Functional genomicists and statistical geneticists live in two different worlds

@minouye271 Very nice work! Is it worth adding some convolution layers to account for LD? At least it should have some benefits of reducing # of parameters

@A_A_Zaidi Great point! I should fix this soon

@SashaGusevPosts I still find that "assuming each variant explains a tiny bit of variance" is quite clever. This way, the standard error for each marker is the same (condition on true effect size), which is 1/sqrt(N). By using iterated expectation, the expected chi2 can be worked out

@epigenci Yep, was it XGboost or something

@nmancuso_ @KaiYuan1990 That’s great context. In 60s where genotyping is unavailable, this method can still be used for predicting traits. All we need is to keep track of the pedigree. Quite brilliant idea!

@KaiYuan1990 far as I can see, is the reduced dimensionality. Further, we can be creative in designing the kernel, even to incorporate interaction terms. This can be done by using the polynomial kernel, for example. The dimensionality will remain unchanged.

LD is also computed in a block-by-block manner, which means if two variants are far away, their correlation is ignored. Would these ignored correlation, or LD, cumulatively play a role in determining the joint effect size?

@SashaGusevPosts @f2harrell I wonder why no negatives in GWAS? Infinitesimal model is biologically impossible IMO

@SashaGusevPosts The answer seems to be yes:

RT @RyanDhindsa: Interested in applying functional genomics to uncover molecular mechanisms of brain disease? We are #recruiting a postdoc…

Why would the gradient of a function (let’s say p —> 1) being a column vector by convention? Wouldn’t a row vector make more sense? The column convention just causes so much trouble when applying the chain rule…

RT @PGCgenetics: 🚨Review alert! Admixed populations make up a large portion of global genetic diversity but are often excluded from genomic…