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A co-dominant genomic model was assumed for SNP effects.

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.

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.

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.

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.

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).

However, as explained by de los Campos et al. [ 27] the fact that multiple markers are likely to track the same QTL questions the assumptions of additivity and independence of SNP allele substitution effects between loci, and therefore the genomic model is a rough approximation to the reality of the additive model of causal effects.

The genomic model is generally described as: y = 1 μ + Xα + e, where y contains phenotypic records, μ is the overall mean, 1 is a vector of 1's, X is an m ×  n matrix that contains the scaled and centered genotypes of all individuals, α contains the (random) allele substitution effects for all loci, and e contains the random residuals.