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Williams MJ, Orlando C, Akisanya J, Amezcua L. Multiple Sclerosis in Black and Hispanic Populations: Serving the Underserved. Neurol Clin 2024; 42:295-317. [PMID: 37980120 DOI: 10.1016/j.ncl.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2023]
Abstract
Multiple sclerosis has historically been characterized as a disease that affects young women of European ancestry, but recent studies indicate that the incidence and prevalence of the disease is much higher in Black and Hispanic populations than previously recognized. There is evidence that there is a more severe disease course in these populations. , but the intersection of genetic underpinnings and social determinants of health (SDOH) is poorly understood due to the lack of diversity in clinical research. Improving health disparities will involve multiple stakeholders in efforts to improve SDOH and raise awareness about research involvement and the importance of developing personalized health care plans to combat this disease.
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Affiliation(s)
- Mitzi J Williams
- Joi Life Wellness Multiple Sclerosis Center, 767 Concord Road, SE, Smyrna, GA 30082, USA.
| | - Christopher Orlando
- Department of Neurology, University of Southern California, Keck School of Medicine, 1520 San Pablo Street, Suite 3000, Los Angeles, CA, USA. https://twitter.com/OrlandoMDMPH
| | - Jemima Akisanya
- Georgetown Department of Neurology, 10401 Hospital Drive, Suite 102, Clinton, MD 20735, USA. https://twitter.com/MimasMyelin
| | - Lilyana Amezcua
- Department of Neurology, University of Southern California, Keck School of Medicine, 1520 San Pablo Street, Suite 3000, Los Angeles, CA, USA
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2
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Prieto-Fernández A, Sánchez-Barroso G, González-Domínguez J, García-Sanz-Calcedo J. Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction. Expert Rev Med Devices 2023; 20:851-864. [PMID: 37522639 DOI: 10.1080/17434440.2023.2243208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/14/2023] [Accepted: 06/22/2023] [Indexed: 08/01/2023]
Abstract
BACKGROUND Proper maintenance of electro-medical devices is crucial for the quality of care to patients and the economic performance of healthcare organizations. This research aims to identify the interaction between Ultrasound scanners (US) maintenance variables as a function of maintenance indicators: US in service or decommissioned, excessive number of failures, and failure rate. Knowing those interactions, specific maintenance measures will be developed to improve the reliability of the US. RESEARCH DESIGN AND METHODS Multifactor Dimensionality Reduction (MDR) method was eployed to analyze data from 222 US and their four-year maintenance history. Models were developed based on the variables with the greatest influence on maintenance indicators, where US were classified according to the associated risk. RESULTS US with more than one major failure or at least one major component replacement had up to 496.4% more failures than the average. Failure rate increased by up to 188.7% over the average for those US with more than three moderate failures, three replacements, or both. CONCLUSIONS This study identifies and quantifies the causes of risk to establish a specific maintenance plan for US. It helps to better understand the degradation of US to optimize their operation and maintenance.
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Affiliation(s)
| | - Gonzalo Sánchez-Barroso
- Engineering Projects Area, School of Industrial Engineering, University of Extremadura, Badajoz, Spain
| | - Jaime González-Domínguez
- Engineering Projects Area, School of Industrial Engineering, University of Extremadura, Badajoz, Spain
| | - Justo García-Sanz-Calcedo
- Engineering Projects Area, School of Industrial Engineering, University of Extremadura, Badajoz, Spain
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3
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Martins J, Yusupov N, Binder EB, Brückl TM, Czamara D. Early adversity as the prototype gene × environment interaction in mental disorders? Pharmacol Biochem Behav 2022; 215:173371. [PMID: 35271857 DOI: 10.1016/j.pbb.2022.173371] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 02/03/2022] [Accepted: 02/28/2022] [Indexed: 10/18/2022]
Abstract
Childhood adversity (CA) as a significant stressor has consistently been associated with the development of mental disorders. The interaction between CA and genetic variants has been proposed to play a substantial role in disease etiology. In this review, we focus on the gene by environment (GxE) paradigm, its background and interpretation and stress the necessity of its implementation in psychiatric research. Further, we discuss the findings supporting GxCA interactions, ranging from candidate gene studies to polygenic and genome-wide approaches, their strengths and limitations. To illustrate potential underlying epigenetic mechanisms by which GxE effects are translated, we focus on results from FKBP5 × CA studies and discuss how molecular evidence can supplement previous GxE findings. In conclusion, while GxE studies constitute a valuable line of investigation, more harmonized GxE studies in large, deep-phenotyped, longitudinal cohorts, and across different developmental stages are necessary to further substantiate and understand reported GxE findings.
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Affiliation(s)
- Jade Martins
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany.
| | - Natan Yusupov
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany; Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30329, USA
| | - Tanja M Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
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4
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Liu Y, Gao Y, Fang R, Cao H, Sa J, Wang J, Liu H, Wang T, Cui Y. Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach. Brief Bioinform 2021; 22:6346804. [PMID: 34373892 DOI: 10.1093/bib/bbab305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 07/01/2021] [Accepted: 07/17/2021] [Indexed: 11/12/2022] Open
Abstract
Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene-gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene-gene interactions associated with complex traits.
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Affiliation(s)
- Yan Liu
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Yuzhao Gao
- School of Statistics, Shanxi University of Finance and Economics, Taiyuan, PR China
| | - Ruiling Fang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Hongyan Cao
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Jian Sa
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Hongqi Liu
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Tong Wang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
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5
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Awany D, Allali I, Dalvie S, Hemmings S, Mwaikono KS, Thomford NE, Gomez A, Mulder N, Chimusa ER. Host and Microbiome Genome-Wide Association Studies: Current State and Challenges. Front Genet 2019; 9:637. [PMID: 30723493 PMCID: PMC6349833 DOI: 10.3389/fgene.2018.00637] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 11/27/2018] [Indexed: 12/20/2022] Open
Abstract
The involvement of the microbiome in health and disease is well established. Microbiome genome-wide association studies (mGWAS) are used to elucidate the interaction of host genetic variation with the microbiome. The emergence of this relatively new field has been facilitated by the advent of next generation sequencing technologies that enable the investigation of the complex interaction between host genetics and microbial communities. In this paper, we review recent studies investigating host-microbiome interactions using mGWAS. Additionally, we highlight the marked disparity in the sampling population of mGWAS carried out to date and draw attention to the critical need for inclusion of diverse populations.
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Affiliation(s)
- Denis Awany
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Imane Allali
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Shareefa Dalvie
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Sian Hemmings
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Kilaza S Mwaikono
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Nicholas E Thomford
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Andres Gomez
- Department of Animal Science, University of Minnesota-Twin Cities, St. Paul, MN, United States
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - 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, Cape Town, South Africa
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6
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Amezcua L, Beecham AH, Delgado SR, Chinea A, Burnett M, Manrique CP, Gomez R, Comabella M, Montalban X, Ortega M, Tornes L, Lund BT, Islam T, Conti D, Oksenberg JR, McCauley JL. Native ancestry is associated with optic neuritis and age of onset in hispanics with multiple sclerosis. Ann Clin Transl Neurol 2018; 5:1362-1371. [PMID: 30480030 PMCID: PMC6243381 DOI: 10.1002/acn3.646] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 08/19/2018] [Accepted: 08/21/2018] [Indexed: 11/24/2022] Open
Abstract
Background and Objective Hispanics with multiple sclerosis (MS) present younger and more often with optic neuritis (ON) as compared to Whites in the western United States. Regional differences related to Hispanic genetic admixture could be responsible. We investigated the association between global genetic ancestry and ON and age at onset of MS in Hispanics. Methods Data were obtained for 1033 self‐identified Hispanics with MS from four MS‐based registries from four academic institutions across the United States January 2016–April 2017. Multivariate regression models, utilizing genetic ancestry estimates for Native American (NA), African, and European ancestry, were used to assess the relationship between genetic ancestry and ON presentation and age of MS onset, defined as age at first symptom. Results Genetic ancestry and ON proportions varied by region where NA ancestry and ON proportions were highest among Hispanics in the southwestern United States (40% vs. 19% overall for NA and 38% vs. 25% overall for ON). A strong inverse correlation was observed between NA and European ancestry (r = −0.83). ON presentation was associated with younger age of onset (OR: 0.98; 95% CI: 0.96–0.99; P = 7.80 × 10−03) and increased NA ancestry (OR: 2.35 for the highest versus the lowest quartile of NA ancestry; 95% CI: 1.35–4.10; P = 2.60 × 10−03). Younger age of onset was found to be associated with a higher proportion NA (Beta: −5.58; P = 3.49 × 10−02) and African ancestry (Beta: −10.07; P = 1.39 × 10−03). Interpretation Ethnic differences associated with genetic admixture could influence clinical presentation in Hispanics with MS; underscoring the importance of considering genetic substructure in future clinical, genetic, and epigenetic studies in Hispanics.
