary Diseases, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - H M Boezen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB, Groningen, the Netherlands. .,Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
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Identifying novel genes and biological processes relevant to the development of cancer therapy-induced mucositis: An informative gene network analysis. PLoS One 2017; 12:e0180396. [PMID: 28678827 PMCID: PMC5498049 DOI: 10.1371/journal.pone.0180396] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 05/30/2017] [Indexed: 12/20/2022] Open
Abstract
Mucositis is a complex, dose-limiting toxicity of chemotherapy or radiotherapy that leads to painful mouth ulcers, difficulty eating or swallowing, gastrointestinal distress, and reduced quality of life for patients with cancer. Mucositis is most common for those undergoing high-dose chemotherapy and hematopoietic stem cell transplantation and for those being treated for malignancies of the head and neck. Treatment and management of mucositis remain challenging. It is expected that multiple genes are involved in the formation, severity, and persistence of mucositis. We used Ingenuity Pathway Analysis (IPA), a novel network-based approach that integrates complex intracellular and intercellular interactions involved in diseases, to systematically explore the molecular complexity of mucositis. As a first step, we searched the literature to identify genes that harbor or are close to the genetic variants significantly associated with mucositis. Our literature review identified 27 candidate genes, of which ERCC1, XRCC1, and MTHFR were the most frequently studied for mucositis. On the basis of this 27-gene list, we used IPA to generate gene networks for mucositis. The most biologically significant novel molecules identified through IPA analyses included TP53, CTNNB1, MYC, RB1, P38 MAPK, and EP300. Additionally, uracil degradation II (reductive) and thymine degradation pathways (p = 1.06-08) were most significant. Finally, utilizing 66 SNPs within the 8 most connected IPA-derived candidate molecules, we conducted a genetic association study for oral mucositis in the head and neck cancer patients who were treated using chemotherapy and/or radiation therapy (186 head and neck cancer patients with oral mucositis vs. 699 head and neck cancer patients without oral mucositis). The top ranked gene identified through this association analysis was RB1 (rs2227311, p-value = 0.034, odds ratio = 0.67). In conclusion, gene network analysis identified novel molecules and biological processes, including pathways related to inflammation and oxidative stress, that are relevant to mucositis development, thus providing the basis for future studies to improve the management and treatment of mucositis in patients with cancer.
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Fang L, Sahana G, Ma P, Su G, Yu Y, Zhang S, Lund MS, Sørensen P. Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection. Genet Sel Evol 2017; 49:44. [PMID: 28499345 PMCID: PMC5427631 DOI: 10.1186/s12711-017-0319-0] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 05/03/2017] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. RESULTS We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P < 0.05). CONCLUSIONS GFBLUP provides a framework for integrating multiple layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set test might be used as a first-step to improve GFBLUP models. Approaches like GFBLUP and SNP set test will become increasingly useful, as the functional annotations of genomes keep accumulating for a range of species and traits.
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Affiliation(s)
- Lingzhao Fang
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark. .,Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
| | - Goutam Sahana
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Peipei Ma
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Guosheng Su
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Shengli Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Mogens Sandø Lund
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Peter Sørensen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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Fang L, Sahana G, Su G, Yu Y, Zhang S, Lund MS, Sørensen P. Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle. Sci Rep 2017; 7:45560. [PMID: 28358110 PMCID: PMC5372096 DOI: 10.1038/srep45560] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 02/28/2017] [Indexed: 02/06/2023] Open
Abstract
Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds (i.e., Holstein, Nordic red cattle, and Jersey) to explore the genetic basis of mastitis resistance and milk production using post-GWAS analyses and a genomic feature linear mixed model. At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits.
