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In the second stage, these residuals were regressed on ROH4Mb using a single marker regression model and a gradient boosted machine (GBM) algorithm.
The second stage involved using the residuals from the first stage as a phenotype and regress these on the ROH4Mb status utilizing a single marker regression and gradient boosted machine (GBM).
The ROH4Mb effect of a SNP was estimated by regressing ROH4Mb of a SNP on the same phenotype as single marker regression and gradient boosted machine and therefore the additive effect explained by the EBV was removed from the phenotype.
single marker regression.
QTL analysis was performed by single marker regression analysis (Kearsey and Hyne [1994]) with Qgene 4.3 (Joehanes and Nelson [2008]).
For 144 RILs, single marker regression based on a GLM was completed to examine the association between each SNP and the anaerobic response index.
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Single-marker regression (SMR; association testing) was also conducted to test the hypothesis that individual marker loci were in partial or complete linkage disequilibrium with a causal gene and to delineate subsets of markers for further marker regressions.
The five SSR markers detected previously by single-marker regression analysis were within this QTL region.
Different methods were compared such as Single-marker regression (SMR), Simple interval mapping (SIM), and Composite interval mapping (CIM).
In this study interval mapping with multiple-marker regression approach was extended to analyse multiple chromosomes simultaneously.
Since this is a single-marker regression QTL mapping, we estimated the physical interval size of a QTL based on most of the original drought QTLs to be around a 1-MB region.
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