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Affiliation(s)
- Lilyana Amezcua
- Department of Neurology Keck School of Medicine University of Southern California Los Angeles California
| | - Ashley H Beecham
- Dr. John T. Macdonald Department of Human Genetics Miller School of Medicine University of Miami Miami Florida.,John P. Hussman Institute of Human Genomics Miller School of Medicine University of Miami Miami Florida
| | - Silvia R Delgado
- Multiple Sclerosis Division Department of Neurology Miller School of Medicine University of Miami Miami Florida
| | - Angel Chinea
- San Juan Multiple Sclerosis Center San Juan Puerto Rico
| | - Margaret Burnett
- Department of Neurology Keck School of Medicine University of Southern California Los Angeles California
| | - Clara Patricia Manrique
- John P. Hussman Institute of Human Genomics Miller School of Medicine University of Miami Miami Florida
| | - Refujia Gomez
- Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles California
| | - Manuel Comabella
- Department of Neurology University of San Francisco School of Medicine Los Angeles California
| | - Xavier Montalban
- Department of Neurology University of San Francisco School of Medicine Los Angeles California
| | - Melissa Ortega
- Multiple Sclerosis Division Department of Neurology Miller School of Medicine University of Miami Miami Florida
| | - Leticia Tornes
- Multiple Sclerosis Division Department of Neurology Miller School of Medicine University of Miami Miami Florida
| | - Brett T Lund
- Department of Neurology Keck School of Medicine University of Southern California Los Angeles California
| | - Talat Islam
- Department de Neurología-Neuroinmunología Centre d'Esclerosi Múltiple de Catalunya (Cemcat) Institut de Recerca Vall d'Hebron Hospital Universitari Vall d'Hebron Universitat Autònoma de Barcelona Barcelona Spain
| | - David Conti
- Department de Neurología-Neuroinmunología Centre d'Esclerosi Múltiple de Catalunya (Cemcat) Institut de Recerca Vall d'Hebron Hospital Universitari Vall d'Hebron Universitat Autònoma de Barcelona Barcelona Spain
| | - Jorge R Oksenberg
- Department of Preventive Medicine Keck School of Medicine University of Southern California Los Angeles California
| | - Jacob L McCauley
- Dr. John T. Macdonald Department of Human Genetics Miller School of Medicine University of Miami Miami Florida.,John P. Hussman Institute of Human Genomics Miller School of Medicine University of Miami Miami Florida
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7
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Zhou X, Chan KCC. Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification. BMC Bioinformatics 2018; 19:329. [PMID: 30227829 PMCID: PMC6145205 DOI: 10.1186/s12859-018-2361-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 09/09/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other methods are still not computationally efficient or effective. RESULTS Generalized Fuzzy Quantitative trait MDR (GFQMDR) is proposed in this paper to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then selecting best sets of genetic markers, mainly single nucleotide polymorphisms (SNPs) or simple sequence length polymorphic markers (SSLPs), as having strong association with the trait through generalized fuzzy classification using extended member functions. Experimental results on simulated datasets and real datasets show that our algorithm has better success rate, classification accuracy and consistency in identifying gene-gene interactions associated with QTs. CONCLUSION The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits.
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Affiliation(s)
- Xiangdong Zhou
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian China
| | - Keith C. C. Chan
- Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong China
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8
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Cole BS, Hall MA, Urbanowicz RJ, Gilbert‐Diamond D, Moore JH. Analysis of Gene‐Gene Interactions. ACTA ACUST UNITED AC 2018; 95:1.14.1-1.14.10. [DOI: 10.1002/cphg.45] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Brian S. Cole
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania
| | - Molly A. Hall
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania
- The Center for Systems Genomics, The Pennsylvania State University, University Park Pennsylvania
| | - Ryan J. Urbanowicz
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania
| | - Diane Gilbert‐Diamond
- Institute for Quantitative Biomedical Sciences at Dartmouth Hanover New Hampshire
- Department of Epidemiology, Geisel School of Medicine at Dartmouth Hanover New Hampshire
| | - Jason H. Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania
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9
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Rivas-Rodríguez E, Amezcua L. Ethnic Considerations and Multiple Sclerosis Disease Variability in the United States. Neurol Clin 2018; 36:151-162. [DOI: 10.1016/j.ncl.2017.08.007] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Wang Z, Hall B, Xu J, Shi X. A Sparse Learning Framework for Joint Effect Analysis of Copy Number Variants. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1013-1027. [PMID: 28991724 DOI: 10.1109/tcbb.2015.2462332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Copy number variants (CNVs), including large deletions and duplications, represent an unbalanced change of DNA segments. Abundant in human genomes, CNVs contribute to a large proportion of human genetic diversity, with impact on many human phenotypes. Although recent advances in genetic studies have shed light on the impact of individual CNVs on different traits, the analysis of joint effect of multiple interactive CNVs lags behind from many perspectives. A primary reason is that the large number of CNV combinations and interactions in the human genome make it computationally challenging to perform such joint analysis. To address this challenge, we developed a novel sparse learning framework that combines sparse learning with biological networks to identify interacting CNVs with joint effect on particular traits. We showed that our approach performs well in identifying CNVs with joint phenotypic effect using simulated data. Applied to a real human genomic dataset from the 1,000 Genomes Project, our approach identified multiple CNVs that collectively contribute to population differentiation. We found a set of multiple CNVs that have joint effect in different populations, and affect gene expression differently in distinct populations. These results provided a collection of CNVs that likely have downstream biomedical implications in individuals from diverse population backgrounds.
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11
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Amezcua L. MS in self-identified Hispanic/Latino individuals living in the US. Mult Scler J Exp Transl Clin 2017; 3:2055217317725103. [PMID: 28979795 PMCID: PMC5617095 DOI: 10.1177/2055217317725103] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Accepted: 07/07/2017] [Indexed: 12/26/2022] Open
Abstract
Self-identified Hispanic/Latino individuals living with multiple sclerosis (MS) in the continental United States (US) are a diverse group that represents different cultural and ancestral backgrounds. A marked variability in the way MS affects various subgroups of Hispanics in the US has been observed. We reviewed and synthesized available data about MS in Hispanics in the US. There are likely a host of multifactorial elements contributing to these observations that could be explained by genetic, environmental, and social underpinnings. Barriers to adequate MS care in Hispanics are likely to include delivery of culturally competent care and social and economic disadvantages. Considerable efforts, including the formation of a national consortium known as the Alliance for Research in Hispanic Multiple Sclerosis (ARHMS), are underway to help further explore these various factors.
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Affiliation(s)
- Lilyana Amezcua
- Department of Neurology, University of Southern California, Keck School of Medicine, USA
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12
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Li R, Kim D, Ritchie MD. Methods to analyze big data in pharmacogenomics research. Pharmacogenomics 2017; 18:807-820. [PMID: 28612644 DOI: 10.2217/pgs-2016-0152] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The scale and scope of pharmacogenomics research continues to expand as the cost and efficiency of molecular data generation techniques advance. These new technologies give rise to enormous opportunity for the identification of important genetic and genomic factors important for drug treatment response. With this opportunity come significant challenges. Most of these can be categorized as 'big data' issues, facing not only pharmacogenomics, but other fields in the life sciences as well. In this review, we describe some of the analysis techniques and tools being implemented for genetic/genomic discovery in pharmacogenomics.
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Affiliation(s)
- Ruowang Li
- Bioinformatics & Genomics Graduate Program, The Pennsylvania State University, University Park, PA 16802, USA
| | - Dokyoon Kim
- Biomedical & Translational Informatics Institute, Geisinger Health System, Danville, PA 17821, USA
| | - Marylyn D Ritchie
- Bioinformatics & Genomics Graduate Program, The Pennsylvania State University, University Park, PA 16802, USA.,Biomedical & Translational Informatics Institute, Geisinger Health System, Danville, PA 17821, USA
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13
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Frånberg M, Strawbridge RJ, Hamsten A, de Faire U, Lagergren J, Sennblad B. Fast and general tests of genetic interaction for genome-wide association studies. PLoS Comput Biol 2017; 13:e1005556. [PMID: 28586362 PMCID: PMC5478145 DOI: 10.1371/journal.pcbi.1005556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 06/20/2017] [Accepted: 05/09/2017] [Indexed: 11/29/2022] Open
Abstract
A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two ‘tag’ variants in the LPA locus (p = 2.42 ⋅ 10−09) as well as replicate the interaction (p = 6.97 ⋅ 10−07). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction. Interaction between organic molecules forms the basis of all biological systems. The availability of high-throughput genotyping and sequencing platforms enables us to cost-effectively genotype a large number of individuals. For sufficiently large datasets it is possible to reconstruct the genetic dependencies that underlie complex traits and diseases. However, there is a need for efficient statistical methodologies that can tackle the large sample size and computational resources required to study interaction. In this work we provide theory that reduces the required computational resources, and enable multiple research groups to effectively combine their results.
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Affiliation(s)
- Mattias Frånberg
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
- * E-mail:
| | - Rona J. Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Ulf de Faire
- Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden
| | - Jens Lagergren
- Science for Life Laboratory, Stockholm, Sweden
- The School of Computer Science and Communications, KTH Royal Institute of Technology, Stockholm, Sweden
- Swedish e-science Research Center (SeRC), Stockholm, Sweden
| | - Bengt Sennblad
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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14
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Abstract
BACKGROUND Detection of gene-gene interaction (GGI) is a key challenge towards solving the problem of missing heritability in genetics. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. MDR reduces the dimensionality of multi-factor by means of binary classification into high-risk (H) or low-risk (L) groups. Unfortunately, this simple binary classification does not reflect the uncertainty of H/L classification. Thus, we proposed Fuzzy MDR to overcome limitations of binary classification by introducing the degree of membership of two fuzzy sets H/L. While Fuzzy MDR demonstrated higher power than that of MDR, its performance is highly dependent on the several tuning parameters. In real applications, it is not easy to choose appropriate tuning parameter values. RESULT In this work, we propose an empirical fuzzy MDR (EF-MDR) which does not require specifying tuning parameters values. Here, we propose an empirical approach to estimating the membership degree that can be directly estimated from the data. In EF-MDR, the membership degree is estimated by the maximum likelihood estimator of the proportion of cases(controls) in each genotype combination. We also show that the balanced accuracy measure derived from this new membership function is a linear function of the standard chi-square statistics. This relationship allows us to perform the standard significance test using p-values in the MDR framework without permutation. Through two simulation studies, the power of the proposed EF-MDR is shown to be higher than those of MDR and Fuzzy MDR. We illustrate the proposed EF-MDR by analyzing Crohn's disease (CD) and bipolar disorder (BD) in the Wellcome Trust Case Control Consortium (WTCCC) dataset. CONCLUSION We propose an empirical Fuzzy MDR for detecting GGI using the maximum likelihood of the proportion of cases(controls) as the membership degree of the genotype combination. The program written in R for EF-MDR is available at http://statgen.snu.ac.kr/software/EF-MDR .
