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After multivariate and conditional analyses, four variants on three chromosomes remained independent contributions.
In the present study, we implemented univariate, multivariate and conditional GWASs using 600 K Affymetrix Chicken SNP array in a total of 1,534 F2 chickens with observations for egg weights at different ages.
In summary, we performed univariate, multivariate and conditional GWASs for longitudinal egg weight data using high-density 600 K SNP arrays, and suggested that longitudinal analysis had higher power to dig out variants that influence phenotype variability over time.
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In stratified analyses, we used unconditional logistic regression, adjusting for matching factors, since results from multivariate unconditional and conditional logistic regression models were essentially identical.
For young sires, TBV and GEBV were simulated jointly (in the GPS population) from multivariate normal distributions and conditional on parent average (EBV before including progeny information).
The parameter vector was augmented with the unobserved liabilities, the location parameters Θ were drawn from multivariate normal distributions, and conditional posterior distributions of the dispersion parameters were scale inverted chi-square.
Analyses were conducted using bivariate and multivariate conditional logistic regression models and generalized additive models [ 28].
The association between ReA, IGE, and potential covariates was assessed using univariate and multivariate conditional logistic regression models.
Compared with control, risk factors with crude and adjusted matched ORs and their 95% CIs associated with primary MDR-TB and primary DS-TB were identified by univariate and multivariate conditional logistic regression models, respectively.
Results are from univariate and conditional multivariate logistic regression models.
We calculated crude ORs, adjusted ORs, and corresponding 95% confidence intervals by univariate and conditional multivariate logistic regression.
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