How correct beta from intercept, and error "Could not determine N"

[Ying Liu]

Sorry if I have newbie questions. 

My summary statistics have a column A1_CT (varies by SNPs) which comes from plink. Also i have a column named OBS_CT which is all the same for all the SNPs. When I tried to use munge_sumstats.py to format the sumstat, I got the error message of Could not determine N. How could I deal with this situation? Just rename the A1_CT or OBS_CT to N? Which one is appropriate? 

*********************************************************************

* LD Score Regression (LDSC)

* Version 1.0.0

* (C) 2014-2015 Brendan Bulik-Sullivan and Hilary Finucane

* Broad Institute of MIT and Harvard / MIT Department of Mathematics

* GNU General Public License v3

*********************************************************************

Call:

./munge_sumstats.py \

--out WHR_all_2 \

--merge-alleles w_hm3.snplist \

--sumstats WHR_all_2

ERROR converting summary statistics:

Traceback (most recent call last):

  File "./munge_sumstats.py", line 643, in munge_sumstats

    raise ValueError('Could not determine N.')

ValueError: Could not determine N.

Conversion finished at Wed Apr 10 12:54:12 2019

Total time elapsed: 0.12s

Traceback (most recent call last):

  File "./munge_sumstats.py", line 746, in <module>

    munge_sumstats(parser.parse_args(), p=True)

  File "./munge_sumstats.py", line 643, in munge_sumstats

    raise ValueError('Could not determine N.')

ValueError: Could not determine N.

The other question is regarding how to use the intercept to correct GWAS data. I learned from the thread: 

In that case, you’ll want to:

1) Convert the summary data to ldsc format using munge_sumstats.py (see example in tutorial)

2) Run ldsc.py using --h2 with the basic arguments as described in the tutorial (you don’t want the --intercept-h2 flag)

3) Find the fitted estimate for the intercept in the ldsc output log.

4) Treat the intercept like lambdaGC for adjusting your GWAS results (assuming intercept > 1 so there’s evidence of something to correct). Method depends on what data you have:

- beta (or odds ratio) and SE: multiply the SE by sqrt(intercept)

- z statistics: divide Z stat by sqrt(intercept)

- chi2 statistics: divide chi2 by the intercept

- p-values: recompute after applying above adjustment to test statistics

I will have beta (not OR) for my quantitative phenotype. How would I use the intercept to correct the original beta computed directly from the GWAS? 

Thanks so much!