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Affiliation(s)
- Sangseob Leem
- Department of Statistics, Seoul National University, Seoul, 08826 South Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, 08826 South Korea
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15
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Evidence for gene-gene epistatic interactions between susceptibility genes for Mycobacterium avium subsp. paratuberculosis infection in cattle. Livest Sci 2017. [DOI: 10.1016/j.livsci.2016.11.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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16
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Frånberg M, Gertow K, Hamsten A, Lagergren J, Sennblad B. Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests. PLoS Genet 2015; 11:e1005502. [PMID: 26402789 PMCID: PMC4581725 DOI: 10.1371/journal.pgen.1005502] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 08/14/2015] [Indexed: 01/26/2023] Open
Abstract
Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PROCARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex diseases, serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences.
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Affiliation(s)
- Mattias Frånberg
- Atherosclerosis Research Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
- * E-mail:
| | - Karl Gertow
- Atherosclerosis Research Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hamsten
- Atherosclerosis Research Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Jens Lagergren
- School of Computer Science and Communications, KTH Royal Institute of Technology, Science for Life Laboratory, Swedish e-Science Research Centre, Stockholm, Sweden
| | - Bengt Sennblad
- Atherosclerosis Research Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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17
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Polonikov AV, Ivanov VP, Bogomazov AD, Solodilova MA. [Genetic and biochemical mechanisms of involvement of antioxidant defense enzymes in the development of bronchial asthma]. BIOMEDITSINSKAIA KHIMIIA 2015; 61:427-39. [PMID: 26350733 DOI: 10.18097/pbmc20156104427] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In the present review we have analyzed and summarized recent literature data on genetic and biochemical mechanisms responsible for involvement of antioxidant defense enzymes in the etiology and pathogenesis of bronchial asthma. It has been shown that the mechanisms of asthma development are linked with genetically determined abnormalities in the functioning of antioxidant defense enzymes. These alterations are accompanied by a systemic imbalance between oxidative and anti-oxidative reactions with the shift of the redox state toward increased free radical production and oxidative stress, a key element in the pathogenesis of bronchial asthma.
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Affiliation(s)
| | - V P Ivanov
- Kursk State Medical University, Kursk, Russia
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18
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Yu Z, Demetriou M, Gillen DL. Genome-Wide Analysis of Gene-Gene and Gene-Environment Interactions Using Closed-Form Wald Tests. Genet Epidemiol 2015; 39:446-55. [PMID: 26095143 DOI: 10.1002/gepi.21907] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Revised: 02/25/2015] [Accepted: 05/06/2015] [Indexed: 01/31/2023]
Abstract
Despite the successful discovery of hundreds of variants for complex human traits using genome-wide association studies, the degree to which genes and environmental risk factors jointly affect disease risk is largely unknown. One obstacle toward this goal is that the computational effort required for testing gene-gene and gene-environment interactions is enormous. As a result, numerous computationally efficient tests were recently proposed. However, the validity of these methods often relies on unrealistic assumptions such as additive main effects, main effects at only one variable, no linkage disequilibrium between the two single-nucleotide polymorphisms (SNPs) in a pair or gene-environment independence. Here, we derive closed-form and consistent estimates for interaction parameters and propose to use Wald tests for testing interactions. The Wald tests are asymptotically equivalent to the likelihood ratio tests (LRTs), largely considered to be the gold standard tests but generally too computationally demanding for genome-wide interaction analysis. Simulation studies show that the proposed Wald tests have very similar performances with the LRTs but are much more computationally efficient. Applying the proposed tests to a genome-wide study of multiple sclerosis, we identify interactions within the major histocompatibility complex region. In this application, we find that (1) focusing on pairs where both SNPs are marginally significant leads to more significant interactions when compared to focusing on pairs where at least one SNP is marginally significant; and (2) parsimonious parameterization of interaction effects might decrease, rather than increase, statistical power.
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Affiliation(s)
- Zhaoxia Yu
- Department of Statistics, University of California, Irvine, California, United States of America
| | - Michael Demetriou
- Department of Neurology, University of California, Irvine, California, United States of America.,Department of Microbiology & Molecular Genetics, University of California, Irvine, California, United States of America
| | - Daniel L Gillen
- Department of Statistics, University of California, Irvine, California, United States of America
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19
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Wang Z, Sul JH, Snir S, Lozano JA, Eskin E. Gene-Gene Interactions Detection Using a Two-stage Model. J Comput Biol 2015; 22:563-76. [PMID: 25871811 DOI: 10.1089/cmb.2014.0163] [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] [Indexed: 10/23/2022] Open
Abstract
Genome-wide association studies (GWAS) have discovered numerous loci involved in genetic traits. Virtually all studies have reported associations between individual single nucleotide polymorphisms (SNPs) and traits. However, it is likely that complex traits are influenced by interaction of multiple SNPs. One approach to detect interactions of SNPs is the brute force approach which performs a pairwise association test between a trait and each pair of SNPs. The brute force approach is often computationally infeasible because of the large number of SNPs collected in current GWAS studies. We propose a two-stage model, Threshold-based Efficient Pairwise Association Approach (TEPAA), to reduce the number of tests needed while maintaining almost identical power to the brute force approach. In the first stage, our method performs the single marker test on all SNPs and selects a subset of SNPs that achieve a certain significance threshold. In the second stage, we perform a pairwise association test between traits and pairs of the SNPs selected from the first stage. The key insight of our approach is that we derive the joint distribution between the association statistics of a single SNP and the association statistics of pairs of SNPs. This joint distribution allows us to provide guarantees that the statistical power of our approach will closely approximate the brute force approach. We applied our approach to the Northern Finland Birth Cohort data and achieved 63 times speedup while maintaining 99% of the power of the brute force approach.
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Affiliation(s)
- Zhanyong Wang
- 1Computer Science Department, University of California Los Angeles, Los Angeles, California
| | - Jae Hoon Sul
- 2Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sagi Snir
- 3Institute of Evolution, Department of Evolutionary and Environmental Biology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Jose A Lozano
- 4Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia Spain
| | - Eleazar Eskin
- 1Computer Science Department, University of California Los Angeles, Los Angeles, California
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20
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Musameh MD, Wang WYS, Nelson CP, Lluís-Ganella C, Debiec R, Subirana I, Elosua R, Balmforth AJ, Ball SG, Hall AS, Kathiresan S, Thompson JR, Lucas G, Samani NJ, Tomaszewski M. Analysis of gene-gene interactions among common variants in candidate cardiovascular genes in coronary artery disease. PLoS One 2015; 10:e0117684. [PMID: 25658981 PMCID: PMC4320092 DOI: 10.1371/journal.pone.0117684] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 12/30/2014] [Indexed: 11/19/2022] Open
Abstract
Objective Only a small fraction of coronary artery disease (CAD) heritability has been explained by common variants identified to date. Interactions between genes of importance to cardiovascular regulation may account for some of the missing heritability of CAD. This study aimed to investigate the role of gene-gene interactions in common variants in candidate cardiovascular genes in CAD. Approach and Results 2,101 patients with CAD from the British Heart Foundation Family Heart Study and 2,426 CAD-free controls were included in the discovery cohort. All subjects were genotyped with the Illumina HumanCVD BeadChip enriched for genes and pathways relevant to the cardiovascular system and disease. The primary analysis in the discovery cohort examined pairwise interactions among 913 common (minor allele frequency >0.1) independent single nucleotide polymorphisms (SNPs) with at least nominal association with CAD in single locus analysis. A secondary exploratory interaction analysis was performed among all 11,332 independent common SNPs surviving quality control criteria. Replication analyses were conducted in 2,967 patients and 3,075 controls from the Myocardial Infarction Genetics Consortium. None of the interactions amongst 913 SNPs analysed in the primary analysis was statistically significant after correction for multiple testing (required P<1.2x10-7). Similarly, none of the pairwise gene-gene interactions in the secondary analysis reached statistical significance after correction for multiple testing (required P = 7.8x10-10). None of 36 suggestive interactions from the primary analysis or 31 interactions from the secondary analysis was significant in the replication cohort. Our study had 80% power to detect odds ratios > 1.7 for common variants in the primary analysis. Conclusions Moderately large additive interactions between common SNPs in genes relevant to cardiovascular disease do not appear to play a major role in genetic predisposition to CAD. The role of genetic interactions amongst less common SNPs and with medium and small magnitude effects remain to be investigated.
