application of CNN in genomics
CNN has been used many times in genomics, what you described is not new. A 5s quick search on Google gave me this paper:


  https://www.biorxiv.org/content/biorxiv/early/2017/12/31/241414.full.pdf
  
  ...predict phenotypes from genotypes by using a deep convolutional neural
  network (CNN)...


"Genotype" is a better terminology than "SNP", as short indels are also important. Your idea is certainly not new, and in fact insufficient as a research topic anymore. 

Predicting phenotype from just genotype is old and quite limitied. No good journal will take a topic like that anymore. No matter how good your model is; no matter how many layers you have (e.g. 1000000 trillion layers), how many GPUs you have (e.g. every single GPU in the world), how many engineers you have (e.g. every single ML engineer in the world). CNN will never solve problem perfectly as it neglects the vital information from proteins. Whatever you see from a paper would be limited in applications (e.g. strong assumptions). Genotype data don't tell you how exactly your genes are transcribed.

Instead, the current research trend is on applying deep learning networks integrating mutliple data sources (proteins, transcripts, genes, genotypes etc), and form a single unifed model. This is why we have AI for precision medicine.