Sentence examples for model the genotypic from inspiring English sources

Unlike a traditional mixture model for QTL mapping, we will model the genotypic values of each QTL genotype in likelihood (2) characterized by a group of nonlinear ODEs.

If the allometric relationship between two biological traits is controlled by a QTL, the linearized power equation (2) can be used to model the genotypic mean vector in the likelihood (3) with genotype-specific parameter sets (α1, β1) or (α2, β2).

Given that the allometric change of one trait is not only regulated by the underlying genes, but also by physiology-and metabolism-related characteristics that contain the influences of both genes and environments [7], we model the genotypic vector of a trait with the phenotypic value of a second allometrically related trait.

The most parsimonious model was the genotypic model (P = 1.18×10−6) indicating that genotypes are predictive of optimum racing distance (Table 1).

Two ways of modelling the genotypic data are presented by these authors.

The total genomic value of an individual is of interest in many cases, favouring the first way of modelling the genotypic data in Gianola et al. [ 4].

For the purpose of controlling population structure in association analysis, adjusted line means from the original model were then used as observations in a mixed model analysis that modeled the genotypic variance-covariance structure as proportional to the realized genomic relationship matrix, thus incorporating the different pairwise relationships among the lines.

Following this, the second way of modelling the genotypic data in Gianola et al. [ 4], as described above, seems to be an interesting option, because it yields directly the additive effects, if the genotypes are modelled appropriately, and no extra computational step for the linear approximation is needed.

If heteroscedasticity is present before modeling the genotypic and environmental effects, but absent when these effects are controlled for, then this can be taken to indicate that the heteroscedasticity was due to the interaction between the locus and environment under study.

All random effects in the model, including the genotypic effects of family and RIL nested in family, were modeled with independent G covariance structures.

Incorporating familial/pedigree information not only provides powerful filtering options for the extensive variant lists that are usually produced by HTS but also allows valuable quality control checks, insights into the genetic model and the genotypic status of individuals of interest.