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Affiliation(s)
- Muntaser D. Musameh
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
- * E-mail:
| | - William Y. S. Wang
- School of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | | | - Radoslaw Debiec
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Isaac Subirana
- Cardiovascular Epidemiology and Genetics, IMIM, Barcelona, Spain
- Epidemiology and Public Health Network (CIBERESP), Barcelona, Spain
| | - Roberto Elosua
- Cardiovascular Epidemiology and Genetics, IMIM, Barcelona, Spain
| | - Anthony J. Balmforth
- Division of Epidemiology, LIGHT, School of Medicine, University of Leeds, Leeds, United Kingdom
| | - Stephen G. Ball
- University of Leeds, MCRC, Leeds Institute of Genetics, Health and Therapeutics, Leeds, United Kingdom
| | - Alistair S. Hall
- Division of Epidemiology, LIGHT, School of Medicine, University of Leeds, Leeds, United Kingdom
| | - Sekar Kathiresan
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - John R. Thompson
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
| | - Gavin Lucas
- Cardiovascular Epidemiology and Genetics, IMIM, Barcelona, Spain
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Maciej Tomaszewski
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
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21
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Polonikov AV, Ivanov VP, Bogomazov AD, Solodilova MA. Genetic and biochemical mechanisms of involvement of antioxidant defense enzymes in the development of bronchial asthma: A review. BIOCHEMISTRY (MOSCOW) SUPPLEMENT SERIES B: BIOMEDICAL CHEMISTRY 2014; 8:273-285. [DOI: 10.1134/s1990750814040076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
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22
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Li D, Zhou J, Thomas DC, Fardo DW. Complex pedigrees in the sequencing era: to track transmissions or decorrelate? Genet Epidemiol 2014; 38 Suppl 1:S29-36. [PMID: 25112185 DOI: 10.1002/gepi.21822] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Next-generation sequencing (NGS) studies are becoming commonplace, and the NGS field is continuing to develop rapidly. Analytic methods aimed at testing for the various roles that genetic susceptibility plays in disease are also rapidly being developed and optimized. Studies that incorporate large, complex pedigrees are of particular importance because they provide detailed information about inheritance patterns and can be analyzed in a variety of complementary ways. The nine contributions from our Genetic Analysis Workshop 18 working group on family-based tests of association for rare variants using simulated data examined analytic methods for testing genetic association using whole-genome sequencing data from 20 large pedigrees with 200 phenotype simulation replicates. What distinguishes the approaches explored is how the complexities of analyzing familial genetic data were handled. Here, we explore the methods that either harness inheritance patterns and transmission information or attempt to adjust for the correlation between family members in order to utilize computationally and conceptually simpler statistical testing procedures. Although directly comparing these two classes of approaches across contributions is difficult, we note that the two classes balance robustness to population stratification and computational complexity (the transmission-based approaches) with simplicity and increased power, assuming no population stratification or proper adjustment for it (decorrelation approaches).
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Affiliation(s)
- Dalin Li
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America; David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
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23
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Antioxidant defense enzyme genes and asthma susceptibility: gender-specific effects and heterogeneity in gene-gene interactions between pathogenetic variants of the disease. BIOMED RESEARCH INTERNATIONAL 2014; 2014:708903. [PMID: 24895604 PMCID: PMC4026955 DOI: 10.1155/2014/708903] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2014] [Revised: 04/05/2014] [Accepted: 04/07/2014] [Indexed: 12/15/2022]
Abstract
Oxidative stress resulting from an increased amount of reactive oxygen species and an imbalance between oxidants and antioxidants plays an important role in the pathogenesis of asthma. The present study tested the hypothesis that genetic susceptibility to allergic and nonallergic variants of asthma is determined by complex interactions between genes encoding antioxidant defense enzymes (ADE). We carried out a comprehensive analysis of the associations between adult asthma and 46 single nucleotide polymorphisms of 34 ADE genes and 12 other candidate genes of asthma in Russian population using set association analysis and multifactor dimensionality reduction approaches. We found for the first time epistatic interactions between ADE genes underlying asthma susceptibility and the genetic heterogeneity between allergic and nonallergic variants of the disease. We identified GSR (glutathione reductase) and PON2 (paraoxonase 2) as novel candidate genes for asthma susceptibility. We observed gender-specific effects of ADE genes on the risk of asthma. The results of the study demonstrate complexity and diversity of interactions between genes involved in oxidative stress underlying susceptibility to allergic and nonallergic asthma.
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24
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Milne RL, Herranz J, Michailidou K, Dennis J, Tyrer JP, Zamora MP, Arias-Perez JI, González-Neira A, Pita G, Alonso MR, Wang Q, Bolla MK, Czene K, Eriksson M, Humphreys K, Darabi H, Li J, Anton-Culver H, Neuhausen SL, Ziogas A, Clarke CA, Hopper JL, Dite GS, Apicella C, Southey MC, Chenevix-Trench G, Swerdlow A, Ashworth A, Orr N, Schoemaker M, Jakubowska A, Lubinski J, Jaworska-Bieniek K, Durda K, Andrulis IL, Knight JA, Glendon G, Mulligan AM, Bojesen SE, Nordestgaard BG, Flyger H, Nevanlinna H, Muranen TA, Aittomäki K, Blomqvist C, Chang-Claude J, Rudolph A, Seibold P, Flesch-Janys D, Wang X, Olson JE, Vachon C, Purrington K, Winqvist R, Pylkäs K, Jukkola-Vuorinen A, Grip M, Dunning AM, Shah M, Guénel P, Truong T, Sanchez M, Mulot C, Brenner H, Dieffenbach AK, Arndt V, Stegmaier C, Lindblom A, Margolin S, Hooning MJ, Hollestelle A, Collée JM, Jager A, Cox A, Brock IW, Reed MW, Devilee P, Tollenaar RA, Seynaeve C, Haiman CA, Henderson BE, Schumacher F, Le Marchand L, Simard J, Dumont M, Soucy P, Dörk T, Bogdanova NV, Hamann U, Försti A, Rüdiger T, Ulmer HU, Fasching PA, Häberle L, Ekici AB, Beckmann MW, Fletcher O, Johnson N, dos Santos Silva I, Peto J, Radice P, Peterlongo P, Peissel B, Mariani P, Giles GG, Severi G, Baglietto L, Sawyer E, Tomlinson I, Kerin M, Miller N, Marme F, Burwinkel B, Mannermaa A, Kataja V, Kosma VM, Hartikainen JM, Lambrechts D, Yesilyurt BT, Floris G, Leunen K, Alnæs GG, Kristensen V, Børresen-Dale AL, García-Closas M, Chanock SJ, Lissowska J, Figueroa JD, Schmidt MK, Broeks A, Verhoef S, Rutgers EJ, Brauch H, Brüning T, Ko YD, Couch FJ, Toland AE, Yannoukakos D, Pharoah PD, Hall P, Benítez J, Malats N, Easton DF. A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium. Hum Mol Genet 2014; 23:1934-46. [PMID: 24242184 PMCID: PMC3943524 DOI: 10.1093/hmg/ddt581] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2013] [Revised: 10/28/2013] [Accepted: 11/12/2013] [Indexed: 12/23/2022] Open
Abstract
Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70,917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46,450 breast cancer cases and 42,461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P < 0.01) of a per-allele main effect, and all two-way combinations of those were evaluated by a per-allele (1 d.f.) test for interaction using logistic regression. Second, all 2.5 billion possible two-SNP combinations were evaluated using Boolean operation-based screening and testing, and SNP pairs with the strongest evidence of interaction (P < 10(-4)) were selected for more careful assessment by logistic regression. Under the first approach, 3277 SNPs were preselected, but an evaluation of all possible two-SNP combinations (1 d.f.) identified no interactions at P < 10(-8). Results from the second analytic approach were consistent with those from the first (P > 10(-10)). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome.
