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We showed that the accuracy of across-breed genomic predictions improved when a subset of SNPs with consistent effects across populations is used to generate the predictions, instead of the randomly selected or equally spaced SNPs currently used for genomic selection.
Apple researchers have used Next-Generation Sequencing (NGS) technologies to detect SNPs across the genome, enabling the development of apple SNP arrays used for genomic selection in apple breeding programs and for fine trait mapping [ 17– 17].
GWAS results allow the identification of candidate regions that can then be used for genomic selection.
GBLUP includes genomic information into breeding value estimation and has been used for genomic selection in dairy cattle [ 15- 18].
However, most SNPs (single nucleotide polymorphisms) used for genomic selection are in linkage disequilibrium (LD) with unknown causative mutations.
These marker sets were tested in three types of linear regression models commonly used for genomic selection and prediction: RR-BLUP, LASSO, and elastic net analysis.
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If variable selection models were used for genomic prediction, higher numbers of QTL resulted in lower accuracies [ 16, 17, 45].
One aliquot was used for genomic DNA (gDNA) extraction immediately after puromycin selection (t0) and the other aliquote was passed in culture.
The effectiveness of genomic selection will depend on the size and composition of the reference population used for genomic predictions [ 8, 9].
Random forest has often been used for imputing markers for genomic selection in plant breeding, especially when a reference genome is not available [ 10, 15, 18].
High-density maps can be used in breeding programs for genomic selection and precise mapping of agronomically important genes for marker-assisted selection.
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