Ammon here. We are in a data-limited environment where regularization is key. That's why LASSO regression has worked so well. Linearity itself (low parameter count) is regularization. Anything else (done naively) does not generalize. Of course, a (surprisingly) large amount of human variation does appear to be due to additive effects, and is thus well explained by linear models. But that's not the entire story. PRS models for many diseases still fall far short of broad-sense heritability. There is a gap to explain. The question, I think, comes does to the nature of that gap.
Talking to researchers, I encounter a few different theories: 1) rare SNPs not yet identified by GWAS, 2) structural variants not included in GWAS, 3) epistasis/dominance, 4) epigenetics, 5) gene/environment interactions (perhaps undermining the twin studies used to calculate broad-sense heritability. I'd love to hear other ideas.
To the extent that 2, 3, or 4 are true (for some important diseases) looking beyond current techniques seems necessary. If 1 is true, I still think there's lift from new approaches. Take a look at GPN-MSA. Reducing the size of the search space (calculating a prior over SNP pathogenicity from unsupervised methods) finds more loci.
Ammon here. We are in a data-limited environment where regularization is key. That's why LASSO regression has worked so well. Linearity itself (low parameter count) is regularization. Anything else (done naively) does not generalize. Of course, a (surprisingly) large amount of human variation does appear to be due to additive effects, and is thus well explained by linear models. But that's not the entire story. PRS models for many diseases still fall far short of broad-sense heritability. There is a gap to explain. The question, I think, comes does to the nature of that gap.
Talking to researchers, I encounter a few different theories: 1) rare SNPs not yet identified by GWAS, 2) structural variants not included in GWAS, 3) epistasis/dominance, 4) epigenetics, 5) gene/environment interactions (perhaps undermining the twin studies used to calculate broad-sense heritability. I'd love to hear other ideas.
To the extent that 2, 3, or 4 are true (for some important diseases) looking beyond current techniques seems necessary. If 1 is true, I still think there's lift from new approaches. Take a look at GPN-MSA. Reducing the size of the search space (calculating a prior over SNP pathogenicity from unsupervised methods) finds more loci.