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Affiliation(s)
- Roger L. Milne
- Human Cancer Genetics Programme and
- Centre for Epidemiology and Biostatistics, School of Population Health and
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Jesús Herranz
- Human Cancer Genetics Programme and
- Biostatistics Unit, IMDEA Food Institute, Madrid, Spain
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care and
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care and
| | - Jonathan P. Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - M. Pilar Zamora
- Servicio de Oncología Médica, Hospital Universitario La Paz, Madrid, Spain
| | | | - Anna González-Neira
- Human Genotyping-CEGEN Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Guillermo Pita
- Human Genotyping-CEGEN Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - M. Rosario Alonso
- Human Genotyping-CEGEN Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care and
| | - Manjeet K. Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care and
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics
| | | | | | - Hatef Darabi
- Department of Medical Epidemiology and Biostatistics
| | - Jingmei Li
- Human Genetics Division, Genome Institute of Singapore, Singapore
| | - Hoda Anton-Culver
- Department of Epidemiology, University of California Irvine, Irvine, CA, USA
| | | | - Argyrios Ziogas
- Department of Epidemiology, University of California Irvine, Irvine, CA, USA
| | | | - John L. Hopper
- Centre for Epidemiology and Biostatistics, School of Population Health and
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, School of Population Health and
| | - Carmel Apicella
- Centre for Epidemiology and Biostatistics, School of Population Health and
| | | | | | | | | | - Anthony Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK
- Division of Breast Cancer Research
| | - Alan Ashworth
- Division of Breast Cancer Research
- Breakthrough Breast Cancer Research Centre and
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Nicholas Orr
- Division of Breast Cancer Research
- Breakthrough Breast Cancer Research Centre and
| | - Minouk Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Jan Lubinski
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Katarzyna Jaworska-Bieniek
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Postgraduate School of Molecular Medicine, Warsaw Medical University, Warsaw, Poland
| | - Katarzyna Durda
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Irene L. Andrulis
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics
| | - Julia A. Knight
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
- Division of Epidemiology, Dalla Lana School of Public Health and
| | - Gord Glendon
- Ontario Cancer Genetics Network, Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | - Anna Marie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada
| | - Stig E. Bojesen
- Copenhagen General Population Study
- Department of Clinical Biochemistry and
| | | | - Henrik Flyger
- Department of Breast Surgery, Herlev University Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Taru A. Muranen
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | | | - Carl Blomqvist
- Department of Oncology, Helsinki University Central Hospital, Helsinki, Finland
| | | | | | | | - Dieter Flesch-Janys
- Department of Cancer Epidemiology/Clinical Cancer Registry and
- Institute for Medical Biometrics and Epidemiology, University Clinic Hamburg-Eppendorf, Hamburg, Germany
| | - Xianshu Wang
- Department of Laboratory Medicine and Pathology and
| | - Janet E. Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Department of Clinical Chemistry and Biocenter Oulu
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Department of Clinical Chemistry and Biocenter Oulu
| | | | - Mervi Grip
- Department of Surgery, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Pascal Guénel
- Inserm (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, Villejuif, France
- University Paris-Sud, UMRS 1018, Villejuif, France
| | - Thérèse Truong
- Inserm (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, Villejuif, France
- University Paris-Sud, UMRS 1018, Villejuif, France
| | - Marie Sanchez
- Inserm (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, Villejuif, France
- University Paris-Sud, UMRS 1018, Villejuif, France
| | - Claire Mulot
- Centre de Ressources Biologiques EPIGENETEC, Paris, France
- Inserm (National Institute of Health and Medical Research), U775, Paris, France
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Aida Karina Dieffenbach
- Division of Clinical Epidemiology and Aging Research
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research
| | | | | | - Sara Margolin
- Department of Oncology - Pathology, Karolinska Institutet, Stockholm, Sweden
| | | | | | - J. Margriet Collée
- Department of Clinical Genetics, Family Cancer Clinic, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Agnes Jager
- Department of Medical Oncology, Family Cancer Clinic and
| | - Angela Cox
- CRUK/YCR Sheffield Cancer Research Centre, Department of Oncology, University of Sheffield, Sheffield, UK
| | - Ian W. Brock
- CRUK/YCR Sheffield Cancer Research Centre, Department of Oncology, University of Sheffield, Sheffield, UK
| | - Malcolm W.R. Reed
- CRUK/YCR Sheffield Cancer Research Centre, Department of Oncology, University of Sheffield, Sheffield, UK
| | - Peter Devilee
- Department of Human Genetics
- Department of Pathology and
| | | | | | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian E. Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Fredrick Schumacher
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Jacques Simard
- Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Quebec Research Center and Laval University, Quebec, Canada
| | - Martine Dumont
- Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Quebec Research Center and Laval University, Quebec, Canada
| | - Penny Soucy
- Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Quebec Research Center and Laval University, Quebec, Canada
| | - Thilo Dörk
- Department of Obstetrics and Gynaecology and
| | - Natalia V. Bogdanova
- Department of Obstetrics and Gynaecology and
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
| | - Asta Försti
- Division of Molecular Genetic Epidemiology and
- Center for Primary Health Care Research, Lund University, Malmö, Sweden
| | - Thomas Rüdiger
- Institute of Pathology, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | | | - Peter A. Fasching
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA, USA
| | - Lothar Häberle
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Arif B. Ekici
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Matthias W. Beckmann
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | | | | | | | - Julian Peto
- London School of Hygiene and Tropical Medicine, London, UK
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Preventive and Predictive Medicine and
| | - Paolo Peterlongo
- IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, Milan, Italy
| | - Bernard Peissel
- Unit of Medical Genetics, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Paolo Mariani
- IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, Milan, Italy
- Cogentech Cancer Genetic Test Laboratory, Milan, Italy
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, School of Population Health and
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Gianluca Severi
- Centre for Epidemiology and Biostatistics, School of Population Health and
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Laura Baglietto
- Centre for Epidemiology and Biostatistics, School of Population Health and
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Elinor Sawyer
- Division of Cancer Studies, NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas’ NHS Foundation Trust in Partnership with King's College London, London, UK
| | - Ian Tomlinson
- Wellcome Trust Centre for Human Genetics and
- Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Michael Kerin
- School of Medicine, Clinical Science Institute, National University of Ireland, Galway, Ireland
| | - Nicola Miller
- School of Medicine, Clinical Science Institute, National University of Ireland, Galway, Ireland
| | - Federik Marme
- Department of Obstetrics and Gynecology and
- National Center for Tumor Diseases, University of Heidelberg, Heidelberg, Germany
| | - Barbara Burwinkel
- Molecular Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Obstetrics and Gynecology and
| | - Arto Mannermaa
- Department of Clinical Pathology and
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine and
- Biocenter Kuopio, University of Eastern Finland, Kuopio, Finland
| | - Vesa Kataja
- Cancer Center, Kuopio University Hospital, Kuopio, Finland
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine and
- Biocenter Kuopio, University of Eastern Finland, Kuopio, Finland
| | - Veli-Matti Kosma
- Department of Clinical Pathology and
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine and
- Biocenter Kuopio, University of Eastern Finland, Kuopio, Finland
| | - Jaana M. Hartikainen
- Department of Clinical Pathology and
- School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine and
- Biocenter Kuopio, University of Eastern Finland, Kuopio, Finland
| | | | | | - Giuseppe Floris
- Multidisciplinary Breast Center, University Hospital Gasthuisberg, Leuven, Belgium
| | - Karin Leunen
- Multidisciplinary Breast Center, University Hospital Gasthuisberg, Leuven, Belgium
| | - Grethe Grenaker Alnæs
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway
| | - Vessela Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway
- Faculty of Medicine (Faculty Division Ahus), UiO, Oslo, Norway
| | - Anne-Lise Børresen-Dale
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway
- Faculty of Medicine (Faculty Division Ahus), UiO, Oslo, Norway
| | - Montserrat García-Closas
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK
- Division of Breast Cancer Research
- Breakthrough Breast Cancer Research Centre and
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology, Warsaw, Poland
| | - Jonine D. Figueroa
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Marjanka K. Schmidt
- Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Annegien Broeks
- Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Senno Verhoef
- Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Emiel J. Rutgers
- Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Hiltrud Brauch
- University of Tübingen, Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr-University Bochum (IPA), Bochum, Germany
| | - Yon-Dschun Ko
- Department of Internal Medicine, Evangelische Kliniken Bonn GmbH, Johanniter Krankenhaus, Bonn, Germany
| | - The GENICA Network
- Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany
- University of Tübingen, Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr-University Bochum (IPA), Bochum, Germany
- Department of Internal Medicine, Evangelische Kliniken Bonn GmbH, Johanniter Krankenhaus, Bonn, Germany
- Institute for Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Pathology, Medical Faculty of the University of Bonn, Bonn, Germany
| | | | - Amanda E. Toland
- Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - The TNBCC
- Department of Laboratory Medicine and Pathology and
| | - Drakoulis Yannoukakos
- Molecular Diagnostics Laboratory, INRASTES, National Centre for Scientific Research ‘Demokritos’, Athens, Greece
| | - Paul D.P. Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care and
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics
| | - Javier Benítez
- Human Cancer Genetics Programme and
- Human Genotyping-CEGEN Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Biomedical Network on Rare Diseases (CIBERER), Madrid, Spain
| | | | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care and
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
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Yoshikawa T, Kanazawa H, Fujimoto S, Hirata K. Epistatic effects of multiple receptor genes on pathophysiology of asthma - its limits and potential for clinical application. Med Sci Monit 2014; 20:64-71. [PMID: 24435185 PMCID: PMC3907491 DOI: 10.12659/msm.889754] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2013] [Accepted: 11/09/2013] [Indexed: 01/31/2023] Open
Abstract
To date, genome-wide association studies (GWAS) permit a comprehensive scan of the genome in an unbiased manner, with high sensitivity, and thereby have the potential to identify candidate genes for the prevalence or development of multifactorial diseases such as bronchial asthma. However, most studies have only managed to explain a small additional percentage of hereditability estimates, and often fail to show consistent results among studies despite large sample sizes. Epistasis is defined as the interaction between multiple different genes affecting phenotypes. By applying epistatic analysis to clinical genetic research, we can analyze interactions among more than 2 molecules (genes) considering the whole system of the human body, illuminating dynamic molecular mechanisms. An increasing number of genetic studies have investigated epistatic effects on the risk for development of asthma. The present review highlights a concept of epistasis to overcome traditional genetic studies in humans and provides an update of evidence on epistatic effects on asthma. Furthermore, we review concerns regarding recent trends in epistatic analyses from the perspective of clinical physicians. These concerns include biological plausibility of genes identified by computational statistics, and definition of the diagnostic label of 'physician-diagnosed asthma'. In terms of these issues, further application of epistatic analysis will prompt identification of susceptibility of diseases and lead to the development of a new generation of pharmacological strategies to treat asthma.
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Affiliation(s)
- Takahiro Yoshikawa
- Department of Sports Medicine, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Hiroshi Kanazawa
- Department of Respiratory Medicine, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Shigeo Fujimoto
- Department of Sports Medicine, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Kazuto Hirata
- Department of Respiratory Medicine, Osaka City University Graduate School of Medicine, Osaka, Japan
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26
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Hu JK, Wang X, Wang P. Testing gene-gene interactions in genome wide association studies. Genet Epidemiol 2014; 38:123-34. [PMID: 24431225 DOI: 10.1002/gepi.21786] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Revised: 10/11/2013] [Accepted: 12/02/2013] [Indexed: 11/07/2022]
Abstract
Detection of gene-gene interaction has become increasingly popular over the past decade in genome wide association studies (GWAS). Besides traditional logistic regression analysis for detecting interactions between two markers, new methods have been developed in recent years such as comparing linkage disequilibrium (LD) in case and control groups. All these methods form the building blocks of most screening strategies for disease susceptibility loci in GWAS. In this paper, we are interested in comparing the competing methods and providing practical guidelines for selecting appropriate testing methods for interaction in GWAS. We first review a series of existing statistical methods to detect interactions, and then examine different definitions of interactions to gain insight into the theoretical relationship between the existing testing methods. Lastly, we perform extensive simulations to compare powers of various methods to detect either interaction between two markers at two unlinked loci or the overall association allowing for both interaction and main effects. This investigation reveals informative characteristics of various methods that are helpful to GWAS investigators.
