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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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2
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Zimoń M, Huang Y, Trasta A, Halavatyi A, Liu JZ, Chen CY, Blattmann P, Klaus B, Whelan CD, Sexton D, John S, Huber W, Tsai EA, Pepperkok R, Runz H. Pairwise effects between lipid GWAS genes modulate lipid plasma levels and cellular uptake. Nat Commun 2021; 12:6411. [PMID: 34741066 PMCID: PMC8571362 DOI: 10.1038/s41467-021-26761-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 10/09/2021] [Indexed: 12/27/2022] Open
Abstract
Complex traits are characterized by multiple genes and variants acting simultaneously on a phenotype. However, studying the contribution of individual pairs of genes to complex traits has been challenging since human genetics necessitates very large population sizes, while findings from model systems do not always translate to humans. Here, we combine genetics with combinatorial RNAi (coRNAi) to systematically test for pairwise additive effects (AEs) and genetic interactions (GIs) between 30 lipid genome-wide association studies (GWAS) genes. Gene-based burden tests from 240,970 exomes show that in carriers with truncating mutations in both, APOB and either PCSK9 or LPL ("human double knock-outs") plasma lipid levels change additively. Genetics and coRNAi identify overlapping AEs for 12 additional gene pairs. Overlapping GIs are observed for TOMM40/APOE with SORT1 and NCAN. Our study identifies distinct gene pairs that modulate plasma and cellular lipid levels primarily via AEs and nominates putative drug target pairs for improved lipid-lowering combination therapies.
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Affiliation(s)
- Magdalena Zimoń
- grid.4709.a0000 0004 0495 846XMolecular Medicine Partnership Unit (MMPU), University of Heidelberg/EMBL, Heidelberg, Germany ,Cell Biology and Biophysics Unit, European Molecular Biological Laboratory, Heidelberg, Germany
| | - Yunfeng Huang
- grid.417832.b0000 0004 0384 8146Translational Biology, Biogen Inc, Cambridge, MA USA
| | - Anthi Trasta
- grid.4709.a0000 0004 0495 846XMolecular Medicine Partnership Unit (MMPU), University of Heidelberg/EMBL, Heidelberg, Germany ,Cell Biology and Biophysics Unit, European Molecular Biological Laboratory, Heidelberg, Germany
| | - Aliaksandr Halavatyi
- Advanced Light Microscopy Facility (ALMF), European Molecular Biological Laboratory, Heidelberg, Germany
| | - Jimmy Z. Liu
- grid.417832.b0000 0004 0384 8146Translational Biology, Biogen Inc, Cambridge, MA USA
| | - Chia-Yen Chen
- grid.417832.b0000 0004 0384 8146Translational Biology, Biogen Inc, Cambridge, MA USA ,grid.32224.350000 0004 0386 9924Psychiatric and Neurodevelopmental Genetics Unit, Mass General Hospital, Boston, MA USA
| | - Peter Blattmann
- grid.4709.a0000 0004 0495 846XMolecular Medicine Partnership Unit (MMPU), University of Heidelberg/EMBL, Heidelberg, Germany ,Cell Biology and Biophysics Unit, European Molecular Biological Laboratory, Heidelberg, Germany ,grid.508389.f0000 0004 6414 2411Idorsia Pharmaceuticals Ltd, Basel, Switzerland
| | - Bernd Klaus
- Genome Biology Unit, European Molecular Biological Laboratory, Heidelberg, Germany
| | - Christopher D. Whelan
- grid.417832.b0000 0004 0384 8146Translational Biology, Biogen Inc, Cambridge, MA USA
| | - David Sexton
- grid.417832.b0000 0004 0384 8146Translational Biology, Biogen Inc, Cambridge, MA USA
| | - Sally John
- grid.417832.b0000 0004 0384 8146Translational Biology, Biogen Inc, Cambridge, MA USA
| | - Wolfgang Huber
- Genome Biology Unit, European Molecular Biological Laboratory, Heidelberg, Germany
| | - Ellen A. Tsai
- grid.417832.b0000 0004 0384 8146Translational Biology, Biogen Inc, Cambridge, MA USA
| | - Rainer Pepperkok
- grid.4709.a0000 0004 0495 846XMolecular Medicine Partnership Unit (MMPU), University of Heidelberg/EMBL, Heidelberg, Germany ,Cell Biology and Biophysics Unit, European Molecular Biological Laboratory, Heidelberg, Germany ,Advanced Light Microscopy Facility (ALMF), European Molecular Biological Laboratory, Heidelberg, Germany
| | - Heiko Runz
- grid.4709.a0000 0004 0495 846XMolecular Medicine Partnership Unit (MMPU), University of Heidelberg/EMBL, Heidelberg, Germany ,grid.417832.b0000 0004 0384 8146Translational Biology, Biogen Inc, Cambridge, MA USA