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Affiliation(s)
- Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark.,Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture &National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture &National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China
| | - Shengli Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture &National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
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Identification of genetic susceptibility loci for intestinal Behçet's disease. Sci Rep 2017; 7:39850. [PMID: 28045058 PMCID: PMC5206652 DOI: 10.1038/srep39850] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 11/28/2016] [Indexed: 12/19/2022] Open
Abstract
Several recent genome-wide association studies (GWAS) identified susceptibility loci/genes for Behçet’s disease (BD). However, no study has specifically investigated the genetic susceptibility loci associated with intestinal involvement in BD. We aimed to identify distinctive genetic susceptibility loci/genes associated with intestinal involvement in BD and determine their roles in intestinal inflammation as well as their interactions with genes involved in inflammatory bowel disease (IBD). GWAS and validation studies showed intestinal BD-specific associations with an NAALADL2 gene locus (rs3914501, P = 3.8 × 10−4) and a YIPF7 gene locus (rs6838327, P = 3.5 × 10−4). Validation, haplotype, and pathway analyses showed distinct genetic architectures between intestinal BD and BD without intestinal involvement. Furthermore, network analysis revealed shared pathogenic pathways between intestinal BD and IBD. Gene functional analyses indicated that down-regulation of NAALADL2 and YIPF7 expression was associated with exacerbating intestinal inflammatory responses both in vitro and in vivo. Our results provide new insights into intestinal BD-specific genetic variations, which represents a distinct pathway from BD without intestinal involvement. Functional consequences of the intestinal BD-specific NAALADL2 and YIPF7 expression patterns proved a suggestive association with intestinal inflammation risk, which warrants further validation.
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Pranavchand R, Reddy BM. Genomics era and complex disorders: Implications of GWAS with special reference to coronary artery disease, type 2 diabetes mellitus, and cancers. J Postgrad Med 2016; 62:188-98. [PMID: 27424552 PMCID: PMC4970347 DOI: 10.4103/0022-3859.186390] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The Human Genome Project (HGP) has identified millions of single nucleotide polymorphisms (SNPs) and their association with several diseases, apart from successfully characterizing the Mendelian/monogenic diseases. However, the dissection of precise etiology of complex genetic disorders still poses a challenge for human geneticists. This review outlines the landmark results of genome-wide association studies (GWAS) with respect to major complex diseases - Coronary artery disease (CAD), type 2 diabetes mellitus (T2DM), and predominant cancers. A brief account on the current Indian scenario is also given. All the relevant publications till mid-2015 were accessed through web databases such as PubMed and Google. Several databases providing genetic information related to these diseases were tabulated and in particular, the list of the most significant SNPs identified through GWAS was made, which may be useful for designing studies in functional validation. Post-GWAS implications and emerging concepts such as epigenomics and pharmacogenomics were also discussed.
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Affiliation(s)
- R Pranavchand
- Molecular Anthropology Group, Biological Anthropology Unit, Indian Statistical Institute, Hyderabad, Andhra Pradesh, India
| | - B M Reddy
- Molecular Anthropology Group, Biological Anthropology Unit, Indian Statistical Institute, Hyderabad, Andhra Pradesh, India
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Zhao X, Luan YZ, Zuo X, Chen YD, Qin J, Jin L, Tan Y, Lin M, Zhang N, Liang Y, Rao SQ. Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data. GENOMICS PROTEOMICS & BIOINFORMATICS 2016; 14:349-356. [PMID: 27965104 PMCID: PMC5200919 DOI: 10.1016/j.gpb.2016.04.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Revised: 03/30/2016] [Accepted: 04/10/2016] [Indexed: 02/06/2023]
Abstract
Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer’s disease, non-alcoholic fatty liver disease, and Huntington’s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer’s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer’s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.
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Affiliation(s)
- Xiang Zhao
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Yi-Zhao Luan
- School of Life Sciences, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiaoyu Zuo
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Ye-Da Chen
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Jiheng Qin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Lv Jin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Yiqing Tan
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Meihua Lin
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China
| | - Naizun Zhang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yan Liang
- Maoming People's Hospital, Maoming 525000, China
| | - Shao-Qi Rao
- Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology, School of Public Health, Guangdong Medical College, Dongguan 523808, China; Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Kao PYP, Leung KH, Chan LWC, Yip SP, Yap MKH. Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions. Biochim Biophys Acta Gen Subj 2016; 1861:335-353. [PMID: 27888147 DOI: 10.1016/j.bbagen.2016.11.030] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 10/17/2016] [Accepted: 11/19/2016] [Indexed: 12/20/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data. SCOPE OF REVIEW 1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data. MAJOR CONCLUSIONS To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions. GENERAL SIGNIFICANCE Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.