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Affiliation(s)
- Jie Kate Hu
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
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Abstract
MicroRNA profiling is an important task to investigate miRNA functions and recent technologies such as microarray, single nucleotide polymorphism (SNP), quantitative real-time PCR (qPCR), and next-generation sequencing (NGS) have played a major role for miRNA analysis. In this chapter, we give an overview on statistical approaches for gene expressions, SNP, qPCR, and NGS data including preliminary analyses (pre-processing, differential expression, classification, clustering, exploration of interactions, and the use of ontologies). Our goal is to outline the key approaches with a brief discussion of problems avenues for their solutions and to give some examples for real-world use. Readers will be able to understand the different data formats (expression levels, sequences etc.) and they will be able to choose appropriate methods for their own research and application. On the other hand, we give brief notes on most popular tools/packages for statistical genetic analysis. This chapter aims to serve as a brief introduction to different kinds of statistical methods and also provides an extensive source of references.
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Millstein J. Screening-testing approaches for gene-gene and gene-environment interactions using independent statistics. Front Genet 2013; 4:306. [PMID: 24416039 PMCID: PMC3874470 DOI: 10.3389/fgene.2013.00306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2013] [Accepted: 12/16/2013] [Indexed: 12/14/2022] Open
Affiliation(s)
- Joshua Millstein
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California Los Angeles, CA, USA
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29
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Kim-Howard X, Sun C, Molineros JE, Maiti AK, Chandru H, Adler A, Wiley GB, Kaufman KM, Kottyan L, Guthridge JM, Rasmussen A, Kelly J, Sánchez E, Raj P, Li QZ, Bang SY, Lee HS, Kim TH, Kang YM, Suh CH, Chung WT, Park YB, Choe JY, Shim SC, Lee SS, Han BG, Olsen NJ, Karp DR, Moser K, Pons-Estel BA, Wakeland EK, James JA, Harley JB, Bae SC, Gaffney PM, Alarcón-Riquelme M, Looger LL, Nath SK. Allelic heterogeneity in NCF2 associated with systemic lupus erythematosus (SLE) susceptibility across four ethnic populations. Hum Mol Genet 2013; 23:1656-68. [PMID: 24163247 DOI: 10.1093/hmg/ddt532] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Recent reports have associated NCF2, encoding a core component of the multi-protein NADPH oxidase (NADPHO), with systemic lupus erythematosus (SLE) susceptibility in individuals of European ancestry. To identify ethnicity-specific and -robust variants within NCF2, we assessed 145 SNPs in and around the NCF2 gene in 5325 cases and 21 866 controls of European-American (EA), African-American (AA), Hispanic (HS) and Korean (KR) ancestry. Subsequent imputation, conditional, haplotype and bioinformatic analyses identified seven potentially functional SLE-predisposing variants. Association with non-synonymous rs17849502, previously reported in EA, was detected in EA, HS and AA (P(EA) = 1.01 × 10(-54), PHS = 3.68 × 10(-10), P(AA) = 0.03); synonymous rs17849501 was similarly significant. These SNPs were monomorphic in KR. Novel associations were detected with coding variants at rs35937854 in AA (PAA = 1.49 × 10(-9)), and rs13306575 in HS and KR (P(HS) = 7.04 × 10(-7), P(KR) = 3.30 × 10(-3)). In KR, a 3-SNP haplotype was significantly associated (P = 4.20 × 10(-7)), implying that SLE predisposing variants were tagged. Significant SNP-SNP interaction (P = 0.02) was detected between rs13306575 and rs17849502 in HS, and a dramatically increased risk (OR = 6.55) with a risk allele at each locus. Molecular modeling predicts that these non-synonymous mutations could disrupt NADPHO complex assembly. The risk allele of rs17849501, located in a conserved transcriptional regulatory region, increased reporter gene activity, suggesting in vivo enhancer function. Our results not only establish allelic heterogeneity within NCF2 associated with SLE, but also emphasize the utility of multi-ethnic cohorts to identify predisposing variants explaining additional phenotypic variance ('missing heritability') of complex diseases like SLE.
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Affiliation(s)
- Xana Kim-Howard
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
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Kim K, Kwon MS, Oh S, Park T. Identification of multiple gene-gene interactions for ordinal phenotypes. BMC Med Genomics 2013; 6 Suppl 2:S9. [PMID: 23819572 PMCID: PMC3654913 DOI: 10.1186/1755-8794-6-s2-s9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Background Multifactor dimensionality reduction (MDR) is a powerful method for analysis of gene-gene interactions and has been successfully applied to many genetic studies of complex diseases. However, the main application of MDR has been limited to binary traits, while traits having ordinal features are commonly observed in many genetic studies (e.g., obesity classification - normal, pre-obese, mild obese and severe obese). Methods We propose ordinal MDR (OMDR) to facilitate gene-gene interaction analysis for ordinal traits. As an alternative to balanced accuracy, the use of tau-b, a common ordinal association measure, was suggested to evaluate interactions. Also, we generalized cross-validation consistency (GCVC) to identify multiple best interactions. GCVC can be practically useful for analyzing complex traits, especially in large-scale genetic studies. Results and conclusions In simulations, OMDR showed fairly good performance in terms of power, predictability and selection stability and outperformed MDR. For demonstration, we used a real data of body mass index (BMI) and scanned 1~4-way interactions of obesity ordinal and binary traits of BMI via OMDR and MDR, respectively. In real data analysis, more interactions were identified for ordinal trait than binary traits. On average, the commonly identified interactions showed higher predictability for ordinal trait than binary traits. The proposed OMDR and GCVC were implemented in a C/C++ program, executables of which are freely available for Linux, Windows and MacOS upon request for non-commercial research institutions.
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Affiliation(s)
- Kyunga Kim
- Department of Statistics, Sookmyung Women's University, 100 Cheongpa-ro, Yongsan-gu, Seoul, South Korea
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Pattin KA, Moore JH. Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge. Bioinformatics 2013. [DOI: 10.4018/978-1-4666-3604-0.ch038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Recent technological developments in the field of genetics have given rise to an abundance of research tools, such as genome-wide genotyping, that allow researchers to conduct genome-wide association studies (GWAS) for detecting genetic variants that confer increased or decreased susceptibility to disease. However, discovering epistatic, or gene-gene, interactions in high dimensional datasets is a problem due to the computational complexity that results from the analysis of all possible combinations of single-nucleotide polymorphisms (SNPs). A recently explored approach to this problem employs biological expert knowledge, such as pathway or protein-protein interaction information, to guide an analysis by the selection or weighting of SNPs based on this knowledge. Narrowing the evaluation to gene combinations that have been shown to interact experimentally provides a biologically concise reason why those two genes may be detected together statistically. This chapter discusses the challenges of discovering epistatic interactions in GWAS and how biological expert knowledge can be used to facilitate genome-wide genetic studies.
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Satagopan JM, Elston RC. Evaluation of removable statistical interaction for binary traits. Stat Med 2013; 32:1164-90. [PMID: 23018341 PMCID: PMC3744333 DOI: 10.1002/sim.5628] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2011] [Revised: 08/13/2012] [Accepted: 09/01/2012] [Indexed: 12/26/2022]
Abstract
This paper is concerned with evaluating whether an interaction between two sets of risk factors for a binary trait is removable and, when it is removable, fitting a parsimonious additive model using a suitable link function to estimate the disease odds (on the natural logarithm scale). Statisticians define the term 'interaction' as a departure from additivity in a linear model on a specific scale on which the data are measured. Certain interactions may be eliminated via a transformation of the outcome such that the relationship between the risk factors and the outcome is additive on the transformed scale. Such interactions are known as removable interactions. We develop a novel test statistic for detecting the presence of a removable interaction in case-control studies. We consider the Guerrero and Johnson family of transformations and show that this family constitutes an appropriate link function for fitting an additive model when an interaction is removable. We use simulation studies to examine the type I error and power of the proposed test and to show that, when an interaction is removable, an additive model based on the Guerrero and Johnson link function leads to more precise estimates of the disease odds parameters and a better fit. We illustrate the proposed test and use of the transformation by using case-control data from three published studies. Finally, we indicate how one can check that, after transformation, no further interaction is significant.
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Affiliation(s)
- Jaya M Satagopan
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.
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Su C, Andrew A, Karagas MR, Borsuk ME. Using Bayesian networks to discover relations between genes, environment, and disease. BioData Min 2013; 6:6. [PMID: 23514120 PMCID: PMC3614442 DOI: 10.1186/1756-0381-6-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2012] [Accepted: 03/10/2013] [Indexed: 01/21/2023] Open
Abstract
We review the applicability of Bayesian networks (BNs) for discovering relations between genes, environment, and disease. By translating probabilistic dependencies among variables into graphical models and vice versa, BNs provide a comprehensible and modular framework for representing complex systems. We first describe the Bayesian network approach and its applicability to understanding the genetic and environmental basis of disease. We then describe a variety of algorithms for learning the structure of a network from observational data. Because of their relevance to real-world applications, the topics of missing data and causal interpretation are emphasized. The BN approach is then exemplified through application to data from a population-based study of bladder cancer in New Hampshire, USA. For didactical purposes, we intentionally keep this example simple. When applied to complete data records, we find only minor differences in the performance and results of different algorithms. Subsequent incorporation of partial records through application of the EM algorithm gives us greater power to detect relations. Allowing for network structures that depart from a strict causal interpretation also enhances our ability to discover complex associations including gene-gene (epistasis) and gene-environment interactions. While BNs are already powerful tools for the genetic dissection of disease and generation of prognostic models, there remain some conceptual and computational challenges. These include the proper handling of continuous variables and unmeasured factors, the explicit incorporation of prior knowledge, and the evaluation and communication of the robustness of substantive conclusions to alternative assumptions and data manifestations.
