SpecVar is a convenient tool for estimating heritability enrichment, identifying relevant tissues and relevance correlation for human complex traits.

1. Run the following commands for installation:
   

  wget https://github.com/AMSSwanglab/SpecVar/archive/master.zip
    
  unzip master.zip
    
  cd SpecVar-master

2. Download the necessary files for SpecVar into SpecVar-master at: https://drive.google.com/file/d/1mm_koaVDlYwrd3IRj76_EvC-iBWhvZC8/view?usp=sharing and run the fowllowing command:
   

  tar -zxvf Prior.tar.gz

3. Install LDSC at: https://github.com/bulik/ldsc
   Install Homer at: http://homer.ucsd.edu/homer/download.html
   

4. Edit the PATHON_HOME and LDSC_HOME path in SpecVar_GC.sh to your personal LDSC home to finish the installation

Taking GWAS of Educational Attainment (EA) as example.

1. Input files: EA.txt, EA.bed
   

  head EA.txt
  MarkerName      A1      A2      EAF     p       N
  rs13090388      C       T       0.6905  4.29e-54        1070751
  rs7630869       C       T       0.6922  4.61e-54        1070751
  rs7623659       T       C       0.3095  4.75e-54        1070751
  rs11922013      G       C       0.6905  5.94e-54        1070751
  rs9859556       G       T       0.6905  6.03e-54        1070751
  rs6779524       C       T       0.6905  6.30e-54        1070751
  rs9871380       A       G       0.3095  6.68e-54        1070751
  rs9878943       G       A       0.6905  6.68e-54        1070751
  rs9814873       G       A       0.3095  6.78e-54        1070751
  
  head EA.bed
  chr3    49391082        49391082        rs13090388      4.29e-54        ###
  chr3    49522543        49522543        rs7630869       4.61e-54        ###
  chr3    49414791        49414791        rs7623659       4.75e-54        ###
  chr3    49458355        49458355        rs11922013      5.94e-54        ###
  chr3    49455986        49455986        rs9859556       6.03e-54        ###
  chr3    49450449        49450449        rs6779524       6.30e-54        120.853
  chr3    49438221        49438221        rs9871380       6.68e-54        ###
  chr3    49434654        49434654        rs9878943       6.68e-54        ###
  chr3    49454112        49454112        rs9814873       6.78e-54        120.853
  chr3    49453834        49453834        rs6997  6.88e-54        120.853

2. After preparing the input files as above, run the following command to identify relevant contexts:

  bash SpecVar_RT.sh EA

3. Output files in Results fold:
   EA.SpecVar.RS.txt: the EA's RS scores to 77 human contexts;
   EA_SigTissue.txt: the EA's relevant human contexts;
   EA_frontal_cortex_SubNetwork.txt: the EA's SNP associated regulatory subnetwork in the most relevant context "frontal cortex";
   

Taking GWAS of Educational Attainment (EA) and Cognitive Performance (CP) as example.

1. Input files: EA.txt, EA.bed and CP.txt, CP.bed with same formats above
   
2. After preparing the input files as above, run the following command to estimate phenotypic correlation:

  bash SpecVar_GC.sh EA CP

3. Output files in Results fold:
   EA.SpecVar.RS.txt: the EA's RS scores to 77 human contexts;
   EA_SigTissue.txt: the EA's relevant human contexts;
   CP.SpecVar.RS.txt: the CP's RS scores to 77 human contexts;
   CP_SigTissue.txt: the CP's relevant human contexts;
   EA_CP_GC.txt: the genetic correlation and p-value of EA and CP;
   EA_frontal_cortex_SubNetwork.txt: the EA's SNP associated regulatory subnetwork in the most relevant common context "frontal cortex";
   CP_frontal_cortex_SubNetwork.txt: the CP's SNP associated regulatory subnetwork in the most relevant common context "frontal cortex".
   

Python
Python package: numpy, sklearn, and scipy
LDSC
Homer
Better if number of processors is more than 8.

If you use SpecVar or SpecVar associated concepts, please cite

Zhanying Feng, et al. Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification. eLife 2022.