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3
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Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study. PLoS One 2020; 15:e0238304. [PMID: 32915819 PMCID: PMC7485803 DOI: 10.1371/journal.pone.0238304] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 08/13/2020] [Indexed: 01/25/2023] Open
Abstract
Epistasis analysis elucidates the effects of gene-gene interactions (G×G) between multiple loci for complex traits. However, the large computational demands and the high multiple testing burden impede their discoveries. Here, we illustrate the utilization of two methods, main effect filtering based on individual GWAS results and biological knowledge-based modeling through Biofilter software, to reduce the number of interactions tested among single nucleotide polymorphisms (SNPs) for 15 cardiac-related traits and 14 fatty acids. We performed interaction analyses using the two filtering methods, adjusting for age, sex, body mass index (BMI), waist-hip ratio, and the first three principal components from genetic data, among 2,824 samples from the Ludwigshafen Risk and Cardiovascular (LURIC) Health Study. Using Biofilter, one interaction nearly met Bonferroni significance: an interaction between rs7735781 in XRCC4 and rs10804247 in XRCC5 was identified for venous thrombosis with a Bonferroni-adjusted likelihood ratio test (LRT) p: 0.0627. A total of 57 interactions were identified from main effect filtering for the cardiac traits G×G (10) and fatty acids G×G (47) at Bonferroni-adjusted LRT p < 0.05. For cardiac traits, the top interaction involved SNPs rs1383819 in SNTG1 and rs1493939 (138kb from 5’ of SAMD12) with Bonferroni-adjusted LRT p: 0.0228 which was significantly associated with history of arterial hypertension. For fatty acids, the top interaction between rs4839193 in KCND3 and rs10829717 in LOC107984002 with Bonferroni-adjusted LRT p: 2.28×10−5 was associated with 9-trans 12-trans octadecanoic acid, an omega-6 trans fatty acid. The model inflation factor for the interactions under different filtering methods was evaluated from the standard median and the linear regression approach. Here, we applied filtering approaches to identify numerous genetic interactions related to cardiac-related outcomes as potential targets for therapy. The approaches described offer ways to detect epistasis in the complex traits and to improve precision medicine capability.
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Moore JH, Olson RS, Schmitt P, Chen Y, Manduchi E. How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases. ARTIFICIAL LIFE 2020; 26:23-37. [PMID: 32027528 DOI: 10.1162/artl_a_00308] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Susceptibility to common human diseases such as cancer is influenced by many genetic and environmental factors that work together in a complex manner. The state of the art is to perform a genome-wide association study (GWAS) that measures millions of single-nucleotide polymorphisms (SNPs) throughout the genome followed by a one-SNP-at-a-time statistical analysis to detect univariate associations. This approach has identified thousands of genetic risk factors for hundreds of diseases. However, the genetic risk factors detected have very small effect sizes and collectively explain very little of the overall heritability of the disease. Nonetheless, it is assumed that the genetic component of risk is due to many independent risk factors that contribute additively. The fact that many genetic risk factors with small effects can be detected is taken as evidence to support this notion. It is our working hypothesis that the genetic architecture of common diseases is partly driven by non-additive interactions. To test this hypothesis, we developed a heuristic simulation-based method for conducting experiments about the complexity of genetic architecture. We show that a genetic architecture driven by complex interactions is highly consistent with the magnitude and distribution of univariate effects seen in real data. We compare our results with measures of univariate and interaction effects from two large-scale GWASs of sporadic breast cancer and find evidence to support our hypothesis that is consistent with the results of our computational experiment.