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Affiliation(s)
- Chengwei Su
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA.
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34
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Li R, Conti DV, Diaz-Sanchez D, Gilliland F, Thomas DC. Joint analysis for integrating two related studies of different data types and different study designs using hierarchical modeling approaches. Hum Hered 2013; 74:83-96. [PMID: 23343600 DOI: 10.1159/000345181] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2012] [Accepted: 10/17/2012] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND A chronic disease such as asthma is the result of a complex sequence of biological interactions involving multiple genes and pathways in response to a multitude of environmental exposures. However, methods to model jointly all factors are still evolving. Some of the current challenges include how to integrate knowledge from different data types and different disciplines, as well as how to utilize relevant external information such as gene annotation to identify novel disease genes and gene-environment inter-actions. METHODS Using a Bayesian hierarchical modeling framework, we developed two alternative methods for joint analysis of an epidemiologic study of a disease endpoint and an experimental study of intermediate phenotypes, while incorporating external information. RESULTS Our simulation studies demonstrated superior performance of the proposed hierarchical models compared to separate analysis with the standard single-level regression modeling approach. The combined analyses of the Southern California Children's Health Study and challenge study data suggest that these joint analytical methods detected more significant genetic main and gene-environment interaction effects than the conventional analysis. CONCLUSION The proposed prior framework is very flexible and can be generalized for an integrative analysis of diverse sources of relevant biological data.
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Affiliation(s)
- Rui Li
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
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35
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Piriyapongsa J, Ngamphiw C, Intarapanich A, Kulawonganunchai S, Assawamakin A, Bootchai C, Shaw PJ, Tongsima S. iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies. BMC Genomics 2012; 13 Suppl 7:S2. [PMID: 23281813 PMCID: PMC3521387 DOI: 10.1186/1471-2164-13-s7-s2] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). Results In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. Conclusion iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.
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Affiliation(s)
- Jittima Piriyapongsa
- National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
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36
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Rajapakse I, Perlman MD, Martin PJ, Hansen JA, Kooperberg C. Multivariate detection of gene-gene interactions. Genet Epidemiol 2012; 36:622-30. [PMID: 22782518 DOI: 10.1002/gepi.21656] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2012] [Revised: 04/27/2012] [Accepted: 05/29/2012] [Indexed: 12/18/2022]
Abstract
Unraveling the nature of genetic interactions is crucial to obtaining a more complete picture of complex diseases. It is thought that gene-gene interactions play an important role in the etiology of cancer, cardiovascular, and immune-mediated disease. Interactions among genes are defined as phenotypic effects that differ from those observed for independent contributions of each gene, usually detected by univariate logistic regression methods. Using a multivariate extension of linkage disequilibrium (LD), we have developed a new method, based on distances between sample covariance matrices for groups of single nucleotide polymorphisms (SNPs), to test for interaction effects of two groups of genes associated with a disease phenotype. Since a disease-associated interacting locus will often be in LD with more than one marker in the region, a method that examines a set of markers in a region collectively can offer greater power than traditional methods. Our method effectively identifies interaction effects in simulated data, as well as in data on the genetic contributions to the risk for graft-versus-host disease following hematopoietic stem cell transplantation.
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Affiliation(s)
- Indika Rajapakse
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA
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Oh S, Lee J, Kwon MS, Weir B, Ha K, Park T. A novel method to identify high order gene-gene interactions in genome-wide association studies: gene-based MDR. BMC Bioinformatics 2012. [PMID: 22901090 DOI: 10.1186/1471‐2105‐13‐s9‐s5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Because common complex diseases are affected by multiple genes and environmental factors, it is essential to investigate gene-gene and/or gene-environment interactions to understand genetic architecture of complex diseases. After the great success of large scale genome-wide association (GWA) studies using the high density single nucleotide polymorphism (SNP) chips, the study of gene-gene interaction becomes a next challenge. Multifactor dimensionality reduction (MDR) analysis has been widely used for the gene-gene interaction analysis. In practice, however, it is not easy to perform high order gene-gene interaction analyses via MDR in genome-wide level because it requires exploring a huge search space and suffers from a computational burden due to high dimensionality. RESULTS We propose dimensional reduction analysis, Gene-MDR analysis for the fast and efficient high order gene-gene interaction analysis. The proposed Gene-MDR method is composed of two-step applications of MDR: within- and between-gene MDR analyses. First, within-gene MDR analysis summarizes each gene effect via MDR analysis by combining multiple SNPs from the same gene. Second, between-gene MDR analysis then performs interaction analysis using the summarized gene effects from within-gene MDR analysis. We apply the Gene-MDR method to bipolar disorder (BD) GWA data from Wellcome Trust Case Control Consortium (WTCCC). The results demonstrate that Gene-MDR is capable of detecting high order gene-gene interactions associated with BD. CONCLUSION By reducing the dimension of genome-wide data from SNP level to gene level, Gene-MDR efficiently identifies high order gene-gene interactions. Therefore, Gene-MDR can provide the key to understand complex disease etiology.
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Affiliation(s)
- Sohee Oh
- Department of Statistics, Seoul National University, Seoul, South Korea
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Oh S, Lee J, Kwon MS, Weir B, Ha K, Park T. A novel method to identify high order gene-gene interactions in genome-wide association studies: gene-based MDR. BMC Bioinformatics 2012; 13 Suppl 9:S5. [PMID: 22901090 PMCID: PMC3372457 DOI: 10.1186/1471-2105-13-s9-s5] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Because common complex diseases are affected by multiple genes and environmental factors, it is essential to investigate gene-gene and/or gene-environment interactions to understand genetic architecture of complex diseases. After the great success of large scale genome-wide association (GWA) studies using the high density single nucleotide polymorphism (SNP) chips, the study of gene-gene interaction becomes a next challenge. Multifactor dimensionality reduction (MDR) analysis has been widely used for the gene-gene interaction analysis. In practice, however, it is not easy to perform high order gene-gene interaction analyses via MDR in genome-wide level because it requires exploring a huge search space and suffers from a computational burden due to high dimensionality. Results We propose dimensional reduction analysis, Gene-MDR analysis for the fast and efficient high order gene-gene interaction analysis. The proposed Gene-MDR method is composed of two-step applications of MDR: within- and between-gene MDR analyses. First, within-gene MDR analysis summarizes each gene effect via MDR analysis by combining multiple SNPs from the same gene. Second, between-gene MDR analysis then performs interaction analysis using the summarized gene effects from within-gene MDR analysis. We apply the Gene-MDR method to bipolar disorder (BD) GWA data from Wellcome Trust Case Control Consortium (WTCCC). The results demonstrate that Gene-MDR is capable of detecting high order gene-gene interactions associated with BD. Conclusion By reducing the dimension of genome-wide data from SNP level to gene level, Gene-MDR efficiently identifies high order gene-gene interactions. Therefore, Gene-MDR can provide the key to understand complex disease etiology.
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Affiliation(s)
- Sohee Oh
- Department of Statistics, Seoul National University, Seoul, South Korea
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Boffetta P, Winn DM, Ioannidis JP, Thomas DC, Little J, Smith GD, Cogliano VJ, Hecht SS, Seminara D, Vineis P, Khoury MJ. Recommendations and proposed guidelines for assessing the cumulative evidence on joint effects of genes and environments on cancer occurrence in humans. Int J Epidemiol 2012; 41:686-704. [PMID: 22596931 DOI: 10.1093/ije/dys010] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
We propose guidelines to evaluate the cumulative evidence of gene-environment (G × E) interactions in the causation of human cancer. Our approach has its roots in the HuGENet and IARC Monographs evaluation processes for genetic and environmental risk factors, respectively, and can be applied to common chronic diseases other than cancer. We first review issues of definitions of G × E interactions, discovery and modelling methods for G × E interactions, and issues in systematic reviews of evidence for G × E interactions, since these form the foundation for appraising the credibility of evidence in this contentious field. We then propose guidelines that include four steps: (i) score the strength of the evidence for main effects of the (a) environmental exposure and (b) genetic variant; (ii) establish a prior score category and decide on the pattern of interaction to be expected; (iii) score the strength of the evidence for interaction between the environmental exposure and the genetic variant; and (iv) examine the overall plausibility of interaction by combining the prior score and the strength of the evidence and interpret results. We finally apply the scheme to the interaction between NAT2 polymorphism and tobacco smoking in determining bladder cancer risk.
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Affiliation(s)
- Paolo Boffetta
- Tisch Cancer Institute, Mount Sinai School of Medicine, NY, USA.
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40
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Wason J, Dudbridge F. A general framework for two-stage analysis of genome-wide association studies and its application to case-control studies. Am J Hum Genet 2012; 90:760-73. [PMID: 22560088 DOI: 10.1016/j.ajhg.2012.03.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Revised: 02/17/2012] [Accepted: 03/09/2012] [Indexed: 02/03/2023] Open
Abstract
Two-stage analyses of genome-wide association studies have been proposed as a means to improving power for designs including family-based association and gene-environment interaction testing. In these analyses, all markers are first screened via a statistic that may not be robust to an underlying assumption, and the markers thus selected are then analyzed in a second stage with a test that is independent from the first stage and is robust to the assumption in question. We give a general formulation of two-stage designs and show how one can use this formulation both to derive existing methods and to improve upon them, opening up a range of possible further applications. We show how using simple regression models in conjunction with external data such as average trait values can improve the power of genome-wide association studies. We focus on case-control studies and show how it is possible to use allele frequencies derived from an external reference to derive a powerful two-stage analysis. An illustration involving the Wellcome Trust Case-Control Consortium data shows several genome-wide-significant associations, subsequently validated, that were not significant in the standard analysis. We give some analytic properties of the methods and discuss some underlying principles.
