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If the number of markers is large, the number of effects in the model is enormous.
The region spanned by these markers is large and covers the promoter region and the first exons and introns.
When the number of markers is large, FAM-MDR relies on the genomic kinship matrix and there is no need to keep individuals with missing trait values in the analysis.
However, if the genetic distance between the linked marker and the flanking markers is large, it is less likely that the causal genetic variation is far away from the linked marker (i.e. close to the flanking markers).
In principle, epistasis can be incorporated when the number of markers is large, but the large number of epistatic effects (two-locus interaction) makes their use unfeasible.
Since the distance between these two markers is large (about 5 Mb), it is possible that these two markers are capturing different signals (see below).
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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.
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).
The number of markers was large; however their distribution was not even.
However, this is only clear when both the sample size and the number of markers are large.
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.
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