Maybe, just maybe, we will be able to find some of the epistatic genetic effects through clever search algos. https://arxiv.org/abs/1610.05108 pic.twitter.com/KlPgz0fIOh
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Maybe, just maybe, we will be able to find some of the epistatic genetic effects through clever search algos. https://arxiv.org/abs/1610.05108 pic.twitter.com/KlPgz0fIOh
Or we can just chuck deep learning at it. :) I've suggested dilated CNNs for multi-trait GWAS and any available annotations. Handles arbitrarily complex interactions...
At the cost of huge variance. I think we are still in the p/n region where it makes sense to strongly direct the model wrt. bias variance trade-off.
I dunno about that. p>>n is precisely where NNs tend to shine empirically, and they should benefit from modeling the distribution of effect sizes, genetic correlations, higher-order interactions, and everything a linear model or something like xyz throws away. Empirical question.
Example: https://www.biorxiv.org/content/early/2017/12/31/241414 … Single trait w/no auxiliary data, very small n/p, shallow CNN, no hyperparameter optimization or data augmentation, but still beats rr-BLUP.
The scaling with 'p' isn't great and something like a 500k SNP chip might be too hard without devoting a whole cluster, but I figure maybe it can be pruned back to 50k or so based on a first-pass. Probably not too many relevant SNPs with exactly 0 additive effects...
Maybe do a rough filtering with lasso/p value, and then do NN on the chosen snps.
I think lasso's behavior in sparsifying is probably the exact opposite of what one would want. The other end of the spectrum, ridge, would be more useful b/c it ranks SNPs while shrinking, and small bets still let you take the top Nk SNPs (whatever you can feasibly fit the CNN).
Well, one will have to try a variety of approaches. Hsu's team currently prune the snp list by ranking them by p value from singular regression first, then taking the top 50k or whatever snps and feeding these to the lasso.
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