[et2o]

Numerous important biomedical findings have resulted from GWAS. Most GWAS today are inherently reproducible since their hits usually come from multi-stage designs with independent samples. Sample sizes are no longer "incredibly small" either; large GWAS often have in the order of 100s of 1000s of patients. Some have over a million.

I suppose the most important idea is that GWAS aren't really supposed to show causality. "Association" is in the name. GWAS are usually hypothesis generating (e.g., identification of associated variants) and then identified variants can be probed experimentally with all of the tools of molecular biology.

In summary, GWAS have their problems, but I think your statement is a bit too strong.

[samwalrus]

Mendelian randomization is a good technique to start thinking about causality for epidemiological studies.

This is a good paper that demonstrates the approach: https://www.nature.com/articles/srep16645 Millard, Louise AC, et al. "MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization." Scientific reports 5 (2015): 16645.

[stochastic_monk]

Thousands of samples and millions of dimensions still doesn’t strike me as an easy problem, but it makes sense to me that downstream molecular biology can verify putative associations. Thank you for weighing in.