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Affiliation(s)
- Jason H Moore
- University of Pennsylvania, Institute for Biomedical Informatics, Perelman School of Medicine.
| | - Randal S Olson
- University of Pennsylvania, Institute for Biomedical Informatics, Perelman School of Medicine
| | - Peter Schmitt
- University of Pennsylvania, Institute for Biomedical Informatics, Perelman School of Medicine
| | - Yong Chen
- University of Pennsylvania, Institute for Biomedical Informatics, Perelman School of Medicine
| | - Elisabetta Manduchi
- University of Pennsylvania, Institute for Biomedical Informatics, Perelman School of Medicine
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Stanaway IB, Hall TO, Rosenthal EA, Palmer M, Naranbhai V, Knevel R, Namjou-Khales B, Carroll RJ, Kiryluk K, Gordon AS, Linder J, Howell KM, Mapes BM, Lin FTJ, Joo YY, Hayes MG, Gharavi AG, Pendergrass SA, Ritchie MD, de Andrade M, Croteau-Chonka DC, Raychaudhuri S, Weiss ST, Lebo M, Amr SS, Carrell D, Larson EB, Chute CG, Rasmussen-Torvik LJ, Roy-Puckelwartz MJ, Sleiman P, Hakonarson H, Li R, Karlson EW, Peterson JF, Kullo IJ, Chisholm R, Denny JC, Jarvik GP, Crosslin DR. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet Epidemiol 2018; 43:63-81. [PMID: 30298529 PMCID: PMC6375696 DOI: 10.1002/gepi.22167] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/10/2018] [Accepted: 08/28/2018] [Indexed: 12/30/2022]
Abstract
The Electronic Medical Records and Genomics (eMERGE) network is a network of medical centers with electronic medical records linked to existing biorepository samples for genomic discovery and genomic medicine research. The network sought to unify the genetic results from 78 Illumina and Affymetrix genotype array batches from 12 contributing medical centers for joint association analysis of 83,717 human participants. In this report, we describe the imputation of eMERGE results and methods to create the unified imputed merged set of genome‐wide variant genotype data. We imputed the data using the Michigan Imputation Server, which provides a missing single‐nucleotide variant genotype imputation service using the minimac3 imputation algorithm with the Haplotype Reference Consortium genotype reference set. We describe the quality control and filtering steps used in the generation of this data set and suggest generalizable quality thresholds for imputation and phenotype association studies. To test the merged imputed genotype set, we replicated a previously reported chromosome 6 HLA‐B herpes zoster (shingles) association and discovered a novel zoster‐associated loci in an epigenetic binding site near the terminus of chromosome 3 (3p29).
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Affiliation(s)
- Ian B Stanaway
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Taryn O Hall
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Elisabeth A Rosenthal
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Melody Palmer
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Vivek Naranbhai
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington.,Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Rachel Knevel
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Bahram Namjou-Khales
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Robert J Carroll
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Krzysztof Kiryluk
- Department of Medicine, Columbia University, New York City, New York
| | - Adam S Gordon
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Jodell Linder
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Kayla Marie Howell
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Brandy M Mapes
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Frederick T J Lin
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | | | - M Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ali G Gharavi
- Department of Medicine, Columbia University, New York City, New York
| | | | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | - Soumya Raychaudhuri
- Harvard Medical School, Harvard University, Cambridge, Massachusetts.,Program in Medical and Population Genetics, Broad Institute of Massachusetts Technical Institute and Harvard University, Cambridge, Massachusetts
| | - Scott T Weiss
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Matt Lebo
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Sami S Amr
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - David Carrell
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland
| | | | | | - Patrick Sleiman
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | - Rongling Li
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Elizabeth W Karlson
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Josh F Peterson
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | | | - Rex Chisholm
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Joshua Charles Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Gail P Jarvik
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
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- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - David R Crosslin
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
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6
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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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Mediterranean Diet Adherence and Genetic Background Roles within a Web-Based Nutritional Intervention: The Food4Me Study. Nutrients 2017; 9:nu9101107. [PMID: 29019927 PMCID: PMC5691723 DOI: 10.3390/nu9101107] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 10/04/2017] [Accepted: 10/09/2017] [Indexed: 01/02/2023] Open
Abstract
Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers.
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Moore JH, Andrews PC, Olson RS, Carlson SE, Larock CR, Bulhoes MJ, O'Connor JP, Greytak EM, Armentrout SL. Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases. BioData Min 2017; 10:19. [PMID: 28572842 PMCID: PMC5450417 DOI: 10.1186/s13040-017-0139-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 05/18/2017] [Indexed: 11/18/2022] Open
Abstract
Background Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway. Results We show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer’s disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer’s disease. Conclusions We discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.
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Affiliation(s)
- Jason H Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Peter C Andrews
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Randal S Olson
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
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