Effective sample size or total N?

[nievergeltlab]

Hi,

I am working with a lot of cohort study data, which has a high ratio of controls to cases (e.g. 4:1). LDSC h2 estimates are highly dependent upon the value put in for N, which is calculated as N cases + N controls. I am fairly sure that because of the high number of controls, calculating N this way is causing my h2 to be under-estimated.

To illustrate the extent of the problem, a response to an earlier issues comment states, "For example, if the entries in you N column are half what they should be, then you will over-estimate h2 by a factor of two." .

Naturally then if the entries in the N column are twice what they should be, you will under-estimate h2 by a factor of two. With cohort data, the N will definitely be overstated, as the excess controls don't actually do much at all to SNP odds ratio estimates or their variances (recall that beyond a 3:1 control to case ratio, excess controls basically add nothing to estimate precision).

Should I use effective sample size instead of overall N? I.e. from https://www.nature.com/articles/nprot.2014.071 , Neff = 2 / (1/Ncases + 1/Ncontrols). Does choice of N also bias rg estimates?

Thanks!
Adam

[rkwalters]

Hi Adam, The case/control imbalance should be mostly handled by the conversion to liability scale by suppling the sample prevalence (--samp-prev) and the population prevalence (--pop-prev) when you run ldsc. See this post <https://groups.google.com/d/msg/ldsc_users/yJT-_qSh_44/MmKKJYsBAwAJ> for detail on how the effective N calculation is a component of the observed-to-liability transform. Cheers, Raymond

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On Dec 1, 2017, at 3:11 PM, Adam X. Maihofer ***@***.***> wrote: Hi, I am working with a lot of cohort study data, which has a high ratio of controls to cases (e.g. 4:1). LDSC h2 estimates are highly dependent upon the value put in for N, which is calculated as N cases + N controls. I am fairly sure that because of the high number of controls, calculating N this way is causing my h2 to be under-estimated. To illustrate the extent of the problem, a response to an earlier issues comment states, "For example, if the entries in you N column are half what they should be, then you will over-estimate h2 by a factor of two." . Naturally then if the entries in the N column are twice what they should be, you will under-estimate h2 by a factor of two. With cohort data, the N will definitely be overstated, as the excess controls don't actually do much at all to SNP odds ratio estimates or their variances (recall that beyond a 3:1 control to case ratio, excess controls basically add nothing to estimate precision). Should I use effective sample size instead of overall N? I.e. from https://www.nature.com/articles/nprot.2014.071 <https://www.nature.com/articles/nprot.2014.071> , Neff = 2 / (1/Ncases + 1/Ncontrols). Does choice of N also bias rg estimates? Thanks! Adam — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#95>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AILEvbhY4TAU64TYhZPXAuSipcMSTpfTks5s8F1kgaJpZM4Qy2nT>.

[nievergeltlab]

I see, great, thanks!

[pjordab]

Hi Raymond and users,

First of all many thanks for your excellent program and previous answers.

I'm using LDSC with the following command:

~/project/tools/ldsc/ldsc.py
--rg sumstats1, sumstats2
--ref-ld-chr /eur_w_ld_chr/
--w-ld-chr ~/project/tools/ldsc/eur_w_ld_chr/
--out sumtats1-sumstats2

In my input file I'm using the total sample size for case-control studies in the n column. I understand from the previous comments that using the flag --samp-prev will account for the proportion of cases and controls and the effective sample size. My question is:
if I haven't use this flag it is still appropiate to use the total sample size in the input file?

Many thanks!

Paloma