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Finally, we propose application of GRAMMAR-GC using the kinship matrix estimated through genomic marker data, instead of (possibly missing and/or incorrect) genealogy.
Genome-wide association analysis was performed using the GenABEL package [46] in R using a three step GRAMMAR-CG approach, (Genome wide Association using Mixed Model and Regression - Genomic Control), with the extension of using the genomic kinship matrix estimated through genomic marker data, instead of the pedigree [39], [47].
GRAMMAR infers pairwise kinship coefficients amongst sampled individuals on the basis of genomic marker data.
Such studies could for example use genomic marker data to distinguish underlying mechanisms of spouse correlations (e.g., assortative mating due to PA would show increased genetic relatedness between spouses relative to random individuals of a population, whereas assortative mating due to SH would not).
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Marker-assisted selection (MAS) and genomic selection (GS) based on genome-wide marker data provide powerful tools to predict the genotypic value of selection material in plant breeding.
Both GWAS and genomic selection (GS), a method for predicting phenotypes by the use of genome-wide marker data, are receiving considerable attention among plant breeders.
Genomic selection, a breeding method that promises to accelerate rates of genetic gain, requires dense, genome-wide marker data.
Since genomic selection relies on marker data, simulations conducted on a genomic dataset are a pre-requisite before selection can be implemented.
Genomic relationships estimated from marker data provide more accurate estimates of genetic covariance between relatives, which in turn leads to more accurate predictions.
However, the databases mentioned above emphasize genomic, genetic, taxonomic and marker data, providing limited information on key functional genes and the relationships between them.
aALL: all markers; AUTO: markers on the autosomes; PAR: markers on the pseudo-autosomal region; X: X-specific markers on the X chromosome; bIMP_test: for the test animals in genomic prediction, the 54K marker data were imputed from LD marker data; IMP_0.5ref: for half (randomly chosen) of the reference animals, the 54K marker data were imputed from LD marker data.
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