LDpred2-auto: automatic determination of use_MLE and shrink_corr

[sritchie73]

Hi,

I've just seen the updated tutorial on LDpred2-auto (last time I ran this, the use_MLE, allow_jump_sign, and shrink_corr arguments didn't exist), and I'm wondering if its possible to provide further guidance on (1) how and when to reduce shrink_corr from 0.95 to 0.4, and (2) what constitutes a "low power GWAS" that would cause convergence issues, ultimately with a goal of programmatically determining both parameters.

For use_MLE, is there a typical (effective) sample size below which you find you have to set use_MLE = FALSE?

For shrink_corr, I'm wondering if it makes sense to set this based on the proportion of GWAS summary statistics passing the QC filters when doing the SNP matching. Is there a cutoff below which this proportion passing QC indicates sufficient LD divergence that the shrink_corr should be set to 0.4 instead of 0.95? Or should the shrink_corr scale between 0.4 and 0.95 somehow proportionally to the % of GWAS summary statistics passing SNP QC?

[privefl]

That's very important questions.
I wanted to investigate automatic values and decisions for this based on some tests, but I never had time to do this (and will be probably never since I'm leaving academia in one month).

What I thought about setting use_MLE to TRUE and then rerunning with FALSE when

• too many chains diverged
• had predicted r2 at low value; predicted r2 is good proxy for GWAS power, but is not directly computed within LDpred2 (some code afterwards); alternatively, an upper bound for r2 is uniroot(function(r2) r2 - h2 / (1 + (1 - r2) * M_c / (N * h2)), interval = c(0, h2))$root, where M_c is the number of causal variants.

I think setting shrink_corr based on the proportions removed by QC can be a good idea.
What we did in some pipeline was to rerun with some lower value (0.05 or 0.1 lower each time down to 0.4) as long as not enough chains were kept.

[sritchie73]

Thanks @privefl ,

So the GWAS power relates to the power of the GWAS to predict the target trait in the data in which LDpred2 was run, rather than the power of the original GWAS?

When you say too many chains diverged / not enough chains kept, do you have a rule of thumb here on what proportion of chains should be kept?

[privefl]

For me, everything is basically the same, a monotonic transformation of N*h2/p.

I would aim to keep at half of them at least.

[sritchie73]

Thanks for the advice,

My plan is to iterate over shrink_corr = c(0.95, 0.9, 0.85, ..., 0.40) and use_MLE = c(TRUE, FALSE) at each shrink_corr, terminating in the first combination of shrink_corr and use_MLE that yield >50% chain convergence.

[privefl]

I'm testing a few things right now.
You are interested mainly in predictive performance, right?