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The method accommodates both binary and quantitative traits.
The GMDR method, unlike the original MDR method [77], permits adjustment for covariates and better handles data with unequal numbers of cases and controls, and can be used to analyze both qualitative (e.g. binary) and quantitative traits via different link functions.
We show that BiForce is more powerful and significantly faster than published tools for both binary and quantitative traits in a series of performance tests on simulated and real datasets.
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In this study, we have developed a novel GWA G × G interaction testing scheme, applicable to both binary phenotypes and quantitative traits.
Simulation was used to test the performance of BiForce in binary traits in comparison with BOOST (Wan et al., 2010) and quantitative traits in comparison with PLINK (Purcell et al., 2007) using 500 replicates for every simulation scenario (Supplementary Note 2).
Mackay, T. F. C. Epistasis and quantitative traits: using model organisms to study gene gene interactions.
Servin, B. & Stephens, M. Imputation-based analysis of association studies: candidate regions and quantitative traits.
Association mapping was conducted to detect genetic variants at quantitative trait SNP (QTS) loci, quantitative trait transcript (QTT) differences, quantitative trait protein (QTP) variability, and quantitative trait metabolite (QTM) changes, which can be summarized as QTX locus variation.
Therefore, the advent of molecular maps for rice (Causse et al. [1994]) and quantitative trait locus (QTL) analysis approaches have facilitated the analysis of these quantitative traits.
CLRT can deal with binary or quantitative traits and the authors have pointed out its potential to be generalized to models with covariates.
These ideas are combined in a unique tool, BiForce, to support high-throughput epistasis analysis for either binary or quantitative traits on commonly used computer systems.
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