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As the lower confidence limits of all the markers were larger than 0.063, none of the markers met the stopping criterion at this early stage of the study.
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Indicine cattle continued to have a lower LD than most of the other breeds when the distances between markers were large, which suggests that they originated from a larger ancestral population.
However, there were only two spaces where the distances between markers were large, i.e. 23.56 cM between PKAM0211 and PKAM0417 on CcLG02 and 28.85 cM between PKAM0671 and PKAM0543 on CcLG06.
Of these, 23 singletons data points were re-coded as missing data for 20 loci where these occurred, except for two loci where distances between flanking markers were large enough to expect recombination.
The fall in both markers was larger with EFV than with ATV/r.
This was explained because average distance and maximum distance between two adjacent markers was larger in C15 than in C14.
However, when the number of markers is larger than the number of test subjects or when variables are highly correlated, standard regression methods become overwhelmed.
IMPUTE2 also uses a strategy to improve speed by dividing the data in smaller chunks, but the number of overlapping markers was larger.
This increase in the scale of molecular information results in data where, typically, the number of predictors (markers) is larger than the number of records (phenotypes).
In previous studies using high-throughput Illumina® SNP chip genotyping for QTL or association genetics in rice, alfalfa, maize and wheat [ 102– 105], the proportion of polymorphic markers was larger, ranging from 52%to81%81 %.
If the number of markers is large, the number of effects in the model is enormous.
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