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Threshold models for ordinal and binary traits have been considered previously by several authors.
While HWE deviation-induced FP for binary traits have been noted previously (Schaid & Jacobsen 1999), we have further demonstrated that the effect extends to categorical-uniform traits and that the effect is likely restricted to low MGF-induced HWE deviation.
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Multilocus association models of binary and ordinal traits have been considered by Hoti and Sillanpää (2006), Iwata et al. (2009), González-Recio et al. (2009), González-Recio and Forni (2011), and Wang et al. (2013).
The first expectation is satisfied by four trait-types; only simulated binary traits have P-values that are significantly non-uniform (median P = 0.02 for tests of uniformity), signifying an increased sensitivity of binary traits to various SNP attributes.
Estimation of the heritability of binary traits is not straightforward and contradictory results have been reported, although the factors responsible for these differences remain unclear [ 17].
Four different claw disorders (digital dermatitis, sole ulcer, wall disorder, and interdigital hyperplasia) have been scored as binary traits and analyzed separately.
This paper tends to review the data mining approach that deals with binary trait outcomes (for e.g., disease status) that have been accepted to detect interactions between genes.
People have lived beyond the binary for probably as long as there have been people.
A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits.
Although sex determination is a simply inherited binary trait in most organisms, the precise genetic processes affecting sex determination have been found to be complex and diverse.
For case-control studies, which formulate the measures for a binary trait, a number of statistical methods for detecting gene-gene interactions have been proposed.
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