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Mellerup E, Møller GL. Combinations of Genetic Variants Occurring Exclusively in Patients. Comput Struct Biotechnol J 2017; 15:286-289. [PMID: 28377798 PMCID: PMC5367802 DOI: 10.1016/j.csbj.2017.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 02/26/2017] [Accepted: 03/06/2017] [Indexed: 11/30/2022] Open
Abstract
In studies of polygenic disorders, scanning the genetic variants can be used to identify variant combinations. Combinations that are exclusively found in patients can be separated from those combinations occurring in control persons. Statistical analyses can be performed to determine whether the combinations that occur exclusively among patients are significantly associated with the investigated disorder. This research strategy has been applied in materials from various polygenic disorders, identifying clusters of patient-specific genetic variant combinations that are significant associated with the investigated disorders. Combinations from these clusters are found in the genomes of up to 55% of investigated patients, and are not present in the genomes of any control persons.
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Affiliation(s)
- Erling Mellerup
- Laboratory of Neuropsychiatry, Department of Neuroscience and Pharmacology, Faculty of Health, University of Copenhagen, Denmark
| | - Gert Lykke Møller
- Genokey ApS, ScionDTU, Technical University of Denmark, Hoersholm, Denmark
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Abstract
BACKGROUND A large amount of research has been devoted to the detection and investigation of epistatic interactions in genome-wide association studies (GWASs). Most of the literature focuses on low-order interactions between single-nucleotide polymorphisms (SNPs) with significant main effects. RESULTS In this paper we propose an original approach for detecting epistasis at the gene level, without systematically filtering on significant genes. We first compute interaction variables for each gene pair by finding its Eigen-Epistasis component, defined as the linear combination of Gene SNPs having the highest correlation with the phenotype. The selection of significant effects is done using a penalized regression method based on Group Lasso controlling the False Discovery Rate. CONCLUSION The method is tested against two recent alternative proposals from the literature using synthetic data, and shows good performances in different settings. We demonstrate the power of our approach by detecting new gene-gene interactions on three genome-wide association studies.
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McKinney BA, Lareau C, Oberg AL, Kennedy RB, Ovsyannikova IG, Poland GA. The Integration of Epistasis Network and Functional Interactions in a GWAS Implicates RXR Pathway Genes in the Immune Response to Smallpox Vaccine. PLoS One 2016; 11:e0158016. [PMID: 27513748 PMCID: PMC4981436 DOI: 10.1371/journal.pone.0158016] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 06/08/2016] [Indexed: 11/24/2022] Open
Abstract
Although many diseases and traits show large heritability, few genetic variants have been found to strongly separate phenotype groups by genotype. Complex regulatory networks of variants and expression of multiple genes lead to small individual-variant effects and difficulty replicating the effect of any single variant in an affected pathway. Interaction network modeling of GWAS identifies effects ignored by univariate models, but population differences may still cause specific genes to not replicate. Integrative network models may help detect indirect effects of variants in the underlying biological pathway. In this study, we used gene-level functional interaction information from the Integrative Multi-species Prediction (IMP) tool to reveal important genes associated with a complex phenotype through evidence from epistasis networks and pathway enrichment. We test this method for augmenting variant-based network analyses with functional interactions by applying it to a smallpox vaccine immune response GWAS. The integrative analysis spotlights the role of genes related to retinoid X receptor alpha (RXRA), which has been implicated in a previous epistasis network analysis of smallpox vaccine.
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Affiliation(s)
- Brett A. McKinney
- Tandy School of Computer Science and Department of Mathematics, University of Tulsa, Tulsa, OK, United States of America
| | - Caleb Lareau
- Tandy School of Computer Science and Department of Mathematics, University of Tulsa, Tulsa, OK, United States of America
| | - Ann L. Oberg
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Richard B. Kennedy
- Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, MN, United States of America
| | - Inna G. Ovsyannikova
- Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, MN, United States of America
| | - Gregory A. Poland
- Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, MN, United States of America
- * E-mail:
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Marceau R, Lu W, Holloway S, Sale MM, Worrall BB, Williams SR, Hsu FC, Tzeng JY. A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction. Genet Epidemiol 2015; 39:456-68. [PMID: 26139508 DOI: 10.1002/gepi.21909] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 05/10/2015] [Accepted: 05/20/2015] [Indexed: 01/27/2023]
Abstract
Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level.
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Affiliation(s)
- Rachel Marceau
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Shannon Holloway
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Michèle M Sale
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.,Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America.,Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Bradford B Worrall
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.,Department of Neurology, University of Virginia, Charlottesville, Virginia, United States of America
| | - Stephen R Williams
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.,Cardiovascular Research Center, University of Virginia, Charlottesville, Virginia, United States of America
| | - Fang-Chi Hsu
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Jung-Ying Tzeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America.,Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America.,Department of Statistics, National Cheng-Kung University, Tainan, Taiwan
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