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Abstract
This chapter reviews the rationale for the use of haplotypes in association-based testing, discusses statistical issues related to haplotype uncertainty that complicate the analysis, then gives practical guidance for testing haplotype-based associations with phenotype or outcome trait, first of candidate gene regions and then for the genome as a whole. Haplotypes are interesting for two reasons: First, they may be in closer LD with a causal variant than any single measured SNP, and therefore may enhance the coverage value of the genotypes over single SNP analysis. Second, haplotypes may themselves be the causal variants of interest and some solid examples of this have appeared in the literature. This chapter discusses three possible approaches to incorporation of SNP haplotype analysis into generalized linear regression models: (1) a simple substitution method involving imputed haplotypes; (2) simultaneous maximum likelihood (ML) estimation of all parameters, including haplotype frequencies and regression parameters; and (3) a simplified approximation to full ML for case-control data. Examples of the various approaches for a haplotype analysis of a candidate gene are provided. We compare the behavior of the approximation-based methods and show that in most instances the simpler methods hold up well in practice. We also describe the practical implementation of genome-wide haplotype risk estimation and discuss several shortcuts that can be used to speed up otherwise potentially very intensive computational requirements.
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Affiliation(s)
- Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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Shi ZY, Li YJ, Zhang KJ, Gao XC, Zheng ZJ, Han N, Zhang FC. Positive association of CC2D1A and CC2D2A gene haplotypes with mental retardation in a Han Chinese population. DNA Cell Biol 2011; 31:80-7. [PMID: 22023432 DOI: 10.1089/dna.2011.1253] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The CC2D1A and CC2D2A genes are involved in Ca(2+)-regulated signaling pathways and have recently been implicated in the etiology of mental retardation (MR). The aim of this study was to investigate whether CC2D1A and CC2D2A polymorphisms are associated with susceptibility to MR in a Han Chinese population using a family based association approach. The sample included 172 trios (parents and offspring), and all subjects were genotyped for several single-nucleotide polymorphisms covering CC2D1A and CC2D2A. Linkage disequilibrium (LD) analysis revealed that the rs6511901 and rs10410239 polymorphisms of CC2D1A were in strong LD (D'=0.865), and haplotype analysis showed evidence for over-transmission from parents to MR offspring (p=0.0009). The LD analysis also revealed that CC2D2A single-nucleotide polymorphisms rs10025837, rs13116304, and rs7661102 were in strong LD (D'=0.848), and haplotype analysis showed significant transmission disequilibrium (p=0.0004). The results suggest the involvement of CC2D1A and CC2D2A in MR in the Han Chinese population, and some specific haplotypes may be susceptible or protective.
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Affiliation(s)
- Zhang-Yan Shi
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Institute of Population and Health, Institute of Application Psychology, Northwest University, 229 Tai Bai Road, Xi'an, China
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Pan W, Basu S, Shen X. Adaptive tests for detecting gene-gene and gene-environment interactions. Hum Hered 2011; 72:98-109. [PMID: 21934325 DOI: 10.1159/000330632] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2011] [Accepted: 07/02/2011] [Indexed: 12/14/2022] Open
Abstract
There has been an increasing interest in detecting gene-gene and gene-environment interactions in genetic association studies. A major statistical challenge is how to deal with a large number of parameters measuring possible interaction effects, which leads to reduced power of any statistical test due to a large number of degrees of freedom or high cost of adjustment for multiple testing. Hence, a popular idea is to first apply some dimension reduction techniques before testing, while another is to apply only statistical tests that are developed for and robust to high-dimensional data. To combine both ideas, we propose applying an adaptive sum of squared score (SSU) test and several other adaptive tests. These adaptive tests are extensions of the adaptive Neyman test [Fan, 1996], which was originally proposed for high-dimensional data, providing a simple and effective way for dimension reduction. On the other hand, the original SSU test coincides with a version of a test specifically developed for high-dimensional data. We apply these adaptive tests and their original nonadaptive versions to simulated data to detect interactions between two groups of SNPs (e.g. multiple SNPs in two candidate regions). We found that for sparse models (i.e. with only few non-zero interaction parameters), the adaptive SSU test and its close variant, an adaptive version of the weighted sum of squared score (SSUw) test, improved the power over their non-adaptive versions, and performed consistently well across various scenarios. The proposed adaptive tests are built in the general framework of regression analysis, and can thus be applied to various types of traits in the presence of covariates.
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Affiliation(s)
- Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, USA. weip @ biostat.umn.edu
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Q. Deng W, Paré G. A fast algorithm to optimize SNP prioritization for gene-gene and gene-environment interactions. Genet Epidemiol 2011; 35:729-38. [DOI: 10.1002/gepi.20624] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2011] [Revised: 06/19/2011] [Accepted: 06/21/2011] [Indexed: 12/18/2022]
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Wang Y, Liu G, Feng M, Wong L. An empirical comparison of several recent epistatic interaction detection methods. Bioinformatics 2011; 27:2936-43. [DOI: 10.1093/bioinformatics/btr512] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Droney J, Riley J, Ross J. Evolving Knowledge of Opioid Genetics in Cancer Pain. Clin Oncol (R Coll Radiol) 2011; 23:418-28. [DOI: 10.1016/j.clon.2011.04.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2009] [Revised: 11/04/2010] [Accepted: 04/22/2011] [Indexed: 01/11/2023]
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Abrera-Abeleda MA, Nishimura C, Frees K, Jones M, Maga T, Katz LM, Zhang Y, Smith RJH. Allelic variants of complement genes associated with dense deposit disease. J Am Soc Nephrol 2011; 22:1551-9. [PMID: 21784901 DOI: 10.1681/asn.2010080795] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
The alternative pathway of the complement cascade plays a role in the pathogenesis of dense deposit disease (DDD). Deficiency of complement factor H and mutations in CFH associate with the development of DDD, but it is unknown whether allelic variants in other complement genes also associate with this disease. We studied patients with DDD and identified previously unreported sequence alterations in several genes in addition to allelic variants and haplotypes common to patients with DDD. We found that the likelihood of developing DDD increases with the presence of two or more risk alleles in CFH and C3. To determine the functional consequence of this finding, we measured the activity of the alternative pathway in serum samples from phenotypically normal controls genotyped for variants in CFH and C3. Alternative pathway activity was higher in the presence of variants associated with DDD. Taken together, these data confirm that DDD is a complex genetic disease and may provide targets for the development of disease-specific therapies.
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Yu Z. Testing gene-gene interactions in the case-parents design. Hum Hered 2011; 71:171-9. [PMID: 21778736 PMCID: PMC3153343 DOI: 10.1159/000327355] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2010] [Accepted: 03/11/2011] [Indexed: 11/19/2022] Open
Abstract
The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used.
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Affiliation(s)
- Zhaoxia Yu
- Department of Statistics, University of California, Irvine, CA 92697, USA.
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Gilbert-Diamond D, Moore JH. Analysis of gene-gene interactions. CURRENT PROTOCOLS IN HUMAN GENETICS 2011; Chapter 1:Unit1.14. [PMID: 21735376 PMCID: PMC4086055 DOI: 10.1002/0471142905.hg0114s70] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene-gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs.
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Affiliation(s)
- Diane Gilbert-Diamond
- Computational Genetics Laboratory, Departments of Genetics and Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire, USA
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Melén E, Kho AT, Sharma S, Gaedigk R, Leeder JS, Mariani TJ, Carey VJ, Weiss ST, Tantisira KG. Expression analysis of asthma candidate genes during human and murine lung development. Respir Res 2011; 12:86. [PMID: 21699702 PMCID: PMC3141421 DOI: 10.1186/1465-9921-12-86] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2011] [Accepted: 06/23/2011] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Little is known about the role of most asthma susceptibility genes during human lung development. Genetic determinants for normal lung development are not only important early in life, but also for later lung function. OBJECTIVE To investigate the role of expression patterns of well-defined asthma susceptibility genes during human and murine lung development. We hypothesized that genes influencing normal airways development would be over-represented by genes associated with asthma. METHODS Asthma genes were first identified via comprehensive search of the current literature. Next, we analyzed their expression patterns in the developing human lung during the pseudoglandular (gestational age, 7-16 weeks) and canalicular (17-26 weeks) stages of development, and in the complete developing lung time series of 3 mouse strains: A/J, SW, C57BL6. RESULTS In total, 96 genes with association to asthma in at least two human populations were identified in the literature. Overall, there was no significant over-representation of the asthma genes among genes differentially expressed during lung development, although trends were seen in the human (Odds ratio, OR 1.22, confidence interval, CI 0.90-1.62) and C57BL6 mouse (OR 1.41, CI 0.92-2.11) data. However, differential expression of some asthma genes was consistent in both developing human and murine lung, e.g. NOD1, EDN1, CCL5, RORA and HLA-G. Among the asthma genes identified in genome wide association studies, ROBO1, RORA, HLA-DQB1, IL2RB and PDE10A were differentially expressed during human lung development. CONCLUSIONS Our data provide insight about the role of asthma susceptibility genes during lung development and suggest common mechanisms underlying lung morphogenesis and pathogenesis of respiratory diseases.
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Affiliation(s)
- Erik Melén
- Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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