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Linakis MW, Van Landingham C, Gasparini A, Longnecker MP. Re-expressing coefficients from regression models for inclusion in a meta-analysis. BMC Med Res Methodol 2024; 24:6. [PMID: 38191310 PMCID: PMC10773134 DOI: 10.1186/s12874-023-02132-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/19/2023] [Indexed: 01/10/2024] Open
Abstract
Meta-analysis poses a challenge when original study results have been expressed in a non-uniform manner, such as when regression results from some original studies were based on a log-transformed key independent variable while in others no transformation was used. Methods of re-expressing regression coefficients to generate comparable results across studies regardless of data transformation have recently been developed. We examined the relative bias of three re-expression methods using simulations and 15 real data examples where the independent variable had a skewed distribution. Regression coefficients from models with log-transformed independent variables were re-expressed as though they were based on an untransformed variable. We compared the re-expressed coefficients to those from a model fit to the untransformed variable. In the simulated and real data, all three re-expression methods usually gave biased results, and the skewness of the independent variable predicted the amount of bias. How best to synthesize the results of the log-transformed and absolute exposure evidence streams remains an open question and may depend on the scientific discipline, scale of the outcome, and other considerations.
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Affiliation(s)
- Matthew W Linakis
- Ramboll U.S. Consulting, Raleigh, NC, 27612, USA, 3214 Charles B Root Wynd #130.
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Reynolds RJ, Irvin MR, Bridges SL, Kim H, Merriman TR, Arnett DK, Singh JA, Sumpter NA, Lupi AS, Vazquez AI. Genetic correlations between traits associated with hyperuricemia, gout, and comorbidities. Eur J Hum Genet 2021; 29:1438-1445. [PMID: 33637890 DOI: 10.1038/s41431-021-00830-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 12/06/2020] [Accepted: 02/10/2021] [Indexed: 01/26/2023] Open
Abstract
Hypertension, obesity, chronic kidney disease and type 2 diabetes are comorbidities that have very high prevalence among persons with hyperuricemia (serum urate > 6.8 mg/dL) and gout. Here we use multivariate genetic models to test the hypothesis that the co-association of traits representing hyperuricemia and its comorbidities is genetically based. Using Bayesian whole-genome regression models, we estimated the genetic marker-based variance and the covariance between serum urate, serum creatinine, systolic blood pressure (SBP), blood glucose and body mass index (BMI) from two independent family-based studies: The Framingham Heart Study-FHS and the Hypertension Genetic Epidemiology Network study-HyperGEN. The main genetic findings that replicated in both FHS and HyperGEN, were (1) creatinine was genetically correlated only with urate and (2) BMI was genetically correlated with urate, SBP, and glucose. The environmental covariance among the traits was generally highest for trait pairs involving BMI. The genetic overlap of traits representing the comorbidities of hyperuricemia and gout appears to cluster in two separate axes of genetic covariance. Because creatinine is genetically correlated with urate but not with metabolic traits, this suggests there is one genetic module of shared loci associated with hyperuricemia and chronic kidney disease. Another module of shared loci may account for the association of hyperuricemia and metabolic syndrome. This study provides a clear quantitative genetic basis for the clustering of comorbidities with hyperuricemia.
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Affiliation(s)
- Richard J Reynolds
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.
| | - M Ryan Irvin
- Department of Epidemiology, UAB, Birmingham, AL, USA
| | - S Louis Bridges
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Hwasoon Kim
- Duke Clinical Research Institute, Durham, NC, USA
| | - Tony R Merriman
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.,Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Jasvinder A Singh
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.,Birmingham VA Medical Center, Birmingham, AL, USA
| | - Nicholas A Sumpter
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Alexa S Lupi
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Ana I Vazquez
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA. .,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
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Mwakalinga VM, Sartorius BKD, Limwagu AJ, Mlacha YP, Msellemu DF, Chaki PP, Govella NJ, Coetzee M, Dongus S, Killeen GF. Topographic mapping of the interfaces between human and aquatic mosquito habitats to enable barrier targeting of interventions against malaria vectors. ROYAL SOCIETY OPEN SCIENCE 2018; 5:161055. [PMID: 29892341 PMCID: PMC5990771 DOI: 10.1098/rsos.161055] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 04/18/2018] [Indexed: 06/08/2023]
Abstract
Geophysical topographic metrics of local water accumulation potential are freely available and have long been known as high-resolution predictors of where aquatic habitats for immature Anopheles mosquitoes are most abundant, resulting in elevated densities of adult malaria vectors and human infection burden. Using existing entomological and epidemiological survey data, here we illustrate how topography can also be used to map out the interfaces between wet, unoccupied valleys and dry, densely populated uplands, where malaria vector densities and infection risk are focally exacerbated. These topographically identifiable geophysical boundaries experience disproportionately high vector densities and malaria transmission risk, because this is where Anopheles mosquitoes first encounter humans when they search for blood after emerging or ovipositing in the valleys. Geophysical topographic indicators accounted for 67% of variance for vector density but for only 43% for infection prevalence, so they could enable very selective targeting of interventions against the former but not the latter (targeting ratios of 5.7 versus 1.5 to 1, respectively). So, in addition to being useful for targeting larval source management to wet valleys, geophysical topographic indicators may also be used to selectively target adult Anopheles mosquitoes with insecticidal residual sprays, fencing, vapour emanators or space sprays to barrier areas along their fringes.
