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VarExp specifications


Unique identifier OMICS_24334
Name VarExp
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Some meta-analysis summary statistics, the sample size, the mean and variance for both the studied outcome and the exposure in each cohort included in the meta-analyses.
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License MIT License
Computer skills Advanced
Stability Stable
Maintained Yes




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  • person_outline Vincent Laville

Publication for VarExp

VarExp citations


Assessing the durability and efficiency of landscape based strategies to deploy plant resistance to pathogens

PLoS Comput Biol
PMCID: 5918245
PMID: 29649208
DOI: 10.1371/journal.pcbi.1006067

[…] expv,p=γminAGGagγ(p),qrγ(v)γ(9)Note, the usual shape and scale parameters of a Gamma distribution, β1 and β2, can be calculated from the expectation and variance, exp and var, with: β1=exp2var and β2=varexp, respectively. […]


Early Events of the Reaction Elicited by CSF 470 Melanoma Vaccine Plus Adjuvants: An In Vitro Analysis of Immune Recruitment and Cytokine Release

Front Immunol
PMCID: 5660290
PMID: 29109725
DOI: 10.3389/fimmu.2017.01342

[…] . All models were tested for homoscedasticity and normality of residuals by visual assessment of plots. When homoscedasticity was not accomplished, models were fitted by the addition of the VarIdent, VarExp, or VarPower variance structure to the random part of the model (). The best variance structure used in the fitted models was determined by comparison of Akaike’s and Bayesian’s Information Cri […]


Maternal Effects on Seed and Seedling Phenotypes in Reciprocal F1 Hybrids of the Common Bean (Phaseolus vulgaris L.)

Front Plant Sci
PMCID: 5259735
PMID: 28174586
DOI: 10.3389/fpls.2017.00042

[…] cture was determined by comparing different variance functions on the GEN4 model for each trait, using wAICc as selection criteria. For all traits the best covariance structure selected from nlme was varExp, an exponential function of the harvest (days) covariate estimated across genotypes, except for cotyledon dry weight decay, which was best modeled by estimating an individual variance term (var […]


Specific arrangements of species dominance can be more influential than evenness in maintaining ecosystem process and function

Sci Rep
PMCID: 5171799
PMID: 27996034
DOI: 10.1038/srep39325

[…] l, we allowed the residual spread to vary with evenness using generalized least squares (GLS) estimation. This procedure uses appropriate variance functions (here varIdent for nominal and varPower or varExp for continuous explanatory variables) to model the variance structure. The optimal variance covariate structure was determined by comparing the initial regression model without variance structu […]


Whole genome sequence analysis of serum amino acid levels

Genome Biol
PMCID: 5123402
PMID: 27884205
DOI: 10.1186/s13059-016-1106-x
call_split See protocol

[…] uency alleles. We also conducted single variant analysis for all individual variants with MAF >5% using an additive genetic model with the same adjustments. For each approach, the variance explained (VarExp) was calculated using the effect allele frequency (p) and beta (β) from the analyses and the variance of the quantitative trait (σ 2) using the formula VarExp = β 2/σ 2 × 2 × p × (1 − p) []. In […]


Factors controlling bark decomposition and its role in wood decomposition in five tropical tree species

Sci Rep
PMCID: 5048430
PMID: 27698461
DOI: 10.1038/srep34153

[…] mposition retained number of days (polynomial 2nd degree), species, litter-bag type and all two-way interactions as independent variables. To correct for heteroscedasticity, the exponential function (varExp (form = ~fitted(.)) was used. After 1 yr incubation, bark litter mass loss ranged from 96.45 ± 0.54% (mean ± standard error, N = 5) for Dipterocarpus turbinatus to 58.49 ± 4.15% for Toona cilia […]

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VarExp institution(s)
Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France; Center for Research on Genomics and Global Health, NHGRI, NIH, Bethesda, MD, USA; Laboratoire TIMC-IMAG, UMR 5525, Université Grenoble Alpes, Grenoble, France; Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA; Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA; Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany; Division of Statistical Genetics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA; Division of Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
VarExp funding source(s)
Supported by the HL118305 grant from the NHLBI; by R21HG007687 from NHGRI; by the Intramural Research Program of the National Human Genome Research Institute in the Center for Research in Genomics and Global Health (CRGGH, Z01HG200362).

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