Peter Sørensen

Most complex diseases such as susceptibility to mammary gland infections (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. Despite the successes, the variants identified as being statistically significant have generally explained only a small fraction of the heritable component of the trait, the so-called problem of missing heritability. Insufficient modelling of the underlying genetic architecture may in part explain this missing heritability. Evidence collected across genome-wide association studies in human provides 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 the associated genetic variants are enriched for genes that are connected in biol ogical 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 single nucleotide polymorphism (SNP) data and trait phenotypes and can account for a much larger fraction of the heritable component of the trait. A disadvantage is that this “black box” modelling approach does not provide any insight into 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 biological findings on the trait phenotype. As a proof of concept we have tested the modelling framework on susceptibility to mastitis in dairy cattle. Research supported by EC-FP7 “Quantomics”, agreement n° 222664