MISA [17] improves upon SNP-at-a-time (marginal) methods by modeling phenotype as a function of a multivariate genetic profile and, as a result, provides measures of association adjusted for the remaining markers.
Finally and most critically, the timing of hTDP-43 overexpression certainly affects the integrity of model phenotype as it relates to FTLD-TDP.
Thus, other methods often model phenotypes as a normal distribution, where the mean and covariance matrix are computed conditional on marker information [ 3, 13, 18- 25].
In particular, the EM-algorithm applied to this marginal likelihood is computationally slow, a marginal likelihood gives biased ML estimates for variances, and the regression approximation confers additional flexibility for modeling different distributions for the phenotype as well as different mean-variance relationships.
For each marker in the dataset, the software calculates the PAVE value [ 64], which measures the contribution of this marker to the model by describing the phenotype as well as the probability (P) of this effect being observed by chance only.
Nonetheless, anticipating the use of its estimate τ ^ a 2 as a summary statistic, we explore its relation to the concept of heritability (Lynch and Walsh 1998) in Appendix A. When outliers are suspected, maybe as a result of erratic measurement error, it can be desirable to model the phenotype as being sampled from a distribution with heavier tails than the normal distribution.
DOI: http://dx.doi.org/10.7554/eLife.01914.004 Consequently, we seek to flexibly incorporate genetic data by modeling antigenic phenotype as an evolutionary diffusion (Lemey et al., 2010), wherein a virus's antigenic character state evolves along branches of the phylogenetic tree according to a Brownian motion process (see 'Materials and methods').
Results indicated that a hierarchical (higher-order) model with a latent g phenotype, as well as specific cognitive domains, was best supported by the data.
These discrepancies may be due to the use of another transgenic mouse model, which differs by cognitive phenotype as well as in brain pathology.
Although animal models could never replicate a phenotype as complex as alcoholism, they can mimic certain facets of the trait, which then can be associated with specific expression signatures using gene expression microarrays.
