21 March, 2021

Genomic Improvement In Laying Hens

Study design

The study used public datasets on 1,000 laying hens with nearly 300,000 SNPs. The data comes from 11th generation pure line Rhode Island Red chickens. The hens originate from a set of 92 sires and 801 dams which have been selected for egg size and quality over many generations. These SNPs were already filtered for standard QC criteria, such as minor allele frequencies (MAF) and Hardy-Weinberg equilibrium.

The Challenge

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. 

Our Solution

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.

GO-terms identified in enrichment analysis
0 %
increase in phenotype prediction accuracy
0 %
reduction in prediction error
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correlation of rankings to standard GBLUP

The Results

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.

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