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Affiliation(s)
- Patrick Y P Kao
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Kim Hung Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lawrence W C Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shea Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Maurice K H Yap
- Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong SAR, China
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Windhorst DA, Mileva-Seitz VR, Rippe RCA, Tiemeier H, Jaddoe VWV, Verhulst FC, van IJzendoorn MH, Bakermans-Kranenburg MJ. Beyond main effects of gene-sets: harsh parenting moderates the association between a dopamine gene-set and child externalizing behavior. Brain Behav 2016; 6:e00498. [PMID: 27547500 PMCID: PMC4980469 DOI: 10.1002/brb3.498] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 11/13/2015] [Accepted: 04/21/2016] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND In a longitudinal cohort study, we investigated the interplay of harsh parenting and genetic variation across a set of functionally related dopamine genes, in association with children's externalizing behavior. This is one of the first studies to employ gene-based and gene-set approaches in tests of Gene by Environment (G × E) effects on complex behavior. This approach can offer an important alternative or complement to candidate gene and genome-wide environmental interaction (GWEI) studies in the search for genetic variation underlying individual differences in behavior. METHODS Genetic variants in 12 autosomal dopaminergic genes were available in an ethnically homogenous part of a population-based cohort. Harsh parenting was assessed with maternal (n = 1881) and paternal (n = 1710) reports at age 3. Externalizing behavior was assessed with the Child Behavior Checklist (CBCL) at age 5 (71 ± 3.7 months). We conducted gene-set analyses of the association between variation in dopaminergic genes and externalizing behavior, stratified for harsh parenting. RESULTS The association was statistically significant or approached significance for children without harsh parenting experiences, but was absent in the group with harsh parenting. Similarly, significant associations between single genes and externalizing behavior were only found in the group without harsh parenting. Effect sizes in the groups with and without harsh parenting did not differ significantly. Gene-environment interaction tests were conducted for individual genetic variants, resulting in two significant interaction effects (rs1497023 and rs4922132) after correction for multiple testing. CONCLUSION Our findings are suggestive of G × E interplay, with associations between dopamine genes and externalizing behavior present in children without harsh parenting, but not in children with harsh parenting experiences. Harsh parenting may overrule the role of genetic factors in externalizing behavior. Gene-based and gene-set analyses offer promising new alternatives to analyses focusing on single candidate polymorphisms when examining the interplay between genetic and environmental factors.
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Affiliation(s)
- Dafna A Windhorst
- Centre for Child and Family Studies Leiden University Leiden The Netherlands; The Generation R Study Group Erasmus University Medical Center Rotterdam The Netherlands; Department of Child and Adolescent Psychiatry Erasmus University Medical Center-Sophia Children's Hospital Rotterdam The Netherlands
| | - Viara R Mileva-Seitz
- Centre for Child and Family Studies Leiden University Leiden The Netherlands; The Generation R Study Group Erasmus University Medical Center Rotterdam The Netherlands; Department of Child and Adolescent Psychiatry Erasmus University Medical Center-Sophia Children's Hospital Rotterdam The Netherlands
| | - Ralph C A Rippe
- Centre for Child and Family Studies Leiden University Leiden The Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry Erasmus University Medical Center-Sophia Children's Hospital Rotterdam The Netherlands; Department of Epidemiology Erasmus University Medical Center Rotterdam The Netherlands; Department of Psychiatry Erasmus University Medical Center Rotterdam The Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group Erasmus University Medical Center Rotterdam The Netherlands; Department of Epidemiology Erasmus University Medical Center Rotterdam The Netherlands; Department of Pediatrics Erasmus University Medical Center Rotterdam The Netherlands
| | - Frank C Verhulst
- Department of Child and Adolescent Psychiatry Erasmus University Medical Center-Sophia Children's Hospital Rotterdam The Netherlands
| | - Marinus H van IJzendoorn
- Centre for Child and Family Studies Leiden University Leiden The Netherlands; School of Pedagogical and Educational Sciences Erasmus University Rotterdam The Netherlands
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Richard AC, Peters JE, Lee JC, Vahedi G, Schäffer AA, Siegel RM, Lyons PA, Smith KGC. Targeted genomic analysis reveals widespread autoimmune disease association with regulatory variants in the TNF superfamily cytokine signalling network. Genome Med 2016; 8:76. [PMID: 27435189 PMCID: PMC4952362 DOI: 10.1186/s13073-016-0329-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 06/21/2016] [Indexed: 01/08/2023] Open
Abstract
