Since CNN has been widely applied in DNA sequence data, I'm wondering why CNN is not often used for predicting phenotype from SNP data, given that SNPs are essentially parts of DNA sequences and retain the nature of ordering.
For instance, this paper https://arxiv.org/abs/1611.09340 states that "... most of these techniques(CNNs) are based on sequence data where convolutional or recurrent networks are appropriate. When the full DNA sequence is unavailable, such as when data is acquired through genotyping, other methods need to be used..."
Please explain regarding the algorithms rather than the biology side(e.g. SNPs only explains a small portion of genetic variation, etc)
Hey guys, I just wanna remind that I'm addressing the use of SNPs(or you can include other types of variants as well)...not full DNA sequence data. It's very common to use CNN on full DNA sequences to predict variation loci. And it's easy to understand as it's natural to use CNN on sequential data.
But what I don't understand is why not using CNN (1)on SNPs (since SNPs retain the major spatial structure of DNA sequences) (2) to predict phenotype?