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We tested for the significance of the additive genetic covariance by a Likelihood Ratio Test (LRT) based on −2×difference in log-likelihood between the unconstrained model specified above and a model with the intersexual genetic correlation set to zero, which has one degree of freedom.
SAS 9.2 was used to analyze the data from the model specified above.
The adjusted trait values were obtained by analysing the raw data using the model specified above without including SNP information.
This hypothesis will be tested through the adjusted contrast between the two interventions at 4 months from the mixed model specified above.
In the measurement model specified above, children at the same level of the latent neurobehavioral function (η3) are expected to have equal test scores on each of the individual tests of that function.
To conduct estimation and subsequent inference, we follow a fully Bayesian analysis of the iBAG model specified above using Markov chain Monte Carlo (MCMC) approaches (Casella and George, 1992).
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To check for possible confounding by recent consumption, we added each food and supplement variable to the logistic regression models specified above and re-evaluated significance and effect size of the validated environmental factors.
For SPP1 and LTBP4 genotypes, patients were grouped based on inheritance models specified above.
We will apply the same models specified above for our secondary analyses, but will use linear regression models for the continuous outcomes.
We simulated adaptation from de novo mutation in cycling populations and an expanding population using the Wright Fisher models specified above.
To investigate whether efficacy is affected by dose, we will compare outcomes across groups defined by the number of PHV visits received using the models specified above.
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