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Affiliation(s)
- Victoria M. Mwakalinga
- School of Urban and Regional Planning, Department of Housing and Infrastructure Planning, Ardhi University, PO Box 35176, Dar es Salaam, Tanzania
- Department of Environmental Health and Ecological Sciences, Ifakara Health Institute, Kiko Avenue, Mikocheni, PO Box 78373, Dar es Salaam, Tanzania
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Benn K. D. Sartorius
- Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
| | - Alex J. Limwagu
- Department of Environmental Health and Ecological Sciences, Ifakara Health Institute, Kiko Avenue, Mikocheni, PO Box 78373, Dar es Salaam, Tanzania
| | - Yeromin P. Mlacha
- Department of Environmental Health and Ecological Sciences, Ifakara Health Institute, Kiko Avenue, Mikocheni, PO Box 78373, Dar es Salaam, Tanzania
| | - Daniel F. Msellemu
- Department of Environmental Health and Ecological Sciences, Ifakara Health Institute, Kiko Avenue, Mikocheni, PO Box 78373, Dar es Salaam, Tanzania
| | - Prosper P. Chaki
- Department of Environmental Health and Ecological Sciences, Ifakara Health Institute, Kiko Avenue, Mikocheni, PO Box 78373, Dar es Salaam, Tanzania
| | - Nicodem J. Govella
- Department of Environmental Health and Ecological Sciences, Ifakara Health Institute, Kiko Avenue, Mikocheni, PO Box 78373, Dar es Salaam, Tanzania
| | - Maureen Coetzee
- Wits Research Institute for Malaria and Wits/MRC Collaborating Centre for Multidisciplinary Research on Malaria, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
| | - Stefan Dongus
- Department of Environmental Health and Ecological Sciences, Ifakara Health Institute, Kiko Avenue, Mikocheni, PO Box 78373, Dar es Salaam, Tanzania
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, PO Box, 4002 Basel, Switzerland
- Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Gerry F. Killeen
- Department of Environmental Health and Ecological Sciences, Ifakara Health Institute, Kiko Avenue, Mikocheni, PO Box 78373, Dar es Salaam, Tanzania
- Vector Biology Department, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
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Assessment of whole-genome regression for type II diabetes. PLoS One 2015; 10:e0123818. [PMID: 25885636 PMCID: PMC4401705 DOI: 10.1371/journal.pone.0123818] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 03/07/2015] [Indexed: 12/29/2022] Open
Abstract
Lifestyle and genetic factors play a large role in the development of Type 2 Diabetes (T2D). Despite the important role of genetic factors, genetic information is not incorporated into the clinical assessment of T2D risk. We assessed and compared Whole Genome Regression methods to predict the T2D status of 5,245 subjects from the Framingham Heart Study. For evaluating each method we constructed the following set of regression models: A clinical baseline model (CBM) which included non-genetic covariates only. CBM was extended by adding the first two marker-derived principal components and 65 SNPs identified by a recent GWAS consortium for T2D (M-65SNPs). Subsequently, it was further extended by adding 249,798 genome-wide SNPs from a high-density array. The Bayesian models used to incorporate genome-wide marker information as predictors were: Bayes A, Bayes Cπ, Bayesian LASSO (BL), and the Genomic Best Linear Unbiased Prediction (G-BLUP). Results included estimates of the genetic variance and heritability, genetic scores for T2D, and predictive ability evaluated in a 10-fold cross-validation. The predictive AUC estimates for CBM and M-65SNPs were: 0.668 and 0.684, respectively. We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066). The highest predictive AUC among the genome-wide marker Bayesian models was 0.681 for the Bayesian LASSO. Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information. Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D. Our results suggest that the Bayes Cπ and the G-BLUP models with a large set of genome-wide markers could be used for predicting risk to T2D, as an alternative to using high-density arrays when selected markers from large consortiums for a given complex trait or disease are unavailable.
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Aslibekyan S, Almeida M, Tintle N. Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18. Genet Epidemiol 2014; 38 Suppl 1:S86-91. [PMID: 25112195 DOI: 10.1002/gepi.21831] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As part of Genetic Analysis Workshop 18, seven research groups applied pathway analysis techniques to whole-genome sequence data from the San Antonio Family Study. Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple-testing burden and incorporating biologic insight remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up. Annotation of genetic variants is inconsistent across databases, incomplete, and biased toward known genes. At the analysis stage insufficient statistical power remains a major challenge. Analyses combining rare and common variants may have an inflated type I error rate and may not improve detection of causal genes. Inclusion of known causal genes may not improve statistical power, although the fraction of explained phenotypic variance may be a more appropriate metric. Interpretation of findings is further complicated by evidence in support of interactions between pathways and by the lack of consensus on how to best incorporate functional information. Finally, all presented approaches warranted follow-up studies, both to reduce the likelihood of false-positive findings and to identify specific causal variants within a given pathway. Despite the initial promise of pathway analysis for modeling biological complexity of disease phenotypes, many methodological challenges currently remain to be addressed.
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Affiliation(s)
- Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
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