Most complex diseases, such as susceptibility to mastitis, have a complex inheritance and may result from variants in many genes, each contributing only a small effect to the trait. Genome-wide association studies have successfully identified numerous loci at which common variants influence complex diseases. However, the variants identified as being statistically significant have generally explained only a small fraction of the heritable component of the trait. Insufficient modelling of the underlying genetic architecture may in part explain this missing heritability. Evidence collected across GWAS for complex diseases reveals patterns that provide insight into the genetic architecture of complex traits. Although many genetic variants with small or moderate effects contribute to the overall genetic variation, it appears that multiple independently associated variants are located in the same genes and that these variants are enriched for genes that are connected in biological pathways or for likely functional effects on genes. These biological findings provide valuable insight for developing better genomic models. These are statistical models for predicting complex trait phenotypes on the basis of SNP-data and trait phenotypes and can account for a much larger fraction of the heritable component. A disadvantage is that this “black-box” modelling approach conceals the biological mechanisms underlying the trait. We propose to open the “black-box” by building SNP-set genomic models that evaluate the collective action of multiple SNPs in genes, biological pathways or other external findings on the trait phenotype. As proof of concept we have tested the modelling framework on several traits in dairy cattle.