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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).
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.
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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.
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).
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.
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).
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.
The linkage group 14 most likely breaks between Bmap 19-PT and Eid 11, as the distance between these two markers is large.
In addition, Long's test might not be valid when the variance among markers is large [ 30] and the structure of larval salamander populations (potentially a few large sibships) might accentuate the influence of genetic drift.
Moreover, when the number of markers is large compared to the number of lines (m ≫ n), S is optimal with respect to mean-squared error (Casella and Berger 2002).
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