Background Tumour necrosis factor (TNF) superfamily cytokines and their receptors regulate diverse immune system functions through a common set of signalling pathways. Genetic variants in and expression of individual TNF superfamily cytokines, receptors and signalling proteins have been associated with autoimmune and inflammatory diseases, but their interconnected biology has been largely unexplored. Methods We took a hypothesis-driven approach using available genome-wide datasets to identify genetic variants regulating gene expression in the TNF superfamily cytokine signalling network and the association of these variants with autoimmune and autoinflammatory disease. Using paired gene expression and genetic data, we identified genetic variants associated with gene expression, expression quantitative trait loci (eQTLs), in four peripheral blood cell subsets. We then examined whether eQTLs were dependent on gene expression level or the presence of active enhancer chromatin marks. Using these eQTLs as genetic markers of the TNF superfamily signalling network, we performed targeted gene set association analysis in eight autoimmune and autoinflammatory disease genome-wide association studies. Results Comparison of TNF superfamily network gene expression and regulatory variants across four leucocyte subsets revealed patterns that differed between cell types. eQTLs for genes in this network were not dependent on absolute gene expression levels and were not enriched for chromatin marks of active enhancers. By examining autoimmune disease risk variants among our eQTLs, we found that risk alleles can be associated with either increased or decreased expression of co-stimulatory TNF superfamily cytokines, receptors or downstream signalling molecules. Gene set disease association analysis revealed that eQTLs for genes in the TNF superfamily pathway were associated with six of the eight autoimmune and autoinflammatory diseases examined, demonstrating associations beyond single genome-wide significant hits. Conclusions This systematic analysis of the influence of regulatory genetic variants in the TNF superfamily network reveals widespread and diverse roles for these cytokines in susceptibility to a number of immune-mediated diseases. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0329-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Arianne C Richard
- Department of Medicine and Cambridge Institute for Medical Research, The University of Cambridge, Box 139, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK.,Autoimmunity Branch, National Institute for Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - James E Peters
- Department of Medicine and Cambridge Institute for Medical Research, The University of Cambridge, Box 139, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK
| | - James C Lee
- Department of Medicine and Cambridge Institute for Medical Research, The University of Cambridge, Box 139, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK
| | - Golnaz Vahedi
- Department of Genetics, Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Alejandro A Schäffer
- Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Richard M Siegel
- Autoimmunity Branch, National Institute for Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Paul A Lyons
- Department of Medicine and Cambridge Institute for Medical Research, The University of Cambridge, Box 139, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK
| | - Kenneth G C Smith
- Department of Medicine and Cambridge Institute for Medical Research, The University of Cambridge, Box 139, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK.
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Covariance Association Test (CVAT) Identifies Genetic Markers Associated with Schizophrenia in Functionally Associated Biological Processes. Genetics 2016; 203:1901-13. [PMID: 27317683 DOI: 10.1534/genetics.116.189498] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Accepted: 06/09/2016] [Indexed: 12/12/2022] Open
Abstract
Schizophrenia is a psychiatric disorder with large personal and social costs, and understanding the genetic etiology is important. Such knowledge can be obtained by testing the association between a disease phenotype and individual genetic markers; however, such single-marker methods have limited power to detect genetic markers with small effects. Instead, aggregating genetic markers based on biological information might increase the power to identify sets of genetic markers of etiological significance. Several set test methods have been proposed: Here we propose a new set test derived from genomic best linear unbiased prediction (GBLUP), the covariance association test (CVAT). We compared the performance of CVAT to other commonly used set tests. The comparison was conducted using a simulated study population having the same genetic parameters as for schizophrenia. We found that CVAT was among the top performers. When extending CVAT to utilize a mixture of SNP effects, we found an increase in power to detect the causal sets. Applying the methods to a Danish schizophrenia case-control data set, we found genomic evidence for association of schizophrenia with vitamin A metabolism and immunological responses, which previously have been implicated with schizophrenia based on experimental and observational studies.
