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Second, we tested trait and gene-specific hypotheses, repeating the analysis above, but using only the number of QTL and the marker intervals for the actual traits that mapped to candidate genes.
Previous work has demonstrated that the predictive capacity of genetic testing is constrained by the heritability and prevalence of the tested trait, although these constraints have only been approximated under the assumption of a normally distributed genetic risk distribution.
Here, we mathematically elucidate the absolute bounds on the specificities, sensitivities, and AUC for genetic testing given any values of heritability and prevalence of the tested trait, without making any assumptions about the risk distribution.
This is due to the fact that the positive predictive value is low if the prevalence of the tested trait is low [ 23].
One of the crucial steps of a gene-based pathway analysis is the assignment of a gene statistic that represents the association of each gene with the tested trait.
Since genotyping more or less test-individuals affected the distribution of genetic gain among traits, the rate of improvement does not depend only on the characteristics of the tested trait but also on the other traits in the breeding goal.
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Through conventional breeding to pyramid traits, individual plants/lines must be phenotypically screened for all tested traits.
There were no significant interactions between planting date and plant density in the majority of tested traits.
However, the RNAi lines showed no significant differences compared with Shengdao 806 for all tested traits (Additional file 7: Figure S5).
All tested traits were considered to be continuous in the analyses.
The remaining tested traits were associated with zero to five SNPs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com