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However, the integrative genomic model is comprised of combinations of multiple linear regression models.
The output of the genomic model is a patient-specific "prognostic index (PI)" score.
The integrative genomic model is built by exploiting the representative genomic data including SNP, CNV, DNA methylation, and gene expression.
To evaluate whether our genomic model is able to discriminate IPF patients from healthy individuals, we conducted the ROC analysis of the PI by plotting the true positive (sensitivity) versus false alarm (1-specificity).
The most interesting feature of our multi-trait genomic model is the ability to estimate covariances between defined genomic regions for each trait, which would be more difficult with other approaches.
Fitting a residual polygenic effect in the genomic model is necessary to avoid the variance of DGV being too high, to make the GEBV of candidates less biased, and to reduce the correlation between reference sires' EBV and animals' DGV.
Using purebred pig data, Su et al. [ 34] showed that when an additive genomic model was extended to explicitly incorporate dominance genomic effects, improved accuracies of predictions of both total genetic values and breeding values were obtained.
The genomic model was displayed in Fig. 3a, where W i and X i represented the weight and log-intensity of the i-th gene in the gene set identified from training cohort.
In comparison, when the same models were tested for the prediction of a continuous trait (total body length), the genomic models were clearly more efficient than pedigree-based BLUP.
For instance, clinico-genomic models were used in breast cancer outcomes prediction where genomic data combined with clinical and demographic characteristics were used for improving predictive accuracy [ 11].