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Reyes-Gibby CC, Wang J, Silvas MRT, Yu R, Yeung SCJ, Shete S. MAPK1/ERK2 as novel target genes for pain in head and neck cancer patients. BMC Genet 2016; 17:40. [PMID: 26872611 PMCID: PMC4752805 DOI: 10.1186/s12863-016-0348-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 02/05/2016] [Indexed: 01/23/2023] Open
Abstract
Background Genetic susceptibility plays an important role in the risk of developing pain in individuals with cancer. As a complex trait, multiple genes underlie this susceptibility. We used gene network analyses to identify novel target genes associated with pain in patients newly diagnosed with squamous cell carcinoma of the head and neck (HNSCC). Results We first identified 36 cancer pain-related genes (i.e., focus genes) from 36 publications based on a literature search. The Ingenuity Pathway Analysis (IPA) analysis identified additional genes that are functionally related to the 36 focus genes through pathway relationships yielding a total of 82 genes. Subsequently, 800 SNPs within the 82 IPA-selected genes on the Illumina HumanOmniExpress-12v1 platform were selected from a large-scale genotyping effort. Association analyses between the 800 candidate SNPs (covering 82 genes) and pain in a patient cohort of 1368 patients with HNSCC (206 patients with severe pain vs. 1162 with non-severe pain) showed the highest significance for MAPK1/ERK2, a gene belonging to the MAP kinase family (rs8136867, p value = 8.92 × 10−4; odds ratio [OR] = 1.33, 95 % confidence interval [CI]: 1.13–1.58). Other top genes were PIK3C2G (a member of PI3K [complex], rs10770367, p value = 1.10 × 10−3; OR = 1.46, 95 % CI: 1.16–1.82), TCRA (the alpha chain of T-cell receptor, rs6572493, p value = 2.84 × 10−3; OR = 0.70, 95 % CI: 0.55–0.88), PDGFC (platelet-derived growth factor C, rs6845322, p value = 4.88 × 10−3; OR = 1.32, 95 % CI: 1.09–1.60), and CD247 (a member of CD3, rs2995082, p value = 7.79 × 10−3; OR = 0.76, 95 % CI: 0.62–0.93). Conclusions Our findings provide novel candidate genes and biological pathways underlying pain in cancer patients. Further study of the variations of these candidate genes could inform clinical decision making when treating cancer pain. Electronic supplementary material The online version of this article (doi:10.1186/s12863-016-0348-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Cielito C Reyes-Gibby
- Department of Emergency Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A..
| | - Jian Wang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A..
| | - Mary Rose T Silvas
- Department of Emergency Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A..
| | - Robert Yu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A..
| | - Sai-Ching J Yeung
- Department of Emergency Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A..
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A.. .,Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A.
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Abstract
OBJECTIVE The aim of this study was to identify candidate single-nucleotide polymorphisms (SNPs) that might play a role in susceptibility to diabetic nephropathy (DN) in type 1 diabetes, elucidate their potential mechanisms, and generate SNP-to-gene-to-pathway hypotheses. METHODS A genome-wide association study (GWAS) dataset of DN in type 1 diabetes, which included 345,363 SNPs from a total of 1,705 samples (820 DN cases and 885 normoalbuminuric controls) of European ancestry, was used in this study. The Identify Candidate Causal SNPs and Pathways (ICSNPathway) analysis was applied to the GWAS dataset. RESULTS ICSNPathway analysis identified 14 candidate SNPs, 10 genes, and 19 pathways, which in turn revealed 10 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was one in which rs4740 altered the role of EBI3 in various pathways and processes, including regulation of the cytokine biosynthetic process, cytokine metabolic process, positive regulation of the cytokine biosynthetic process, regulation of the interferon gamma biosynthetic process, and interferon gamma production (0.008 ≤ p < 0.001; 0.047 ≤ false discovery rate [FDR] ≤ 0.002). This next most strongly supported hypothesis was the modulation of NMUR2 by rs982715, rs4958531, 4958532, rs1895245, and rs4958535 to affect its role in various pathways and processes, including calcium-mediated signaling and peptide receptor activity, and G-protein activity (p < 0.001, 0.002; FDR = 0.005, 0.049, respectively). CONCLUSIONS By using the ICSNPathway to analyze the DN GWAS data, we identified 14 candidate SNPs, 10 genes (including EBI3, NMUR2, and EFNA1), and 19 pathways that likely contribute to the susceptibility to DN in type 1 diabetes.
