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CS with individual level data #131
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@CharliStoneman To check on some basics, is there a reason you set scaled prior variance to 0.1? This, interpreted as the percentage of variance explained per SNP in this context, is very high for a GWAS study. What if you let SuSiE estimate it and not specify it? |
Hi @gaow no there was not a reason i set it to that, I was just using the settings applied in the vignette here: https://stephenslab.github.io/susieR/ |
@gaow maybe we should remove the fixed prior variance from the vignette
since
we would encourage estimating it.
…On Wed, Jun 30, 2021 at 8:54 AM Charli Stoneman ***@***.***> wrote:
Hi @gaow <https://github.com/gaow> no there was not a reason i set it to
that, I was just using the settings applied in the vignette here:
https://stephenslab.github.io/susieR/
I can give it a go rerunning with no scaled prior variance set.
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Note that if the number of individuals (rows) in your data matrix is much larger than the number of SNPs, it might be faster to use |
so it looks like there are no mappable signals in the region.
What are the single-snp z scores? any significant?
…On Wed, Jun 30, 2021 at 12:26 PM Charli Stoneman ***@***.***> wrote:
I have re-run without the scaled_prior_variance option but set
estimate_prior_variance = TRUE and get a resulting plot like the following
We also now get credsets=NULL
[image: image]
<https://user-images.githubusercontent.com/38356770/124005157-aa636e00-d9d0-11eb-8863-33992dc0cfd9.png>
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@stephens999 yes I just update the vignette. We did discuss choice of priors in the vignette but the ordering of the narrative is not very good now that I read it again. It is now improved along with the R code updates. |
@CharliStoneman are you referring to running |
We used susie_suff_stat for fine-mapping using the summary stats and the input for susie was the summary stats where we got the betas & se from and also calculated the z-scores. We also input an ld matrix for the region we were looking at. |
@CharliStoneman i think you will need to share code and data to get further feedback. |
Close this ticket due to a lack of follow up. Discussions related to complications with using external LD reference are |
Hi, I have been trying to run susie with individual level data from UKB. I convert the genotypes into dosages and then create a genotype matrix. I also perform the covariates adjustment as suggested before to create the final input files for susie. Each time susie is run however I am getting very large cred sets which have over 11,000 variants in them. I have been running susie with the default settings and have adjusted some of the options in order to see if any of these would give more meaningful cred sets. I wonder if you could offer any advice or help with this?
Here is an examples of the cred sets I am getting:
Command to run
fitted_adjusted <- susie(raw2, pheno_susie[,1],
L = 10,
estimate_residual_variance = TRUE,
estimate_prior_variance = FALSE,
scaled_prior_variance = 0.1,
min_abs_corr = 0.0,
verbose = TRUE)
Cred set output
![image](https://user-images.githubusercontent.com/38356770/123949838-f5628e80-d99a-11eb-8db2-33aaaaf53aba.png)
$purity
min.abs.corr mean.abs.corr median.abs.corr
L9 1.939965e-05 0.05201907 0.01831098
L7 1.774827e-05 0.05630721 0.01949726
L6 1.054947e-05 0.05143106 0.01714920
L3 1.034395e-05 0.05039611 0.01706981
L5 8.795920e-06 0.06292139 0.02208106
L2 6.810453e-06 0.05276931 0.01733549
L8 3.465249e-06 0.05655636 0.01968297
L1 9.722116e-07 0.04467518 0.01365854
$cs_index
[1] 9 7 6 3 5 2 8 1
$coverage
[1] 0.9500093 0.9500169 0.9500354 0.9500451 0.9500114 0.9500218 0.9500402 0.9500175
$requested_coverage
[1] 0.95
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