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n k (i) models the additive thermal noise at the receiver, and is a zero-mean, circularly symmetric, complex Gaussian random variable with variance E { | n k ( i ) | 2 } = σ n k 2 for all i.

In many quantitative-genetic models, the additive genetic variance is assumed to be constant.

∙ Reaction norm model In reaction norm models, the additive genetic effect is described as a continuous function of environmental parameters.

To analyze the effect of SNPs comprehensively, we applied three different genetic models: the additive model, the dominant model and the recessive model.

In the BPRHS, when the GxE term was not included in the models, the additive genetic variance contribution for HOMA-IR, fasting insulin and glucose was 8.83% (P-nominal = 0.02), 6.24% (P-nominal = 0.06) and 13.8% (P-nominal = 0.006), respectively.

In DYD models, the additive genetic variance (σ ^ a 2 ) was underestimated while the genetic variance explained by the QTL (σ ^ q t l 2 ) and the ratio of σ ^ a 2 and σ ^ q t l 2 were overestimated.

Similar(54)

We compared the strictly linear models with the additive models and the geoadditive models using the Deviance Information Criterion DICC) values [ 26].

For all models, except the additive model, Pearson's Chi-square test of association was used.

As the equivalent constant noise model, the additive zero-mean Gaussian noise with σ=2.3 meters was considered.

In this model, the additive effects of the markers are considered as independent random normal variables.

In the genotypic model, the additive variance is the variance of additive effects (average difference between homozygotes), whereas in the breeding model, additive effects are functions of allele substitution effects.

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