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Affiliation(s)
- Young Ho Lee
- a Division of Rheumatology , Department of Internal Medicine, Korea University College of Medicine , Seoul , Korea
| | - Gwan Gyu Song
- a Division of Rheumatology , Department of Internal Medicine, Korea University College of Medicine , Seoul , Korea
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Su YC, Gauderman WJ, Berhane K, Lewinger JP. Adaptive Set-Based Methods for Association Testing. Genet Epidemiol 2015; 40:113-22. [PMID: 26707371 DOI: 10.1002/gepi.21950] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Revised: 11/02/2015] [Accepted: 11/17/2015] [Indexed: 12/31/2022]
Abstract
With a typical sample size of a few thousand subjects, a single genome-wide association study (GWAS) using traditional one single nucleotide polymorphism (SNP)-at-a-time methods can only detect genetic variants conferring a sizable effect on disease risk. Set-based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. Although self-contained set-based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set-based approaches (those that rely on comparison of variants in the set of interest with variants not in the set), there is no consensus as to which self-contained methods are best. In particular, several self-contained set tests have been proposed to directly or indirectly "adapt" to the a priori unknown proportion and distribution of effects of the truly associated SNPs in the set, which is a major determinant of their power. A popular adaptive set-based test is the adaptive rank truncated product (ARTP), which seeks the set of SNPs that yields the best-combined evidence of association. We compared the standard ARTP, several ARTP variations we introduced, and other adaptive methods in a comprehensive simulation study to evaluate their performance. We used permutations to assess significance for all the methods and thus provide a level playing field for comparison. We found the standard ARTP test to have the highest power across our simulations followed closely by the global model of random effects (GMRE) and a least absolute shrinkage and selection operator (LASSO)-based test.
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Affiliation(s)
- Yu-Chen Su
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - William James Gauderman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Kiros Berhane
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Juan Pablo Lewinger
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
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Winham SJ, Jenkins GD, Biernacka JM. Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias. Genet Epidemiol 2015; 40:123-32. [PMID: 26639183 DOI: 10.1002/gepi.21946] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 10/29/2015] [Accepted: 10/29/2015] [Indexed: 12/12/2022]
Abstract
Machine learning methods, including Random Forests (RF), are increasingly used for genetic data analysis. However, the standard RF algorithm does not correctly model the effects of X chromosome single nucleotide polymorphisms (SNPs), leading to biased estimates of variable importance. We propose extensions of RF to correctly model X SNPs, including a stratified approach and an approach based on the process of X chromosome inactivation. We applied the new and standard RF approaches to case-control alcohol dependence data from the Study of Addiction: Genes and Environment (SAGE), and compared the performance of the alternative approaches via a simulation study. Standard RF applied to a case-control study of alcohol dependence yielded inflated variable importance estimates for X SNPs, even when sex was included as a variable, but the results of the new RF methods were consistent with univariate regression-based approaches that correctly model X chromosome data. Simulations showed that the new RF methods eliminate the bias in standard RF variable importance for X SNPs when sex is associated with the trait, and are able to detect causal autosomal and X SNPs. Even in the absence of sex effects, the new extensions perform similarly to standard RF. Thus, we provide a powerful multimarker approach for genetic analysis that accommodates X chromosome data in an unbiased way. This method is implemented in the freely available R package "snpRF" (http://www.cran.r-project.org/web/packages/snpRF/).
