sociogenomics2023

This part of the lab needs python 2.7 installed in your computer. We recommend the anaconda distribution

The next commands show how to install it in google shell

First, some data cleaning to free some space on the google shell account

cd /home/nicola_barban/Sociogenomics/Data/
rm *.*

cd /home/nicola_barban/Sociogenomics/Results/
rm *.*

This requires time and installs miniconda (which is a python distribution) in the system

cd /home/nicola_barban/Sociogenomics/Software
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p 
rm ~/miniconda.sh
source $HOME/miniconda3/bin/activate

This installs LDSC in your system

cd /home/nicola_barban/Sociogenomics/Software
git clone https://github.com/bulik/ldsc.git


cd ldsc

Congratulations you have installed LDSC now you have to install some packeges that LDSC uses for its calculations

conda env create --file environment.yml
source activate ldsc

Now, let’s check if everything works

./ldsc.py -h
./munge_sumstats.py -h

This is the LDSC command to estimate heritability. We need the following files:

1. Giant_Height2018.txt.gz
2. w_hm3.snplist

cd /home/nicola_barban/Sociogenomics

First we need to “munge” summary statistics, that is to make ti comparable with reference data that have info on LD scores

##This takes a lot of time. you can skip this and find the prepared data in the folder

python Software/ldsc/munge_sumstats.py \
  --sumstats Data/Giant_Height2018.txt.gz \
  --snp SNP \
  --N-col N \
  --a1 Tested_Allele \
  --a2 Other_Allele \
  --p P \
  --frq Freq_Tested_Allele_in_HRS \
  --out Results/height \
  --merge-alleles Data/w_hm3.snplist

The folder eur_w_ld_chr/ contains the LD scores that are merged with our summary statistics

python Software/ldsc/ldsc.py \
 --h2 Data/height.sumstats.gz \
 --ref-ld-chr Data/eur_w_ld_chr/ \
 --w-ld-chr Data/eur_w_ld_chr/ \
 --out Results/height_h2

Now we can try to estimate genetic correlation between height and educational attainment.

First, we need to prepare data for Educational Attainment

##This takes a lot of time. you can skip this and find the prepared data in the folder

python Software/ldsc/munge_sumstats.py \
   --sumstats Data/EA2_results.txt.gz \
   --snp SNP\
   --N 500000 \
   --a1 A1 \
   --a2 A2 \
   --p Pval \
   --frq EAF \
   --out Reults/Educ \
   --merge-alleles Data/w_hm3.snplist

And this is finally the command to estimate genetic correlation

python Software/ldsc/ldsc.py \
 --rg Data/height.sumstats.gz,Data/Educ.sumstats.gz  \
 --ref-ld-chr Data/eur_w_ld_chr/ \
 --w-ld-chr Data/eur_w_ld_chr/ \
 --out Results/height_educ