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Once all the shared markers are identified, the original marker sets are recollected as a unique set, and each projection maps redefined in relation to the first series a, that is (P_{omega _b} rightarrow P_{omega _b} circ T_{ab}).
However, when using the Fluidigm system, this is difficult because all marker sets are run under the same conditions.
Denser marker sets are more likely to provide sufficient LD between QTL and SNPs, which can lead to a higher predictive ability and higher accuracy of GEBV [ 2, 5].
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All the analyzed individuals, typed with differing marker sets, were reclassified into 27 possible Y chromosome haplo/paragroups to allow population comparisons.
Because the results from both marker sets were broadly similar, only results from the larger marker set are presented here.
Two different marker sets were created from the combined dataset for population structure analysis.
The two marker sets were subsequently used separately for population structure analysis.
The marker sets were obtained from the Natural website (CviLer, www.dpw.wau.nl/natural/), the BaySha website (http://dbsgap.versailles.inra.fr/vnat/Documentation/33/DOC.html) and Singer et al. [41] for ColLer.
In fish, the marker sets were not totally conserved: on one side, the CITED4 gene was present, on the other side, two additional flanking genes MANEAL and SF3A3 (Table S4) were present in inverse orientation to the conserved LIN28.
In addition to standard deviation, the similarity between the two marker sets was reported using ipCMC (Table 4).
We conclude that high levels of missing data in dense marker sets is not a major obstacle for genomic selection, even when marker order is not known.
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