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Affiliation(s)
- Stacey J Winham
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Gregory D Jenkins
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Joanna M Biernacka
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.,Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, United States of America
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Hamzić E, Buitenhuis B, Hérault F, Hawken R, Abrahamsen MS, Servin B, Elsen JM, Pinard-van der Laan MH, Bed'Hom B. Genome-wide association study and biological pathway analysis of the Eimeria maxima response in broilers. Genet Sel Evol 2015; 47:91. [PMID: 26607727 PMCID: PMC4659166 DOI: 10.1186/s12711-015-0170-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2015] [Accepted: 11/05/2015] [Indexed: 02/22/2023] Open
Abstract
Background Coccidiosis is the most common and costly disease in the poultry industry and is caused by protozoans of the Eimeria genus. The current control of coccidiosis, based on the use of anticoccidial drugs and vaccination, faces serious obstacles such as drug resistance and the high costs for the development of efficient vaccines, respectively. Therefore, the current control programs must be expanded with complementary approaches such as the use of genetics to improve the host response to Eimeria infections. Recently, we have performed a large-scale challenge study on Cobb500 broilers using E. maxima for which we investigated variability among animals in response to the challenge. As a follow-up to this challenge study, we performed a genome-wide association study (GWAS) to identify genomic regions underlying variability of the measured traits in the response to Eimeria maxima in broilers. Furthermore, we conducted a post-GWAS functional analysis to increase our biological understanding of the underlying response to Eimeria maxima challenge. Results In total, we identified 22 single nucleotide polymorphisms (SNPs) with q value <0.1 distributed across five chromosomes. The highly significant SNPs were associated with body weight gain (three SNPs on GGA5, one SNP on GGA1 and one SNP on GGA3), plasma coloration measured as optical density at wavelengths in the range 465–510 nm (10 SNPs and all on GGA10) and the percentage of β2-globulin in blood plasma (15 SNPs on GGA1 and one SNP on GGA2). Biological pathways related to metabolic processes, cell proliferation, and primary innate immune processes were among the most frequent significantly enriched biological pathways. Furthermore, the network-based analysis produced two networks of high confidence, with one centered on large tumor suppressor kinase 1 (LATS1) and 2 (LATS2) and the second involving the myosin heavy chain 6 (MYH6). Conclusions We identified several strong candidate genes and genomic regions associated with traits measured in response to Eimeria maxima in broilers. Furthermore, the post-GWAS functional analysis indicates that biological pathways and networks involved in tissue proliferation and repair along with the primary innate immune response may play the most important role during the early stage of Eimeria maxima infection in broilers. Electronic supplementary material The online version of this article (doi:10.1186/s12711-015-0170-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Edin Hamzić
- UMR1313 Animal Genetics and Integrative Biology Unit, AgroParisTech, 16 rue Claude Bernard, 75005, Paris, France. .,UMR1313 Animal Genetics and Integrative Biology Unit, INRA, Domaine de Vilvert, 78350, Jouy-en-Josas, France. .,Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, P.O. Box 50, 8830, Tjele, Denmark.
| | - Bart Buitenhuis
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, P.O. Box 50, 8830, Tjele, Denmark.
| | - Frédéric Hérault
- UMR1348 Physiology, Environment and Genetics for the Animal and Livestock Systems Unit, INRA, Domaine de la Prise, 35590, Saint Gilles, France.
| | | | | | - Bertrand Servin
- UMR1388 Genetics, Physiology and Breeding Systems, INRA, 24 chemin de Borde-Rouge, 31326, Castanet-Tolosan, France.
| | - Jean-Michel Elsen
- UMR1388 Genetics, Physiology and Breeding Systems, INRA, 24 chemin de Borde-Rouge, 31326, Castanet-Tolosan, France.
| | - Marie-Hélène Pinard-van der Laan
- UMR1313 Animal Genetics and Integrative Biology Unit, AgroParisTech, 16 rue Claude Bernard, 75005, Paris, France. .,UMR1313 Animal Genetics and Integrative Biology Unit, INRA, Domaine de Vilvert, 78350, Jouy-en-Josas, France.
| | - Bertrand Bed'Hom
- UMR1313 Animal Genetics and Integrative Biology Unit, AgroParisTech, 16 rue Claude Bernard, 75005, Paris, France. .,UMR1313 Animal Genetics and Integrative Biology Unit, INRA, Domaine de Vilvert, 78350, Jouy-en-Josas, France.
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50
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Lu ZH, Zhu H, Knickmeyer RC, Sullivan PF, Williams SN, Zou F. Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection. Genet Epidemiol 2015; 39:664-77. [PMID: 26515609 DOI: 10.1002/gepi.21932] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Revised: 07/23/2015] [Accepted: 08/18/2015] [Indexed: 11/07/2022]
Abstract
The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.
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Affiliation(s)
- Zhao-Hua Lu
- Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, United States of America.,Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Stephanie N Williams
- Department of Genetics, University of North Carolina at Chapel Hill, North Carolina, United States of America
| | - Fei Zou
- Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, United States of America
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