Your English writing platform
Discover LudwigSuggestions(5)
Exact(8)
Nevertheless, the KLC2 gene was not found in one of these modules, neither in the WISH based on genomic correlations nor in the WISH based on epistatic interaction.
However, the WISH network based on genomic correlations is created based on extreme phenotypes (EBVs).
From here on, the methods are comparable to the methods used in the WISH based on genomic correlations.
The WISH method based on genomic correlations was tested using HTG data of an F2 pig resource population [ 26], with carcass weight as the trait of interest.
Both methods, WISH based on genomic correlations and WISH based on epistatic interactions, result in the construction of a scale-free network.
In the WISH based on genomic correlations the connectivity is measured over > 5000 SNPs and the most highly connected SNPs are concordantly selected for network construction.
Similar(52)
The WISH network based on genomic correlation was applied directly to real data.
In the WISH network based on genomic correlation, a total of 5219 SNPs and 75 animals from the Duroc*Göttingen Minipigs based on their estimated breeding values (EBVs) for carcass weight (25 high, 25 intermediate, and 25 low) were selected for network construction.
These findings helped in understanding the intraspecies (strains) diversity and/or similarity and its correlation with that of phylogenomic variations based on genomic contents.
Genomic evaluation was performed based on genomic best linear unbiased prediction and its accuracy was evaluated as the Pearson correlation coefficient between genomic estimated breeding values using either observed (12k/50k) or imputed genotypes with varying levels of imputation accuracy and accurate estimated breeding values based on progeny-tests.
Collecting genomic observations improves one's understanding of the genome, which in turn improves the ability to deliver value through the application that is based on genomic information, which then enables further data collection.
More suggestions(16)
based on genomic co-ordinates
based on temporal correlations
based on genomic drivers
based on genomic comparisons
based on genomic data
based on genomic distances
based on experienced correlations
based on partial correlations
based on interregional correlations
based on genomic differences
based on possible correlations
based on international correlations
based on empirical correlations
based on genomic studies
based on genomic profiles
based on polychoric correlations
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com