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The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.
In particular, three genes i.e. BCL2, PAK7, and NIRP1 shared pathways in cancer and other pathways, supporting our hypothesis that multivariate phenotypes have common etiology pathways when they are affected by pleiotropic genetic factors.
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Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions.
Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions by using more information from data.
In summary, our results suggest that the genetic markers identified with multivariate phenotype highLDLhighTG have phenotypic associations with common traits in MS. The common traits in MS, particularly hyperlipidemia, may be linked to pathogenic associations with osteosclerosis and neurodegenerative disorders including AD and PD influenced by pleiotropic genetic factors.
On the other hand, standard methods for multivariate phenotypes (Ferreira and Purcell, 2009) have not been thoroughly investigated in the context of rare variants, and we will see later in the text that severe overfitting may occur.
Much work have not investigated what types of single traits can be correlated to induce multivariate phenotypes.
Even if there exists a large number of methods for analyzing rare variants, none of those has been tailored for multivariate phenotypes.
We have showed a methodology to identify genetic markers associated with multivariate phenotypes derived from patterns of phenotypic associations discovered by ARM.
Statistically classic multivariate methods have been applied to GWAS of multivariate phenotypes to tackle in an effective manner.
Previous work has contributed to addressing association tests for multivariate phenotypes.
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