Synomics’ combination of a proprietary technology platform and deep industry experience offers unique potential to identify trait-associated, combinatorial and predictive SNPs that standard GWAS does not find and translate these SNPs to genomic prediction models which out-perform current best-in-class.
Egg weight (EW) is an economically important trait which displays a consecutive increase with a hen’s age. As EW is lowly heritable in older birds, this case study focused on EW at week 56 (EW56). We used a publicly available dataset of 1,027 hens, to investigate the additional, detailed insight our platform was able to discover above standard industry analyses and quantified the impact this additional insight has when translated to a genomic prediction model and genomic selection.
Genotype and phenotype datasets were analysed in Synomics’ proprietary platform, identifying high-order combinations of SNPs (including epistatic interactions). These combinations capture the non-linearity of biological effects and the impact they have on phenotypes much better than existing methods based on single features (such as GWAS). These novel, biologically, and statistically relevant genetic variants were then incorporated into our next-generation genomic evaluation method.
Even with the comparatively small dataset, Synomics’ platform identified signals sufficient to detect 2,018 highly-predictive SNPs which mapped to 122 genes, which are potential targets for intervention. We were able to find these SNPs despite the small sample size and relatively high SNP chip density. We also identified 3 significant GO-terms, one supported in literature and the others interesting targets for further investigation. Conversion of the unique SNP combinations into a standard genomic prediction model showed step-change improvement in key metrics as well as a materially wider EBV distribution compared with standard breeding models. This results in an increased genetic gain in laying hen breeding.