Integrating DNA Methylation with other 'Omics Data Sets for Prediction of Meat Tenderness in Sheep

Tenderness, juiciness, odour and flavour are the key eating quality phenotypes that influence consumer appreciation of meat.  Eating quality is critical with respect to product quality and value, and is recognised by the meat industry as a key product parameter.  However, eating quality is a particularly difficult phenotype to understand and influence. It is affected by a complex matrix of underlying factors that span animal genetics through to on-farm production and protein/lipid modification during processing, packaging and retail display. 

As many of the factors affecting eating quality are environmental in nature, epigenetics is likely to play an important role in the final eating quality of meat.  However, measurement and analysis of epigenetic factors such as DNA methylation in isolation of phenotype and other  ‘omics datasets such as transcriptomics, lipidomics, and proteomics is of little value. DNA methylation alone provides little insight into underlying molecular mechanisms that may be used to predictor influence meat eating quality.  Unfortunately integration of these large datasets is challenging and still in its infancy. Using meat tenderness as a model system we have begun correlation of genome-wide DNA methylation with other ‘omics data sets derived from sheep longissimus dorsi muscle samples.  The need for better tools for analysis of DNA methylation in non- human species is also discussed in relation to understanding the molecular mechanisms driving meat eating quality and other desirable production traits.