Background: The use of DNA marker information for the prediction of genetic merit in animal and plant breeding, and susceptibility to disease in human medicine has become widespread. Therefore, an increasing number of methods have been proposed for more accurate and efficient genomic prediction. However, most of the commonly used models for genomic prediction only account for additive effects since most of them are designed based on the linear model.
Results: Here, we proposed a GpNet, a deep learning network for genomic prediction in Korean beef cattle. With a locally connected layer, GpNet can estimate LD-block effects of single nucleotide polymorphisms (SNP) with adjacent two or more SNPs closer to 3’-end. This operation is quite similar to how the DNA sequence is used in the translation process in which the RNA polymerase interprets DNA sequence by units of codons to downstream (3’ to 5’). GpNet archived a superior performance than previous state-of-arts methods for beef carcass weight with a predictive ability of 0.721%. GpNet also found two significant quantitative trait locus (QTL) on the regions (bta 6:38464203–39816133, bta 14:25307116–29987025) for carcass weight. However, GpNet showed less performance than linear methods in backfat thickness and eye-muscle area.
Conclusions: GpNet outperformed the previous state-of-arts methods for beef carcass weight. However, GpNet cannot achieve superior performance in backfat thickness and eye-muscle area. We noticed that the lack of ability to estimate distant epistasis and dominance was the weakness of GpNet. Therefore, it remains a future research issue to expand GpNet to resolve these flaws and this further study will accelerate the new phase of the genomic prediction.