SpecVar is a convenient tool for estimating heritability enrichment, identifying relevant tissues and relevance correlation for human complex traits.
- Run the following commands for installation:
wget https://github.com/AMSSwanglab/SpecVar/archive/master.zip
unzip master.zip
cd SpecVar-master
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1NNK_R_1DQo8wxkIoXLE2ntM4GilqmP60' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1NNK_R_1DQo8wxkIoXLE2ntM4GilqmP60" -O SpecVar_Data.tar.gz && rm -rf /tmp/cookies.txt
tar -zxvf SpecVar_Data.tar.gz
rm -f SpecVar_Data.tar.gzNote: if you can't use wget command to download files in google drive, you can manually download SpecVar_Data.tar.gz at https://drive.google.com/file/d/1NNK_R_1DQo8wxkIoXLE2ntM4GilqmP60/view?usp=share_link
-
Install LDSC at: https://github.com/bulik/ldsc
Install Homer at: http://homer.ucsd.edu/homer/download.html
Install bedtools at: https://bedtools.readthedocs.io/en/latest/content/installation.html -
Edit the PATHON_HOME (python2), PATHON3_HOME (python3), LDSC_HOME, Bedtools_HOME, and HOMER_HOME path in SpecVar_RT.sh, SpecVar_GC.sh, SpecVar_FE.sh to your personal software location to finish the installation
Taking GWAS of Educational Attainment (EA) as example.
- 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- After preparing the input files as above, run the following command to identify relevant contexts:
bash SpecVar_RT.sh EA- 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.
- Input files: EA.txt, EA.bed and CP.txt, CP.bed with same formats above
- After preparing the input files as above, run the following command to estimate phenotypic correlation:
bash SpecVar_GC.sh EA CP- 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".
This program takes a set of genomic loci (SNPs or all kinds of QTLs) as input.Taking GWAS of COVID19 as example:
head COVID19.bed
chr12 103014757 103014757 12:103014757:C:A 1.652e-10
chr12 113352159 113352159 12:113352159:T:C 2.739e-08
chr12 113357193 113357193 12:113357193:G:A 3.097e-09
chr12 113357209 113357209 12:113357209:G:A 5.386e-09
chr12 113357442 113357442 12:113357442:G:A 5.575e-09
chr12 113359132 113359132 12:113359132:A:G 4.568e-08
chr12 113360737 113360737 12:113360737:G:T 2.933e-08
chr12 113361117 113361117 12:113361117:C:T 4.526e-09
chr12 113361158 113361158 12:113361158:C:T 4.889e-08
chr12 113361174 113361174 12:113361174:T:C 4.861e-08Note: the last column contains the P-values of SNPs, which is required. If you don't have P-values for your loci, this can be replaced by any biologically meaningful scores or blank characters.
Then run the following command to do FE analysis of SpecVar.
bash SpecVar_FE.sh COVID19The Output files in ./Results/COVID19/ fold:
COVID19_FE.txt: The COVID19's FE scores to 77 human contexts;
COVID19_Primary_monocytes_from_peripheral_blood_network.txt: The COVID19's associated regulatory network in "Primary_monocytes_from_peripheral_blood". Each row means one SNP with P-value is inside of RE, which will potentially affect TF's binding and TG's expression with regulatory strength TRS.
Python2 and Python3
Python package: numpy, sklearn, and scipy
LDSC
Bedtools
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.