Your English writing platform
Discover LudwigExact(1)
The k-NN imputation tested with different values of k ranging from 1 to 10, have shown that, k = 10 was the optimal choice with relatively smaller RMSECV%% value.
Similar(59)
Imputation test sets and reference sets were subsets of genotype data selected from a large multi-breed sheep resource flock.
These results show that the size of the reference set is more important when genomic relationships between imputation test set and reference set animals are lower.
Imputation test sets (target animals) consisted of 1000 purebred Merinos, 1000 mixed crossbred Merinos, or 500 crossbred Merinos (BLxM or PDxM or WSxM).
Genotype imputation refers to statistical inference of un-typed marker genotypes in a set of low-density genotyped animals (imputation test set) based on a group of animals that are genotyped with higher density marker arrays (imputation reference set) [ 13].
In addition to imputing from a random reference set, we also tried to impute from a reference set that was chosen to be informative for all animals in the imputation test set.
For this, genomic best linear unbiased prediction (GBLUP) was performed based on 1000 purebred Merino as the genomic prediction reference population (which was also used as imputation test set (see Fig. 1)).
When the logP value of the imputation test is lower than that of the haplotype analysis, the SNPs at the QTL peak are unlikely to be causal; in contrast when the merge logP is higher, it suggests that the tested variant is consistent with being a quantitative trait nucleotide (QTN), with the caveat that there may be multiple variants with the same SDP that are equally likely to be causal [ 34].
Since our aim was to improve imputation accuracy in breeds with a small reference population size, multi-breed imputation was tested.
These distributions are shown for all imputation methods tested in Figure S3.
> -wrap-foot> > -wrap-foot> All of the imputation methods tested produced more efficient parameter estimates than complete-record analysis.
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